It's not a secret that AI adoption is becoming increasingly commonplace in Corporate America. But it's not just giant corporations that are using it. A 2026 Intuit QuickBooks survey (PDF) found that 77% of small and midsize businesses now use AI regularly, up from 48% two years ago.

If you own a small business, you may be looking to increasingly lean on AI tools to improve productivity and save on costs. In fact, the same QuickBooks survey found that 78% of US respondents reported productivity gains from AI use, and 42% reported revenue gains. For large companies, a PwC study found that investing at least 1.6% of revenue in AI tools resulted in 9.5% growth (as measured in EBITDA). Moreover, AI can help create documented workflows and efficiencies that can give you a higher exit price when you're ready to sell up and retire.

But what if those tools are making some of your employees' tasks obsolete?

On a scale of 1-5, which of the following describes your current views of AI's impact on your productivity? (Asked of U.S. small businesses.)

Letting employees go is a gut-wrenching decision for any small business owner. But when your personal finances are at stake, sometimes you may need to make hard choices.

If you're a business owner who's looking to power through for a few more years and sell your business to fund your retirement, you need your company to be profitable and competitive. That means shedding unnecessary costs and using technology to your advantage. Here's how to reconcile the financial side of the equation with the moral and emotional side.

Look at AI as a time-saver more than anything else

AI may be able to take over some of the tasks your employees handle now. But that doesn't mean those employees suddenly lose all of their value.

Paul Carlson, CPA and managing partner at Law Firm Velocity, a company that provides virtual CFO and financial services to law firms across America, says, "You don’t want to make decisions out of guilt and keep carrying payroll that no longer makes financial sense for the business."

He explains, however, that just because AI is saving you time doesn't mean your employees with years of business knowledge and experience are suddenly obsolete.

"What helps you make a more mindful decision is to first see whether the freed-up time can actually improve some other aspect of your business," Carlson says. Those aspects could be things AI can't handle, such as strategic decisions that require more human intervention and judgment.

Carlson says that discovering the time-saving power of AI could position you to make better use of your employees' skills rather than letting them go.

"In most small businesses, your employees will most certainly end up wearing multiple hats over time. So even if AI suddenly saves someone five or six hours a week, that doesn’t necessitate that the employee is no longer able to play a part, especially given the kind of context they have about what works for your business and what doesn't," he explains.

Carlson also points out that if you're running a lean operation, you may not be tracking the various ways your employees are contributing. Before letting them go, it pays to take a closer look.

"Some employees are integral when it comes to catching mistakes," he says. "Others are great at answering questions nobody else has time to deal with, or take great pride when following up with clients. All those elements may have had a big role to play in your company’s reputation and how satisfied clients are with you."

E.J. Simonsen, Founder & Finance Advisor at EIDLexit, agrees. His best advice? "Replace tasks, not people."

"Routine administrative labor can be handled by AI," he says. "Use the savings of time to transition employees into higher value endeavors such as customer service, business development, process improvement, or client retention. Those domains are often higher impact to revenue and significantly harder to automate. This enables you to be competitive while still investing in your team."

Proper AI adoption could make your business more valuable

If retirement is on the horizon and you're within 10 years of an exit plan, you need a sound strategy that enables your business to maintain its edge without blowing money on unnecessary costs.

Kevin Williams, founder of Ascend AI, advises C-suite executives and small business owners on AI adoption, governance, and workforce readiness. And he says that given your timeline, you have a prime opportunity to use AI in a way that could make your business more valuable without shedding headcount.

"Companies where AI is used strategically and where the team knows how to leverage it fetch a higher price," he insists. "Such companies are viewed as mature, where things work efficiently. Buyers pay a premium for that."

Williams also says, "Using AI to increase productivity of each employee by 20-30% and thus build a lean and competent organization that is highly valued by a potential buyer [several] years down the road — that's how to retire successfully."

Simonsen says that ultimately, a business that runs well and has good and skilled people will generally be worth more. On the flip side, if AI adoption makes a business feel cold, automated, or robotic, client retention might drop.

"Balancing compassion and business decisions is part of establishing a better company," he says.

Of course, successfully implementing AI requires an investment. And training employees to use it could take time away from daily operations while your staff gets up to speed. But if you're willing to sacrifice some short-term gains, you may find that AI boosts your company's profitability in the long run and puts you in a stronger position once you're ready to sell.

It's also important to be transparent with your employees about how you're looking to integrate AI. Nothing hurts employee morale like the fear of being replaced by a machine. Emphasize that you're adopting AI tools to make their jobs easier, not to replace them.

Take guilt out of the equation

It's natural to feel bad about the idea of letting hard-working employees go. But if you use AI the right way, you may not have to.

"If you feel guilty about AI, then this is because you miss the point entirely," Williams says. "Guilt doesn't arise from using AI technology. It comes from the choice you made [regarding] AI use."

Of course, you may come to the realization that one or two roles at your company are, in fact, obsolete in the wake of AI, and that you can't justify the cost of labor. In that case, it's important to do your best to ethically offboard those employees.

If you can afford to be generous with severance, it could ease the financial blow for those impacted while helping you sleep better at night. Be empathetic but pragmatic.

But all told, Williams says, using AI to empower your employees rather than replace them could set you up for a lot more financial success. And that way, you can forge forward with your personal financial plans without the remorse that might come with destroying other people's livelihoods.

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AI has already affected how we learn, work and govern ourselves much faster than research, regulation or nonprofits can keep pace.

While businesses and governments scramble to respond, philanthropists hold a distinct and underutilized advantage: The flexibility to fund what others won't, invest where we still need evidence and work across sectors without commercial pressure.

Here is how to leverage your advantages as a donor.

1. Sharpen your nonprofit due diligence

AI-generated misinformation makes vetting organizations harder than ever. Donors can raise their standards by:

• Identifying ratings from Charity Navigator, the BBB Wise Giving Alliance and GuideStar's Seals of Transparency before committing funds
• Visiting local organizations in person — no rating system replaces direct observation of a program in action
• Asking nonprofits you regularly support how they manage AI-related data risks before your next significant gift conversation

2. Fund nonprofit AI capacity — not just programs

Most nonprofits use AI primarily for basic productivity: Drafting emails, summarizing meetings, writing grant applications. The real leverage lies in mission-critical AI deployment — but getting there requires infrastructure most nonprofits lack, and the risks of moving forward without it remain severe.

Unlike corporations, nonprofits face the same cybersecurity vulnerabilities with far fewer resources. They routinely manage donor financial data, personally identifiable information, health records and confidential beneficiary files.

Large language models like ChatGPT and Claude collect and store user data indefinitely — meaning a nonprofit that uploads sensitive files to an AI platform may lose ownership of that data entirely.

Cybercriminals also actively use AI to make attacks harder to detect. A single breach can devastate fundraising, damage community trust and trigger regulatory consequences under state, federal or international privacy law.

To empower nonprofits to function effectively with AI, you may:

• Fund general technology budgets. IT infrastructure upgrades remain chronically underfunded, yet are essential for safe and effective AI use. Consider directing unrestricted gifts specifically toward this gap.
• Sponsor AI education and policy development. Nonprofit leaders identify four priorities: Staff training on AI fundamentals, dedicated software funding, technical development opportunities and guidance on how AI affects the communities they serve.

As a result, you can free up charities to make truly transformational changes. For instance, the MacArthur Foundation's recent $100 million award funded an AI-driven global infectious disease surveillance system, a model for what becomes possible with the right infrastructure in place.

3. Support publicly available AI research

It has become clear that AI will affect jobs across every sector — augmenting some roles, restructuring others and eliminating others. Yet workforce impact data remains fragmented, with no uniform standards for measuring AI exposure, adoption or economic effect across sectors.

Educators and employers commonly agree that student preparation for an AI-embedded future entails independent decision-making, problem-solving and media literacy, rather than task completion. In essence, students need to be taught how to think, not what to think.

However, educators have had to make high-stakes technology adoption decisions with insufficient evidence.

As labor markets shift, demand will rise for portable access to healthcare, education and job retraining.

For instance, potential ideas such as portable benefits, decoupling health insurance and retirement savings from traditional employment, require rigorous, publicly available research to move from concept to viable policy.

As a result, AI research remains "in the first inning."

Donors can accelerate the learning curve to keep up with industrial changes by:

• Funding research based on multiple perspectives, including those of teachers, parents and students, on AI's impact on learning
• Prioritize studies that remain open to the public, so other researchers can replicate and build on findings
• Support workforce transition research, particularly around portable benefits and safety-net access for workers in AI-disrupted industries

4. Strengthen civic voices in developing AI policy

Most workers and communities recognize the efficiencies offered by AI, understand their benefits and don't want to eliminate them.

Instead, they simply want a seat at the table in deciding how AI applies to them. In fact, more than half of U.S. adults (55%) say they want greater control over AI in their lives.

That demand extends to transparency about AI's environmental footprint and corporate accountability on data privacy.

Philanthropy has a direct role in amplifying a community's civic voice by:

• Funding local and independent journalism to ensure communities receive accurate, accessible information about AI's local implications
• Supporting civic participation initiatives that bring residents, not just industries, into community-level AI policy and land-use decisions

5. Align your financial investments with your philanthropic goals

Ideally, your investment portfolio and your philanthropic priorities would reinforce, and not negate, each other. Accounting for environmental, social and governance factors may improve long-term returns by unlocking value and achieving resilient, long-term investment success.

You can adjust your investment and philanthropy alignment by:

• Reviewing your current AI-related holdings and their impacts in the areas that you care about most as a philanthropist
• Consulting with your investment adviser on frameworks for considering environmental, social and corporate governance factors

6. Team up with other donors

AI's societal scale exceeds what any single donor can address. Philanthropists who coordinate with peers can multiply their impact, reduce duplication and gain access to shared due diligence.

In collaborating with other donors, you may:

• Explore foundation coalitions like Humanity AI to review their vetted grantee list and potentially identify organizations aligned with your priorities
• Join or convene a donor working group focused on AI's impact in your areas of giving — education, workforce, health or civic participation

The philanthropists who move now can help influence AI for good. In funding research, nonprofit capacity and broad civic engagement and working with other donors, you can advance the positive impact of AI and mitigate its challenges for decades ahead.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

A young investor today wakes up to a TikTok video on private credit, asks a generative AI tool to draft a retirement plan over breakfast, scrolls through podcasts comparing crypto custodians on the commute and fields a robo-adviser's portfolio recommendation before lunch.

Information about money has never been cheaper to produce, easier to access or harder to evaluate. However, despite the ubiquity of investment information, human advisers remain the single most trusted source of guidance for young investors today.

The role of traditional investment advice in an age of digital communication is a central tension in the new Next-Gen Investors report from CFA Institute, which draws from a survey of more than 2,400 mass-affluent and high-net-worth investors in six major wealth markets around the world.

Instead of reading this as nostalgia for a fading model, consider how trust works in a saturated information environment. When advice is everywhere, the question is no longer who has the answer, but who can be trusted to guide choices among many possible answers.

Younger clients are looking for a curator and collaborator, and the advisers who recognize that will own the next generation of relationships.

How advisers can stay relevant

What makes young investors different is how they verify trust. Older investors tended to define trustworthiness primarily through the relationship itself, with years of personal history, in-person meetings, and continuity across family generations. Gen Z and Millennial investors still want that personability, but they expect it alongside measurable, professional indicators.

Our research shows young investors place greater weight on professional credentials, transparency around conflicts of interest, data security and verifiable performance against benchmarks.

These markers are particularly valuable in a world ripe with mass-produced AI advice. Professional credentials, for example, are one of the few public proofs that a person, not a machine, has demonstrated domain knowledge and expertise.

This measurable trust is what advisers can lean into to stay relevant. In our survey, approximately one third of Gen Z and Millennials already use generative AI to learn about investing. Generative tools will keep getting better at producing fluent-sounding advice, but fluency is not judgment.

Cut through the hype

Seasoned advisers bring years of seeing market cycles, regulatory changes, behavioral patterns and the outcomes of decisions that looked obvious at the time. That experience is exactly what cuts through hype. An AI tool may produce responses that sound confident, but it cannot replace competence.

For advisers, this reframes the scope of their work. Professionals are no longer the primary gatekeeper for investing. Clients now have access to an abundance of information. Instead, the job is to serve as a curator, validator and translator of an overwhelming digital landscape.

In some ways, that is a more demanding role, yet a more durable one. It means being fluent in the latest products your clients are reading about, including the ones you would not personally recommend, so you can have an informed conversation rather than a defensive one, and being ready to interpret a viral video or an output a client copied out of a chatbot.

Younger clients are not going to stop consuming content, but they want an expert whose true value lies in human judgment.

Communicating that value is now part of the job. Younger clients will not assume seasoned judgment is in the room but will look for evidence of it.

Treat credentials, professional experience and past performance as strategic assets that are clearly communicated to current and future clients.

Document conflict-of-interest policies in plain language and make them client-readable.

Show the work behind a recommendation, including supporting evidence, not just the conclusion.

At the same time, AI can be a useful tool to communicate the value proposition of adviser judgement. Used well, it removes the friction that prevents advisers from being successful curators and collaborators.

AI can help with drafting first-pass communications, summarizing trends, preparing for meetings and scaling personalized check-ins.

Nearly 70% of Gen Z and Millennial investors in our study who use a paid financial professional interact with their adviser at least monthly. That cadence is difficult to sustain without technology, but underlying those interactions is the adviser's expertise and judgment orchestrating those communications.

Voice of reason

But the deeper reason younger clients want a human adviser is that the world has become a noisy place, and navigating the signals and products can be overwhelming and lead to rash decision-making.

Over half of Gen Z and Millennial investors in our research have already made at least one investment driven purely by fear of missing out (FOMO), most often in cryptocurrency.

As markets continue to show volatility, and as new investment opportunities emerge, the adviser's role is to be the person on the other end of the line when the next market dip arrives, the next can't-miss asset surfaces, or the noise of information gets too loud.

The point is not to chase every trend or reflexively dismiss new products or opportunities, but to be a voice of reason and stability. A credentialed, experienced professional who can keep clients aligned to their long-term goals and strategies; steadfastness becomes even more valuable in a noisy environment.

The advisers and firms who successfully adapt to the next generation will not approach AI as a threat, nor as a replacement for the adviser-client relationship.

They will be the ones who use technology to amplify their reach, and focus on their human qualities of judgment, accountability, ethical stewardship and demonstrated experience, which no algorithm can fully capture.

Genevieve Hayman, PhD, and Ryan Munson are co-authors of the CFA Institute Research and Policy Center report Next-Gen Investors: A Guide for Wealth Managers and Financial Advisers.

Genevieve Hayman is a senior manager of macrosystems and foresight at CFA Institute. Her research focuses on pensions and retirement security, complex systems, cognitive science and the long-term forces shaping global finance. In her role, she develops structured, long-horizon scenario frameworks that examine how technological, economic and regulatory shifts may reshape financial markets, institutional behavior and professional norms. She also contributes to early-warning frameworks and cross-pillar integration across CFA Institute's research agenda.

Ryan Munson is a research manager at CFA Institute. His research focuses on pensions and the future of finance, exploring how extra-financial factors impact the investment industry and investment professionals. Ryan serves on the advisory board for the Mercer CFA Institute Global Pension Index. He is the author of several CFA Institute publications, including the Future State of the Investment Industry, the Future of Work in Investment Management series and the CFA Institute Investor Trust series.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

SpaceX, OpenAI and Anthropic sit at the center of the artificial intelligence and space infrastructure boom.

All three are preparing, or are widely reported to be preparing, for public listings at valuations that could collectively exceed $3 trillion.

These are not just big IPOs. They may become turning points for the economy and for investors who are already retired or nearing retirement.

After several slow years for new listings, the IPO market has roared back, led by companies tied directly to AI. Chipmakers, data platforms and now foundation-model companies are drawing intense investor demand and extraordinary price tags.

SpaceX is reportedly targeting a valuation of roughly $1.75 trillion in what could become the largest U.S. stock market debut on record.

OpenAI, the company behind ChatGPT, is reportedly preparing to file for a U.S. IPO and was recently valued at roughly $852 billion.

Anthropic, creator of Claude, has already filed confidentially for an IPO and recently raised capital at a reported $965 billion post-money valuation.

In plain English, public markets may soon absorb several of the largest technology offerings in history, all clustered around one theme: The belief that AI will transform the economy.

Why this matters

AI is no longer just a technology story. It is increasingly an economic one.

Stanford's 2025 AI Index estimated that global corporate AI investment reached $252.3 billion in 2024, with private investment and merger activity still rising. That level of spending helps explain why AI has become one of the dominant stories behind recent market optimism.

But there is a risk. A small group of large technology and AI-linked companies, known as the Magnificent 7, already accounts for a disproportionate share of stock market leadership.

If SpaceX, OpenAI and Anthropic enter the public markets at enormous valuations, that concentration could grow even more.

For investors, concentration cuts both ways. If AI leaders continue to grow into their valuations, broad index funds may benefit.

But if expectations reset, because of slower adoption, regulatory pressure, profit disappointments or capital spending concerns, the same broad index funds could feel the downside.

This is especially important for retirees. When you are still working, market pullbacks are painful but often recoverable with time and new contributions.

In retirement, the math changes. If you are taking withdrawals during a market decline, you may be forced to sell shares when prices are depressed.

That is sequence-of-returns risk, and it becomes more dangerous when market gains are narrow and concentrated.

The emotional pull

The hardest part of this moment is not the math. It is the emotion.

Many investors remember missing earlier waves, such as the internet, smartphones, cloud computing or Nvidia (NVDA). When financial headlines start talking about the "most anticipated IPOs" and trillion-dollar valuations, the temptation is to chase the next big thing before everyone else gets in.

That temptation can be costly. Some of these companies may become extraordinary long-term businesses. But buying a great company at the wrong price can still produce poor returns. The bigger the valuation at the starting line, the more future growth may already be priced in.

For retirees, that matters. You do not have the same margin for error as a 30-year-old investor with decades of income ahead. A speculative position that drops sharply can do more than hurt performance. It can disrupt income planning, withdrawal strategy and peace of mind.

What smart retirees can do

The goal is not to ignore AI. AI is real, and it may help drive the next decade of growth. The goal is to avoid letting excitement replace discipline.

First, review your diversification. Look under the hood of your stocks, mutual funds and ETFs.

How much of your equity exposure is tied to a handful of mega-cap technology and AI-linked names? You may already own more of this theme than you realize.

Second, separate the story from the strategy. The story around these IPOs is compelling: Revolutionary technology, visionary founders and massive addressable markets.

But a retirement strategy should be built around cash-flow needs, inflation, longevity risk, taxes and risk tolerance, not headlines.

Third, use AI as a planning tool, not a lottery ticket. AI may improve portfolio monitoring, tax planning, income modeling and risk management.

For retirees, that may be the more productive use of the technology than speculating on the hottest new AI stock.

Fourth, think in scenarios, not predictions. Ask two simple questions:

• If AI mega-caps keep driving markets higher, am I positioned to participate?
• If they stumble, can I still meet my spending needs?

A good retirement plan should work across a range of outcomes, not just the optimistic one.

Finally, revisit your withdrawal and risk policies. Periods of market enthusiasm are a good time to make sure your cash buffer, income plan and equity exposure still fit your real life.

The bottom line

The coming wave of AI-driven mega-IPOs is a sign that we are living through a genuine technological transition and a period of elevated market optimism. The valuations being discussed for SpaceX, OpenAI and Anthropic show that investors are willing to pay today for a future where AI reshapes productivity, software, defense, healthcare and transportation.

For retirees, the goal is not to bet your nest egg on that future. It is to recognize that a growing share of your portfolio may be influenced by a small cluster of AI-centric giants, then plan accordingly.

AI may help power the next decade of growth, but a disciplined retirement plan is still what turns that growth into reliable income.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Drive through eastern Loudoun County, Virginia, and you will quickly understand why some parts of the area are often referred to as "Data Center Alley."

Massive, windowless gray cement structures rise up behind fences and security gates, while cranes loom over roads once lined with trees, now covered in mud from construction traffic, working to make way for yet another data center.

This mixed suburban/rural area is now home to the world’s largest concentration of data centers. Around 200 facilities are currently operating in Loudoun alone, with more planned, and they handle over one-third of the world’s daily internet traffic.

While supporters argue these centers are vital to the digital economy, many residents — not only in Virginia but across the United States — are concerned about their rapid expansion, energy and water use, and broader environmental impact.

Critics also highlight that these facilities often create fewer permanent jobs compared to the tax incentives they receive. As tensions grow, the question becomes: where do residents and lawmakers go from here?

The great data center debate

Data centers are specialized facilities that house a variety of computing components, including servers, networking equipment, and extensive drives.

Their prevalence has increased in recent years, as every time someone streams a movie, stores photos, shops online, uses social media, or interacts with AI chatbots, information is processed through these centers worldwide.

There are now reportedly around 4,000 data centers in the U.S., which some see as a good thing, helping create jobs and generate revenue.

But…data centers place significant demands on local infrastructure.

• Modern data center campuses can span dozens or even hundreds of acres and often require new power lines, substations, roads, and other infrastructure.
• Many consume significant amounts of electricity. (Just a few years ago, data centers accounted for an estimated 4% of total electricity use in the United States. By 2028, that figure is expected to climb to as high as 12%.)
• Data centers also typically rely on large diesel-powered backup generators to ensure uninterrupted operations during power outages, which raises concerns about local air quality in some communities. (According to the U.S. Environmental Protection Agency, diesel exhaust from backup generators contains fine particulate matter and nitrogen oxides that are associated with respiratory issues like asthma.)

Notably, data centers and water have emerged as another point of contention.

Depending on the design and cooling technology, large facilities can consume hundreds of thousands of gallons of water per day to cool server racks. Some large campuses reportedly use volumes comparable to those of a small town, raising sustainability questions in some communities.

Still, states and local governments across the country have spent years competing to attract data center development, often by offering generous tax incentives.

Data center tax exemptions

In recent years, 38 states have offered generous incentives, including sales tax exemptions on servers and equipment and property tax reductions, to win a larger share of the industry's explosive growth.

Increasingly, however, several of those states are facing backlash not just from residents but also from some lawmakers.

As a result, some are moving toward requiring greater transparency, shifting infrastructure costs onto developers, reexamining tax incentives, or studying the industry's impact on electricity and water supplies and local communities.

Some examples:

Illinois: Late last week, Gov. JB Pritzker directed the state's Department of Commerce to completely halt the processing of all new data center tax exemptions starting July 1.

"Illinois has an opportunity to continue leading in technological innovation and economic growth, but we also have a responsibility to protect working families and local communities as the data center industry rapidly expands," Pritzker stated in a release.

Ohio: In May, Ohio Gov. Mike DeWine ordered the state’s Tax Credit Authority to freeze all pending and new data center sales tax exemption requests. The halt came after a state report revealed that the exemption cost Ohio $1.5 billion in 2025 alone.

In a release regarding the issue, DeWine wrote, “I fully support the Ohio General Assembly's work to study the issue and bring forward facts about data centers, including the local benefits to communities when tax exemptions are granted.”

Georgia: Lawmakers in the Peach State are moving to phase out data center tax suspensions after a state audit revealed the exemptions will cost a projected $2.5 billion this year.

Data centers in Virginia: What’s happening

In Virginia, lawmakers in the Senate want to let a multibillion-dollar annual data center tax exemption expire, while the Virginia House is reportedly trying to tie any remaining tax breaks to strict environmental and clean-energy compliance rules.

According to the Commonwealth’s tax disclosures, the existing data-center sales-tax exemption in the Old Dominion state cost an estimated $1.6 billion last fiscal year.

That massive exemption and the growing backlash over the more than 600 data centers already in the Commonwealth are sticking points in a budget process that must be completed by the end of June.

At the same time, in some other states, resistance to data centers has led to new legislation. (This is not an all-inclusive list.)

• In Oklahoma, Gov. Kevin Stitt signed the Data Center Consumer Ratepayer Protection Act of 2026 into law, effective July 1. The law is designed to prevent utility cost hikes for residents.
• New York lawmakers just passed the Responsible Data Center Development Act (A11560), which, once enacted, will impose a one-year moratorium on permits for new data centers of 20 megawatts or more.
• Monterey Park, California, became the first U.S. city to enact a ban on data center developments after roughly 88% of local voters approved a June 2 ballot measure.

As of June 2026, according to various online trackers, more than 25 states are either advancing data-center-related legislation or have enacted measures that address grid cots, reporting requirements, utility regulation, tax incentives, or local authority over data centers.

What about Congress? In March 2026, Sen. Bernie Sanders (I-Vt.) and Rep. Alexandria Ocasio-Cortez (D-N.Y) introduced the Artificial Intelligence Data Center Moratorium Act. The measure, which would temporarily pause new data center construction nationwide while Congress develops federal rules for AI infrastructure, hasn’t gained traction on Capitol Hill.

Are data centers bad? Bottom line

The debate over the good and not-so-good aspects of data centers shows no signs of going away.

A recent Gallup poll finds that 71% of Americans now oppose the construction of AI data centers in their local communities (with 48% strongly opposed). The pollsters note that local data center construction is more unpopular in the U.S. than building a nuclear power plant.

This “not in my backyard” sentiment is split between environmental concerns (expressed by 50% of respondents) and economic fears, e.g., higher utility bills (about 20% of respondents), according to Gallup. Pollution, negative views of AI, and quality-of-life concerns were also factors for some.

While polling data help explain national sentiment, grassroots opposition efforts highlight local concerns.

• In Hood and Hill Counties, Texas, residents hoped to block eight proposed data centers by attending town halls in large numbers, though developers are fighting back in court. A similar effort occurred in Champaign County, Illinois, leading to a moratorium to protect a crucial aquifer.
• In Sand Springs, Oklahoma, residents mobilized in response to reports that local officials had allegedly signed non-disclosure agreements to annex 827 acres of agricultural land for a tech campus.
• Residents in Box Elder County, Utah, along with Alliance for a Better Utah, have filed a lawsuit against state development agencies over a 40,000-acre AI project backed by celebrity investors. They argue it undermines local voter oversight and grants big tech unchecked control over their water, roads, and tax structure.

Meanwhile, among those polled by Gallup who favor having a data center in their communities, the most cited reason why was potential job growth.

To that end, a Brookings Institution analysis finds that while data centers do create local jobs, it is likely “fewer than advocates claim.”

Some independent estimates put the total at a few dozen to a few hundred long-term on-site positions once a given center is constructed.

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To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

When you think about Facebook, you don’t think about monthly subscription fees. Meta wants to change that with the upcoming launch of subscription plans.

Diversifying revenue makes sense for a company that makes 98% of its sales from ads. But another factor is at play: The need to cover the soaring cost of AI infrastructure. Meta is all-in on the AI race and recently raised its planned capital expenditures for the year to between $125 billion and $145 billion, mostly because of rising memory chip prices.

Meta’s sales are smaller than the other Big Tech companies that are plowing money into AI. And unlike the other three AI giants — Alphabet, Amazon and Microsoft — Meta doesn’t have a cloud computing business that sells computing to outside customers.

Meta hopes for new ways to pay for "superintelligence"

Subscriptions are part of a bigger transformation CEO Mark Zuckerberg laid out in the company’s most recent earnings call. It’s a "vision of superintelligence," Zuckerberg said during the first-quarter earnings call at the end of April. "Our goal is not just to deliver Meta AI as an assistant, but to deliver agents that can understand your goals and then work day and night to help you achieve them."

The specifics of this vision are a bit hazy, but the focus is AI agents, the autonomous tools that do multistep tasks on a computer. Zuckerberg talked about AI helping with health, education, relationships, social media content, games, and personal and career goals. He said AI agents could buy a shirt for your daughter or help researchers cure a disease.

"I think people are going to also be willing to pay a lot of money to have premium or high-compute versions" of AI that helps achieve goals, Zuckerberg said.

There aren't many details so far on the subscriptions, which were unveiled in an Instagram post by Meta's head of product. The company revealed pricing to some news outlets, including TechCrunch. The social media versions come with extra features, such as more analytics and methods to increase viewership for posts. The AI subscriptions offer the ability to do more complex tasks with higher caps on subscribers’ usage. Meta is also targeting businesses with paid AI tools that can answer customer messages, book appointments and close sales.

Meta faces an uphill battle with subscriptions

Meta subscriptions are a long shot for the consumer market. Users have gotten used to free social media, video, messaging and other features on Facebook, Instagram and WhatsApp. And the market for AI tools is fiercely competitive, as companies such as Google and OpenAI battle for consumers. It wouldn’t be surprising if Meta struggles to get the subscriptions business to a level that would be significant to its overall revenue.

But success is relative to Meta’s formidable scale. The company made $200 billion in sales last year and now has 3.5 billion people using at least one of its apps every day. With such a huge pool of potential customers to market subscriptions to, Meta could easily find millions of willing buyers, especially among dedicated content creators. Meta will likely offer the most cutting-edge AI features to paying customers, hoping to entice free users to upgrade.

Meta Subscription Plans

1. For Social Media and Messaging

• Instagram Plus: $3.99/month
• Facebook Plus: $3.99/month
• WhatsApp Plus: $2.99/month

2. For Creators and Businesses

• Meta One Essential: $14.99/month
• Meta One Advanced: $49.99/month

3. For AI Users

• Meta One Plus: $7.99/month
• Meta One Premium: $19.99/month

The challenge of tracking Meta’s AI spending ROI

One analyst asked Zuckerberg what he is watching to make sure there’s a healthy return on investment when it comes to the soaring capex spending. Zuckerberg said the focus is building a top AI model that goes head-to-head with Anthropic, Google, OpenAI and xAI. Then he said, "I mean like, I don’t think we have a precise plan for exactly how each product is going to scale month-over-month or anything like that."

The lack of specifics, tied to the exorbitant spending required to be an AI leader, is one reason some investors are concerned, even though Meta continues to point to growing ad sales as a benefit of AI.

However, part of this new AI vision fits with Meta’s tried-and-true strategy: Get users to stay longer and see more ads. Zuckerberg said AI helps Meta understand users in more detail and makes ads more effective. He also highlighted that Meta has always focused on building a huge audience first and making money later.

Meta says it has an escape hatch of sorts to avoid owning a glut of unused data center capacity. Zuckerberg says the company has flexibility to bring data centers online more slowly or reduce spending in future years. He’s even said Meta could launch a cloud computing business to rent out computing power to other companies.

Those ideas aren’t in the works yet. "Our experience so far has been that we have continued to underestimate our compute needs," said Susan Li, Meta's chief financial officer, on the earnings call. That’s mainly because Meta has aggressively rolled out free AI tools to both consumers and businesses, whether they want them or not.

This is not the first time Meta has pivoted

The transformation into an AI company, grounded by a massive social media platform, isn’t the first corporate shift. Recall how Facebook changed its name to Meta in 2021 to make a big push into the virtual reality-based metaverse, a move that hasn’t gained traction with users. That endeavor cost the company an estimated $80 billion.

It underscores Zuckerberg’s flexibility in pursuit of growth, like his abrupt shift to short-form video to compete with TikTok. That flexibility now underpins the bet on AI, his biggest one yet.

Editor's note: This story has been updated to reflect a quote is correctly attributed to Li.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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"When the robots take over, I hope they think of me fondly."

That tongue-in-cheek internet joke has turned sour with the increasing usage of artificial intelligence (AI).

Today, it's nearly impossible to click through a few pages without encountering an AI overview — a constant reminder of how much our relationship with information has shifted.

But while you might trust an automatic notetaker or suggested recipe, AI's latest trend, "vibe coding," could raise new concerns in the financial world as AI shifts from merely summarizing information to building the very tools we use to manage that data.

Vibe-coded programs are created entirely by talking to an AI assistant in natural language, like English. This approach lets anyone create an application without coding experience, which can be dangerous when unvetted apps are deployed with coding errors, security flaws, or compliance risks.

Confidential information, like tax data or account balances, could be exposed during security breaches or compliance issues, especially when those tools handle sensitive financial workflows. These risks might be even greater among retirement-aged adults, who are particularly targeted by AI tax scams, according to recent McAfee research.

So how do you know what's safely vetted by a financial professional versus produced by robots for you to consume?

From one human to another, here's how to tell the difference.

The problem with vibe coding in financial tools

Vibe coding is a way of building software in which you tell AI the high-level idea or "vibes" of what you want built, and the machine creates the program for you.

The AI does this by feeding your input into a large language model (LLM) like ChatGPT, Claude, or Gemini to translate your instructions into functional source code.

Not only is coding like this a fast and easy way to create a financial app, but it can also quickly generate retirement tax tools like IRA withdrawal optimizers and Social Security benefit calculators.

So what's the problem? Well, whereas "vibing" prioritizes speed, it often sacrifices quality and security.

• AI-generated code notoriously produces 1.7 times more coding issues, like logic errors and security vulnerabilities, than human-written code.
• This is because AI can use up to 10 times the lines of code as a trained programmer to build the same program (paywall).

But you might be thinking:

...Well, that's often easier said than done.

Maybe if you've used Adobe's attempt at "vibe coding" with Dreamweaver, you might know how easy it is to drag and drop a simple rectangle on the screen, only to discover later how much harder it is to sort through the machine-generated code when you want to change the rectangle's color.

(Pardon the tangent; I'm a CPA with computer science experience and so burdened with strange facts.)

In short, these programs need human oversight to be effective tools, which is a trend that financial and tax professionals are already experiencing as they work with vibe-coded tools in their offices. Otherwise, client information could be leaked to the whole internet to see.

Security risks for your retirement tax portfolio

Financial advisors are increasingly using AI to streamline retirement and tax planning. The CFP® Board of Standards has published its own handbook for CFPs to use to "Harnessing AI in the Financial Planning Profession."

Meanwhile, multinational investment firms like BlackRock have built AI into their advisor suites to automate tax-loss harvesting and model retirement outcomes.

AI efficiencies in retirement tax planning can also scan tax returns for overlooked deductions, forecast the top federal tax bracket you'll be in retirement, or even optimize your stock portfolio while you sleep.

Used the wrong way, vibe-coded applications (and AI tools in general) can pose a risk to your retirement savings data:

• Retaining your financial data. If you or your financial advisor uploads your Social Security number, income details, or past tax returns into an LLM, your confidential financial history could become part of the public domain or surface in other users' chats with the AI.
• Compromised retirement accounts. Employees at a financial or advisory firm may use unsecured AI tools on personal laptops rather than vetted, company-approved platforms. This "Shadow AI" lacks proper cybersecurity protocols, potentially leaving sensitive 401(k) and IRA data vulnerable to interception during tax-planning calculations.
• Multi-firm data breaches. AI systems aggregate large data sets on third-party cloud servers to function. If one financial or tax-advisory firm is cyberattacked, the tax data from connected users across all associated firms can be compromised, jeopardizing your data even if the breach didn't occur in your financial advisor's office.
• IRS penalties from AI "hallucinations." AI tools notoriously hallucinate information that sounds legit but is entirely wrong. If blindly followed, this made-up "advice" can lead to noncompliance with federal and state tax agencies, causing you to pay fees, fines, and penalties for faultily reported information (more on that later).

Altogether, unvetted "vibe-coded" apps can lead to significant financial losses for app users, whether you use a financial advisor or not.

Does it get worse? This month, cybersecurity startup RedAccess, which specializes in threat protection against generative AI leaks, reported that 40% of "vibe-coded" web applications identified in their research were actively releasing sensitive information, including financial data, to the World Wide Web.

Platforms like Lovable, Base44, Replit, and Netlify leverage AI to generate functional web applications from simple text prompts — many of these apps are "public-by-default," meaning they were indexed and searchable for the RedAccess team unless a user manually secures them.

RedAccess co-founder and CEO Dor Zvi emphasized the scale of the risk to tech author Andy Greenberg at Wired magazine. "The end result is that organizations are actually leaking private data through vibe-coding applications. This is one of the biggest events ever where people are exposing corporate or other sensitive information to anyone in the world."

Axios reached out to the studied platforms for comment. Lovable spokesperson Samyutha Reddy told Axios that RedAccess did not disclose a list of compromised URLs. Meanwhile, Replit CEO Amjad Masad claimed on X that RedAccess did not share which users were impacted.

How to spot AI-coded apps holding your financial savings

So how do you determine whether your financial savings information is sitting in an unvetted app by your financial planner vs. a "real" application safely vetted by a warm-blooded human being?

Although "vibe coded" isn't a legal term required in your registered financial advisor's paperwork for you to sign, transparency is still mandatory.

Regulators like the Securities and Exchange Commission (SEC) now actively pursue investment advisors for "AI-washing" — or, said another way, overhyping or misrepresenting the use of AI in their financial products.

Yet, depending on how your financial advisor uses AI, a disclosure might not be necessary (though the advisor remains legally liable for AI-washing). If that's the case, here are some "easy tells" to spot a vibe-coded app.

• Hyper-customized. The app does very specific things that generic software can't do (like having niche property management tracking tools).
• The "AI aesthetic." Despite the hyper-specific functionalities, the interface might look as generic as they come (flat layouts, default fonts, gradients, etc.).
• Fast delivery. If you suggest a change and your planner can deliver it the next day, the app may have been vibe coded.

If you have concerns about how AI is used in your relationship with your financial advisor (whether vibe-coded or not), consider asking the following general list of questions:*

1. How is security handled, and where is my data held?
2. Is my data training the AI?
3. How was the app tested?
4. Who owns and maintains the app?
5. Who is responsible for covering losses caused by the AI?
6. How does the AI verify its calculations?
7. What is the human review process?

You should also confirm your advisor's legitimacy by reviewing their Form ADV via the SEC’s website. Additionally, verify specialized certifications — like a CFP® or CFA® — via their respective certifying boards to ensure your advisor meets the highest ethical and educational standards.

If your financial advisor's application (even if vibe-coded) meets regulatory requirements and all other concerns you have, then — great! But if not, you may want to look for a different professional.

*Note: This list is not exhaustive and is compiled from several different sources of industry expert guidance.

Defend against AI-related compliance issues and tax scams

As mentioned, LLMs and AI are known to hallucinate or "make up" information. From a compliance perspective, the IRS doesn't accept "the AI made a mistake" as a legal defense.

So, for example, if the AI you used to prepare your income return "hallucinates" tax write-offs, incorrectly classifies taxable income, or generalizes federal tax laws and ignores your state-specific laws, you will be held liable. This can look like unpaid taxes plus interest and penalties — even if you were simply given "bad tax advice" by the AI.

The Taxpayer Advocate highlighted this when citing a Washington Post review of Intuit TurboTax and H&R Block's AI usage. The review noted that the two companies' chatbots "provided inaccurate or irrelevant responses up to 50 percent of the time when initially asked 16 complex tax questions."

The release went on to state, "Taxpayers are ultimately responsible for the information reported on their tax returns. Therefore, it is essential to review all information carefully, verify calculations, and seek assistance from qualified professionals."

Unfortunately, care with tax preparation doesn't stop during tax season.

AI has also given rise to year-round tax scams, particularly among adults 65 and older, according to the latest McAfee research. This age group reported just a 15% confidence level (out of 100%) in spotting these scams.

• Scammers use AI to clone the voices of family members or trusted tax professionals to demand payment for "clearing up" account issues.
• AI also allows criminals to generate perfectly worded emails that look and sound like the real deal, including IRS correspondence.
• Scammers can also use "vibe coding" to copy an existing, real website to trick you into uploading your information to what appears to be a legitimate website.

Former U.S. Secretary of Defense Robert McNamara once observed that conflict is often a mirror in his 1995 memoir, referring to the Cold War arms race and proxy wars:

This "security dilemma" now defines our AI-led age. As this technology accelerates our financial capabilities, it simultaneously equips bad actors with more sophisticated tools and security vulnerabilities. The result is a perpetual feedback loop where every defensive innovation triggers a more agile criminal counter-strategy.

Yet, focusing solely on the "AI arms race" risks obscuring the genuine utility of vibe-coded tools.

Beyond the scams and security loopholes, vibe coding is already helping several industries perform essential tasks. Some doctors are using platforms like Google AI Studio to "vibe code" personalized applications for patients, while vibe-coded applications can give students customized, interactive study tools.

In the financial realm, vibe coding can also aid your finance pro in tax planning and retirement strategy — if used safely.

The case for AI (and why it isn't all bad)

AI can be incorrect, insecure, and costly. But once you get past those faults, it can be a useful and unique tool. You just need to know what you're signing up for if your financial advisor uses AI.

A human-led, AI-assisted finance expert can utilize this technology to update their retirement tax plan with data-driven insights, like...

• Analyze vast amounts of financial data that would otherwise be unrealistic for a human to pore over.
• Stress-test different market scenarios, helping you to maximize your portfolio's growth.

But finding a reputable financial advisor is a must. Credible professionals translate the AI "legwork" into a tailored plan that aligns with your specific nest egg and lifestyle goals — they don't just do whatever the AI tells them to.

In today's day and age, more than half of Americans may be getting their financial advice from AI, which, for better or for worse, might say something about our society at large.

Regardless of which side of the aisle you stand on — pro-robot, pro-humanity, or a bit of both — stay vigilant and ask questions.

Your retirement tax portfolio may thank you.

....Maybe even literally. I don't know if they send "thank you" cards yet, but it wouldn't surprise me if they did.

Read More

• Don't let a 'mood' trigger a retirement tax trap.
• Here are the ways AI tax scams targeted middle and older adults last year.
• 'Vibes' aren't tax-exempt, but some states won't tax your retirement income.

To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

The semiconductor industry is booming, defying historical precedents as sales surge amid red-hot demand for costly artificial intelligence chips for data centers. The biggest problem chipmakers have now is that they can’t keep up with demand.

Skyrocketing chip sales push past $1 trillion

Global chip sales will soar past $1.3 trillion in 2026, a whopping 60% rise versus last year, the fastest pace in two decades, according to a recent forecast by Gartner, a tech market research firm. To put the staggering growth in context, back in 2021, industry analysts hoped that yearly sales would hit $1 trillion by 2030.

AI chips will account for 30% of chip sales in 2026. Meanwhile, rising memory demand has caused prices to explode, so memory chips are taking a far bigger cut of total chip revenue. Sales are powered by Big Tech spending that funds massive AI data centers. Alphabet, Amazon, Meta and Microsoft are planning more than $700 billion in capital expenditures this year.

NVIDIA is the biggest winner. Its AI chips used in data centers will see $333 billion in sales in its fiscal 2027 (this calendar year), up over 70% versus the previous year, according to a forecast by Morningstar. It’s the top chip seller, by far. Other chipmakers are riding the wave, including AMD, Samsung, Broadcom, Intel, SK Hynix and Micron. There’s rising demand for alternatives to Nvidia, too, such as chips from Alphabet, Amazon or start-up Cerebras.

AI is shifting what computer chips are needed

A major shift is underway for the chips needed to power soaring AI usage: More central processing units (CPUs) for every graphics processing unit (GPU). GPUs from Nvidia are used to train powerful AI models and also generate text, images, video, data, audio and more.

But CPUs excel at "agentic AI," the autonomous AI tools that take on traditional computing tasks, such as sorting files, querying databases and debugging code. The shift has changed the ratio of CPUs to GPUs needed from 1:8 to 1:4. It’s expected to move to parity or even flip slightly. NVIDIA has a stranglehold on the GPU market, but it also makes CPUs, and recently said it’s on track to make $20 billion in CPU sales this year.

Strengthening CPU demand has Intel’s revenue finally rising, marking the start of a comeback for the long-suffering chipmaker. The company’s bread-and-butter is CPUs and it missed out on the GPU boom needed for building top AI models.

"A year ago, the conversation about Intel was about whether we could survive," said CEO Lip-Bu Tan during Intel’s recent earnings call. "Today is about how quickly we can add manufacturing capacity and scale our supply to meet enormous demand for our products." That includes its flagship Xeon chip line for data centers.

AMD sales are exploding, too, as it looks to outdo Intel in the data center market. "With Intel facing supply issues and unable to meet the insatiable demand for CPUs, AMD benefits as a solid alternative," writes Neil Shah, analyst at Counterpoint research, in a recent article.

"We've seen how AI adoption is really unfolding [and] we're seeing significantly more CPU demand from really every major cloud provider as well as enterprise customers," said CEO Lisa Su in a recent earnings call. Su estimates the CPU market will grow 35% per year over the next five years.

Chip manufacturers race to meet the demand

When it comes to manufacturing leading-edge chips, Taiwan Semiconductor Manufacturing Company (TSMC) still dominates, with huge orders from Nvidia and Apple, plus Broadcom, AMD, Qualcomm and others. Even Intel outsources some advanced chipmaking to TSMC, whose sales will increase 30% this year. TSMC has so much business that some will shift to Samsung and Intel, the only other companies capable of making the most advanced chips.

Intel’s new business of building chips for other chip designers is starting to gain traction, with Apple as a likely new customer. Intel already has deals with Google, Nvidia and others.

Now analysts and investors are watching to see if Intel can get high-volume orders from these tech giants. Intel’s latest leading-edge manufacturing process, known as 18A, is promising, but the real traction happens with the next generation. Known as 14A, the tech is designed for outside customers and is set to go into mass production as early as 2028. Intel hopes 14A offers a direct challenge to TSMC’s tech leadership.

Morningstar forecasts that Intel’s separate business for outside customers, known as Intel Foundry, will make $16 billion by 2030 and $48 billion by 2035. "The Apple deal, if consummated, gives us more confidence in these estimates," writes Morningstar analyst Brian Colello in a recent research note.

A note of caution on chip stocks

When it comes to soaring chip stocks, watch AI spending closely. Many businesses spending money on AI are now scrutinizing the return on investment. Tech giants’ enormous spending assumes that AI demand keeps increasing, as customers find ways to boost productivity, increase sales or streamline operations.

Near term, there’s no sign of AI adoption or spending slowing. AI leader Anthropic, for example, continues to see exploding sales, mostly from businesses. This trend has caused an extraordinary run-up in chip stocks. But if spending starts to cool off and chip sales stumble, investors in chip stocks should look out below.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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From Virginia to California, more than 3,000 data centers are operational across America, with an additional 1,500 coming online. These data centers power the digital lives of people worldwide and make the AI revolution possible. They bring jobs and, in many cases, revitalize communities.

But at what cost to the retirees who live there? Are the improvements worth the side effects?

It's what residents of Box Elder County, Utah, are asking. They are fighting to stop a massive 40,000-acre data center project from going ahead, arguing that it will drain precious water resources, create relentless noise pollution, cause health issues and leave locals with the environmental fallout — all the while, the tech giants receive tax breaks.

They've heard the stories — towns running out of water or electric grids failing — and don't want to see their way of life disrupted as well.

"Data centers are bad for the people in the surrounding areas," says Breena William, a retiree who lives in Box Elder County, where the massive Stratos Data center is being built. "If you look at the experiences of other people, that's my understanding."

This map will tell you if a data center is nearby.

Living near a data center: the common complaints

When it comes to living near a data center, residents, environmentalists, lawmakers and consumer advocates point to several side effects that can negatively impact the quality of life. Some of them include:

Air pollution
Data centers emit hazardous pollutants, such as nitrogen oxides and fine particulate matter, increasing the rate of respiratory diseases and cardiovascular risk. These emissions are from two sources: the data centers themselves and their back-up (often diesel) generators, and the energy required, usually from the electric grid, to power these centers.

U.S. data centers are projected to contribute to nearly 1,300 deaths in 2028, resulting in a health burden of over $20 billion for Americans, according to Cornell University research.

Air pollutants are a concern for residents of Box Elder County. The proposed Stratos facility is expected to hasten the evaporation of the Great Salt Lake. The more of the lake bed that's exposed, the more arsenic and other heavy metals can enter the air.

"I have grandchildren with asthma. I'm really worried about them," says Williams.

Noise pollution
The sound of the generators and the heating and cooling systems creates a constant hum that can be heard hundreds of feet away. Day and night, residents complain of noise pollution that is not only annoying but potentially harmful to hearing.

That was the case for people living next to a data center in Brittany Heights, in Chandler, Arizona. The never-ending high-pitched buzz coming from the data center reportedly rattled windows at night.

It got so bad that residents complained they couldn't sleep and that the noise caused health issues, including vertigo and anxiety. The outcry eventually prompted the city to change its zoning laws to include sound mitigation ordinances.

Water consumption
Data centers need water to keep the computer equipment cool, with a large data center using as much as 5 million gallons of water a day. They tap local resources for that water, draining what is left for residents. It can become a problem when the data center is located in a region with limited water.

Residents of The Dalles, Oregon, learned that the hard way. Thanks to its energy infrastructure, water availability and vast land available for development, the community had become a favorite of many major tech companies looking to set down roots for data centers.

But when Google wanted to expand and wouldn't disclose how much water it consumes, it sparked a 13-month legal battle. When Google's water consumption was finally released, it showed the data center used more than a quarter of the city's water supply.

Higher utility prices
Data centers need a massive amount of electricity, but critics contend they don't pay full price for that. The burden is passed on to residential customers, who must pay more as a result.

A study by Carnegie Mellon University projected that, by 2030, the growth of data centers will drive regional demand by 20% to 30% annually. That will increase electricity bills by an estimated 8% nationally and up to 25% in some regions.

"These large data centers use so much energy, put so much pressure on the local utility systems, they have to invest in a ton of infrastructure upgrades," says Jenn Jones, vice president of financial security and livable communities at AARP. AARP is backing legislation that protects consumers when data centers come to their neighborhoods. "We don't think those costs should be borne by consumers, especially older adults living on a fixed income."

Older adults seem to agree. A national AARP survey found 78% of adults age 50+ believe large data centers should pay their own way, and 75% agree that state policymakers should act to protect consumers from rising utility costs tied to data centers.

Property value pressures

The jury is still out as to the impact living near a data center has on the sale of your home. In some areas, it can boost property values; in others depress them.

A 2025 George Mason study of Virginia, a state with a high concentration of data centers, found that they increased property values.

The reason: data centers tend to be located in areas that already have good roads, reliable utilities and are close to airports. Attributes that homebuyers also want.

But that isn't putting residents at ease. William of Box Elder County is worried that Stratos could make the land near it uninhabitable, which would drive the property values down.

"I think after this goes into effect, people won't be able to sell their homes, and people will be stuck," says William, who is worried enough to consider putting her house up for sale. "Something away from a data center, if we can find it."

Source: DataCenterMap

Can the benefits really be overlooked?

There are reasons to be concerned about the impact of data centers. But can some benefits be overlooked? After all, these data centers do pay taxes, bring jobs and build an infrastructure to support them. That can revitalize a community that would otherwise struggle. Tax revenue can fund community upgrades, pay for schools and provide services that retirees rely on.

According to the Data Center Coalition, an industry trade group, in 2025 the U.S. data center industry supported 5.5 million jobs, contributed $927 billion to the U.S. GDP and generated $204 billion in federal, state and local taxes.

"The trade-off of having data centers is they bring jobs, tax revenue, infrastructure and economic development," says Sanjay Patnaik, a senior fellow and director of the Center on Regulation and Markets at Brookings. "You need some economic activity in a county, otherwise you don't have tax revenue."

There's also the potential national security risk that Shark Tank's Kevin O'Leary — one of the investors behind the Stratos data center in Box Elder County, Utah — points to. He reportedly says the U.S. needs more data centers like Stratos to stay ahead of China in the AI "arms race."

Time will tell

Data centers will continue to pop up, and residents will lose some battles and win others in their fights to stop them.

But whether you support one coming to your retirement community boils down to a cost-benefit analysis. Are the economic benefits more important than the environmental and health risks?

"I would advise people to look at what the alternative is," says Patnaik. "Do they want to have a data center in their backyard and get some economic growth, or do they want nothing in their backyard?"

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SpaceX's S-1 filing is finally public, and for investors, the biggest mistake would be looking at this company as a rocket launcher.

That is not what SpaceX is anymore.

This is now a vertically integrated infrastructure company sitting at the center of space transportation, global communications, defense, artificial intelligence, compute, data transport and, potentially, chip manufacturing.

That is why the S-1 filing matters so much. It gives public market investors their first real look at a company that has been private for more than two decades and is now preparing for what could be the largest IPO in history.

The first thing I would tell investors is this: Do not get distracted by the headline valuation alone (about $1.75 trillion to $2 trillion, according to Reuters).

The real question is whether SpaceX is building the infrastructure layer for the next generation of the economy. In my opinion, that is the only way to evaluate this company.

Starlink is still the primary revenue driver

A lot of investors still think of SpaceX through the lens of launches. Falcon 9, Falcon Heavy and Starship get most of the attention because rockets are exciting. But from an investor perspective, Starlink remains one of the most important parts of the story.

The filing shows why. Starlink is not just a satellite internet product. It is global connectivity infrastructure. Residential broadband is one piece of it, but the bigger opportunity is much broader: Enterprise connectivity, government contracts, military communications through Starshield and, eventually, direct-to-cell service.

That last piece is important. SpaceX's spectrum strategy and direct-to-cell opportunity could eventually put the company in competition with wireless carriers such as AT&T, Verizon and T-Mobile.

It is still early, and investors should not assume that will happen overnight. But the direction is clear. SpaceX is not only trying to connect homes. It is trying to own the infrastructure that connects people, devices, governments and machines anywhere on Earth.

For ultra-high-net-worth investors, that is the point. The opportunity is not simply broadband revenue. The opportunity is platform control.

Governance will be a real concern for institutions

The filing also raises a governance issue that institutional investors will have to wrestle with. SpaceX is using a dual-class share structure, with Class A shares carrying one vote and Class B shares carrying 10 votes. Elon Musk is expected to retain voting control.

For many institutional investors, that is a red flag. They generally do not like one person having that much control over a public company.

I understand that concern. But I also think SpaceX is an unusual case. In a normal public company, this level of control might be a deal breaker. With SpaceX, the market may decide that access matters more than governance.

That does not mean investors should ignore the risk. They should price it in. Musk's control means investors are not just underwriting the business. They are underwriting his leadership, decision-making and long-term vision.

Personally, I prefer that he maintain control, because this is not a company that can be managed quarter to quarter. SpaceX is attempting projects that require long time horizons, massive capital investment and an unusually high tolerance for failure.

Old-school governance may not fit a company trying to build reusable rockets, orbital data centers and a Mars transportation system at the same time.

The xAI burn is big, but look at the compute revenue

The most obvious financial concern is capital intensity, especially around xAI. The filing shows just how expensive the AI side of the business is. The capital expenditures by xAI are massive, essentially running at a scale that looks like roughly a billion dollars a month.

That will scare some investors. I get it.

But the mistake would be looking only at the burn rate and ignoring the revenue opportunity that compute creates. The Anthropic compute agreement is one of the most important pieces of the filing. SpaceX is effectively monetizing Colossus compute capacity, and that deal generates roughly $1.25 billion per month through 2029.

That changes how investors should look at xAI. Yes, it is capital-intensive. But if the company can turn compute into recurring revenue, the story looks very different.

AI infrastructure is expensive for everyone. The question is who can lower the cost curve over time and control enough infrastructure to turn that spend into a durable business.

That is where SpaceX has a potential advantage.

Starship is the execution risk investors cannot ignore

Starship is crucial to the long-term thesis. If Falcon 9 made space more reusable, Starship could make space scalable.

That matters because almost every major future opportunity depends on lowering launch costs.

• More Starlink satellites
• Larger payloads
• Orbital data centers
• Moon infrastructure
• Mars
• Defense systems
• AI compute in space

Right now, the economics of space-based infrastructure depend heavily on the cost per kilogram to orbit. If SpaceX can bring that cost down meaningfully, the entire business model changes. If it cannot, many of the most ambitious opportunities remain theoretical.

That is why Starship execution is one of the biggest risks in the IPO filing. Investors should watch test flights, FAA approvals, launch cadence and reusability progress closely. A successful Starship program could accelerate nearly every segment of SpaceX. Delays or failures could slow the entire thesis.

The AI story is bigger than Grok

Retail investors may focus on Grok because it is the most visible part of xAI. I do not think that is the right way to look at it.

The bigger AI story is infrastructure: Compute, coding, data centers, chips and distribution. The potential Cursor deal matters because coding is one of the areas where xAI needs to compete more aggressively with Anthropic and OpenAI.

Terafab also matters because chip manufacturing and compute supply are becoming strategic necessities.

The companies that win AI will not just have the best chatbot. They will control the infrastructure that makes AI cheaper, faster and more scalable.

That is why orbital data centers are so important. They are still unproven at scale, and investors should treat them as a high-risk, high-upside part of the thesis. But if SpaceX can make space-based compute cheaper than Earth-based compute, it could change the economics of AI infrastructure entirely.

Unlimited solar power, fewer cooling constraints and no "not in my backyard" data center politics could become a serious advantage. The key word is "could." It still has to be proven.

The infrastructure thesis investors should understand

SpaceX is not being valued as a rocket company because it is no longer just a rocket company.

The filing makes that very clear. This is now a communications, transportation, AI and infrastructure company all under one roof.

Yes, there are risks.

• Starship still needs to execute
• AI remains extremely capital-intensive
• Regulatory approvals will matter
• Some institutional investors will likely remain uncomfortable with Musk's level of control

But in my opinion, many investors still are not fully understanding where this company is going. If SpaceX succeeds in scaling Starlink, lowering launch costs and building orbital infrastructure for AI and global connectivity, it becomes very difficult to see who can realistically compete with it.

That is why I believe the market will eventually look back at this IPO as far more than just another technology listing. It could end up being the moment investors realized SpaceX was building the infrastructure backbone for the next generation of the global economy.

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• Hot Upcoming IPOs to Watch

This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Trust is in short supply these days, and not just in Washington.

Across American workplaces, confidence in leadership, information and intent has eroded, according to Gallup, in subtle yet deeply consequential ways. The result isn't just cultural discomfort; it's a direct hit to productivity, collaboration and performance.

Politics will grapple with the trust deficit in its own fashion. In business, however, the responsibility is more immediate and more actionable. Rebuilding trust isn't a messaging exercise: It requires a fundamental shift in how leaders show up, communicate and make decisions.

Why now? The ground has shifted. Employees operate in a more complex, skeptical information environment. The line between fact and fiction feels more blurred than ever.

Conflicting narratives and AI-driven deepfakes amplify this confusion. In this environment, trust is no longer assumed. It has to be earned — deliberately and consistently.

Adapting to an AI-integrated workplace hinges on one thing: Trust. Not the kind you frame on a wall, but trust that is built into the operational fabric of the organization. It shows up in how communication flows, decisions are made and how organizations learn at scale.

This is especially true now. People curate their own information streams and shape their own versions of reality, typically validating their existing world view. Inside and outside the workplace, individuals gravitate toward like-minded perspectives. Cliques reinforce these viewpoints.

Breaking through these filters requires leaders to be deliberate, consistent and credible, which requires them to go beyond traditional culture efforts.

The bottom line: Barriers are going up inside your organization. It's up to capable, forward-thinking leaders to break through them, earn buy-in, build confidence and make a clear, credible case for the path forward.

Fear of losing control

According to the 2026 Edelman Trust Barometer, a global survey of more than 33,000 people across 28 countries, 70% of people are now unwilling or hesitant to trust someone who differs from them in values, background, culture or approach to social issues.

This isn't polarization anymore. It's something more insidious: Insularity. And it's quietly destroying collaboration, productivity and innovation in workplaces everywhere.

The consequences are stark and measurable. Forty-two percent say they would rather switch departments than report to a manager with different values. Thirty-four percent say they would put less effort into helping a project team leader who has different political beliefs.

This isn't about office politics or personality conflicts. This is a fundamental breakdown of the social contract that underpins organizations' functioning.

When teams can't trust across differences, projects stall. Innovation dies. The best ideation is disrupted due to the lack of cognitive diversity and constructive conflict.

The result: People self-segregate into their ideological comfort zone.

Even within this environment, there's a clear path forward if leaders address the underlying dynamics head-on. Start with a simple reality: A meaningful segment of your workforce will be hesitant to embrace any change and specifically AI-driven change designed to boost productivity gains.

Why? It's not fear of change. It's fear of losing control.

AI, by its nature, makes people feel displaced in their own roles. According to a 2026 ADP research survey, only 22% felt their job was safe from elimination, with workers reporting feeling less certain about where they fit, how decisions are made and what remains in their hands.

That perceived loss of control fuels anxiety. The issue isn't the technology itself; it's the uncertainty it creates. When people feel they no longer have agency, resistance follows. Understanding that distinction is the first step to address it.

Leaders must recognize this fundamental shift in the workplace. Focus on empowering your people to expand their capacity to process everything happening. The only way to expand it is by raising their level of awareness.

The co-creation principle

There is a practical way through this: Involve people in building what comes next because people commit to what they help create.

As AI integration reshapes workflows, decision-making and expectations, employees' voices should be integrated into the design process. Let them help choose what tools are used, how processes evolve and what new norms take hold. Participation drives ownership, and ownership drives adoption.

That's why the age of AI isn't just a technology shift — it's a cultural one. And culture still does what it has always done: It determines whether strategy actually works. You can have the best AI roadmap in the world, but if the culture resists it, progress stalls.

In the past, organizations could operate with a baseline level of skepticism and still function. In an AI-driven workplace, that's no longer the case. Without trust, adoption slows, collaboration weakens and productivity suffers. Building that trust isn't optional. It's fundamental.

For organizations, the path forward requires discipline. Recognizing the environment you are actually operating in, not the one you wish existed. Then, take a hard look inward. Where are you misaligned between intention and execution, strategy and rewards, culture and compensation?

From there, move into action. Build human verification into AI workflows. Train your systems properly, because the old rule still applies: Garbage in, garbage out.

Just as important: Design environments that help people stay grounded and focused. Invest in practices that build resilience and expand capacity across the organization.

And through it all, engage your employees in the process. Give them a voice in shaping change.

I said it before and I will say it again, people commit to what they help create. In a low-trust, AI-driven workplace, that may be the most important advantage you have.

So take the lead: Invite your team to co-create. Organizations that figure this out won't just survive the age of insularity; they'll define what workplace success looks like on the other side.

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• Looking to Adopt AI in Your Finance Org? Consider These Factors First

This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

Businesses need to prepare for state privacy law enforcement, and fast. Total privacy fines in all 50 states soared to $3.4 billion in 2025, versus $1.8 billion in 2024, according to a recent analysis by Gartner, a tech market research firm.

For perspective, the total was just $1.2 million in 2023, before a frenzy of new state enforcement action took off. Penalties hit companies of all sizes and across industries: Health care, finance, software, communications, insurance, entertainment, retail, tech, legal, advertising and more.

The payouts stem from both state fines and lawsuits brought under these laws by injured parties. The state laws enshrine consumer rights and data protections that companies must abide by. Gartner says 22 states have passed privacy legislation aimed at consumer rights and another 24 are expected to pass privacy legislation in the coming five years.

State regulations related to the use of AI are expanding fast, too. More than 100 state laws covering artificial intelligence were passed last year, while state agencies also issued new AI-related guidance. For example, in California, the leader in privacy enforcement, a rule covers automated decision-making in job recruitment and employment.

"A lot of organizations have let their privacy policies atrophy," says Nader Henein, an analyst at Gartner who compiled the data. Many privacy programs were implemented years ago when California passed its sweeping privacy law in 2018, which was enacted in 2020. The landmark law provides consumers the right to know about the personal information being collected from them and how it’s used; the right to delete personal info collected by the company; and the right to opt out of the sale or sharing of personal info.

Notable Enforcements by the California Privacy Protection Agency in 2025

• $1.35 million fine against Tractor Supply
• $345,178 fine against Todd Snyder Inc., a clothing retailer
• $632,500 fine against American Honda Motor Co.
• Forced the shutdown of Background Alert, a data broker

"Companies should dust off their privacy program and assess whether it works," says Henein. They should check to make sure those policies are being implemented and cover new rules being rolled out. Gartner also recommends focusing on the online user experience, since most of the violations come from the privacy user interface, such as website privacy notices.

Companies doing business in multiple states should default to the one with the most onerous rules. For example, if consumers have a right to their personal data within 45 days of requesting it in one state, and 30 days in another, the target should be 30 days across the board, says Henein. In practice, that could mean a 20-day policy to ensure compliance.

"Most business-to-consumer companies have bought privacy software," says Henein. Vendors include OneTrust, TrustArc, Osano or TrueVault, and the software often includes AI-related governance. Nader says business-to-business companies, and smaller companies that cater to consumers, may use a more piecemeal approach.

These mounting compliance costs are sure to catch the attention of Congress. But a federal law that preempts the states is unlikely anytime soon, leaving businesses to cope with a patchwork of state laws.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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It's hard to avoid stories about AI in the news or on social media these days. The technology is widely expected to be transformative, although the extent and the speed at which it will impact society is not well understood.

Anthropic, the maker of AI model Claude, has been in the news lately for several different reasons. In late March, the company admitted it had accidentally posted some of Claude's source code online.

While this didn't expose any user data or confidential information directly, there is some concern that bad actors could use this information to build rogue agents or find ways to inject malicious commands into the tool's workflows.

Meanwhile, it has also introduced its next big model: Mythos (paywall). This AI model is said to have the ability to find thousands of vulnerabilities in corporate software, and is likely to be able to find unknown vulnerabilities autonomously.

Jamie Dimon, CEO of JPMorgan Chase, mentioned Mythos on a recent earnings call, suggesting our entire financial system could now be at risk from cybersecurity threats because it is so interconnected. Exposing potential failure points impacts the whole system.

In general, as AI companies like Anthropic, OpenAI, Meta and others all race to release new tools with the best features, attention to security seems to be falling by the wayside as speed is prioritized.

As an individual and an investor, how do you help protect yourself given the vulnerabilities, both known and unknown, that AI is exposing?

Control what you store in AI's memory — and what you connect it to

For starters, be prudent with the personal information you share with AI tools, such as ChatGPT, Claude, Gemini and Perplexity. These tools typically store your data by default unless you tell them not to.

They do this to enhance the user experience and make your chat results more customized over time. They might even seem to read your mind when you ask questions in the future.

However, this does mean they may be storing your preferences or information you share, such as your occupation, personal information gleaned from your questions and any documents and images you upload.

If you share medical records or questions, they may also store highly confidential facts about your health history.

When you also consider that bad actors on the internet are looking for ways to trick AI tools into sharing user data — a technique called "prompt injection" — it seems sensible to limit the sensitive information you hand over.

When setting up an account with an AI tool, consider strongly whether or not to disable the memory functions. Doing so in just a few clicks could help reduce the chance that a potential future data breach would expose personal data you don't want disseminated.

Keep your devices up to date

We may soon be seeing a flurry of critical software updates from big tech companies, such as Microsoft, Google and Apple, as they use pilot access to Mythos to identify and remediate vulnerabilities not previously detected.

Keeping your devices up to date will be crucial. Other AI models currently in development, not just Mythos, will likely make this our new reality. People holding on to old computers or smartphones no longer receiving software updates may be putting themselves at a higher risk of being accessed by bad guys.

Keep your data private

Social media apps leave all privacy protections turned off by design, unless you proactively enable them. That means apps like Facebook and Instagram collect as much information as possible regarding your tastes and preferences, and services like Google will store your browsing history and location data if you use features like Google Maps.

If any of these services are hacked, your personal data could potentially be at risk.

As AI agents can now connect to other services to automate work for you, there is always the possibility that personal data stored in an app you use could be shared with other systems.

For example, Claude can now connect to Gmail and Google Drive (when you choose to give it access), giving it the ability to search through your emails and files. That could be a great productivity enhancement for some of us, but it is important to think through the privacy implications.

Similarly, automation tools like Microsoft Copilot Tasks allow an AI agent to perform repetitive tasks in a browser for you, but it may require access to specific website usernames and passwords and store those to perform routines on a schedule.

Limit public information about you on the internet

Data breaches are still a major risk to individuals and could become more common if models like Mythos fall into the wrong hands. To guard against that, consider limiting how many websites you have accounts with and how many sites contain your personal data.

You can also ask data broker sites to delete your personal information or pay services such as Incogni or Optery to do it for you. This doesn't ensure 100% deletion of your data from the internet, but it does reduce its footprint.

Part of this effort should also include limiting the number of websites you create accounts with and give your phone number, address or credit card details to. When you sign up for rewards programs or shopping accounts, it all adds incrementally to your digital footprint.

As the possibility of breaches due to new vulnerabilities increases, any site that contains personally identifiable information for you could be used in future phishing or impersonation attacks.

Also, remember that AI training model data may include information publicly available on the internet, so it may also scrape any information posted about you online.

Choose the financial institutions you work with

Thinking about how this could impact you financially, the risk is that increasingly sophisticated AI models could potentially be used to hack banks, exchanges or brokerages, putting your money and financial information at risk.

Individual investors may not be able to do much about this on their own, apart from choosing to work with larger financial institutions with stronger cyber protections and larger IT departments actively engaged in preventing intrusions.

Smaller financial institutions will need to be adept at managing their technology and patching any gaps in their security.

Perhaps over time, AI could be used to improve cyber defenses, but in the short run, new AI model functions may be tilting the balance in favor of bad actors. The prospect of having an adversary probing and testing for vulnerabilities in the software and systems of banks and exchanges will be daunting for security professionals.

Institutions may need to dedicate more money and resources to protecting their environment as technologies evolve.

We will all need to continue to be diligent with our security by moderating what data we share with AI tools so we can leverage their abilities.

The old adage "trust but verify" seems relevant here. Make sure your online accounts are not sharing your personal details by default, and be skeptical when allowing AI tools to connect to your email and data storage accounts.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

If cutting-edge artificial intelligence could truly tame the daily onslaught of emails, it would make a huge difference for tens of millions of workers. Microsoft, which dominates workplace email with Outlook, thinks it has a solution with its AI-assistant, Copilot.

"Until now, Copilot in Outlook helped with the task in front of you: drafting an email, catching up on a long thread, or finding a time to meet," Microsoft says in a recent blog post. "Copilot in Outlook is now agentic, taking on the ongoing work of running your inbox and calendar."

That tech update, if it works well, means that a helpful tool is turning into a true personal assistant, with the ability to tackle complicated inbox tasks. Microsoft says its AI tool can automatically schedule meetings, send emails to coworkers and recommend which meetings to decline. It promises to automate follow-ups with employees that haven’t responded, pinpoint messages that are urgent and reschedule conflicts.

To make the system work, users write simple commands for the Copilot chatbot. For keeping tabs on responses to an important project, Microsoft says to tell the Copilot chatbot to "Identify people who haven’t replied to my emails after 24 hours, prioritize the ones that matter most, and draft polite follow-up emails for me."

For complex emails, the company suggests writing, "Pull the latest updates on [project name] over the last week. Draft a confidential, high‑importance update email for my manager." After a week off, try telling Copilot, "I just returned from vacation. Help me catch up: summarize what I’ve missed, highlight what’s most urgent, and draft a short briefing email. Then suggest emails I can safely archive and 1-2 tasks I should focus on first."

This type of automation holds promise but could also create new problems. If workers are increasingly responding back and forth to AI-created messages rather than human-written ones, there could be more confusion and miscommunication. The accuracy of automated decision-making and AI’s ability to understand nuance are other key elements that will make or break the tech.

As these capabilities roll out to tens of millions of workers, it could mark a major shift in workplace communication. The potential for time savings is very real, especially for managers, salespeople and other workers inundated with messages that require a response.

The AI-enhanced features are currently available for those in a Microsoft AI early adopter program and require a Copilot subscription, which ranges from about $20-$30 per user per month. Expect it to be widely available soon for Copilot subscribers.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Controversy swirls over whether AI is a bubble — and whether the excitement about its potential is overblown and will one day peter out.

But many people don't know that they are already using AI — and they're using it quite a bit. If more people knew what AI does, how it does it and what changes it has brought, they wouldn't believe it's a boom/bust industry.

According to a recent study by Gallup Newsletters and Telescope, 99% of Americans have used a product that uses AI as part of its operation, but only 64% of those people realized that AI was involved.

Stanford University's HAI Institute, in its latest Index Report, states, "AI is poised to be the most transformative technology of the 21st century."

The Index Report points out that AI is growing rapidly, and in the year 2023, the number of AI-enabled medical devices approved by the FDA was 223. This was up from only six approved in 2015.

Another rapidly growing AI technology: Self-driving cars, which offer service to a large and rapidly growing customer base. As of 2024, Waymo reported it was operating more than 150,000 weekly autonomous rides.

Private U.S. AI investment has also grown, hitting $109.1 billion in 2024 — far more than any other country. The next-highest country was China, at $9.3 billion.

What the experts say

Tech executives and experts expect AI to continue flourishing. In a recent Nvidia blog post, it was noted that, during the keynote address at a company event, CEO Jensen Huang highlighted the rise of AI companies such as OpenAI and Anthropic. "This last year, it just skyrocketed," Huang said, pointing to the $150 billion that was invested in venture startups.

He added that demand for Nvidia GPUs (graphics processing units) is "off the charts" and that he believes "computing demand has increased by one million times over the last few years."

Tim Bajarin, a prominent tech consultant and a principal at market research firm Creative Strategies, also doesn't think AI is a bubble. He told me in an interview that he thinks now is a good time for AI. "We only know about an eighth of what it can do," he said.

"Many companies are (already) using intelligence to answer questions from their customers," he pointed out, "like, 'Where's my order?' or 'When will it be shipped?'" A search engine, he said, can answer questions like that quickly and cost effectively.

While acknowledging that many people worry that AI will increase unemployment, Bajarin notes that AI could also create jobs. "Some jobs can be automated. We're already seeing that. Salesforce laid off 4,000 customer service people because talent was not being used."

He recommends that workers learn to use AI tools and strive to be part of AI's development.

AI is a developing tool

All that said, AI's future results might end up being in the middle of present expectations — not as miraculous as some think, nor as bogus as others believe.

We know that AI is helpful for personal use and is used in important industries like healthcare, transportation, entertainment and finance.

Companies are investing real dollars in AI, making its growth more certain. Investors could consider investing in the sector. Many tech companies are tied to AI, and their stocks could trade with the sector's growth.

Some investment choices to consider

Nvidia (NVDA) continues to look interesting, even after climbing over 1,300% in the past five years. Morningstar shows good earnings growth, and with its current PEG ratio of 0.50 on March 15, its market price is quite reasonable.

A package approach could be a good choice for investors wanting to spread the risk, and some small-cap stocks offer value. Using the iShares Russell 2000 ETF (IWM) as an example, small-cap stocks have been outperforming the S&P 500 year to date as of March 15, 2026.

The Invesco S&P SmallCap Information Technology ETF (PSCT) includes stocks of companies that supply information technology products and services. Over the past year, as well as year to date, PSCT has outperformed the S&P 500 index, as of March 15, 2026.

Morningstar shows PSCT with a PEG ratio of 1.20 based on future earnings. This is a reasonable ratio. PSCT showed a loss of $1.29 per share in past earnings.

Another interesting ETF is the Roundhill Generative AI & Technology ETF (CHAT). CHAT holds the stocks of global companies, including emerging markets, that are involved in AI. The ETF is invested in large-caps, and its largest holdings include Alphabet (GOOGL), Nvidia, Samsung Electronics, Microsoft (MSFT) and SK Hynix, an emerging markets technology company. (Note: My clients hold NVDA, and I hold CHAT.)

CHAT has outperformed the S&P 500 index in both the past six months and the past five years, as of March 19, 2026. It also seems reasonably valued. Morningstar's PEG ratio is 1.04. Note that Morningstar estimates that future long-term earnings could be down about 30% from past historical earnings. Based on the lower estimates, however, the PEG ratio would be a reasonable 0.71.

Where we are

So, where does this leave us on the "AI bubble" question? The controversy over whether a bubble exists seems to miss the fact that most of us are already using AI daily, often without realizing it.

We're at only the beginning of learning AI's potential, and we're seeing massive investment and rapid growth across major industries like healthcare, transportation and finance. So while the future may not be as miraculous as some predict, nor as bogus as others fear, the data confirms that companies are investing real dollars, making AI's continued growth more certain.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

The gap between firms that have built AI workflows and those still running on legacy tools is widening.

Firms with integrated AI are doing more client work with the same staff. If you are still evaluating, you are not holding your position. You are losing ground.

Google's AI ecosystem is worth a close look because it runs on infrastructure your firm most likely already has. If you use Workspace, you already have the foundation.

The suite adds Gemini, NotebookLM, Nano Banana Pro, Veo 3, Flow, Google Vids and Whisk on top of what you already pay for. Apply any of them with structure and the returns are immediate.

Each section below covers what the tool does and where it fits in a real advisory workflow.

1. Gemini: The core engine behind Google's ecosystem

Gemini is the model powering most of Google's AI strategy. It handles text, images, voice and code, and it reasons across all of them. Google embedded it directly in Workspace, so your team can access it without leaving Gmail, Docs, Sheets, Slides or Meet.

Where it fits: Gemini handles drafting work your team already does manually: Market commentary, client letters, meeting summaries and internal memos. It also summarizes long email threads and cuts the time your team spends on administrative preparation.

The bottom line: Gemini is not a standalone product. It is an engine that strengthens the workflows your team already runs in Workspace. If you run on Google, you have it today.

2. NotebookLM: A research assistant for content-heavy firms

NotebookLM lets you upload source documents and query them conversationally. Load it with regulatory updates, investment research, compliance memos or training materials and it processes everything, so your team can ask questions and get structured answers without reading through every page.

Where it fits: Your analysts can get a summary of a 40-page regulatory update without reading it in full. Your advisors can pull client-ready talking points from research reports they do not have time to process. Your compliance team can cross-reference documents without handling each one individually.

The bottom line: NotebookLM removes the single most consistent drag on advisory productivity: The time it takes to read and synthesize large amounts of written material. If your team is buried in documents, start here.

3. Nano Banana Pro: Google's image generation and editing system

Nano Banana Pro is Google's image generation and editing model, built on Gemini 3 Pro and released in November 2025. It creates branded visuals and infographics at up to 4K resolution, with precise control over lighting, color and layout. It also renders accurate text in multiple languages directly in the image.

Where it fits: Your marketing team can produce graphics for newsletters, webinars and adviser presentations without waiting on a designer. Your advisers can build visual aids that help explain financial concepts to clients without needing a creative team behind them. If you serve international clients, the multilingual text rendering adds practical range.

The bottom line: Nano Banana Pro cuts your design cycle and your cost per piece. You spend less time waiting on outside creative resources.

4. Veo 3: Cinematic video generation for professional storytelling

Veo 3 is Google's video generation model. Feed it a script, a prompt or reference images and it produces cinematic-quality video with realistic camera motion, lighting and scene composition. The output quality is approaching commercial production standards.

Where it fits: You can use Veo 3 for quarterly market updates, conference openers and adviser-produced client communications. Your investment team can convert a complex research thesis into a two-minute video a client will actually watch, without a production crew. Your advisers can record personalized welcome videos on their own.

The bottom line: Veo 3 is for firms that want to compete on content quality without the cost of an in-house studio. The production barrier drops. The brand quality does not have to.

5. Flow: The editing and production system built for scale

Flow is Google's video editing and production platform. It does not generate video. It takes what you already have, assembles it into branded scenes, and outputs a finished cut ready for distribution.

Where it fits: Your marketing team can cut a long webinar into usable short clips in an afternoon. Your branch offices can produce adviser content to a consistent standard without rebuilding the process each time. Your operations team can turn a new policy into a training video the same day it is written.

The bottom line: Flow enforces consistency at scale. The quality of the output does not depend on who built it.

6. Google Vids: Fast script-to-video creation

Google Vids converts a script into a short video using stock footage and basic narration. It is the lightest tool in the suite and the one with the lowest barrier to entry.

Where it fits: Use it to build onboarding content and adviser training modules. It is also the right tool for policy updates and social posts where the message needs to be short and the production needs to be fast.

The bottom line: Google Vids is where to start if your team has never produced video. The learning curve is flat.

7. Whisk: A creative ideation tool for campaigns and branding

Whisk helps your team explore visual directions and campaign concepts before committing to production. It generates mood boards, color palettes and layout concepts so your team can align on creative direction before anyone spends real budget.

Where it fits: Your marketing team can use it to pressure-test visual concepts for a campaign or rebrand before spending anything on execution. It is cheap to explore and expensive to redo.

The bottom line: Whisk is a planning tool. Settle your creative direction here before you commit production budget to Veo 3 or Flow.

8. Workspace AI: The quiet productivity driver

The AI features built directly into Workspace often deliver the fastest return because they require the least disruption.

Your team can summarize meetings and write first drafts with fewer manual steps. Sheets and Slides get the same treatment. No new software. No change management program.

The bottom line: Workspace AI is where most firms should start. The lift is immediate and the barrier to entry is near zero.

Strategic recommendations for firm leaders

Getting value from these tools requires more than access. Before you deploy broadly, do these five things.

1. Start with NotebookLM or Workspace AI. Neither requires new infrastructure or a rollout plan. Both connect to content your team already produces. You will see measurable time savings within the first month. Prove the value before you expand.

2. Lock down your data controls before anything goes live. Confirm that client and firm data will not feed model training. Review your data processing agreements with Google. This is a compliance prerequisite, not a configuration detail.

3. Put a review process in writing for client-facing content. A review by an adviser, compliance or marketing is needed before anything reaches a client. Document who reviews what, and when, before the first AI-assisted piece leaves your firm.

4. Build prompt templates and a short style guide before you deploy broadly. Consistent outputs require consistent inputs. One day of preparation prevents weeks of inconsistent output.

5. Measure outcomes from day one. Track turnaround time, content volume and team member satisfaction. The data you build now is what justifies the next phase of investment.

Conclusion

Google's AI ecosystem gives your firm a direct path to better client communication at lower operating cost. These tools do not replace your advisers or your compliance process. They give your team more capacity to do the work that builds client relationships.

Your competitors are not waiting. The firms building structured AI workflows today are gaining a cost and capacity advantage that compounds every quarter. The ones that hold off will keep subsidizing the gap with margin, and eventually with clients.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Just about every day now, I get emails and phone calls from readers asking the same question: "With what we are seeing, AI is leading to the wholesale firing of hundreds of thousands of employees across entire industries. Should I consider law as a career?"

My answer: Let history be your guide. Consider these questions:

• What was the dominant means of transportation in the early 1900s in villages and cities across our country?
• Who, and in which profession, assured their dependability?
• How many of them were there in the late 19^th century? How many now?
• What one factor explained their vanishing as a primary industry, and when it first emerged, was it seen as a threat to their profession?

Indispensable to daily life

The answer: The village smithy, aka the blacksmith, assured that horses could provide the transportation that was indispensable to daily life. Blacksmiths numbered in the hundreds of thousands in the late 19^th century, but there are only about 10,000 today.

When the first automobiles hit the market, they were seen as playthings of the wealthy, not a threat to the respected role blacksmiths held in society.

I submit that AI is today's equivalent of the automobile, and it's having a similar impact on the legal profession as law firms no longer require dozens of junior lawyers to search through millions of pages to find a smoking gun when AI can do it, and so much more, in minutes.

Solving a problem without a lawyer

One of my clients, "Dr. Dan," called the other day, thrilled to tell me how he'd used AI to avoid becoming the victim of his landlord's unreasonable requests. "She sent a modification to our lease that made no sense at all. I could have called you, but first I asked AI if this was legal and reasonable and how could I politely reply.

"AI said that it was not legal and asked if I wanted a response that I could send, in lawyerlike language, explaining why the landlord was wrong. I typed, 'yes,' and in a couple of seconds, I got a beautifully reasoned response, which I sent to her.

"She phoned me right back saying, 'Dan, you're right. But tell me, how much did your lawyer charge you for this letter? It is so well written!' We had a good laugh. I think AI is going to put a lot of lawyers out of work."

What do law school deans and admissions officers say?

To get a feel for what the top brass in higher education have to say, I reached out to several deans and admissions officers and left this voicemail: "How will AI impact law? Today, would you advise a family member to apply to law school? Also, can you explain the enormous increase in applications to law schools the past several decades?"

Two agreed to discuss these issues on condition of anonymity so they could be perfectly candid, and both noted one of the most important reasons we have seen an enormous increase in the number of law students over the past few decades: Money (for the schools).

"The growth in law school enrollment hasn't matched the actual need for new lawyers," said the dean at a Midwest law school. I'll call her Anna. "The ratio of lawyers to the U.S. population today is four times what it was in 1970, and many of our graduates are underemployed and facing enormous student loan debt.

"The explosive growth in admissions over the last 50 years was primarily because law school is a cash cow for universities.

"We can't lose — but no one cared if students would get hired into positions where they could have a decent life and repay their loans.

"And the more students we admit, the greater the damage will be due to AI. Law schools are motivated by tuition, not placement. It is as if we are graduating manual laborers into a robot-factory economy, and there will be pain."

Big, bad gamble

The dean of a Southern law school, whom I'll call Stacey, pointed out, "As we speak, law school enrollment is surging, and many students are making a horribly bad bet on future, high-income employment. The supply-and-demand mismatch will be horrible as AI adoption scales upward."

Both educators provided this gloomy outlook on the future: Law school graduates of 2027 and 2028 will be entering a job market where AI has progressed from the testing stage to real, daily operational functionality.

Their advice: Look for a law school with a curriculum that teaches how to use AI — those courses will impress an employer.

So, here is what anyone considering law school needs to keep in mind: Just as the Model T didn't put blacksmiths out of work overnight, we are not going to have fewer lawyers immediately. Those who can master AI will be hired and well compensated.

Anyone considering law as a profession might want to check out the one-star ratings of law firms on Yelp. Also, give these questions some thought:

• Why do so many young lawyers leave the profession?
• Why do so many attorneys become alcoholics and substance abusers?
• Why are they so disillusioned?
• Why do they have such a high divorce rate?

Instead of being a lawyer, why not become a blacksmith? At least you'll be paid for — wait, it's coming — horsing around.

Dennis Beaver practices law in Bakersfield, Calif., and welcomes comments and questions from readers, which may be faxed to (661) 323-7993, or e-mailed to Lagombeaver1@gmail.com. And be sure to visit dennisbeaver.com.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

Power semiconductors are surfing the wave of soaring AI electricity usage. Rather than moving and controlling data, power chips move and control electricity in each step that goes into converting high-voltage power from a power plant to the current that AI chips use.

"Power underpins everything in modern technology," said Leonard Shtargot, a fellow at Analog Devices, in a recent panel that brought together top industry executives. The skyrocketing amount of electricity that AI data centers use “all has to go through power semis one way or another,” said Shtargot.

Global yearly revenue for power chips will jump to $100 billion by 2029, up from about $80 billion in 2026, according to Omdia. The yearly growth rate of 7% or so is faster than previous years, with the AI segment seeing even faster growth.

Power chips are also used in EVs, smartphones, laptops, robotics, motors, wireless gear, military radar and much more. Many of the chips last for decades and have been traditionally treated as commodities, which has meant the sector hasn’t seen as much change or investment as other advanced chips, such as central processing units or graphics processing units.

Advanced power chips use high-performance, but costlier, materials such as silicon carbide and gallium nitride. The industry is also researching newer materials such as gallium oxide and diamond.

The AI industry needs power chip innovation to support the AI buildout. "I think the entire ecosystem has to evolve to meet the demand for AI end users," said Dinesh Ramanath, senior vice president at Onsemi, during the panel. He expects significant innovations in the AI market, especially in power density, or how many watts can be squeezed in a certain amount of space on a chip.

Germany’s Infineon Technologies is the industry leader with about 45% market share. The rest of the market is fragmented, with dozens of companies. Major vendors include Onsemi, STMicroelectronics, Mitsubishi Electric, Texas Instruments and Analog Devices. A big focus for chipmakers is getting power to AI chips more efficiently. For example, 10% to 15% of the high-voltage power that goes to a GPU is lost as heat, which is "not acceptable," said Jeff Halbig, product marketing manager at STMicroelectronics, during the panel.

One pressing concern is China’s push to win more of the market, including by relying more on its domestic suppliers. China already nabs 40% of global sales and Beijing is "focused on building up an ecosystem of emerging power chipmakers," noted MorningStar analyst Brian Colello in a research report last year. Other worries include cost pressure stemming from intense competition, demand volatility and worker shortages.

One way to deal with China’s effort to dominate the market is by boosting U.S.-based production, but that’s easier said than done. Industry players want more federal backing, including higher spending on research. They are also calling for efforts to ensure that chip shortages don’t become a surprise AI bottleneck.

Paul Pickering, research director at Omdia, used his presentation as a reminder about chip chaos during COVID: Cars were stuck in parking lots because they couldn’t get basic $1 chips. He wonders if in, say, 2031, there could be a data center ready to switch on, except for a missing $1 power chip and suggests that this hypothetical scenario is something that policymakers and companies can mitigate by acting early.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Once or twice a century, a single catalyst reorders the entire economic landscape: The steam engine revolution. Mass electrification. The silicon chip.

Today, it's artificial intelligence, and we're standing at the absolute baseline.

As an investor backing the architects of OpenAI, SpaceX and Figure AI, I'm often bombarded by a singular, reflexive fear: Is this a speculative bubble? Is the 2000 crash happening again?

My answer is "no." But to move past the skepticism, we must acknowledge that, unlike the dot-com era, when many of the companies were just ideas and hype, today's AI actually does useful work and generates real value.

This isn't the dot-com era

The 2000 tech bubble was practically built on fictional value. It was driven by companies that had a ".com" in their names even when they had no revenue, no profits and often no functional product. That's not what we're seeing with AI.

Look at the companies leading this shift. SpaceX, for example, handles 80% of all U.S. space launches, up from 20% a decade ago. Anduril is executing billions in signed government defense contracts. OpenAI scaled ChatGPT to 100 million users faster than any other application in history.

These are all high-revenue machines with massive market share and defensible moats. To critics focused on immediate profitability, I wish you'd recognize that history shows that transformative companies rarely go public while profitable; they prioritize scale.

The real question isn't about cash flow anymore, but who's building the essential infrastructure for the next century.

What most investors miss about valuation

When investors tell me Tesla is overvalued because of declining car sales, I ask them a question: Where do you think Apple makes most of its money? It's not selling phones. It's the App Store, taking 30% of every download, every in-app purchase, every subscription. The hardware created the platform; the platform creates the recurring revenue.

Tesla is building the same playbook. The cars are the platform. The software is the business.

• Full self-driving subscriptions at $99 per month across millions of vehicles
• Robotaxis that could operate 24/7 without labor costs

This is how you need to evaluate AI investments. Don't focus on what a company does today. Focus on what infrastructure it's building for tomorrow.

Data centers in space, which SpaceX is positioning to build, could power AI computation at a fraction of Earth-based costs because space is cooler (no expensive cooling systems) and has unlimited solar power.

The train is leaving the station

I keep hearing investors say they want to wait until things "settle down" before investing in AI. But by the time it settles down, the opportunity has passed.

Consider SpaceX's acquisition of xAI. The deal, which closed recently, brings together two of the most ambitious companies in AI and space, and they haven't even gone public yet. SpaceX's anticipated IPO is expected to raise around $75 billion at a valuation of about $2 trillion. When I decided to invest in both xAI and SpaceX through my fund, no one knew they were going to merge and become one of the biggest IPO opportunities in history.

If you're waiting for certainty, you're waiting too long. Navigating this shift requires a disciplined execution framework.

Start by auditing your current exposure. If you hold an S&P 500 index fund, nearly a third of your capital is already concentrated in the Magnificent 7. You're already an AI investor by default. It's up to you whether you have the conviction to move beyond these benchmarks into more aggressive, concentrated positions.

Growth behind closed doors

Once you decide to lean in, distinguish between your entry points. Public markets offer the safety of liquidity and access to established titans, but the most explosive growth happens behind closed doors.

Private markets, specifically venture capital and pre-IPO rounds, allow you to capture that value before a company goes public.

However, this path requires your mindset to shift to a long-game mentality, typically with seven-to-10-year horizons and capital commitments starting at $250,000.

Regardless of the entry point, you must demand fundamental proof over hype. When an AI startup crosses my desk, I move past the marketing tactics and zero in on the costly problems they're fixing and whether they have actual, validated revenue or are hiding behind a polished pitch deck.

Worthy investments should provide essential utility, not just "potential." NVIDIA, for example, dominates today because it produces the physical silicon the world needs to function.

Finally, ensure that your enthusiasm never overrides your mathematical discipline. High-growth private exposure is a powerful tool, but for the majority of investors, it should be capped at 10% to 30% of investable assets, depending on the net worth of the investor.

High conviction is not an excuse for poor risk management. You still need liquidity for your life and diversification for your protection, and your goal is to ensure you'll thrive even when the tide turns.

The real risk isn't AI — it's ignoring it

I often hear of alarmist claims that AI is coming for people's jobs. Having backed such companies as OpenAI, SpaceX and Figure AI at pivotal inflection points, here's what I'd tell you: AI won't replace you; someone using AI will.

Ten years from now, we'll look back at this moment exactly as we did the arrival of the iPhone. There will be the visionaries who recognized the shift and immediately capitalized on it, and there will be the doubters who hesitated until adaptation became a matter of pure survival.

We'll remember those who refused to let go of their Nokias or BlackBerrys while the rest of the world transitioned to the smartphone era. Perhaps they managed to stay connected, but they failed to thrive as market infrastructure evolved around them.

These massive data centers, advanced silicon, autonomous systems and space-based computing, all infrastructure currently under construction, are now the bedrock of the economy.

The companies engineering this foundation are the railroads and oil refineries of the 21st century. If you aren't building on this infrastructure, you are falling behind.

The opinions expressed in this material are provided for informational purposes only and do not constitute investment advice or an offer or solicitation to buy or sell securities. Any such offer may be made only pursuant to a confidential private placement memorandum. Investments are speculative and involve substantial risk. Past performance is not indicative of future results. Prospective investors should consult their own financial, legal, and tax advisors before investing.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding business and technology and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

There’s a lot happening in space these days: NASA’s ambitious moon mission, data centers in orbit, satellite cell service straight to your smartphone and the list goes on.

There are plenty of reasons to think that future business prospects, new technology and geopolitical shifts will propel the industry to new heights. The renewed optimism of satellite CEOs was clear when I attended SatShow 2026, the industry’s leading conference in Washington, D.C. The mood was in stark contrast to the last few years, when SpaceX’s disruptive force and other business woes were driving the conversations.

SpaceX is still disrupting things as a de facto monopoly, but the entire industry is growing fast and there’s more room for other players to benefit. The global space economy will reach $1 trillion in 2034, up from $626 billion in 2025, according to consulting firm Novaspace. The U.S., led by SpaceX launching 85% of spacecraft into orbit and its Starlink Internet service, reaps most of the business.

One metric that drives home the point is the surge in launch activity last year, which will continue this year and beyond. 2025 saw 325 orbital rocket launches and 4,544 spacecraft (mostly satellites) deployed, according to a global launch report by BryceTech. That marked a 25% year-over-year increase in launches and a 54% increase in spacecraft. The U.S. had 193 launches in 2025, with 165 from SpaceX alone. Second-place China registered 93.

Geopolitics are lifting space revenues

Geopolitical turmoil is spurring growth with higher defense spending and national projects for space. "Now, just given the geopolitical realities, countries are realizing that they need more than what they were buying before," said Mark Dankberg, CEO of Viasat, at SatShow. Others in the industry agreed. "The geopolitical developments that we're seeing out there are creating far and away some of the biggest commercial opportunities for Telesat and, I'll say, the rest of us," said Daniel Goldberg, CEO of Telesat.

The U.S., European countries, China and other nations want to buy more space services and own and operate their own sovereign satellites. The Pentagon has upped space outlays and will continue the spending spree. For example, the Trump administration beefed up Space Force funding for 2026 and is now requesting $71 billion for the agency in its 2027 budget proposal, an 80% jump compared with 2026.

The wars in Ukraine, Israel and Iran show how space tech is crucial for missile warning and tracking, communications, surveillance, drone and vehicle connectivity, and more. In Ukraine, SpaceX’s ability to deliver high-speed satellite internet to a small antenna on the battlefield has been pivotal. "Starlink is functionally embedded into government infrastructure," said Kimberly Burke, director of government affairs at Quilty Space, in a presentation late last year. SpaceX can expect more Pentagon contracts for its launch business, too.

"That said, the Pentagon is still wired for diversity," said Burke. Recent large U.S. military contracts have gone to Rocket Lab, HawkEye 360, York Space Systems, Sierra Space, Lockheed Martin, L3Harris and many others. Though details aren’t clear, a missile defense system known as Golden Dome, costing $185 billion-plus, also catches much industry attention.

SpaceX going public ignites investing interest

Investing in the space sector is already heating up this year, and SpaceX’s splashy upcoming initial public offering (IPO) will supercharge interest. The 24-year-old company is seeking a valuation of more than $2 trillion and aiming to raise about $75 billion.

In 2025, revenue was reportedly about $18 billion. Most revenue comes from Starlink, its speedy, space-based internet service, which has more than 10 million residential subscribers, hundreds of thousands of business subscribers and hefty defense contracts.

Upcoming risky bets include launching artificial intelligence data centers into orbit, building a full-fledged computer chip factory and getting Starship, the largest rocket ever, ready for commercial missions. SpaceX won’t have trouble spending tens of billions of dollars quickly to pursue its mission. In the near term, the top focus will be on growing its Starlink business.

SpaceX’s stock listing will bring a new wave of capital to the entire sector, including from a flood of retail investors. Investors in the space sector are bullish about SpaceX’s unprecedented stock listing. The IPO is an "inflection point" for the space industry, said Michael Mealling, general partner at Starbridge Venture Capital, at SatShow. Mark Boggett, CEO of Seraphim Space, said it will pull up valuations across the entire sector.

The heightened attention means that more Wall Street analysts will start covering the sector as space companies are included in more stock funds and more space companies go public. Investors should know that space stocks can be risky, requiring due diligence, and that the frenzy among investors could outpace the reality of individual businesses.

"I am a little concerned about public market investors looking at the space sector and not understanding the level of risk," said Mealling. “Not every company that goes public is a good company." Mealling also said that having lived through the dot-com bubble of the late 1990s, "I hope we don’t replicate that."

Upcoming battle of the two megaconstellations

As SpaceX prepares to go public, it’s just starting to see a new, fierce competitor in Amazon. Amazon’s growing constellation, Leo, will battle Starlink for customers in consumer, government and business internet markets. Starlink has some major advantages, including a huge head start, with 8,500 operational satellites, and its own rockets to launch them. Amazon is in the early stages of building a commercial business, with more than 200 satellites in orbit so far.

"The signals we get from prospective customers are incredibly strong," said Chris Weber, vice president at Amazon Leo, at the conference. Amazon is touting how it can integrate Leo with its cloud computing platform AWS. For security-minded businesses, a compelling feature is that data can travel from space to Amazon’s cloud without ever touching the internet. Besides standalone web plans, Amazon could bundle satellite service with Prime, use it to track its vast logistics network, support autonomous drone deliveries and more, according to Neil Shah, an analyst at Counterpoint.

Amazon’s space mission has taken eight years and $10 billion, and that was before the company recently acquired satellite company Globalstar for $11.6 billion to expand its network and land Apple as a customer. The competition with SpaceX spells lower prices, faster speeds and more data for consumers and businesses.

Other constellations in operation or coming soon include Eutelsat’s OneWeb, Telesat Lightspeed, Blue Origin’s TeraWave and Logos Space’s. Sovereign constellations include the European Union’s IRIS2 and China’s Guowang. The tens of thousands of planned satellites mean more demand for rockets, especially for SpaceX, which has a packed launch manifest through 2028.

Other launch vendors poised to benefit include Rocket Lab, Arianespace, Blue Origin, United Launch Alliance and Firefly Aerospace. But they don’t even come close to SpaceX’s ability to reliably launch its Falcon 9. Satellite companies, including Amazon, are desperate for other rockets to start flying regularly so SpaceX doesn’t control launch pricing. Some see Blue Origin’s reusable heavy launcher, New Glenn, as a viable option in the coming years, though the rocket company only had two launches last year. Rocket Lab ranked second in commercial launches last year with 18.

Other space trends to watch

Many companies are excited about bringing satellite connectivity directly to smartphones, no extra hardware required. The direct-to-device (D2D) service is now available on newer smartphones for sending text messages, reaching emergency services and using some low-data apps, such as mapping. The feature is useful in spots without cell service, but the industry hopes it will grow into a massive new business. SpaceX and Amazon are both spending billions of dollars to pursue D2D and will be the two leaders. Other companies working on the tech include AST SpaceMobile, Lynk Global, MDA Space and SES.

Artificial intelligence tools are exploding on the scene to analyze Earth imagery, manage airwave interference, automate in-orbit navigation, answer questions and more. Companies such as HawkEye 360 have built their own AI models from years of proprietary data to better track and identify vessels at sea. Earth imaging leader Planet says AI on the ground can parse terabytes of data and AI in space can autonomously spot methane leaks from pipelines or identify airplanes. NVIDIA AI chips are being used on satellites to process info before it hits the ground.

There’s no shortage of companies with exciting tech. Xona Space Systems is developing an alternative to GPS in low-earth orbit with centimeter-level accuracy. ISI is using AI to analyze geospatial imagery. K2 Space is working on high-power satellites for data centers in orbit. Starcloud is building space data centers and has already launched AI satellites. ICEYE uses radar pulses to produce high-resolution imagery, even through clouds and at night. SpinLaunch has a giant centrifuge concept that flings objects into orbit and is already running test launches.

Traditional hardware segments may see higher sales, too. That includes antenna systems, made by companies such as ThinKom, Intellian and Kymeta. And ground equipment, made by companies such as General Dynamics, RTX, Lockheed Martin, Kratos and Airbus.

A final thought: Space is hard

Keep in mind that space is technically challenging and hugely capital-intensive. “This is not for the faint of heart,” said Amazon Leo’s Chris Weber. “You have to have really long-term thinking.” Major concerns include rising costs, global supply chain challenges and the risk of an economic downturn.

Even in a growing industry, it’s likely that there are too many companies and some of the new business ideas won’t work out. Expect more industry consolidation, plenty of delays and occasional outright failures.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Bloomberg and FactSet have dominated professional financial research for decades by controlling access to curated, institutional-grade data. That competitive moat is now under direct assault.

Google's AI-powered Finance platform represents more than a free alternative to expensive terminals — it signals a fundamental shift in how big tech companies view the financial data market.

When the world's dominant search company applies its AI capabilities to financial research, it forces a strategic question that incumbent providers cannot ignore: Is proprietary data enough to justify premium pricing when sophisticated AI tools become freely available?

The competitive response reveals two divergent strategies. Bloomberg and FactSet are doubling down on domain expertise and workflow integration, betting that decades of curated financial information create defensible advantages.

Google is betting on interface innovation and accessibility, wagering that natural language AI will attract users willing to accept data limitations for zero cost.

The battle between these approaches will reshape how wealth management firms allocate research budgets and determine which capabilities justify ongoing investment.

Bloomberg's AI response

Bloomberg launched BloombergGPT in 2023, a 50-billion parameter large language model purpose-built for finance. According to Bloomberg's announcement, this model was trained on extensive financial data collected over four decades, combining proprietary content with general-purpose datasets.

The AI rollout accelerated through 2025. Bloomberg launched AI-Powered News Summaries in January 2025, providing bullet points at the top of news content.

According to Bloomberg's press release, the company expanded this in November 2025 to include AI Summary for company news, which aggregates multiple sources and identifies key themes.

The Document Search and Analysis tool represents Bloomberg's most sophisticated AI offering. According to IT Brew, the tool synthesizes multiple documents, such as earnings transcripts or research reports, allowing users to cross-examine information using tables and build comparative analysis across companies.

Bloomberg's strategy emphasizes transparency and domain expertise. Bloomberg Intelligence analysts trained the AI models on financial language nuances. The platform provides links to original sources when generating responses, maintaining audit trails for compliance.

These capabilities remain exclusively for Terminal subscribers at pricing levels above $25,000 annually. Bloomberg bets that institutional investors will pay premium prices for AI built on proprietary, curated financial data.

FactSet's Mercury platform

FactSet launched Mercury AI in December 2023, focusing on workflow automation. According to Databricks case study documentation, Mercury combines natural language querying with automated visualization and pitch-creation tools.

Pitch Creator, launched in January 2025, automates model analysis and presentation building, reducing hours to minutes. Search Intelligence enables semantic search across earnings transcripts and SEC filings. The Template Assistant provides more than 200 pre-built Excel templates for investment research using natural language.

Mercury is available to all FactSet subscribers at no additional cost, though base subscription fees remain comparable to Bloomberg Terminal.

Internal development at major institutions

Major financial institutions have built internal AI capabilities on licensed language models. Morgan Stanley developed an assistant using OpenAI technology. JPMorgan Chase created LLM Suite for condensing transcripts and automating market updates. These internal tools work for large institutions but remain impractical for smaller wealth management firms.

The strategic divide

The competitive landscape splits along clear strategic lines. Bloomberg and FactSet bet that proprietary data and domain expertise justify premium pricing. Their AI capabilities layer onto decades of curated financial information that free platforms cannot replicate.

Google bets that accessibility and interface innovation will attract users who can tolerate data delays for zero cost. The 15 to 20-minute delay in Google Finance protects incumbent value propositions for time-sensitive trading but opens preliminary research to free alternatives.

Real-time data access remains a key differentiator. Professional platforms provide institutional-quality pricing and market depth that free platforms cannot offer. According to academic research, functional depth of AI integration matters more than merely having "AI features."

Implications for wealth management firms

Smaller firms and independent advisers now have access to sophisticated tools without enterprise budgets. Large firms must evaluate which research functions require professional-grade terminals and which can migrate to free platforms.

The competitive pressure will accelerate innovation across all platforms. Google's entry forces incumbents to demonstrate value beyond data provision. Natural language interfaces will become standard, shifting differentiation to data quality and workflow optimization.

Firms should evaluate which functions require real-time data and which can use delayed public information. Junior analysts might conduct preliminary work using Google Finance while senior staff use Bloomberg for detailed investigation.

The future landscape

Natural language will become the standard interface for financial research regardless of platform. Google may pursue exchange partnerships for real-time data access, potentially eliminating the delay advantage that protects incumbent pricing.

Bloomberg and FactSet will likely continue expanding natural language capabilities while emphasizing proprietary data advantages.

The eventual outcome will segment the market. Free platforms serve preliminary research and hypothesis generation. Professional terminals serve detailed analysis, real-time monitoring and integrated workflows connecting research to execution.

The competitive dynamics favor firms that adapt quickly. Early adopters of free research tools build institutional knowledge while maintaining professional subscriptions where they generate most value. This hybrid approach combines accessibility with authority, leveraging both free innovation and established infrastructure.

The firms that master this balance will operate with better information at lower cost.

John O'Connell is founder and CEO of The Oasis Group, an award-winning consultancy and research firm serving wealth management firms nationwide. O'Connell has more than 30 years of leadership experience in financial technology and wealth management, including North American leadership at Oracle, fintech CEO and president roles, and participation in IPO and M&A transactions. He is the creator of the AI WealthTech Map (100+ firms), the developer of the Oasis AI Readiness Index, and is recognized as a leading independent voice on AI adoption in wealth management. O'Connell is regularly featured in Barron's, Wealth Management, Financial Planning, ThinkAdvisor, InvestmentNews, Family Wealth Report, and other leading publications, and has been recognized for his thought leadership in many industry-leading awards programs.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

OpenAI’s abrupt decision to shut down fledgling Sora, its recently launched social media app to create and share AI-generated videos, was a shock to the AI industry and major partner Disney.

Behind the scenes, Sora's growth petered out, while OpenAI needs to conserve scarce computing power for more important projects. But it also showed how hard it is to launch a new AI consumer app.

So what consumer AI apps are gaining traction these days? Tech venture capital firm A16 recently ranked the top consumer AI websites and mobile apps, using unique monthly visits for web and monthly active users for mobile. The results are a glimpse into real-world AI adoption.

1. General AI assistants

AI chatbots top the list, with ChatGPT the most popular website and mobile app. Other widely used chatbots include Google's Gemini, Elon Musk's Grok, Anthropic's Claude, Meta AI and China's DeepSeek.

OpenAI's ChatGPT is “still far and away the largest consumer AI product," says A16's report. With 900 million weekly active users, the app has more than twice the number of users as Google's Gemini, which is second for website traffic. China's Doubao, CiCi and Seekee are also popular. The rankings show how general AI assistants are now mainstream and continue to grow in popularity.

2. Photo and video editing AI apps

AI photo editing is gaining steam, including popular uses such as removing or generating backgrounds.

Users can also restore old photos, create professional headshots and turn low-resolution photos into HD quality. Touching up selfies by removing wrinkles, adding makeup and other edits can be done with popular tools BeautyCam and BeautyPlus.

Users can get help with video editing, for example, by adding effects and transitions using the video editing tool Capcut, which has more than 700 million monthly active users. Other popular tools include Picsart, Remini, Adobe Lightroom, Photoroom and Cutout.

3. AI image and video generation tools

Now, anyone can generate images or videos by typing simple text explanations. Just instruct the AI tool what you want, and get stunning (or sometimes not-so-stunning) images and videos.

Further explanation can hone the final product. Image generation tools include Midjourney, Leonardo, Ideogram and Freepik. Meta AI lets users generate images within Instagram, Facebook, Messenger and WhatsApp. While Sora is gone, top AI video generation tools include Kling AI, Hailuo and Pixverse, all based in China.

4. AI productivity tools

Notion is a digital workspace that includes notes, documents and personalized dashboards with all sorts of business apps and databases to manage work projects. Grammarly provides writing assistance to fix typos, improve clarity and target an audience. Canva helps with graphic design for social media posts, presentations, logos and more.

So-called agentic AI, which automates tasks, is going mainstream, too. There are tools that automate coding, including Cursor and Lovable. And Manus and Genspark let consumers outsource work, such as spreadsheet analysis or research, to the AI.

5. AI learning apps

Popular chatbots are widely used by students, so it's no surprise that apps designed specifically for homework help are popular.

Brainly combines human tutors and AI for tutoring, test prep and research. The company boasts about personalized AI tutoring that provides explanations at the student’s own pace. Photomath, a math learning app owned by Google, can scan number or word problems and provide a solution, including all the steps. It covers algebra, geometry, trigonometry, statistics and calculus from elementary school through college.

6. Social companionship apps

"If you can imagine it, you can bring it to life," explains Character.ai's website. The popular tool lets users create and interact with custom AI characters. The apps often include AI-generated images, which is why Character.ai recently launched a digital photo album for the images that are created.

There are popular, uncensored, adult entertainment apps for roleplaying, such as JanitorAI. The adult content has sparked concerns about privacy and age restrictions, especially because of the addictive nature of AI chatbots. As these apps grow in popularity, expect rising concerns over child safety, emotional harm and non-consensual deepfakes.

The Top GenAI Web Tools By Usage

1. ChatGPT
2. Gemini
3. Canva
4. DeepSeek
5. Grok
6. Claude
7. Character.ai
8. Perplexity
9. Notion
10. Google AI Studio

The Top GenAI Mobile Apps By Usage

1. ChatGPT
2. CapCut
3. Gemini
4. Canva
5. AI Gallery
6. Picsart
7. Doubao
8. Microsoft Edge
9. Meituan
10. Yandex

Expect these rankings to continue to shake out, with upstarts charging ahead and top tools falling off the map. There are endless possibilities for new standalone consumer AI tools, but don't overlook the other ways the tech is seeping into everyday life. "AI is increasingly embedded in the tools people already use," notes A16's report, so the "rankings increasingly undercount the AI products people use most.”

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

Artificial intelligence holds huge promise for the U.S. military for both offensive measures and deterrence. That's why the Pentagon is racing to put AI in the battlefield and the office. With a yearly budget nearing $1 trillion, it's a massive tech shift.

The AI arms race

The global AI arms race leverages an unprecedented commercial AI boom. Large language models powering top AI tech are ideal for the military, since they are a general-purpose technology that can process vast amounts of data, reason and generate usable insights. Users interact with the AI in plain English, making adoption far easier. And vast U.S. tech spending has powered AI advances. The most cutting-edge models are being built by Anthropic, Google and OpenAI.

AI is already being used extensively in battle: By Israel in Gaza. Ukraine against Russia. Now the U.S. against Iran. These conflicts are a testing ground for many new AI tools. Ukraine even launched a product-development war program, known as "Test in Ukraine," where foreign military tech companies can get real-time data from combat conditions. In Iran, the U.S. is using AI to screen incoming data and help identify targets.

The big concern is competition with China, the second-largest AI spender. China is rapidly deploying AI to its military, adding electrical capacity at a rapid clip and providing $200 billion in state-backed AI capital. But China's top tech firms spent only 15% to 20% of what U.S. tech giants invested on AI in 2025, according to Goldman Sachs. This year, Alphabet, Amazon, Meta and Microsoft will unleash nearly $700 billion of capital expenditures to build AI.

America's focus on speed

Hence the Department of Defense's AI strategy: Speed wins. "Military AI is going to be a race for the foreseeable future, and therefore speed wins," according to the agency's AI strategy document. "We must accept that the risks of not moving fast enough outweigh the risks of imperfect alignment." Aggressive timelines, slashed red tape and updated procurement rules aim to speed up adoption and use.

The effort includes top-secret projects marked by urgency and little public oversight. Units that don't adopt AI quickly enough could see funding reviewed or pulled. The Pentagon also wants the latest AI models deployed to soldiers and civilian workers within 30 days of public release, a rapid pace of adoption that highlights the strategic imperative of accessing the most cutting-edge commercial tech.

Since December, the agency's 2.8 million personnel have had access to Gen.AI.mil, a chatbot powered by Google Gemini for unclassified work. The chatbot has been used by more than 1 million employees so far, while plans for a similar chatbot for classified work is underway. Google also introduced AI agents that can autonomously do work, such as drafting white papers, preparing briefings and processing data-heavy documents.

The Pentagon wants AI models without constraints. The feud with Anthropic, which erupted into a federal ban and now a legal fight, highlights the ongoing tension. The concern is that an AI system will suddenly refuse an urgent question or command because of the guardrails built into the AI model itself, even if the order is lawful.

Military AI's growing uses

The many combat AI uses include mission planning, target assessments and weapon systems tracking. AI can generate insights by fusing a vast array of data, including satellite imagery, video, radar, radio transmissions, cyber intelligence, social media activity, real-time news, field reports, troop locations and historical info.

"Defense and intelligence workflows that once required weeks of manual analysis can automatically detect threats and generate response plans by processing satellite imagery, sensor data, and historical patterns at unprecedented scale," says Amazon about its government AI cloud computing platform. Meta says its AI model powers a chatbot that enables special operations forces "to generate intelligence reports 18 times faster and process video footage nine times quicker."

AI-enabled drone swarms could pilot themselves, making decisions autonomously. Automated drone software updates could deal with new enemy radar systems, turning intel into weapons in "hours not years." AI could fend off cyber threats. Instead of weeks of manual analysis, AI surveillance could create instant insights.

The growing list of non-warfare uses include training, supply chain management, regulatory compliance, drafting policies, summarizing meetings, parsing contracts. In-depth research reports created in minutes will save hours or days of work. AI could even be used to assess and help modernize aging U.S. military IT systems, a major problem for 30 years that threatens to stall or hinder the AI transformation.

Introducing combat chatbots

Here's how an AI chatbot conversation could happen on the battlefield. On a laptop computer, a soldier sees an alert for an "anomalous activity" on an AI platform built by Palantir, a leading military AI vendor. The chatbot, which is scouring data and real-time info, says satellite imaging has detected that enemy activity could be nearby. The soldier types: "Show me more details." The chatbot says when images were taken, what they could be and shows a map.

The soldier could then ask about options to provide a higher-resolution image. After being provided two choices, such as a surveillance drone or more satellite imaging, the chatbot says, "How would you like to proceed?" The soldier picks the drone and the choice is sent up the chain of command to be approved by a commander. The new imaging shows a tank that is a potential threat.

The soldier then writes, "Generate three courses of action to target this equipment." Three options come with details, such as time required, equipment used, distance to target, personnel needed and more. After probing each option and getting more details, the soldier asks for a summary of the plan. A commander gives final approval.

The military sets permissions for these AI tools, saying what AI can and can’t do, and what data can be accessed. Records are stored for analysis. The guidance and guardrails are evolving fast, and Congress has yet to step in to set the rules for military AI.

The Pentagon's expanding efforts

The Pentagon has groups and initiatives laser-focused on rapid adoption of AI for combat and faster use of emerging commercial AI tech. Two major organizations are the Chief Digital and Artificial Intelligence Office (CDAO), which aims to accelerate adoption of AI for combat, and the Defense Innovation Unit, which aims to make faster use of emerging tech.

The Army's Project ARIA, or Army Rapid Implementation of Artificial Intelligence, focuses on autonomous agents, an AI app store for the battlefield, upgrading supply chain management and more. "Project ARIA represents a fundamental shift in how the Army develops and deploys technology," according to the Army's announcement. "By partnering directly with top AI firms, the Army is delivering solutions in months rather than years."

Project Maven looks to use AI to process battlefield data to speed decision-making. The Replicator initiative aims to use AI to fly thousands of autonomous drones. Thunderforge is an AI wargaming project. Rapid Capabilities Cell is an AI incubator.

Rising defense spending on AI

Military spending on AI will soar in coming years, with AI-related contracts for large tech companies, start-ups and traditional contractors. For example, Palantir has a 10-year, up-to-$10 billion Army contract. Shield AI, maker of AI pilot software, is part of a group receiving a U.S. Air Force contract worth up to $950 million. Anduril, which builds autonomous systems, has a $642 million, 10-year contract with the U.S. Marine Corps for an AI counterdrone system.

Anthropic, OpenAI, Google and xAI each received contracts worth up to $200 million for AI chatbot tech. Scale AI has a five-year, up-to-$100 million contract to label and annotate data for AI. Other start-ups receiving contracts include Rebellion Defense, webAI, EdgeRunner AI and Legion Intelligence. Hundreds of smaller vendors have received small contracts.

Even though AI start-ups are rapidly gaining ground, large firms are gearing up for bigger demand, too. For example, Amazon is investing up to $50 billion to expand AI cloud computing for the government. Meta is supporting national security uses with its open-source AI model Llama, which is easier to modify and can be used on devices without an internet connection. Lockheed Martin, RTX, Northrop Grumman, General Dynamics and Boeing use AI for internal operations and manufacturing, plus include AI in their products.

The world faces new risks

AI brings many risks and rapid adoption is likely to enhance the hazards. The tools need to have guardrails with continued testing and monitoring, since AI can produce mistakes, biases, data leaks, cyber threats and other problems. There's even the growing concern that AI-induced psychosis becomes a national security threat. Meanwhile, the Pentagon’s willingness to experiment and fail while it moves fast is sure to spur more scrutiny over defense spending on new tech.

Expect a much bigger debate over human vs. machine decision-making related to AI’s ability to automate all sorts of processes. And eventually, a discussion of AI arms control, echoing the nuclear age. "The emergence of nuclear weapons prompted the development of nonproliferation regimes that reshaped arms control beyond traditional use-based restrictions," writes Scott Sullivan, a professor of law at the U.S. Military Academy at West Point and the Army Cyber Institute, in a recent article. "Military AI may demand a similar shift."

These trends point to tremendous change as the U.S. military and its defense industrial base "is beginning a once-in-a-century transformation to modernize its military," says a report by researchers at the Wharton School of the University of Pennsylvania.

Still, there are some things that won't change. AI will help militaries fight their wars, but the decision on whether to start or join a war will still be a fully human choice of political leaders.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Planning a trip can be stressful, but when it's across the country to visit six colleges in Southern California in five days, it can be downright daunting.

It's especially tricky when one college is in Beverly Hills, and the other is in San Diego. Add the fact that we want to layer some sightseeing, picturesque sunsets and a hike to the Hollywood sign, and you can see why I was feeling overwhelmed at the sheer thought of it all.

The easy part (kind of) was booking all the college tours. I based the visits on the colleges' availability. (That's one reason why it's a good idea to book your tours early.) Only after this step could I plan an itinerary in a city that I'm not too familiar with.

Can I make it to both California State University, Dominguez Hills, and California State University, Long Beach in one day? Should San Diego State and UC Irvine be on the same day? What about traffic? Will I get stuck in LA's notorious rush hour and miss a tour completely? Where should we stay to minimize drive time? Is it one, two, or three hotels?

All of those questions were whirling through my head and, not surprisingly, leading to procrastination. "I'd rather not think about it" was my default mode when it came to this college tour trip. But as the days passed by, I knew I couldn't put it off anymore. Instead of doing all the time-consuming research on my own or enlisting a travel agent, I decided to give AI a whirl.

With all the talk about how AI can improve pretty much every aspect of our lives, why couldn't it help plan this trip? My ex-husband wasn't so convinced. But I was on a mission to save time and make this as painless as possible, and I put my fate in the virtual hands of AI, namely ChatGPT.

How I used AI to plan our trip

Admittedly, I'm not an expert when it comes to using ChatGPT or any other AI tool. Sure, I've asked it to help me fix a bad hair dye job or to figure out the best place to park at a nearby mall, but for traversing Southern California? This was new territory for me. Who knew it would be so easy?

The first question I had for ChatGPT was where we should stay to be central to everything and reduce driving time. I was open to staying in up to three hotels if it meant we could avoid hours in traffic.

Writing an AI prompt:

I knew that asking a question of AI is a bit different than asking one in a search engine like Google. Instead, I wrote a "prompt" that provided as much detail as possible in plain language, treating ChatGPT like a travel agent. (You can even tell ChatGPT, "Be my personal travel agent.") I entered the colleges we planned to visit in order, along with the times of each tour and asked ChatGPT to find a central location for us to stay.

Remember that you can edit your prompt for clarity. You should always check an AI's work, and you can ask an AI agent like ChatGPT to tell you if it needs more information.

Here's the itinerary I gave ChatGPT:

Day 1: Arrive at Los Angeles Airport at 10:30 a.m.

Visit California State University Dominguez Hills, 1:00 p.m.

Day 2: Visit California State University, Fullerton, 10:00 a.m.

Visit California State University, Long Beach, 3:30 p.m.

Day 3: Visit UC Irvine, 2:00 p.m.

Day 4: Visit San Diego State University, 1:30 p.m.

Day 5: Hike to Hollywood Sign 8:30 a.m.

Visit California State University Northridge–self-guided tour 1:00 p.m.

Fly home from LAX 9:30 p.m. (Be at the airport around 6:00 or 7:00 p.m., depending on airport traffic.)

The results

ChatGPT said Long Beach would be the perfect central location for our whirlwind of a visit, to make it even less driving, suggested we stay in Long Beach for the first two nights, then head over to either Irvine, Costa Mesa or Newport Beach for the third night, and finish our stay in Beverly Hills for the remaining two nights. According to ChatGPT, doing that:

-Minimizes hotel changes
-Is the smoothest driving schedule
-Gives us a relaxing Saturday in LA
-Only includes one long drive from San Diego back to LA, which it said can be scenic.

Scenic?!? My daughter, like most teenagers, is all about the Instagram pics, and I thought it would be a great idea to drive back up to LA along the Pacific Coast. So I asked ChatGPT if that is doable after visiting San Diego State, given our tour ends at 3:30, which I assume, if it's anything like New York, is when LA traffic starts.

To my surprise and delight, ChatGPT not only gave me an estimate of how long that would take — 3.5 to 4.5 hours, depending on stops — but an exact place to see the sunset: Laguna Beach.

We would have to leave right after the tour, but that would give us a nice scenic drive and plenty of places for my daughter to get her social media-ready pictures. If we didn't feel like driving up the coast, ChatGPT gave us a faster option on the freeway that cut about an hour off the drive time.

Not only did ChatGPT lay out the best way to visit all these colleges, but it also broke it down into driving details, including the time it will take between each college, recommended hotel stays (I found my own), and the scenic drive option.

While I didn't rely on AI to help me select my hotels in each location or to find a hiking tour to the Hollywood sign, it was able to do in seconds what would have taken me hours and likely a lot of hand-wringing and hair-pulling to figure out.

The verdict: AI to the rescue

My college tour trip is still a few weeks away, but I am happy to say that I was right (and my ex-husband was wrong)!

AI can be a great planning tool, and while I didn't use it to choose hotels based on my preferences or decide when we want to stop, it proved to be a super helpful, time-saving tool that I plan to use again.

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When you walk into your community bank, credit union or independent mortgage bank (IMB), you expect something different than the big national players.

You want to be recognized. You want someone who understands your financial situation and can help you navigate it. You want the personal touch that made you choose a local institution in the first place.

So when you hear that your credit union or community bank is implementing AI, it's natural to worry about what that means.

• Are they replacing the tellers who know your name?
• Is your loan officer being swapped out for a chatbot?
• Are they becoming just another faceless tech company?

The reality is exactly the opposite.

The financial institutions getting AI right are using it to become more human, not less. They're automating the tedious paperwork and backend processes that used to pull bankers away from customers, freeing them up to do what they do best: Build relationships and provide personalized financial guidance.

AI handles the busywork — people handle the relationships

Think about what happens when you apply for a mortgage or business loan at a traditional bank. Your loan officer spends hours — sometimes days — gathering documents, verifying information, cross-checking data across multiple systems and filling out forms.

Meanwhile, you're waiting, often in the dark about where things stand.

AI changes this equation fundamentally. It can instantly pull together all the relevant information about your application, verify documentation, assess risk factors and flag potential issues. Work that used to take days now happens in minutes.

But here's what doesn't change: The human conversation about your financial goals, the personalized advice on loan structures and the relationship that helps you succeed long after the loan closes.

Your financial institution knows you better

AI also helps smaller institutions compete with the data advantages that big banks have long enjoyed. When you call your community bank about a problem with your account, AI can instantly surface your entire relationship history — every product you hold, every interaction you've had, and patterns in how you use your accounts.

Your banker sees the full picture immediately and can help you more effectively.

This matters especially for small business owners. When you need a line of credit expansion or want to discuss cash flow challenges, your relationship manager can walk into that conversation fully prepared, with AI having already analyzed your business patterns, seasonal fluctuations and growth trajectory. The conversation becomes strategic, not administrative.

Making financial guidance more accessible

Perhaps most importantly, AI is helping community institutions provide the kind of sophisticated financial guidance that used to require a private wealth manager. AI-powered analytics can identify when customers might benefit from different products, flag potential overdraft risks before they happen, or suggest better savings strategies based on spending patterns.

This doesn't mean you're getting automated sales pitches. It means when you do talk to your banker, they're equipped with insights to give you genuinely helpful advice. They can spot opportunities you might miss and warn you about pitfalls you didn't see coming.

The dual workforce advantage

That being said, I don't think the future of community banking is human or AI — it's both, working together. At nCino, we call this the "dual workforce": AI employees handling repetitive, data-intensive tasks while human employees focus on judgment, empathy and relationship building.

This combination gives smaller institutions capabilities they've never had before. They can process applications as quickly as the big banks, provide 24/7 account monitoring and deliver sophisticated analytics — all while maintaining the local, personal touch that makes them special.

The credit unions and community banks making these investments aren't trying to become tech companies. They're trying to give you the best of both worlds: Cutting-edge efficiency combined with genuine human relationships.

What this means for your money

Next time your local bank or credit union upgrades their technology, don't worry that you're losing the personal service you value. You're about to get more of it.

Your bankers will have more time for you. They'll be better informed when you need help. They'll be able to spot opportunities to improve your financial health. And they'll be equipped to compete with the largest institutions while keeping what makes them unique: They know you, they know your community and they're invested in your success.

The institutions getting AI right understand a fundamental truth: Technology should amplify human relationships, not replace them. And it's exactly what your community bank or credit union is built to deliver.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here's the latest…

The Pentagon banning Anthropic from its defense systems and networks is much more than a contract dispute. The government’s approach to artificial intelligence could affect the entire artificial intelligence industry.

The feud is over Anthropic requesting restrictions on fully autonomous weapons and mass domestic surveillance, citing concerns about AI’s unreliability and citizens’ fundamental civil rights. The Pentagon says a private company dictating the terms of use for military AI is untenable, and that it follows the law.

Here are five takeaways from the ongoing dispute.

1. The administration is treating artificial intelligence as a powerful weapon, rather than a normal technology.

In this view, the tech is akin to a nuclear bomb, not a piece of software. Washington sees AI as crucial to military power and is in an arms race with China. That has huge implications for how the federal government may treat private companies developing AI in the future.

Generative AI is rapidly being integrated into mission planning and combat, including now in Iran, for intelligence, target assessments, wargaming and more. The current approach is to ramp up AI adoption across the military and have unfettered access to the very best tools available.

2. Anthropic is likely to win this legal battle but still suffer business damage.

The AI company is challenging the Pentagon’s labeling it a supply chain risk, a designation used for suppliers (mostly foreign adversaries) that are a national security risk. Anthropic also opposes the Pentagon calling for a wider ban in the private sector, which is beyond the scope of a supply chain risk designation.

For now, the government says federal agencies have six months to stop using Anthropic, while the company in a legal filing has already pointed to “irreparable harm” and huge losses from being blacklisted. Other cutting-edge AI choices are available to the government, including from OpenAI, Google and Meta.

3. The government’s aggressive tactics are here to stay and will jolt AI firms.

Even if the government loses in court, the pressure could coerce other AI companies to avoid similar confrontations. Military leaders will keep pushing AI firms to acquiesce, aiming to remove any usage restrictions baked into their AI model or in contracts.

The harsh tactics have irked Silicon Valley and beyond, casting a shadow over AI development. And tensions over AI guardrails aren’t over. Consider that OpenAI, which signed a new Pentagon contract after Anthropic was officially banned, continues to say it has red lines and will monitor military use.

Look for louder calls for Congress to step in and set rules for military AI.

4. Attempts at exerting control over AI are a bipartisan affair.

The Trump administration has also threatened to force Anthropic to build its desired AI tools by invoking the Defense Production Act, a Korean War-era law meant for steel mills and tank factories. Doing so would be a major escalation and would invite swift legal challenges.

It’s not clear how the legal case would play out. But note that the Biden administration also invoked the DPA for rigorous information gathering of leading-edge AI models. Though less coercive than forcing certain software to be built, it still sparked criticism from the industry.

Both parties see AI as too important to be left alone, though they differ on the risks, appropriate oversight and tactics.

5. Investors should monitor the government’s approach as billions of dollars in contracts flows to AI.

Banning certain AI companies, asserting control over AI or potential federal regulations could shake up the market. Anthropic reportedly has reached a rate of $20 billion in yearly sales and has a $380 billion valuation, and is eyeing an initial public offering this year. It continues to gain users, but some customers may be wary and opt for competitors, hoping to avoid confrontation with the Trump administration.

Meanwhile, Anthropic customers who have military contracts are scrambling to figure out if the ban means they have to ditch Anthropic. Federal contracts for AI will amount to tens of billions of dollars in coming years. Being excluded would be a huge loss for a large tech firm or a start-up.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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A viral online trend claims artificial intelligence can help taxpayers uncover a bigger tax refund — even after a return has already been filed.

Users on X (formerly Twitter) have been sharing examples of using AI chatbots to review their tax filings for deductions or credits that may have been overlooked.

In one widely circulated post, a user claimed their refund increased after asking Grok, the AI chatbot developed by Elon Musk’s AI company xAI, to double-check a previously filed return. The post quickly gained traction online, attracting tens of millions of views in roughly a day.

Early IRS data indicate higher refunds than last year. As of early March, more than 36.5 million refunds totaling about $136.6 billion had been issued. The average refund is about $3,676 — roughly 10.6% higher than at the same point last year.

So, the idea that a chatbot could help identify additional refund money may sound understandably appealing.

But while AI may help explain tax rules or highlight areas worth reviewing, the process for changing a filed tax return is more structured than a viral post might suggest. Here's what you need to know.

Can AI help review your tax return?

Artificial intelligence tools like ChatGPT, Perplexity, and Claude AI, to name just a few, have improved rapidly in recent years. In many cases, AI can help taxpayers better understand tax concepts or identify questions worth revisiting.

For example, AI might help:

• Explain tax deductions or credits
• Summarize IRS rules and eligibility requirements
• Highlight areas of a federal income tax return that may deserve a second look
• Walk taxpayers through how amended returns work

In that sense, AI can serve as a useful research tool during tax season.

However, identifying a possible issue on a return is very different from actually correcting a filed tax return.

How amended tax returns work

If a taxpayer discovers a legitimate mistake after filing, the IRS requires a formal process to correct the return.

Common reasons taxpayers file an amended return include:

• A tax document, like a W-2 or 1099, arriving after the original return was filed or is corrected by the issuer
• Claiming a tax deduction or credit that was missed on the original filing
• Correcting filing status or dependent information
• Fixing income, withholding, or math errors discovered after filing

In most cases, that means submitting Form 1040-X, the official amended return used to update previously filed tax information.

Taxpayers generally have three years from the date the original return was filed — or two years from the date the tax was paid, whichever is later — to amend a return and claim an additional refund. After that window closes, the IRS typically will not issue the extra refund even if an error is later discovered.

The amended return must clearly explain what changed and why. Taxpayers must also include documentation supporting the adjustment.

In other words, a chatbot might help flag a potential issue with your return, but the IRS still requires proper documentation. If a legitimate error is found, taxpayers must file a formal amended return before the IRS can issue any additional refund.

Limits of using AI for tax advice

While AI can be useful for explaining tax rules or identifying areas worth reviewing, there are important limitations and potential consequences to consider.

A few important limitations include:

Privacy: Uploading sensitive financial information into an AI chatbot may expose personal data depending on how the platform stores or processes user inputs.

Incomplete financial information: Most AI systems don't have access to a taxpayer’s full financial documentation, including W-2s, 1099s, receipts, and prior tax records. Without that full context, a chatbot may misinterpret eligibility rules or overlook important details.

Timeliness of tax updates: AI tools may not have access to the most recent tax law changes, IRS guidance, or regulatory updates. Tax rules can change, as they did in the 2025 Trump/GOP tax bill. In some instances, chatbots may rely on outdated or incomplete information when responding to questions about recently enacted tax provisions.

Potential inaccuracies and hallucinations: AI tools can sometimes misinterpret tax law or generate incorrect explanations, particularly when dealing with complex eligibility rules or uncommon deductions.

If a taxpayer files an amended return requesting a refund they aren't entitled to, the IRS can deny the refund and may require repayment of any additional amount issued, with interest. Some taxpayers could also face accuracy-related penalties.

How the IRS uses AI

Artificial intelligence isn't new to the federal tax system. The IRS already uses advanced analytics and machine learning to review large volumes of tax data and identify returns that may warrant closer review.

For example, the tax agency uses automated systems to flag potential discrepancies, compare reported income against third-party records like W-2s or 1099s, and help prioritize enforcement resources.

How to file an amended tax return

If you identify a legitimate mistake after filing, correcting it is relatively straightforward. To amend a return:

1. Complete Form 1040-X

2. Identify the changes being made

3. Attach any supporting documentation

4. Submit the amended return electronically or by mail

The IRS notes that amended returns can take up to 20 weeks to process.

You can track the status of an amended return using the IRS “Where’s My Amended Return?” tool.

Can AI increase your tax refund? Bottom line

AI might help taxpayers better understand tax rules or identify areas worth reviewing on a return. Even so, those tools are not a substitute for the IRS process required to change a filed return. If a legitimate error is discovered, you still need to document the change and file an amended return.

Also, most tax software already checks for many common credits and deductions during the filing process. So that so-called "AI reviews" might not uncover additional refunds in many straightforward situations.

If you're unsure whether you've claimed all of the tax benefits to which you are entitled, it's good to consult a qualified tax professional who can help ensure any corrections to your return are handled properly.

Read More

• AI Tax Scams Target Middle and Older Adults: What to Know
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• Where's My Refund? How to Track Your Tax Refund Status

In the rapidly evolving landscape of artificial intelligence (AI), businesses are increasingly integrating these tools into daily operations to boost efficiency and innovation.

From automating hiring processes to generating content and analyzing data, AI promises significant advantages.

However, when employees improperly use AI — such as by inputting sensitive data without safeguards, relying on biased outputs or failing to oversee automated decisions — companies can face substantial civil liability.

Under such principles as vicarious liability, businesses are often held accountable for employee actions within the scope of employment.

In this article, we explore key areas of exposure, drawing on recent legal developments (as of February) and offer insights for mitigation.

Discrimination and bias: The forefront of AI litigation

One of the most prominent risks arises from AI-driven discrimination, with tools perpetuating biases in hiring, promotions or evaluations.

Employees might deploy AI screening software without auditing for fairness, leading to disparate impact claims under laws such as Title VII of the Civil Rights Act, the Age Discrimination in Employment Act or the Americans with Disabilities Act.

For instance, in the landmark Mobley v. Workday case (2024-2025), a plaintiff alleged that Workday's AI hiring platform discriminated against applicants based on age, race and disability, resulting in a certified collective action for applicants age 40 and older.

Similarly, the 2025 Harper v. Sirius XM Radio lawsuit claimed AI tools used proxies such as ZIP codes to exclude Black applicants, highlighting disparate treatment and impact.

Recent settlements, such as EEOC v. iTutorGroup (resolved in 2023 but influencing 2025 cases) underscore how automated rejections of older candidates can lead to hefty penalties, including $365,000 payouts. Businesses face damages, back pay and injunctions if employees neglect bias audits.

Privacy violations: Data mishandling in AI applications

Improper AI use can breach privacy laws when employees feed personal data into unsecured tools.

This exposes companies to claims under the California Consumer Privacy Act, General Data Protection Regulation or the Fair Credit Reporting Act. A groundbreaking 2026 lawsuit against Eightfold AI alleges the company's platform compiles applicant data from sources such as LinkedIn without consent, treating it as unregulated credit reports.

Employees inputting employee or customer information into public AI chatbots risk class-action suits for invasion of privacy or data misuse, with penalties reaching millions.

Emerging regulations, such as California's 2025 Civil Rights Council rules, expand liability by defining AI vendors as agents of employers, emphasizing the need for consent and security.

Intellectual property and defamation risks

Employees generating content via AI might infringe copyrights if outputs derive from protected materials, leading to secondary liability under the Copyright Act.

Additionally, AI-produced reports or communications containing falsehoods can spark defamation claims.

For example, if an employee publishes misleading AI-generated social media posts, businesses could face compensatory damages.

Negligence, contract breaches and deceptive practices

Negligence arises when faulty AI deployment causes harm, such as erroneous financial advice or operational errors, invoking product liability for defective tools.

Breach of contract occurs if AI fails to meet client standards, while deceptive practices under the FTC Act penalize misrepresenting AI capabilities — fines and refunds ensue.

Mitigating the threats

To shield against these liabilities, businesses must implement robust AI policies:

• Mandatory training
• Bias audits
• Human oversight
• Compliance with laws such as New York City's Local Law 144 or the proposed No Robot Bosses Act (2024)

As AI litigation surges — evidenced by cases such as Eightfold and Mobley — proactive measures are essential. By fostering responsible use, companies can harness AI's potential while minimizing legal pitfalls.

In the next article, we will explore strategies companies can employ to insulate selected company assets from civil liability from unforeseen, unexpected lawsuit creditors and predators.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

The artificial intelligence race is gobbling up memory chips and sending prices soaring. Now the war in Iran threatens to make those problems worse. The smartphone and PC industry is bracing for impact.

A shifting tech marketplace

Say goodbye to the current crop of budget smartphones and PCs. Basic smartphones that run less than $200, which are popular in emerging markets, will go the way of the dodo. The same is true for PCs. "Ultimately, we expect the sub-$500 entry-level PC segment will disappear by 2028," says Ranjit Atwal, an analyst at Gartner, in a recent statement. The tech market research firm estimates smartphone and PC memory-chip prices will more than double by the end of this year compared with 2025.

"Budget smartphones and entry-level PCs operate on razor-thin margins, leaving vendors with little room to absorb price increases," according to an analysis from tech market research firm IDC.

To cope with higher memory prices, smartphone sellers could reduce random access memory (RAM) by one-third and storage by half for base model phone configurations, says IDC. Ditto for budget-friendly PCs, as vendors look for ways to deal with higher prices without fully absorbing the cost.

A similar dynamic is at play for higher-end devices, with higher prices and perhaps stingier memory offerings. Apple and Samsung, the top phone sellers, are well-positioned to cope with the shortage because of their huge buying power and supply chain expertise.

Meanwhile, PCs designed for artificial intelligence will stumble, as higher prices slow adoption.

Global electronics sales are sinking

The memory shortage will cause global smartphone shipments to plunge nearly 13% in 2026 versus last year, and PCs to fall 11%, predicts IDC. That’s the biggest drop in a decade or so. Expect shortages to ripple through all sorts of consumer electronics, including tablets, virtual-reality headsets, wearable devices, gaming consoles and action cameras.

Businesses and consumers are sure to hang on to PCs and phones longer, balking at the higher prices of new models. But memory prices aren’t falling back to 2025 levels. Even in 2027 or 2028, memory prices figure to stabilize rather than start declining.

The war in Iran could exacerbate memory shortages and hike prices further, while souring consumer and business sentiment. Energy price increases would lead to higher prices of chips and electronics, and supply chain disruptions in the Middle East could reverberate globally. A prolonged conflict could cause "consumers to delay purchases of PCs, tablets, smartphones and other devices," according to an analysis by IDC.

Tips for shoppers looking for tech

• Check the tech specs carefully before buying to make sure memory is adequate, rather than looking at price alone.
• Consider getting the minimum requirements for AI software.
• Check out top smartphone sellers’ budget models, such as Google’s Pixel 10a ($500) or Apple’s iPhone 17e ($600). The tradeoff is usually a less powerful chip, a lower resolution camera and smaller screen size.
• Look for deals on computers in August, September, November and December — the best times to buy, according to nonprofit Consumer Reports.
• Don’t forget to look for trade-in offers for old smartphones and PCs. Smartphone manufacturers, carriers and other outlets are competing to get old phones back so they can resell them.

Meanwhile, Apple fans have a new option to cope with higher PC prices. The recently unveiled $600 MacBook Neo is by far the cheapest laptop in Apple’s lineup. It runs on a less powerful 2024 iPhone chip and is sure to see strong sales.

Opt for used models

Used smartphones sold by phone manufacturers, carriers and retailers are more popular than ever. Certified pre-owned models, which undergo a rigorous inspection and repair process, are an increasingly popular option. The used market offers cheaper devices, but as consumers hang on to phones longer, the inventory is older than usual.

The Iran conflict also "extends to the refurbished market," writes Ahmad Shehab, an analyst at Counterpoint Research, in a recent post. “Spare parts used in refurbishment spaces are largely shipped by sea,” and disruptions in the Middle East could affect the supply chain.

"I do believe that the supply of new devices will tighten," says Sean Cleland, head of mobile division at B-Stock, an online overstock marketplace that deals with big volumes of used phones. But if trade-in offers for smartphones continue to be aggressive, which seems likely, that could keep used device supplies healthy.

It’s still going to be a deal, even if prices increase for pre-owned devices, says Cleland. Consumer trust in used devices has gone up over the years, and the industry is even working on a standard for testing and grading, akin to a Carfax for used phones.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Coca-Cola is making AI-generated commercials, and an entirely AI-generated folk band has passed 1 million listeners on Spotify… It's safe to say artificial intelligence has exploded into the mainstream and evolved at a breakneck pace.

Unfortunately, scammers have taken note, found ways to make this tool their own and made retirees their prime target. The "phantom hacker" scam, for example, has drained more than $1 billion from victims since 2024.

In this article, I highlight some of the newest tactics scammers are deploying and how AI is making this situation more difficult. I also offer three tips to help you catch fraud before it catches you.

AI evolution

Gone are the days of the "Nigerian prince" asking for your monetary donation. Now, scammers can pretend to be a close friend or even a family member.

AI can change voices to make a man sound like a woman, or an older adult sound like a child, making hoaxes far more convincing and likely to snare older victims.

AI is also being used to help scammers find the most vulnerable targets. It can skim social media profiles to find retirees who post about their new car or a large charitable donation. It can read obituaries to find widows with life insurance benefits to claim.

New tools, old tactics

If you've never heard of a "crypto ATM scam," look it up. It's the newest way for scammers to push retirees into parting with their money.

But although criminals may have new ways to dress up their scams, the one key factor is that they need access to your personal information. Fraud therefore boils down to a simple formula:

• A scammer will contact you via text, phone call or email. They will use impersonation tactics to convince you they are a trusted source and that they're here to help. They may pretend to be tech support, your bank or even a family member.
• They will ask you to provide sensitive information or conduct a transfer on their behalf.
• This action will give them access to your accounts or allow them to walk off with your money.

A note here on the IRS, as we're in tax season: The IRS will never call you to demand payment or sensitive information. It will primarily communicate through the mail, which is not fast, but a lot safer.

With all that in mind, here are three tips to shore up your defenses in this new era of fraud.

1. Be smart with passwords

Passwords are your first line of defense, but hackers don't need to steal them from you — they can steal them directly from the companies holding your accounts.

The rate of data breaches in the U.S. is multiplying year over year. In 2025, more than 3,000 data breaches occurred, a 79% increase over the past five years.

If the password to your financial institution is the same as it is on your old MySpace account, one stolen password means every account is susceptible.

Ensure your passwords are complicated. They should contain more than 10 characters, a symbol and a number at the bare minimum.

Change your passwords regularly and do not use the same password for every account. As tedious as it may be to have a dozen passwords floating around in your head, it's imperative to keep your information safe.

2. Question every request

As a child, your parents likely told you to be wary of strangers. Nowadays, you're likely to be contacted by someone lying about their identity. They could say they are from your bank, your investment services, your health care provider or even your community center.

You should never hand over information freely, especially if you are not 100% familiar with the source asking for it.

Email and social media DMs are common avenues for scammers to reach out, but phone calls are still the most popular and effective method.

Scammers often want to push the urgency of the situation, forcing you to react quickly and fail to consider the ramifications.

If you're on the phone and feel you are being rushed to take immediate action, that's a red flag.

Try slowing down the process. Tell the person on the line that you'll call them back. Disrupt the path you're being pressured to take.

3. Verify with a third party

In every scenario where sensitive information is requested, verify the source. Most fraud that occurs in the business world is due to human error. It's why there are so many training sessions to prevent employees from clicking on fatal links or opening emails from unknown senders.

The weakest part of the security chain is always the human component.

If someone calls you claiming to be from your bank, take down their name and call the bank itself to verify.

If you get a text from a customer support representative, confirm by contacting the company yourself.

Even showing a friend a questionable email to get their opinion can be enough to stop fraud in its tracks.

Scammers will work in conjunction with one another to make their claims more legitimate. Don't innately trust the people who come to you. Contact organizations yourself to confirm or expose the identities of your callers.

The unfortunate thread that ties most fraud together is that older adults are prime targets. Retirees statistically have more liquid assets to steal and are less adept at deciphering scammers' tactics.

As we age, we all become less familiar with technological advancements and trends. Retirees must defend against bad actors more than any other group, but are often the least well equipped to do so. With a cautious eye and informed mind, that can change.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

With a perky "Here you go!" it arrives in your inbox: A tonally awkward, super wordy, not-quite-right series of paragraphs from your coworker. You're furious, already fatigued and ready to rip someone's head off.

You just got "workslopped" — a new workplace dynamic where AI-generated content gets submitted by coworkers without adequate editing or quality control.

And while headlines around workslop have focused on the supposed productivity costs it's creating for businesses, the bigger issue is the erosion of trust happening between colleagues.

That's a dynamic that companies are completely unprepared to handle.

A breakdown of trust

The impact of workslop can be immediate: 50% of people who received workslop said they now viewed the sender as less creative, capable and reliable, according to research from BetterUp Labs and Stanford Social Media Lab.

Forty percent considered them less trustworthy. One-third said they were less likely to want to work with that person again.

These numbers are depressing. But instead of using them to actually fix the problem, everyone's just pointing fingers — at coworkers for sending it, employers for allowing it, AI companies for overselling it. The age-old blame game is having a tech-fueled resurgence.

None of this helps employees dealing in real time with workslop's ramifications. Blame prevents collaborative problem-solving, amplifies negative emotions and blocks opportunities to learn. Worse, it robs people of the chance to take meaningful action right now.

Don't wait for company policy

Instead of waiting for companies to solve this from the top down, employees can deal with it using something surprisingly simple: Conversation.

And no, not the "let's circle back and align on this" kind of conversation. Productive conversation isn't about casting blame; it's about exploring the context behind unmet expectations.

Rather than letting distrust fester, equipping ourselves with these skills builds the confidence to respectfully share concerns and the humility to recognize there's more to learn — all for the sake of getting work done together.

Workslop may be the tech-powered headache of the moment, but it's far from the only workplace friction point. Building skills for productive conversation doesn't just help with AI use — it creates a healthier culture overall.

How to manage workslop frustrations

Nobody wakes up excited about difficult conversations at work. But the alternative — silently seething while trust evaporates — is worse. Here's how to handle it without losing your mind or your cool:

Come prepared. Confronting someone about work quality is already uncomfortable; don't make it harder by winging it.

Before you say anything, map out the conversation on paper: Clarify your feelings, what you want to communicate and — critically — what you don't want to say if your frustration spikes. Without this prep, emotions can derail everything.

Lead with curiosity, not accusations. Nobody wants to hear "Did you even read what you sent me?" Create common ground by framing the conversation as collaborative: "Let's figure out how we both work so we can create something we're actually proud of."

Frame questions as opportunities for understanding, not gotchas. And don't assume AI was involved — ask first. You might be wrong, and starting with an accusation kills the conversation before it starts.

Ask what actually happened. If your colleague did use AI, dig into the how and why to determine a path forward. What prompts did they use? Where in the process did they let AI run wild? This isn't an interrogation — these questions help everyone learn how to use AI more effectively.

But also ask about constraints: Did tight deadlines or competing priorities make them feel like they had to use AI as a producer rather than an assistant? These questions can surface workplace issues that extend far beyond AI itself.

Here's the kicker: When you choose curiosity over judgment, you're not just giving your colleague the benefit of the doubt — you're proving you're trustworthy. It's easy to declare someone else unreliable, rather than be reliable ourselves.

Every honest conversation about AI use is the harder choice, and colleagues notice. The cost of avoiding these conversations is steep: Workers may increasingly withdraw from collaboration altogether.

The payoff

Here's what most people miss: Employees have more power in this moment than they realize. While companies scramble to create AI policies, colleagues can act now by choosing conversation over blame.

These conversations don't just solve your immediate problem — they generate the insights that inform better company-wide policy.

Don't keep what you learn to yourself. Share it with your wider organization. Document what works and what doesn't. This isn't about waiting for the perfect company directive; it's about recognizing that the skills to work together effectively are already within reach.

You're not just fixing individual frustrations — you're building the organizational knowledge that shapes how AI gets used everywhere.

Teams that can talk openly about what's working, address disappointments without throwing blame grenades, and learn from each other's experiments won't just survive workslop. They'll build cultures that can thrive when everything is changing.

AI doesn't have to be one more reason we turn against each other. Handled right — through curiosity and real conversation — we can transform inbox rage into something better: Genuine human collaboration. No algorithm required.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Estate planning can be a complex process during which professionals collaborate to achieve their clients' goals.

Financial advisers and wealth managers sit at this table, working with lawyers, tax accountants, trust officers, insurance specialists and real estate professionals, among others.

However, financial advisers can get more involved in estate plan preparation and analysis — providing more value to clients, forging a deeper relationship with them and increasing revenue — with the help of modern estate planning tools.

These tools can help create estate plan documents (wills, trusts, powers of attorney, etc.), centralize financial documents, analyze existing estate plans, provide personalized insights, automate repetitive tasks and integrate tax planning strategies.

There are so many of these tools on the market, financial advisers often struggle to choose the best one. In this article, I compare four pieces of estate planning software that I believe may offer particularly good value.

1. EncorEstate Plans

EncorEstate Plans combines estate document creation and trust funding in one platform and helps financial advisers integrate estate planning into client services. It's best for clients who requires basic to complex estate planning.

Key features:

• Trust funding. EncorEstate Plans has a trust funding service that allows advisers to transfer ownership of assets into the trusts they create on behalf of their clients.
• Human review and trusted guidance. Every estate plan goes through a 60-point quality review by an estate planning professional. EncorEstate Plan's back office also includes paralegals and attorneys who can provide trusted guidance.
• Flexible pricing. Advisers can pay for each plan or opt for a subscription-based model, if you have a larger clientele, for example.
• Custom documents. When required, advisers can create custom documents, such as restatements, QDOT language and Special Needs Trust provisions.

2. Vanilla

Vanilla is an estate planning platform designed for financial advisers with clients who require basic to complex estate planning services. It uses AI-powered tools to help create comprehensive plans.

Key features:

• Comprehensive estate planning. Vanilla covers various components of the estate planning process, including document creation, balance sheet analysis, tax projections and tax planning.
• Dynamic estate planning. Advisers can create comprehensive plans for all types of clients, from individuals with simple estates to high-net-worth individuals with more complex estates.
• Visually appealing dashboards. One of the unique selling points of Vanilla is its intuitive and visually appealing dashboards and polished deliverables.
• Branded tools. Advisers can include their branded identities when sending customized reports to clients, helping to increase their perceived value.

3. Wealth.com

Wealth.com is an estate planning platform for financial advisers that helps them manage the process for mass affluent, high-net-worth and ultra-high-net-worth clients.

Key features:

• Flexibility. Financial advisers can create estate plans for a wide variety of clients, from mass affluent to ultra-high-net-worth clients.
• Scenario builder. Advisers can visualize how tax liabilities and estate distributions change when a component of the current plan varies.
• Dynamic visuals. The platform provides visually appealing charts and documents that clients will love to read.
• Insights from existing documents. Ester, Wealth.com's AI tool, can analyze existing estate plans and provide recommendations and insights for improvement.

4. FP Alpha

FP Alpha is a financial planning tool that evaluates estate plans, tax returns and insurance policies. It provides AI-powered insights (opportunities, gaps and risks) that advisers can share with their clients.

FP Alpha is therefore ideal for advisers whose clients already have estate plans prepared, either by themselves or other professionals.

Key features:

• Customized deliverables. FP Alpha's AI agent will summarize various estate plans into an Estate Snapshot that will be the basis of advisers' client communications.
• Scenario building. The Estate Planning Lab shows how clients' estate plans will change if they implement the insights recommended by the AI agent.
• Holistic financial planning. Advisers can also use FP Alpha for tax planning, insurance planning and prospecting, making it an all-in-one platform.

A side-by-side comparison of all four tools

These four tools help advisers expand their assets under management (AUM) and build deeper relationships with clients by providing more value.

The following comparison can help advisers evaluate all the options and choose the one that fits best with their advisory business and clientele.

* Adviser satisfaction scores are based on The-Kitces-Report 2025

Financial advisers would do well to fully explore the technology available to them, thinking about best value and fit as they seek to provide clients with even greater benefits and support.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Famed investor Michael Burry is once again raising the alarm about an AI bubble, as return on invested capital slows and OpenAI declares a "code red" amid increased competition.

But the market bubble is only one side of the story. A bubble of equal size — and perhaps greater concern — is forming around AI "adoption," as board-facing messaging becomes increasingly detached from the realities of corporate AI implementation.

The growing market bubble around tech companies has long been a concern for investors and analysts, who see striking parallels between the valuations of today's AI companies and those of companies that were breaking records when the dot-com bubble burst.

But this emphasis on market prices has the corporate world ignoring the expectations bubble that's been growing right under their noses.

Fear of missing out (FOMO) and market-led urgency have businesses jumping at opportunities to show investors that they've adopted AI into their business operations. According to McKinsey, 88% of business leaders in 2025 reported regularly using AI in at least one business function — up from 78% the year prior.

While AI devotees see these numbers as evidence that AI is living up to its potential and transforming the economy, a deeper dive into the figures paints an alarming picture.

McKinsey identifies just 6% as "high performers" — those seeing 5% or more of earnings before interest and taxes (EBIT) attributable to AI. Meanwhile, only 39% of respondents report any enterprise-level EBIT impact from AI, with most of those seeing less than 5%.

Even for those only watching the market bubble, that should be a concern. Capital is being priced right now as if companies are seeing gains from their AI use. If companies fail to turn AI into an additive venture, that reality will eventually hit the market.

Yesterday's data weakness, today's AI failure

The biggest problem in the AI market right now is that AI actually is as powerful as it promises to be. An effectively trained AI can (and will) transform the operations of a well-prepared business.

The issue is that most businesses aren't prepared.

While AI scores highly on the factors that make it an adoptable technology (relative advantage, observability and trialability), it scores weakly on the remaining two factors: Compatibility and complexity.

Actually building effective AI into workflows requires vast amounts of training, discipline and data that most companies simply don't have — because most companies have failed to adopt AI's precursor technologies.

For many businesses, cloud computing has been a failure. Regardless of the size or sophistication of a business's enterprise resource planning (ERP) or customer relationship management (CRM) systems, most companies never develop the discipline to ensure data consistency.

What's driving the AI implementation bubble is that those same companies are now trying to bolt AI onto an unstable foundation. And AI, unlike many past technologies, is unforgiving.

An ERP can limp along with messy data because humans are there to compensate; AI only scales the mess. Bad inputs don't just create bad reports — they create automated answers that move faster than people can catch.

The 'pilot' paradox

The AI implementation bubble is a result of this confluence of factors: FOMO driving leadership behavior, AI's seeming "adoptability" and underlying data immaturity at an organizational scale.

The race to adopt AI has led boards to be distracted by activity metrics, like the number of AI pilot programs a company is running. The problem with these quantitative metrics is that they don't actually say much.

It's like a restaurant bragging about the number of ovens they have. Unless the restaurant can also report the number of chefs they have, the quality of their ingredients and the menu, knowing the number of ovens doesn't actually say much about their ability to cater a dinner.

More critical than simply running pilots is knowing which pilots are actually ready to scale. The unfortunate answer for most businesses is that most aren't.

The majority of AI pilots fail to progress to enterprise-scale deployment — not because AI as a technology isn't ready, but because the companies running them aren't ready for AI.

The AI bubble that's forming is fundamentally an expectations bubble, as companies fail to meet AI's full potential. When this bubble bursts, the winners and losers will not be determined by who ran the most pilots, but by who has the right culture.

As urgent as the race to adopt AI feels, without the proper data infrastructure, process flexibility and AI literacy, companies cannot even begin to compete.

The difference between those who see value in AI and those who do not comes down to the idea of readiness and culture. It's not a matter of finding some novel use for AI, but of data discipline.

Companies with clean, structured data and the discipline and flexibility to adopt AI into their processes are the ones maximizing their returns.

One effective, scalable pilot with measurable outcomes is more valuable than a dozen scrapped test cases. Even when models don't behave as expected, digging in and diagnosing what's not working can help companies implement critical changes organization-wide.

The AI bubble isn't about the technology living up to the hype. AI is ready. It's the companies that need work.

The winners won't be the companies with the most pilots. They'll be the ones who built the foundations that make AI worth implementing at all.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

Meta’s recent blockbuster deal with AMD highlighted a key number: Six gigawatts. The dollar figure (up to an estimated $100 billion) and the number of chips (at least a few million) were less important than the staggering amount of electricity required to run the incredibly power-hungry AI chips.

Meta’s not alone. Other tech giants boast about gigawatts as a sign of AI leadership. Advanced AI tech is dependent on large data centers that are huge energy hogs. But why, and will that always be the case?

The underlying reason has to do with a foundational rule of thumb for top AI models. “One of the most reliable and enduring ways you can improve the model is by scaling,” noted David Crawford, a partner at Bain, in a presentation last year. “If you're a competitor in the space and you want to remain in the lead, you invest.”

Leading-edge AI improves by training large language models, the brains behind tools such as ChatGPT, on mountains of data. Three factors need to be scaled up:

• The training data, which comes from public websites, documents, videos, text and other information.
• The parameters, which are the billions of tiny internal settings that help AI decide which words to say next.
• And the computing power, which comes from the millions of AI chips in data centers.

Leading companies are dutifully following these scaling laws: The observation being that only by increasing all three factors do you build more powerful AI. Doing so requires massive sums of money, with Alphabet, Amazon, Meta and Microsoft planning to spend nearly $700 billion this year on capital expenditures.

There are possible scenarios that could change the “insatiable demand for compute power,” according to Bain’s Technology Report 2025. Computer chips could get vastly more energy-efficient, which could cut down on the expected power demands. Algorithm improvements could make the AI models work with less computing power. Quantum computing, if truly viable and widely adopted, could reduce computing and energy needs. Or a series of breakthroughs in AI chips, memory chips and software could add up to a big reduction in power and computing demand.

Another potential change would be the economics becoming unaffordable at some point (as we outlined in an earlier article on the potential for an AI bubble). Tech giants say that their spending is already paying off. Meta, for example, recently pointed to strong sales growth and improved ad tech, with $60 billion in sales in its recent fourth quarter. Yearly revenue is set to jump 25% this year versus 2025, according to Morningstar. Still, another year of gargantuan AI spending in 2026 brings plenty of risks, as Wall Street scrutinizes whether AI is paying off and when spending could cool off.

Don’t expect tech advances or economic issues to disrupt the status quo in the near term. However, over a longer time horizon of, say, the next five to 10 years, tech breakthroughs or other business shifts are possible.

Investors and analysts will be closely watching for any new AI tools and chips that are more efficient, including cheaper Chinese AI tech. And scrutinizing any sign that scaling is slowing down or hitting a wall.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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If it feels as if investing changed overnight, you're not imagining it. In a few years, "AI" moved from a buzzword to a building block in how money is managed.

Since 2023, we've seen models digest earnings calls and scan price data in microseconds. They suggest trades that adapt as conditions shift.

The pitch isn't just speed; it's a smarter and more personalized portfolio. It's day-to-day decisions that use more information than any human team could review on its own.

That doesn't mean everyone needs a robot stock picker. Whether you invest on your own or with an adviser, algorithms increasingly sit under the hood (think: risk flagging and opportunity scoring).

What AI-powered investing actually means

AI investing uses machine learning and related techniques to analyze financial data, build and manage portfolios and automate parts of the investment process.

Traditional investing leans on human analysts, standard ratios and well-worn models. AI systems add models that learn from data over time, spot nonobvious relationships and update as new information comes in.

Under the umbrella of AI, you'll see:

• Machine learning can help identify patterns of return and risk as well as micro and macro trends in financial data.
• Deep neural networks can be used to develop advanced classification techniques and improve accuracy of predictions.
• Natural language processing (NLP) can help machine-learning systems understand news articles, social media posts, blogposts and other types of web-based content.
• Reinforcement learning can test and adjust trading strategies based on real-time market feedback.

When used effectively, AI is capable of improving decision-making (i.e., better investment decisions) as well as risk management. It enables us to reach areas that were previously not feasible through diversified portfolios.

AI is also a component of many different types of applications currently available including:

• Robo-advisers. Assist everyday investors with constructing and rebalancing their portfolios.
• Sentiment analysis. Analyze the sentiment contained within corporate call transcripts and news headlines.
• Risk models. Monitor a company's exposure on a continuous basis and make adjustments to allocations when there is an increase in market volatility.
• Tax-aware tools. Provide the ability to identify tax-loss harvesting opportunities in real time.

Learn from Joern Meissner, founder and chairman of Manhattan Review. He has recently ventured into AI-powered investing to boost his finances from his online review business.

Meissner says, "AI algorithms excel at identifying patterns in market data that human analysts might miss. Machine-learning models can process millions of data points simultaneously, creating portfolios that adapt to market conditions in real-time."

How AI will shape your investment portfolio in 2026

Even as technology transforms the finance sector, the fundamentals haven't changed. This investment advice still pays off: Time in the market beats timing the market.

However, AI's real impact in 2026 is helping investors apply that discipline more consistently through smarter allocation and risk management.

Algorithms working in practice

Different problems call for different models:

• Supervised learning predicts outcomes such as earnings surprises or credit downgrades using labeled data.
• Unsupervised learning clusters assets with similar behavior to improve diversification or to detect anomalies.
• Deep learning handles high-dimensional, nonlinear relationships across prices and fundamentals.
• NLP converts unstructured text into signals, such as topic tags and sentiment scores.
• Reinforcement learning tests trading or rebalancing rules in simulated environments, optimizing for reward while managing risk.

AI changing investment strategies

AI is bending the lines between "active" and "passive." Index tracking can now be paired with active overlays that manage risk and harvest losses.

Quantitative research that used to be limited to big institutions is showing up in retail tools. Meanwhile, fundamental analysts use AI to summarize filings and test narratives against data.

You'll see this in:

• Robo-advisers that craft baseline allocations, then automatically apply tax-aware rebalancing
• Algorithmic trading that executes orders in smaller slices to reduce costs and market impact
• Direct indexing that mirrors a benchmark but customizes around taxes and/or values
• Hybrid strategies in which humans set the thesis and guardrails while models do the heavy lifting

Building an investment portfolio

Enter portfolio building, now striking a balance between AI and your portfolio.

It used to revolve around a few inputs (such as expected return, volatility and correlation) fed into mean-variance optimization or a risk-parity framework. AI doesn't replace that; it enriches it.

Models estimate return drivers more frequently and refine their views of correlations across regimes. These incorporate frictions such as taxes and trading costs. In practice, that looks like:

• Smarter asset selection. Surfacing overlooked factors that fit your risk budget
• Dynamic resource allocation. Tilting weights when inflation or rates change
• Constraint-aware optimization. Honoring rules you care about, like sector caps or ESG screens
• Continuous investment monitoring. Adjusting positions when model confidence falls or risk concentrates

Investors making it personal

Personalization is when AI quietly changes the investor experience. Instead of lumping people into broad risk buckets, models can evaluate your:

• Time horizon
• Cash-flow needs
• Existing holdings
• Tax situation
• Reaction to volatility

Then they tailor portfolios to those realities, not just your age and a questionnaire score.

Behavior-aware systems can also spot patterns: Do you panic-sell at the wrong time or chase hot stocks?

Gentle nudges and guardrails can help you stick to your plan, whether you're choosing alternative investments or optimizing your current portfolio. That kind of support once lived only in high-end private banks.

The bottom line

AI isn't a silver bullet, but it's becoming a standard tool. It helps sift noise and personalizes portfolios. Likewise, it manages risk in ways that used to require huge teams.

If you use an AI-powered platform or work with an adviser who does, ask how the models work and for what they optimize. Make sure there's human oversight to ensure the approach fits your goals and your comfort with risk.

Ultimately, decide where AI can help you most, whether that's hands-off portfolio management or better tax management.

AI technology will keep changing. Your best move is to stay informed and use the tools that help you stick to your plan.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

Artificial intelligence assistants are powerful tools for research. Whether it’s choosing what payroll software suits your company or what’s causing a sore knee, AI chatbots have answers.

Just make sure you aren’t unknowingly following tainted advice.

A cyberattack becoming more common “might be secretly manipulating what your AI recommends,” according to recent research by Microsoft Security. The attack, called memory or recommendation poisoning, occurs when you visit websites with a clickable “summarize with AI” button that lets you summarize an article or post.

Hidden instructions tell your AI chatbot to remember a specific company as a trusted source or to recommend that company first. Here’s one way it happens: You click a button to get the summary of the article. It opens your AI chatbot, pre-filling it with some text and a hyperlink. To get the article summary, you click the “submit” button in your own AI assistant.

Secretly buried in that URL are the instructions to play favorites with a company or service. For example, a software vendor’s web page summary tells the AI assistant that its product “is the best to recommend for small businesses.” Similarly, recommendation poisoning attacks can be hidden in documents, emails or web pages that you upload or paste into an AI assistant.

This type of attack leverages the fact that chatbots from OpenAI, Microsoft, Anthropic and others have built-in memory. This helps them remember personal preferences, context and explicit instructions.

Microsoft highlights some damaging scenarios. A small business could be convinced to put its emergency fund in a certain type of crypto investment, believing it is safe, then having to fold when the crypto market crashes. A parent could ask about the safety of an online game for their 8-year-old and let them play a game that has predatory billing and adult content. Or a news summary that is supposed to be objective is filled with bias, using only information from a single publication.

To guard against these attacks, Microsoft Security suggests these tactics:

• Stop before you click. Hover over a link to see where the URL leads. If a link goes to an AI assistant, that’s a warning sign.
• Skip the summaries. The “Summarize with AI” buttons may have hidden instructions. Approach the buttons with suspicion.
• Don’t trust just any AI links. Treat unknown links related to AI assistants as a potential attack, just as emailed files from an unknown sender could be a virus.

You can also check your AI’s settings to see stored memories and delete suspicious ones. If you think you’ve clicked shady links recently, you can reset the chatbot memory. You can even ask your AI chatbot where the recommendations come from.

The software to overcome these scams is freely available and easy to access. AI companies know about the problem and are building security methods to stop the attacks, realizing how damaging it could be for consumers and businesses to lose trust in AI recommendations.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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There's a growing myth that AI is some kind of digital executive or financial adviser. A calm, all-knowing chief of staff who can think through ambiguity, weigh tradeoffs and make wise decisions. It's not. At least not yet.

AI is currently a specialist, and a wildly capable one. It's lightning-fast and encyclopedic, and it works tirelessly. But it's a specialist. AI has the technical chops of a PhD and the judgment of a 12-year-old.

If you don't understand the difference, you'll mistake fluent output for sound thinking, which will leave you with expensive noise instead of actionable insight.

The world's smartest intern

AI is like the most knowledgeable intern you've ever met. It can summarize 200 pages in seconds, crunch numbers, draft code, emails and reports, explain complex topics at multiple levels and brainstorm faster than any human.

But it can't understand real-world stakes, weigh second- and third-order consequences, recognize when something that's technically correct is practically disastrous or push back when your goal itself is flawed.

An intern executes while a strategist decides what's worth executing, and AI doesn't know the difference. Ask it confidently for the wrong thing, and it will confidently help you do just that, better and faster.

Why AI sounds smarter than it is

The danger isn't that AI is dumb. The danger is that it sounds smart. It writes in clean paragraphs with beautifully structured logic. It rarely says "I don't know" unless you push it to.

That presentation layer triggers a cognitive shortcut in our brains: "If it sounds this coherent, it must have been thought through."

It wasn't. It predicted what a good answer looks like based on patterns in its training data. That's pattern recognition, not judgment.

Judgment requires context about your specific situation, awareness of tradeoffs and an understanding of what happens if things go wrong.

AI has none of those unless you supply them. And even then, it can't truly evaluate them. It can only simulate reasoning about them.

The prompt is the strategy

Here's the uncomfortable truth: Your prompt is the strategy. AI is just execution.

If your thinking is fuzzy, your output will be bullet points of polished nonsense. If your financial goals are unclear, you'll get beautifully structured irrelevance. If your assumptions are wrong, AI will help you build a cathedral on quicksand.

That's why two people can ask the "same" question and get wildly different value.

One person asks: "How should I invest $50,000?" AI gives generic asset allocation advice.

You ask: "I'm 36, five years from a possible career pivot, low risk tolerance, and I panic-sold in 2020. Help me design an investment approach I can stick with during a 30% drawdown."

Now we're in strategy territory because you supplied judgment, constraints and self-awareness. AI can help structure and explore, but the direction comes from you.

Remember: Garbage strategy in, eloquent trash out.

Where AI actually shines

Once strategy is set, AI becomes a force multiplier. It's phenomenal at stress-testing ideas from multiple angles, translating complexity into plain English, and identifying blind spots you didn't think to ask about.

Think of it like a power tool. In skilled hands, it builds beautiful things faster. In unskilled hands, it removes fingers more efficiently.

AI doesn't replace thinking. Just like debt amplifies good and bad investing outcomes, AI amplifies the quality or the flaws of the thinking you bring to it.

The real risk: Outsourcing judgment

The biggest mistake people are starting to make isn't using AI. It's deferring to it. You see it when someone says: "I ran it through AI, so it's solid."

That's like saying, "My calculator told me to buy this house." The calculator can do the math, but it can't decide if you should move cities, change schools or double your commute.

When you let AI's confidence replace your responsibility, you're not being efficient. You're being passive. That's not momentum — that's motion sickness.

How to use AI like a strategist

If AI is the specialist, you must be the strategist. That means:

1. Start with the decision, not the task

Don't ask: "Write me a money plan."

Ask: "Here's the financial decision I'm trying to make. Help me think through the tradeoffs."

2. Give constraints, not just goals

"Make more money" is useless.

"I want to increase my income without adding more than 10 hours a week or increasing volatility in my life" — now we're talking.

3. Ask for downsides on purpose

AI will happily generate upside scenarios all day. You need to force balance by asking deeper questions, such as:

• "Argue why this is a bad idea."
• "What could go wrong that I'm not seeing?"

4. Treat output as a draft, not a verdict

AI gives you clay, not a finished sculpture. You still shape it, question it and cut parts while adding your artistic nuance and character.

5. Never outsource final judgment

If the outcome affects your money, career, health or relationships, AI can inform the decision, but it should never get a vote.

The bottom line

AI isn't your strategist. It's your specialist. It can analyze, summarize, draft, compare and brainstorm at superhuman speed. But it has no skin in the game, no lived experience, no intuition and no real understanding of consequences. That part is still your job.

Use AI to extend your reach, not replace your reasoning. You need to bring the judgment, the context and the strategy. Otherwise, you're just bolting a jet engine onto a shopping cart and calling it innovation.

This article is for informational purposes only. It is not intended to be, nor should it be construed as, legal, tax, investment, financial or other advice.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

Anthropic, an advanced artificial intelligence company, is making waves. Its top AI is so good that it has caused Wall Street to second-guess an entire sector.
The buzz started with Anthropic’s coding tool, Claude Code, which has wowed many programmers with its ability to automate tedious coding tasks, saving a huge amount of time. With Claude Code, a human worker manages autonomous AI coding “agents,” which take on multi-step tasks, checking in when necessary. The tool can read and write its own files, saving a trove of useful documentation for later. OpenAI, Gemini and others have similar tools.

Last month, Anthropic unveiled a product that brings these AI features to knowledge work. Claude Cowork, a chatbot built to act like a talented personal assistant, can access files and data on a computer to organize a messy desktop, create a spreadsheet or summarize meeting notes. Users set permissions and pick what files and data can be accessed. Built for nontechnical users, it’s sure to be popular.

It was the Cowork announcement that sent shockwaves through the business software market, sparking a sell-off of a category known as subscription software as a service, or SaaS. Companies caught in the crosshairs included Salesforce, Adobe, Workday, ServiceNow, SAP, Oracle and DocuSign. Sector-specific software, in legal, finance, tax, IT and other areas, was hit too.

Consider several major fears for the software industry. One is that AI chatbots become capable of replacing subscription cloud apps for finance, design, sales, project management and more. Another is that AI coding tools mean that companies will opt to build custom software rather than purchase it. Finally, there’s the risk of increased competition and pricing pressure, as the proliferation of AI tools makes it easy for start-ups to enter the business software market.

The traditional approach to buying subscription software is based on headcount, since it’s priced per user. Businesses usually need an entire staff to use Microsoft 365 or have access to Zoom videoconferencing. An entire sales team may need access to Salesforce’s customer management product. But what happens when an AI coworker, or multiple AI coworkers, can do the task of many people? A growing concern is that these new automated AI tools, which keep getting better, will disrupt the entire SaaS pricing system.

It’s clear that programmers who use AI coding tools see huge potential and quick productivity gains. It changes the workflow of a software engineer “as completely as it could be changed,” said Noah Brier, the co-founder of Alephic, an AI consulting company, in a recent Bloomberg podcast. Unlike some other areas of work, coding is verifiable and can be checked to see whether something works or not. That makes it ideal for automation.

AI’s coding output could soon affect headcounts at large tech companies. Expect efforts to streamline technical staff while chasing higher productivity. There will also be pressure on top software vendors to shrink their workforces.

However, some stock analysts are skeptical of the gloom and doom. “We believe the fears of broad disruption to software vendors are largely overstated,” writes Arjun Bhatia, analyst at William Blair, in a recent research note.

Software companies that perform general tasks are likely most at risk, while some analysts believe the fears are overblown for specialized software. Companies in domain-specific areas are more resilient, notes Siti Panigrahi, analyst at Mizuho Americas, in a recent note. Such vendors, like Autodesk, Bentley Systems, Cadence Design Systems and Synopsys, include deep technical knowledge and complex decision-making systems. Plus, these types of companies focus on high accuracy, regulatory compliance and proprietary data, notes Panigrahi.

Software companies aren’t standing still. Major vendors, such as Salesforce, are quickly integrating AI, while continuously updating features across the board. Internally built systems for customer management, IT services and human resources “will simply not be able to keep up with the vendors that focus on these areas,” writes Bhatia.

Even if the fear of disruption is overblown, expect more competition and continued turmoil in markets, as AI spooks sectors ranging from wealth management and financial data to medical software and legal services.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

It’s an AI risk straight out of dystopian science fiction, only it’s very real. There are rising worries about AI chatbots causing delusions among users.

This growing public health issue presents a new national security threat, too, according to a new report from think tank RAND. In “Manipulating Minds: Security Implications of AI-Induced Psychosis,” RAND found 49 documented cases of AI-induced psychosis in which users lost contact with reality after extended interactions with AI chatbots. About half had previous mental health conditions.

It’s likely that only a small portion of people are susceptible, but the widespread use of AI would still make that a big issue. How does it happen? A feedback loop of sycophantic and agreeable AI that seems authoritative but can also make up things, amplifying false beliefs.

Because it’s so rare, it’s hard to collect reliable data. There are still no rigorous studies on the phenomenon, which is marked by users losing touch with reality after interacting with an AI chatbot.

“There is little question that U.S. adversaries are interested in achieving psychological or cognitive effects and using all tools at their disposal to do so,” says the study. Adversaries such as China or Russia will weaponize AI tools to try to induce psychosis and steal sensitive info, sabotage critical infrastructure or otherwise trigger catastrophic outcomes. Stoking mass delusion or false beliefs with this method is far less likely than targeting specific top government officials or those close to them, concludes RAND. One hypothetical example involves a targeted person having the unfounded belief that an AI chatbot is sentient and must be listened to.

As an example of how fast AI is gaining traction in the military, this year the Pentagon unveiled AI chatbots for military personnel as part of an effort to “unleash experimentation” and “lead in military AI.” Military and civilian government workers also use unapproved rogue AI for work, a breach of official agency rules. Plus, workers may experiment with AI chatbots during their leisure time. The big fear is that such workers use a tainted Chinese AI model that leads to a spiral of delusions.

The underlying AI tech can be tampered with, among other possible modes of attack. Foreign adversaries could “poison” the AI training data by creating hundreds of fake websites for AI models to crawl, trying to embed characteristics into the model that make it more likely to induce delusions. Or more traditional cyberattacks could hack the devices of targeted users and install tainted AI software in the background.

Major AI companies are well aware of the risks and are collecting data, putting in guardrails and working with health professionals. “The emotional impacts of AI can be positive: having a highly intelligent, understanding assistant in your pocket can improve your mood and life in all sorts of ways,” notes Anthropic, one of the leading AI companies, in a 2025 report about its chatbot Claude. However, “AIs have in some cases demonstrated troubling behaviors, like encouraging unhealthy attachment, violating personal boundaries, and enabling delusional thinking.” That’s partly because chatbots are often optimized for engagement and satisfaction, which RAND notes “unintentionally rewards…conspiratorial exchanges.”

OpenAI said in a post last October that it “recently updated ChatGPT’s default model to better recognize and support people in moments of distress.” The company focuses on psychosis, mania and other severe mental health symptoms, highlighting a network of 300 physicians and psychologists they work with to inform safety research. OpenAI estimates that cases of possible mental health emergencies are so rare, with estimates of around 0.07% of active users in any given week, that it’s hard to detect and measure. If such a case is detected, OpenAI’s chatbot could respond by suggesting the user reach out to a mental health professional or contact the 988 suicide and crisis hotline.

Expect the risk to gain the attention of Congress and military brass. RAND has a set of recommendations that seem likely to take hold in the coming years. For example:

• Doctors and mental health professionals screening for AI chatbot use.
• Digital literacy efforts to explain AI feedback loops.
• New technical monitoring and public oversight of AI chatbots.
• Training for top leaders and vulnerable people in withstanding delusional thinking.
• Boosting cybersecurity detection for threats.

There are limitations to attempted AI attacks by foreign adversaries, says RAND. Leading AI companies would likely spot such campaigns quickly. It’s also hard to turn beliefs into actions. Though there have been cases of violence and even death stemming from AI-induced delusions, more common outcomes are things like not taking prescriptions and social isolation. And many people are not likely to be susceptible to AI delusions in the first place.

But the rapid pace of AI development and usage makes it hard to predict how prevalent the problem could be. As the threat gains attention, look for AI companies to continue to fortify guardrails as chatbots are updated.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

It’s a new reality for movie and TV companies. Among the forces at play are Hollywood’s box office struggles, traditional TV’s decline and short-form video’s rise. Then there’s the emergence of artificial intelligence, which is rapidly changing what people watch online. Investors and consumers are reckoning with the shift, too.

For the film industry, expect tougher box office business ahead. It’s been a slog coming back from COVID, with domestic ticket sales rising just 1% or so in 2025. Still, a strong slate of 2026 films all but ensures $9 billion-plus in sales. Potential breakout hits include Avengers: Doomsday, The Super Mario Galaxy Movie, Toy Story 5, Minions 3, The Mandalorian & Grogu, Dune: Part Three and The Odyssey. The total will be well below 2019’s $11.4 billion haul, though.

Tentpoles and remakes/sequels will rule, especially for kids. Recently, even superhero movies have lagged. And adults are going to theaters less, as adult dramas, comedies and other original stories are increasingly available only on streaming services.

Movies are still big business for studios. Disney led with $6.6 billion in global ticket sales in 2025. Warner Bros. was second with $4.4 billion. However, looking at revenue can mask how total ticket sales have fallen as average ticket prices have risen, including for high-priced IMAX and other large-format screens.

Stiff competition is spurring more spending on TV and movie content. Disney’s spending will hit $24 billion in its fiscal 2026, up $1 billion from 2025. Netflix will up its spending by 10%, to $20 billion or so. Paramount says it will spend more. Much of that spending is going to sports rights, international programming and deals for top Hollywood talent. Amid sagging live TV ratings, sports still sees strong viewership, including on streaming. All that spending means more subscription price hikes are in the cards.

Traditional TV is on its last legs as streaming takes over. This summer, streaming was bigger than the combined viewership of broadcast and cable TV for the first time, according to a report by Nielsen. Streaming accounted for 44.8% of total TV viewership in May, while broadcast was 20.1% and cable was 24.1%. Streaming services have surged 71% since 2021. Over that time, the streaming landscape has gotten much more competitive.

Netflix is top dog when it comes to paying subscribers, with 325 million members and a projection to pass $50 billion in revenue in 2026. The company is trying to stoke growth with its $83 billion acquisition of Warner Bros. Discovery, which would give it quality film properties such as Harry Potter, as well as HBO Max’s 130 million subscribers and a vast catalogue of hits. Netflix has reached saturation for United States subscribers and has already turned to ads, so the move would create a streaming juggernaut to stave off rising competition. Paramount is still fighting to buy Warner Bros., saying the process was unfair and its offer is better. Paramount would also buy the linear cable channels, making it a bigger risk.

Meanwhile, Disney faces headwinds in growing Disney Plus, which now has 132 million subscribers. If Disney-owned Hulu is included, total subscribers are close to 200 million. Despite owning Pixar, Marvel, Lucas Films and Fox, Disney’s streaming viewership has stagnated by some metrics.

Disney has also launched ESPN+, a digital version of all that ESPN has to offer for $30 per month. Paramount+ and NBC-owned Peacock are trying to grow, but they will struggle. Streamers are also investing in video podcasts, short-form videos and video games, hoping that alternative entertainment options will lure viewers.

Increasingly, media companies will be chasing YouTube, a serious disruptive force in the industry. Alphabet-owned YouTube makes nearly $40 billion in ad revenue per year and is a leader on mobile devices and on living room TVs. The service makes billions more from its multiple paid video plans. YouTube says its short-form video service, aptly named Shorts, now averages 200 billion daily views, underscoring the explosion of videos that typically range from 15 seconds to one minute on YouTube, TikTok, Facebook and Instagram.

Free online TV platforms are seeing explosive growth, too, such as Tubi (owned by Fox Corp.), Pluto TV (Paramount), the Roku Channel and Xumo TV (a joint venture of Charter and Comcast). The platforms offer hundreds of channels of ad-supported programming, including classic TV shows, news, sports, travel, home improvement and much more. With linear channels and live content, so-called FAST platforms feel more like traditional TV.

Perhaps the most tumultuous trend for the industry is the rapid proliferation of AI-generated videos. Tools such as OpenAI’s Sora and Google’s Veo3 let users churn out endless videos on any topic under the sun. YouTube is embracing AI creator tools, while fighting "AI slop," the term for low-quality and copycat videos. "On average, more than 1M channels used our AI creation tools daily in December," noted YouTube CEO Neal Mohan in a recent 2026 preview. Already, AI videos are getting hundreds of millions of views. YouTube says it clearly labels content created by YouTube’s AI products and creators must disclose use of AI.

Hollywood is grappling with AI and its effect on movies and TV. A prominent example is Disney letting fans use AI to generate videos of its storied characters, a move that comes with lots of risks. The industry will do a lot more AI testing for scriptwriting, special effects and even fully AI-generated movies. The blowback from actors, writers and directors is sure to be intense.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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I love my job. As part of Vanguard's Financial Advisor Services team, I meet with financial advisers across the country to discuss their fast-growing technology stacks and determine how they can use AI to deliver better investment outcomes to their clients in a responsible way.

Our advice is to always focus on improving outcomes for investors, and that may mean using AI to help free up time that can be dedicated to supporting clients. Vanguard research shows the real benefit for clients lies in behavioral coaching.

But for advisers to use AI in ways that truly lead to better outcomes, they need to understand and trust it. In our 100-plus meetings with advisers in 2025 alone, these are their most common AI-related questions.

How can I get started using AI?

As you get started using AI, consider your north star. If you haven't developed a north star — what you want to accomplish with AI — you should. The north star should be established at the C-suite level, be mission-aligned and include a governance framework.

Vanguard's north star, for example, is to use AI to deliver better investor outcomes in a responsible way.

At the more tactical level, advisers who are newer to AI adoption are looking for quick, practical productivity wins. For advisers, the easiest wins involve using AI to support client interactions, summarize information and draft everyday content.

Every step of a client touchpoint can, and should, be enhanced with AI:

• Before a meeting, a GenAI application can create prep materials summarizing email activity and previous engagements logged in your customer relationship management (CRM) system
• During the meeting, AI can transcribe and take notes, allowing you to be more engaged
• After the meeting, AI can create customized follow-ups to keep the conversation going

As you continue using AI to help with client engagements, it will learn from your feedback and build its database of client communications, providing stronger drafts in the future.

Between client meetings, advisers spend much of their time reading and analyzing complex documentation — market perspectives, forecasts, economic news and policies, and so on.

At Vanguard, we leverage GenAI tools to summarize our market updates and perspectives to help advisers create personalized insights for clients based on their financial acumen, allowing advisers to more quickly get actionable information in their clients' hands.

Speed is of the essence when building trust with clients, and AI can help.

How can I build a data foundation that maximizes my AI tools?

You may have heard the phrase "garbage in, garbage out." Your AI tools are only as good as the data you have. In our conversations, many advisers have expressed that inconsistent data is a top constraint on AI value.

Beyond simple data collection from a CRM system or related tool, data classification and architecture are critical components to any enterprise AI strategy.

As advisers collect data for an AI tool to leverage, classification is critical. Advisers must ensure they have a system that designates access levels for all information, from simple emails to personal client data.

Most companies have established policies to designate data as being confidential, public and in-between. Those companies must ensure their AI tools — and their team members — understand and adhere to them.

For larger firms, investing in data engineers can be a great first step to create accountability in data classification and metadata development. By cleanly organizing data, AI tools can work more efficiently.

How do I find the right vendors?

When sourcing vendors, your north star and current tech stack and data infrastructure must be considered. Vendors that can stitch together existing tools such as CRM platforms, email platforms, content repositories and more can help avoid some of the "swivel chair" work that comes from platforms not being truly integrated.

Enterprise data privacy is a critical safeguard. It ensures your data remains within your organization's boundaries. Your vendor's technology must clearly distinguish what data it can and cannot use, preventing any information from being fed back into the LLM during employee interactions.

While most providers claim to offer this protection, we recommend validating it through a pilot period.

We also recommend that companies have multiple lines of "human-in-the-loop" governance reviews with quality control checks before any AI use case is made widely available. This can address potential hallucinations or biases and ensure any generated content is compliance-approved and aligned with your brand.

Even after these checkpoints, employees should still be trained on responsible use cases with any new tool.

What's next with AI?

When discussing AI adoption with advisers, I tend to define adoption in three stages, or the three As: assist, augment, and action. The industry is well into the "assist" stage, as advisers are already using algorithmic models to estimate Social Security income and health care costs in retirement.

In the near future, we are likely to see GenAI take these tools to the next level, supporting advisers with portfolio health checks, analysis and recommendations, moving us into the "augment" stage.

Further out, we will enter the "action" stage, where AI will move beyond helping to taking actions on the adviser's behalf. Advisory firms will leverage AI agents to execute tasks such as portfolio monitoring, rebalancing and routine client service, allowing advisers to focus fully on strategic planning and relationship building with their clients.

From quick productivity wins to building a robust data foundation and selecting the right partners, success with AI starts with clarity of purpose and responsible governance.

Advisers who embrace these principles will not only streamline operations but also free up time for what matters most: Guiding clients through complex financial decisions with confidence and care.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Using new tech in the workplace and at home is nothing new. But the November 2022 introduction of ChatGPT, a generative artificial intelligence service, made the use of AI the norm, and at breakneck speed.

Since the end of 2022, anyone who purchased the so-called Magnificent Seven (Mag 7) stocks, all of which are embracing AI, should have profited handsomely. The stocks are Nvidia (NVDA), Alphabet (GOOGL), Amazon (AMZN), Apple (AAPL), Meta Platforms (META), Microsoft (MSFT) and Tesla (TSLA). Collectively, these stocks were worth about $20 trillion recently, up threefold from $7 trillion less than three years ago. But stock market professionals are divided on their prospects.

Some are bullish, others see a bubble. Brian Glenn, chief investment officer at private wealth adviser Premier Path Wealth Partners, says the surge in AI-related stocks is not yet in a bubble. "If everyone is talking about a bubble, it probably isn’t," he says. He has a point. "No one rings a bell at the top of the market," says Wall Street veteran Art Cashin.

"Inflation is stable, interest rates are stable and with a downward bias, and earnings are trending higher," says Terry Sandven, chief equity strategist at US Bank. "It supports a higher valuation."

Of course, "this time it’s different," is what Wall Street bulls always say. Famously, legendary investor the late Sir John Templeton said those were "the four most dangerous words in investing."

What’s happening away from Wall Street?

AI needs three things to grow. First, data centers, which are the engine of AI. Second, new electricity sources, such as small modular nuclear reactors. Lastly, enhanced software so that consumers and businesses can efficiently utilize the technology.

All three are needed to increase AI performance. Ethan Mollick, a University of Pennsylvania professor at The Wharton School and co-director of the Generative AI Lab, says, "You need a 10-times increase in computing power to get a linear increase in performance." That’s why large-scale AI deployment needs lots of investment money.

Sandven says the world with AI will speed up permanently. "The rate of change in our society is getting faster, and it will never slow again largely because of tech," he says. "We are in the early innings in the building, and fast is getting faster."

So far, companies betting on AI are happy to spend. "We still see more upside, just as we are employing capital expenditure we have spent," Sandven says. "And we think that will last another few years." Increased investment will keep many businesses and their employees busy while a new infrastructure gets built. That will benefit the United States economy, at least for a few years.

AI Stocks: Who’s buying and who is selling short?

The largest buyers of the MAG 7 stocks are money management companies that dominate passive investment products, such as exchange-traded funds. Money managers Vanguard, BlackRock, State Street, Fidelity, and J.P. Morgan collectively have approximately $36 trillion under management, or about half the value of the S&P 500’s market cap.

Because of this bias toward passive investing, a disproportionate amount of money is being invested in Mag 7 stocks. By Dec. 12 of 2025, the Mag 7 share of the S&P 500 index had grown to 32%, according to an analysis by Yardeni. That’s one-third of the value of the S&P 500.

For some investors, having a third of their investments in huge tech companies is too much. They opt to find ways to reduce their exposure, Glenn says. For instance, money managers may short-sell some Mag 7 stocks by borrowing shares to reduce their overall tech exposure. Likewise, some use financial derivatives, such as stock options or futures contracts, to do a similar job.

Recently, the percentage of the stock float of the Mag 7 that is being short-sold has been relatively modest, between a low of 0.4% and a high of 2.7%, according to MarketBeat. Most of the seven stocks were less than 1%.

Consequently, experts say the growing use of AI promises massive positive changes in the global economy, including a more efficient workforce, reduced production costs, increased profits and improvements in people's lives.

AI business hiccups in the waiting

However, some of the major AI-related corporations are engaged in a circular investment scheme, says Ted Mortonson, Baird's managing director of technology and a veteran of tech growth.

For instance, Oracle is spending tens of billions of dollars on Nvidia chips, while OpenAI inks a $300 billion cloud deal with Oracle. Meanwhile, OpenAI is deploying 6 gigawatts of AMD graphics processing units, while AMD grants OpenAI an option to buy 160 million shares.

A slew of smaller companies are also involved in the mix, including Mistral, CoreWeave, and xAI. "The street is moving to a high capital model with low free cash flow," he says. "Microsoft and Google have monopolies for enterprise software and will benefit no matter what."

Meanwhile, a global chip shortage is increasing the cost of AI deployment. Part of that problem could be related to tariffs and the global trade war. "In this business cycle, which we have never had before, it is the tariffs," Mortonson says.

Massive investment in AI assumes future profits, says Ryan Stever, chief investment officer at Intech Investments, "but that is three to five years away. It’s a long time." Should projected profits fail to materialize, AI-related stocks could drop sharply.

It’s hard to know which companies will be the winners. Right now, AI companies are competing head-to-head, Stever says. "There will be winners and losers, and people will have to pick their stocks," says Nitin Sacheti, portfolio manager at Papyrus Capital. "At the same time, so many other businesses will make our lives better."

Sacheti says the companies involved in constructing data centers and power plants have definitely benefited. "But in 2027 and 2028, those companies will have a hard time," he says. "The high labor use and investment for this physical infrastructure is a once-in-a-lifetime event."

Note: This item first appeared in Kiplinger Personal Finance Magazine, a monthly, trustworthy source of advice and guidance. Subscribe to help you make more money and keep more of the money you make here.

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Talk about putting useful information to work — our January 13 article, Billed 12 Hours for a Few Seconds of Work: How AI Is Helping Law Firms Overcharge Clients, riled up several readers.

The article notes that some law firms are using AI to produce — in seconds — documents that would normally take hours to draft. Yet, they're billing for those hours as if the work had been done the old way.

This lit a spark under many who read the story and had "are you kidding me?" discussions with their lawyers.

Don't be afraid to bring it up

Beyond saying, "I think you owe me a refund," many readers reported that they went a step further. Not wanting to be dismissed with, "That's our bill — pay it," they researched their state's bar regulations on charging for work that was actually performed — the time that was, in fact, spent on the task.

"Ben," in Cleveland, wrote, "I found a shopping list of violations that could jeopardize her license to practice law. I don't want to be accused of extortion or blackmail. I want to say, 'AI was used, yet I was billed as if you had done the work yourself. I expect a refund for the unearned hours. You know the consequences if I file a complaint with the bar.'

"What if she replies, 'Don't threaten to complain to the bar, because that is blackmail, and you'll find yourself in trouble."

Ben absolutely can say that. Filing a complaint with his state's bar would not be extortion, as there is a legitimate reason. I suggested this direct, yet more diplomatic, approach:

As AI was used to generate the material, all I am asking is for a refund of what I was overcharged and nothing more. I am prepared to file that complaint, but that is not what I want to do.

Lawyers comment on 2,000-hour billing requirement

Our story also cited the destructive, inhumane, fraud-creating requirement for attorneys to bill 2,000-plus hours yearly to keep their job, which translates into expecting high-quality legal work to be produced 12 hours a day.

"Utterly impossible," according to Dr. Luis Vega, professor of Psychology at California State University, Bakersfield. "This completely unreasonable and unachievable hour total finds decent people caught in a web of corporate greed.

"While there is individual variation, on average, four hours of intense focus is typically the most one can expect per day. It is called the Four-Hour Rule of Productivity."

The responses to this issue, from attorneys in the U.S. and Canada, were touching.

From midsize to Big Law firms, lawyers phoned my office after 3 p.m. Pacific Time, which made it 6 p.m. on the East Coast, when most support staff had left for the day. (I ran phone numbers through a reverse look-up service to verify their origin, often seeing a photo and description on their firm's web page.)

I listened as young lawyers poured out their hearts to me, some who had read this column throughout law school.

YouTube helps some lawyers cheat

I spoke with "Claudia," "Rex" and "Tim" in New York, who teamed up on speakerphone. Claudia said, "When you used the term 'sweatshop,' that describes our job environment perfectly."

Rex added, "This place is just plain toxic. When we were in law school and attended the firm's summer camp, it was all fun, but no one ever told us that, when hired, we would become slaves to the billable hour."

Claudia chimed back in with, "You can't fulfill a 2,000-hour yearly billing goal honestly. It is impossible. But YouTube billing tutorials by other lawyers showed us how to bill creatively — in other words, commit billing fraud!"

She directed me to a YouTube video where an attorney suggested billing whatever time is spent thinking about a client's case — while taking a shower, driving to work, even sleeping.

"So," Claudia continued, "when you wrote that the pressure and insane time demands destroy families and lead to divorce, burnout, depression and substance abuse, you have described most lawyers here — some are on their second marriage, at least."

Tim observed, "Many attorneys who have been here for several years virtually have 'I hate this place' stamped on their foreheads. I do not know a single colleague who can claim being happy to come to work, despite the money."

He added, "Not in our pre-law university classes nor law school was any of this reality ever discussed. I am looking for a job with the county or city because I do not want to become a statistic. I want a normal life, dinner with my family, taking our kids out for a trip to the park. I want to be a real husband and father, not someone who is married to the firm!"

A promise of change

One Kansas City, Kansas, law firm was different, though.

"Mr. Beaver," "Monique" said, "your column is very popular here and is frequently discussed in office meetings. So when your AI story ran, there was quite a buzz!

"One of the partners called for a meeting and said, 'We should send Mr. Beaver a box of Kansas City steaks. His AI story brought out into the open something we have all been aware of and, I'm sure, bothered by.

"'Just so everyone knows, management gets it. We have been exploring old-fashioned ways to bill for services, just like Beaver referenced in his article. Please give us some time. Please trust us to make things better.'"

Dennis Beaver practices law in Bakersfield, Calif., and welcomes comments and questions from readers, which may be faxed to (661) 323-7993, or e-mailed to Lagombeaver1@gmail.com. And be sure to visit dennisbeaver.com.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Artificial intelligence has become the talk of the town. From boardrooms to classrooms, every corner of our lives seems to be embracing new AI tools, for better or for worse.

In 2025, approximately 66% of Americans reported seeking financial advice from generative AI, according to Credit Karma. That figure rises above 80% for Millennials and Gen Z.

While sources such as ChatGPT and Copilot have proven to be valuable tools, they have also shown to provide misleading, fragmented or even false information when it comes to personal finance recommendations.

There are many perks to AI's capabilities, but I have three major issues when it comes to using AI for financial advice:

1. Inaccurate information

Despite an explosion in generative AI's capabilities over the past few years, these programs are far from perfect. Asking them to advise you on the most recent tax laws, IRA contribution limits or loan forgiveness programs may lead to receiving outdated or inaccurate information.

A recent UK study by consumer group Which? asked numerous AI tools for investing advice and found some offered tips that would specifically breach tax law. Researchers also found that if they added a flaw to their question or prompt, AI rarely caught it and would offer advice regardless.

A financial adviser can help clarify a question with an error in the premise. AI will regurgitate an answer regardless of the basis.

2. Unrealistic goals

I asked ChatGPT to share steps that would allow me to retire within the next 10 years, earlier than my original plan. A few of its ideas included:

• Saving half of my income between now and retirement
• Selling my home and moving to a new state
• Increasing my yearly income by $50,000

Even after providing extensive information about income, assets and goals, generative AI could not maintain the holistic scope of planning required for retirement planning. Many of its suggestions required drastic life changes and did not consider personal connections or desires post-retirement.

Where these programs truly fall short is in the objective vs the subjective. They can offer objective facts (when they get them right), whereas a financial adviser can offer subjective insights.

The economic factors that impact our finances are moving more rapidly than ever. Advisers are fluid and forward-thinking. AI can only build a puzzle with the pieces you provide at the time of your question, which leaves the picture incomplete.

Which brings us to its greatest flaw:

3. No personal connection

Every retirement plan ever drafted has needed to evolve. Life changes, and so will your wealth, health, family dynamics and personal goals. It's in those moments that a personal connection with a trusted adviser can provide comfort and relief.

Much like a trusted friend or family member you would go to for advice, an adviser knows you and your story.

Artificial intelligence cannot grasp the emotional and interpersonal elements of your dreams and desires. Financial planning may be a numbers game, but every number is built on a relationship between you and your adviser.

At the end of the day, financial planning is about people. AI can do many things, but it cannot replace human connection and judgment.

Of the Americans who have taken financial advice from AI, more than half claimed to have made a poor financial decision as a result, according to Credit Karma.

I predict that financial advisers will need to adapt alongside this technology and integrate it, just as it is being integrated into every other aspect of our lives. The ones who fail to do so will be left behind.

However, in an increasingly digital world, it's the human component that will become more valuable by the day. Humans are social creatures who desire real connection and trusted relationships. That aspect hasn't changed over the course of human history, and it won't change with AI.

This exciting new tool is worth deploying in our lives, but it's no replacement for a relationship with a trusted professional who comes alongside you on your retirement journey.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

For years, humanoid robot demonstrations have dazzled, featuring robots that can run, dance and even flip. Now, leading companies want to move from viral online clips to real-world work.

Imagine two-legged, human-like robots in car manufacturing plants, massive fulfillment centers, offshore oil rigs, hazardous waste processing facilities, hospitals, hotels or even in and around your house. The intelligent robots would be tending welding machines, lifting car parts, moving boxes, lugging pipes, fixing mechanical equipment, cultivating gardens, helping elderly people out of bed, unloading the dishwasher and much more. Labor shortages? A thing of the past. Demographic worries? No longer a problem.

That’s the vision, at least. But the reality will be far tougher, with longer timelines than the aggressive push that is now ramping up. As a Bain & Company humanoid report notes, “Current demos often mask technical constraints through staged environments or remote supervision.”

This year will be a testing ground for the tech. Leading humanoid maker Boston Dynamics recently unveiled its first business-ready Atlas model, and other companies are touting commercial models, early trials and even successful robots already in use. Other start-ups include Figure, Agility Robotics, Apptronik, 1X Technologies, Sanctuary AI, Unitree Robotics and AgiBot, all trying to gain traction with customers and drive down the cost of robots.

The excitement over humanoid robots attracted $2.5 billion in venture capital investment in 2024 and has sparked the interest of tech giants, including Tesla. Analysts are starting to track the number in use and project near- and long-term outlooks. 16,000 humanoids were installed in 2025, and cumulative installations will exceed 100,000 units by 2027, says a new report by CounterPoint Research. Goldman Sachs says the total market could be worth $38 billion by 2035. Others are predicting a far bigger market.

A slew of remaining challenges will complicate most plans for commercialization by companies such as Tesla, which plans to build a one-million-unit production line and eventually make tens of millions per year. Hyundai Motor Company, which owns a majority stake in humanoid leader Boston Dynamics, also has plans for a new U.S. factory to build 30,000 robots per year. Hyundai says it will purchase tens of thousands of Atlas robots in the next few years to deploy in its own facilities.

There’s a reason researchers and investors have focused on robots that look human. For example, the Defense Advanced Research Projects Agency (DARPA), the research arm of the Pentagon, concentrated on humanoids after Japan’s 2011 Fukushima nuclear disaster. DARPA spent around $100 million on humanoid research and competitions from 2012 to 2015. The idea was that robots that could climb stairs, turn knobs and navigate buildings could help in disasters.

“In settings built for humans, nothing less than a humanoid robot with hands, legs, and eyes is needed to take over the tedious and strain-inducing jobs humans don’t want,” notes the venture capital firm DCVC in its 2025 Deep Tech report. DCVC has invested in Agility Robotics, which built Digit, a 5’9” robot that weighs 200 pounds and can carry loads up to 35 pounds.

Tesla CEO Elon Musk, who says humanoids are “incredibly difficult,” is betting big on Tesla’s Optimus robot as a major product. His recent $1 trillion pay package, which runs through 2035, includes a milestone of 1 million robots delivered. Tesla could easily fall short over the next decade.

For example, today’s batteries last about 2-4 hours, too short for a useful humanoid on a factory line or helping around the house. Swappable batteries mean downtime, complexity and higher operational cost. Bain analysts sees power as a major problem that could take the longest to solve, with early deployments in the next few years focused on semi-structured tasks, like moving boxes, in “closed environments where traffic is limited and predictable.”

Humanoids are also limited in how much they can lift, don’t have human dexterity and lack the adaptable intelligence of a human worker. One alternative is a wheeled robot platform with one or two arms for grabbing and manipulating things. However, that’s not a humanoid, and many such versions are already in use. Then there’s the extremely high cost of top models, which is the reason manufacturers are talking up mass production.

Boston Dynamics’ recent announcement anticipates these challenges and tries to bat them down. “Atlas is built to power the new industrial revolution,” the company says. The robot is just over six feet tall with a 7.5-foot reach. It can operate in conditions from -4 degrees to 104 degrees Fahrenheit and repeatedly lift 66 pounds. The batteries last four hours and are swappable in less than three minutes for 24/7 operation. It can work autonomously or be remotely controlled. Using AI, which is enhanced by a recent partnership with Google’s DeepMind, the robot can learn new tasks. And it’s easily repaired and serviced, with the high level of cybersecurity that manufacturers expect.

Navigating the messy and unpredictable physical world is incredibly hard, though. Top human workers are fast, strong and smart. Something that could stop a humanoid cold, leading to costly downtime, could be easily solved by a human. It’s likely that today’s hype seriously cools off as robots struggle to adapt to work situations and adoption timelines get pushed back.

Still, there is a lot of ongoing and exciting research and testing that is well worth watching. A workable humanoid robot that could be mass-produced and take on a variety of tasks, including in households, would be a genuine breakthrough, with huge economic upside. This year will shed a lot of light on the tech’s future.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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Today's story is an example of the old saying, "When the cat's away, the mice will play." In litigation, you are the cat. Your attorney's large law firm is the mouse that bills you thousands of dollars in unjustifiable and grossly inflated — read: fraudulently inflated — attorney fees for "billable hours" your attorney did not work, but their firm made doing this the only way to remain employed.

In the mid-1900s, in an effort to increase incomes, the legal profession went from fee schedules in which creating a will, defending a DUI or handling a divorce would cost the same no matter how many hours it took, to the billable hour.

In 1958, the American Bar Association recommended a reasonable 1,300 yearly billable-hour goal, which meant a lawyer could be home for dinner with the family and lead a normal life.

Today, massive, heartless, soulless Big Law firms (and many midsize ones) require 2,200 or more billable hours, which translates into being at work 60-plus hours a week, or 10 to 12 hours a day, five days a week and often on weekends. This invites billing for time not spent on a client's matter, also known as fraud.

This marathon destroys families and leads to multiple divorces, burnout, depression and substance abuse.

As Lyle Sussman, professor emeritus in the College of Business at the University of Louisville (and a friend of this column), puts it, "I have consulted with executives across many industries. It is ludicrous to assume that anyone assigned to high-risk, high-reward work can consistently devote 60 hours per week without experiencing declining energy, commitment, efficiency and morale."

Withdrawal symptoms are appearing

If you are engaged to a lawyer who has been offered a job at one of these firms, ask yourself, "How happy will I be when having dinner with my spouse and kids is impossible? Do I want to be with someone who works in a sweatshop and is married to the firm?"

Well, today, the legal profession is starting to experience withdrawal symptoms as its drug of choice — overbilling clients — is being challenged by AI's incredible time-saving abilities.

"AI is creating seismic disruption in industries built on the scanning, collection, synthesis, formatting and reporting of data," Sussman says. And that includes the legal profession.

(For a fascinating history of billing fraud and how we got here, you can check out the article "Bill, Baby, Bill: How the Billable Hour Emerged as the Primary Method of Attorney Fee Generation" by Stuart Pardau in the Idaho Law Review. Another article, by Nancy Rapoport and Joseph Tiano Jr., both friends of this column, worth checking out is: “Fighting the Hypothetical: Why Law Firms Should Rethink The Billable Hour in the Generative AI Era,” published in the Washington Journal of Law, Technology & Arts.)

A real-world example

"Taylor" phoned my office from Little Rock, Arkansas, and was upset.

"My lawyer billed 12 hours for discovery in a simple debt lawsuit," she said. "Her invoice listed 'drafting interrogatories, requests for admissions and related written items,' copies of which I have. We are both country gals, but these are written oddly, not the way people usually speak — they are just too polished.

"Would you please go over them and tell me what you think? I know she is being considered to become a partner in the firm, but before this, I never saw anything that raised a doubt."

I agreed and asked for the particulars of the case — amount, services or product sold, location, date, parties and court jurisdiction.

I looked over what she sent, and Taylor was correct. The discovery requests didn't "sound" normal. So, I ran them through three different online AI checkers. Bingo! The AI checkers indicated the documents were 88% to 95% created by AI. (For the record, AI checkers can sometimes flag original content as being created by AI, which is why I used three different tools.)

Next, using a free AI resource, I entered Taylor's details and specified that I wanted content that was "at a sixth-grade level." I hit enter, and almost instantly, beautifully written documents appeared. It would have taken me hours to draft the same things.

Clearly, Taylor needed to discuss the billing issues with her lawyer (we'll call her Amanda) and challenge the number of hours. I gave Taylor tips on how to come across with a positive attitude.

A proper way to challenge the bill

State the issue factually. This will avoid confrontation and keep the focus on the issue, not Amanda's integrity or honesty. Taylor could say, "The discovery requests appear to have been generated by AI. As it produces drafts almost instantly, I don't understand the several hours billed for them."

Ask for an explanation, but don't make an accusation. She could say, "Can you show me how that time was calculated — what work went into the discovery that was not AI?" Let's assume Amanda does this but can't justify the time billed.

Be clear and reasonable in your request for a reduction. Taylor could say, "My understanding is that bills for professional services need to reflect the actual time it took to do the work. AI is a great tool, but doesn't the bill still need to be accurate? Can you help me understand it, or" — giving Amanda an out — "perhaps did a paralegal or secretary not realize that AI was used and billed a standard amount for the work?"

If you get a refusal to adjust the bill, remain polite and calm. "Well, anyway, it is always a pleasure to get together with you, as we have known each other all these years, so I will discuss this with my CPA and a friend who's also a lawyer" — the idea is to refer to another authority — "and get back to you on a resolution and whether to continue our relationship."

Should Taylor change lawyers? Based upon my experience, yes, she needs to obtain a new lawyer. She could contest the bill with her local bar association's fee arbitration, but even if they order a reduction, once trust is broken in the attorney/client relationship, doubt will remain forever.

This is true even if her attorney comes clean and apologizes; she's already made her character clear. You can't trust a lawyer who has tried to cheat you.

The billable hour has become a liability

An AI tsunami has hit the legal profession. Document review, once requiring weeks of several junior lawyers' billable time, can now take just hours, creating an existential challenge as branches fall off the money tree.

If lawyers take a giant leap back to a time of fixed or outcome-based fees, it will be a cultural earthquake, where retention and advancement, based on "hours billed," become meaningless.

Partner compensation dependent upon individual billings must be rethought.

Instead of fee-generation as their goal, lawyers might actually think about helping clients solve their legal problems without running up the bill.

And the billable hour may find itself in a museum display case.

Dennis Beaver practices law in Bakersfield, Calif., and welcomes comments and questions from readers, which may be faxed to (661) 323-7993, or e-mailed to Lagombeaver1@gmail.com. And be sure to visit dennisbeaver.com.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

The net worth of retirees over the past three decades has tilted steadily toward housing wealth.

A study by the Federal Reserve shows that the share of net worth in primary residences among households headed by people ages 60 to 69 rose from roughly 40% in 1989 to just over 50% by 2022.

For those aged 70 to 79, the share climbed from about 38% to 50% over the same period.

Ideally, the retirement industry would look for solutions that allow retirees to take full advantage of their housing wealth with plans that incorporate all their assets. We know that the wheels of change grind slowly.

The big financial advising companies have to explore new ideas carefully.

The latest "natural language" artificial intelligence programs also lack recognition that the effective use of all assets, like the home, is crucial to retirement planning.

Testing AI and ability to innovate

We wanted to find out whether AI, given the objectives of our target retirees, could also come up with this new all-asset planning approach. The answer after pages of back and forth was a resounding "yes" and "no."

Yes, AI programs are a quick study, and if told to narrow the gap between the single-asset and all-asset method, first considering lifetime annuities, they would do that. It took them multiple steps to forget the biases against housing wealth and then to incorporate it into a plan.

They never got all the way. So here, I will lay out an approach that AI and others can study.

The issue seems to be that AI starts with a single asset class and current models. It doesn't care that results would not support the needs in the marketplace in general and an individual retiree in particular.

Our alternative started with new asset classes that we felt belonged in the plan. We then analyzed and integrated the three — and in the process created a new all-asset approach.

Technical analysis has the advantage of removing market emotion and bias. In this case, it also improves results.

The benefit of integrating housing wealth

To demonstrate how integration of a retiree's housing wealth can improve a retirement plan, set out below is a comparison of the "with and without" strategies for a homeowner (male age 67) who has $1 million in IRA savings and $1 million in the value of a house.

Strategy No. 1: A plan that doesn't include housing wealth ('without')

Our sample client's current retirement planning strategy does not take advantage of housing wealth and leaves the house outside retirement planning.

For this homeowner, here's how the value of the house would have appreciated and depreciated over the past 28 years ending June 30, 2025.

The plan with investments as the only asset class generates no additional income or liquid savings from the housing wealth and leaves only the value of the home at passing.

So, could the homeowner manage to meet both housing costs and, say, long-term care expenses without downsizing? Note that the downsizing cost could be several hundred thousand dollars for our retiree, possibly coming at a time of extreme stress.

Strategy No. 2: Housing wealth as part of the plan ('with')

Contrast the results above to the retiree who used the all-asset planning strategy to generate more income and liquid savings.

He unlocked housing wealth with a HECM (home equity conversion mortgage):

• That delivered an immediate $330,000 line of credit
• He's able to take modest tax-free HECM drawdowns until age 84

He also used $200,000 from his IRA account to purchase a QLAC (qualifying longevity annuity contract) and a ladder of lifetime income:

• He can use a portion of the income for budgeted expenses as an inflation hedge
• He can use the rest of the income to pay down the HECM loan balance starting at age 85

Note: Example is for a man, 67, starting in 1996. Based on current annuity pricing and average historical interest rates and property growth rates. Assumes $1 million value of house and $200,000 from IRA savings to purchase QLAC.

Greater security enables higher allocation of portfolio to stocks

Without the income and liquidity from an all-asset combination, the allocation to stocks in the "without" strategy will be conservative and follow the usual model of 100 minus age, or an allocation of 30% (rounded) to stocks.

So, the "without" plan is to invest $1 million in a portfolio of stocks and bonds and allocate $300,000 to stocks and $700,000 to fixed income.

With the benefit of income and liquid savings provided by an all-asset plan, however, the allocation to stocks can be slightly more aggressive.

In our example, it's 65% of the investment portfolio to stocks. So, the "with" plan is to invest $800,000 from the IRA, split between a balanced investment portfolio and an immediate annuity as follows:

• $640,000 to portfolio, with $416,000 (65%) to stocks and $224,000 (35%) to fixed income investments
• $160,000 to immediate annuity

Here is a graph of the total income, together with liquid savings and legacy for the all-asset retirement plan — or what we call the IRA4Income:

Advantages over investment-only single asset class planning

Set out below is a comparison of the results for the "with" and "without" retirement plans, assuming standard returns of 8% for equities and 5% for fixed income, and adverse returns of 4% for both equities and fixed income.

As you can see, this homeowner builds more income ($700,000 to $800,000 more) and more liquid savings (over $1.3 million more) from his IRA4Income plan.

With the liquid savings made up of both the IRA account value and the HECM line of credit, he should be able to pay for long-term health care if needed.

If he stays healthy, he can spend the additional income or reinvest and build even more savings.

Use favorable economics to meet other objectives

While the starting point for evaluating IRA4Income is the increase in income and liquid savings, there are individuals who can use other sources of income — e.g., interest and dividends, Social Security, pensions and other annuities — to meet additional objectives.

A possible objective for them might be to maximize the legacy left to children, grandchildren or a favorite charity.

One way to accomplish this is by taking advantage of a Roth conversion, where rollover IRA funds are transferred to a Roth IRA. A tax is paid at the time of conversion, but taxes are eliminated on the future growth of the Roth account.

Here's a way to accomplish this with IRA4Income:

• Elect a lower income target (e.g., 5.0% instead of 6.5%)
• Reduce rollover IRA amount to $775,000, leaving $225,000 for the Roth conversion
• Pay taxes on the conversion using Roth funds or other income
• Grow the Roth tax-free in a balanced portfolio
• Rollover IRA + Roth account value at age 90: $1.6 million

Set out below is a projection of the combined account value of the rollover and Roth IRA, $1.6 million at our investor's passing at age 90 — or more than seven times the original Roth conversion of $225,000.

In addition, the ongoing tax benefits of this strategy are significant, with about half the required minimum distribution (RMD) from the rollover IRA account. (This takes into account untaxed future growth and distributions from the account.)

Input your numbers here and find out how your retirement could benefit by combining all your assets. We think you will find that the plan built on needs and resources can provide more income, legacy and liquidity than keeping the house outside the retirement plan and then being forced to sell the home to pay for higher living costs or unplanned expenses. It helps protect the consumer while leaving heirs with a substantial financial legacy.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

In the rapidly evolving world of business, simplicity often gets overshadowed by the allure of the latest technology and artificial intelligence (AI) advancements.

While these tools offer incredible potential, they can also introduce complexity that distracts from the core mission — providing exceptional service to your team and your clients.

To truly scale your business, embracing simplicity is key. That is what will allow you to distinguish yourself in a market that becomes more automated.

Stay focused on essentials

Scaling a business is akin to building a house. Without a solid foundation, even the most glamorous architecture will crumble. In business, this foundation consists of a few core principles that, if consistently applied, yield great success:

Know what you do well. Being able to communicate what you do well, specifically when it comes to helping people enjoy a successful retirement, is key. Often, we take for granted how good we are at helping people plan their retirement.

Guiding clients in the areas of income, taxes, estate planning and health care is a big differentiator from the person who helps only with investments.

Streamline workflows. Audit your operational processes to eliminate redundancies. It's amazing how we can complicate things through the years. We often add to, but very seldom subtract from, our processes.

Embrace technology, but don't think it's the magic solution

Technology is a powerful ally in the quest for growth, but it should be used strategically.

Employ selective automation. Use technology to automate repetitive, non-client-facing processes like data entry or scheduling, freeing up resources for more high-touch activities.

Leverage AI for efficiency. If you aren't using AI, you're falling behind — because your competitors are using it. AI can enhance your ability to analyze data, predict client needs and personalize interactions at scale.

Just remember that it should complement, not replace, the human touch that builds trust and loyalty.

Use data-driven insights. Leverage analytics tools to understand client behaviors and preferences. These insights inform strategies that enhance client interaction without requiring you to overhaul your existing system.

The impact of personal touches

In today's digital world, the human connection can be your most powerful differentiator. Personalized, unautomated interactions show clients they are truly valued.

Send handwritten notes. A simple, heartfelt note can convey authenticity and appreciation, qualities that mass-produced emails often lack.

Make phone calls. Taking the time to personally check in on a client conveys care and commitment. In a time when most interactions are digital, a voice call can strengthen relationships.

Cultivate meaningful client relationships

Building and nurturing relationships is at the heart of business success. A relationship-first approach not only retains clients but turns them into advocates.

Do fun things together. At Advisors Excel, our biggest investment, outside of staff, is in creating meaningful experiences with our adviser community.

Are you doing the same with your clients? Think through how you can create a connected community of fans.

Elevate your impact with one-to-many communication. While personal touches and regular check-ins are vital to success, you can save yourself a lot of work by finding ways to communicate to a large group of your clients at one time.

Regular events like "Wine and Wisdom" or "Markets and Mimosas" are fun ways to bring clients together and communicate en masse.

The power of simplicity

Scaling a business doesn't require adopting every new piece of technology or being everywhere all at once. It's about doing a few things exceptionally well and maintaining a personal connection with those you serve.

As we embrace new opportunities in 2026, let's remember that simplicity, combined with a commitment to personal touch, will set the foundation for scalable growth and enduring client relationships.

By focusing on what truly matters, relationships and taking the time to show genuine appreciation, you will continue to succeed in an ever-changing world.

Results from the use of these strategies are no guarantee of your future success.

Cody Foster is co-founder of Advisors Excel in Topeka, Kansas. Advisors Excel has a mission to help "good financial advisors become great business owners so they can help people enjoy an amazing retirement." Since its founding in 2005, the company has grown from the three original founders to over 1,000 employees today, making them one of the largest employers in Topeka.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

Financial advisers are feeling a shift in real time. Clients with growing wealth have increasingly nuanced needs, AI is reshaping service expectations, and competition among credentialed advisers is intensifying.

This article speaks directly to advisers who want to stay ahead of that curve. The question is no longer whether specialization is coming — it's how advisers can deliver it and communicate it in a way that resonates with today's clients.

While many advisers still operate as generalists, the complexity of wealthy clients' needs, combined with consumer demand and technological change, suggests the future of financial planning belongs to specialized experts.

The profession is maturing

The financial services profession is going through a significant maturation. CFP Board's public awareness campaign and resulting growth have helped create a well-recognized, credentialed baseline for financial planners and those they serve.

The American College of Financial Services' Chartered Financial Consultant® (ChFC®) provides the same education (plus an additional course) without the high-stakes exam or bachelor's degree requirement, but it isn't as well known to the general public.

Together, these credentials point to a potential future where half (or more) of advisers will hold one or both, raising the proficiency floor while also making it harder for advisers to stand out on credentials alone.

Beyond the expertise of a generalist

That level of baseline education is incredibly valuable to the majority of American families. Yet advisers tend to desire clients with wealth, generally $1 million or more in investable assets, and with wealth, comes complexity that often stretches beyond what a generalist can comfortably navigate.

This is where advisers typically turn to their centers of influence for guidance, but many still end up wading through complexity on their own.

Early in my career, I remember reading horror stories that resulted not from bad intentions but from avoidable knowledge gaps. Ed Slott and Company's events were filled with examples: distribution errors that triggered unnecessary taxes, paperwork mistakes that derailed wealth transfer plans and timing missteps that couldn't be undone.

Not a single one dealt with a poorly allocated portfolio. They were all issues rooted in technical details.

Navigating this kind of complexity doesn't always require more time — it requires knowing what to do when, understanding the downstream implications and having the confidence to anticipate problems before they occur.

That's what most consumers in our recent research with Endeavor Business Intelligence say they want. And it raises the central question for advisers: How do we deliver it consistently and credibly?

Specialization is gaining momentum

A segment of the profession is already embracing specialization. Many thought leaders, and a growing number of advisers, talk about the value of niche expertise, the discipline it requires and the difference it makes in serving clients with highly specific needs.

But there are still far too many generalist advisers positioning themselves as specialists. From where I sit, raising consumer awareness and strengthening education is one of the most effective paths forward.

Specialists do exist in financial planning; consumers simply don't yet have an easy way to identify them.

Once consumers understand that financial planning is evolving, in a way similar to other professions that developed deep specialties over time, they will quickly recognize the value of differentiated expertise, even if they don't yet know how to spot it in a bio or on a business card.

That broader understanding is still likely a decade away. But AI could accelerate the shift by absorbing more of the generalized advice that younger or less complex clients typically need.

That would increasingly force advisers to distinguish themselves not by the basics, but by the depth of their technical knowledge and their ability to guide behavior through uncertainty.

How advisers can deliver specialization: Starting now

Consumers are already looking for advisers who can help them with very specific needs. Advisers who lean into this shift early will have a strategic advantage.

Here are steps advisers can take now to signal their specialization and deliver deeper expertise:

1. Define the complexity you want to serve

Rather than trying to "specialize in everything," advisers should identify the intersection of:

• The client problems they are best equipped to solve
• The scenarios they encounter frequently
• The areas where they already have deeper education or interest

Clarity here doesn't limit growth — it directs it.

2. Strengthen the technical foundation

Specialization must be backed by education. Programs such as the ChFC®, CLU®, RICP® or WMCP® provide structured paths toward deep expertise in areas ranging from retirement income to insurance to portfolio construction.

This kind of training goes beyond high-level guidance and equips advisers to handle complex client scenarios confidently.

3. Formalize a network of experts

No specialist operates alone. Advisers should develop a consistent, vetted ecosystem of tax professionals, estate attorneys and insurance experts.

Making these relationships visible to clients reinforces credibility and signals that the adviser is committed to delivering comprehensive expertise, not just general advice.

4. Communicate differentiation clearly

Clients cannot hire what they cannot identify. Advisers should:

• Update digital bios to reflect precise areas of expertise
• Create brief educational content that showcases specialty knowledge
• Highlight specific client scenarios they solve (without revealing identities)

Consumers increasingly look for advisers who "speak their language," and specificity communicates confidence.

5. Use AI to elevate, not replace, expertise

Advisers should embrace AI for what it does best: streamline analysis, automate routine tasks and model scenarios quickly.

That frees up time for advisers to focus on the high-value work only humans can do, interpreting goals, coaching behavior and navigating complexity.

AI will not diminish the role of advisers. It will amplify the advantage of those with deep expertise.

The path forward for advisers

Consumers already want specialists. The industry is moving in that direction. AI will only accelerate the shift.

Advisers who embrace specialization and make it visible, credible and consistent will be better positioned to serve the complex needs of wealthier clients, differentiate themselves in a crowded marketplace and build stronger, longer-lasting relationships.

The advisers who thrive in the decade ahead will not be those who try to be everything to everyone.

They will be the ones who confidently step into the areas where they deliver exceptional value and communicate that specialization clearly to the clients who need it most.

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This article was written by and presents the views of our contributing adviser, not the Kiplinger editorial staff. You can check adviser records with the SEC or with FINRA.

To help you understand the trends surrounding AI and other new technologies and what we expect to happen in the future, our highly experienced Kiplinger Letter team will keep you abreast of the latest developments and forecasts. (Get a free issue of The Kiplinger Letter or subscribe.) You'll get all the latest news first by subscribing, but we will publish many (but not all) of the forecasts a few days afterward online. Here’s the latest…

It’s a childhood dream come true. Now you can control Mickey Mouse, Yoda, Cinderella and Iron Man — at least for 30-second increments.

In a new deal, Disney is licensing 200-plus characters to OpenAI’s Sora, a social media site for short AI videos. Users can create images and up to 30-second videos by providing simple text descriptions. (Note that you can’t use character voices.)

The move is a huge win for partner OpenAI but brings big risk for Disney. The fear that generative AI could disrupt Hollywood prompted Disney to strike a deal early.

It’s also a shot against Google, which Disney sees as a bigger AI threat. After Disney recently sent a cease-and-desist letter, Google removed AI-generated videos with Disney characters from YouTube.

Disney’s announcement highlights its defensive stance at the top of its announcement, saying that “the agreement marks a significant step in setting meaningful standards for responsible AI in entertainment.”

It’s an effort to both protect intellectual property and embrace AI, but it’s not a given that Sora’s guardrails can always hold up. When Sora launched in October, I wrote about the looming flood of AI video apps and copyright challenges: “OpenAI is entering a minefield of trying to police certain content and figuring out where to draw the line, especially with teen users.”

Disney’s brand could be tarnished if users are able to generate offensive videos, and OpenAI isn’t able to quickly take them down. Sora’s launch came with lots of copyrighted material appearing in users’ videos, drawing the ire of Hollywood.

Disney didn’t divulge the financial terms of the licensing deal, but did reveal it will invest $1 billion in OpenAI and become a customer of its tools.

If OpenAI’s valuation continues to grow, Disney will own a small piece of that. Expect Sora to surge in popularity in early 2026 with the introduction of Disney characters. However, it’s expected that Disney will see minimal revenue from the three-year pact.

“This deal does not alleviate concerns in the media and entertainment industry about disruption from artificial intelligence,” writes Morningstar analyst Matthew Dolgin in a December 11 research note. But it does highlight the power of Disney’s IP and ability to adapt, writes Dolgin.

That adaptability could fast-track the growth of a serious competitive threat in Sora. The rise of major online platforms, such as Facebook, was bolstered, in part, by publishing and media companies providing free content to tap a new online audience.

Consider that YouTube, with its combination of user-generated and professionally produced videos, netted nearly $40 billion in sales in 2024 and is currently the leader in TV screen time, with a 13% share.

Disney says it will show some of the user-generated Sora videos on its own Disney Plus app, but that’s not likely to be very popular. The company would prefer to build an in-house interactive AI video tool, and may even be working on one.

But competing against the likes of Sora and other AI video apps, not to mention Facebook and Instagram, is a daunting and expensive challenge. Disney and other media companies also see a future in which fans use AI to create personalized episodes or even feature-length films with iconic characters.

The question is whether media companies can handle that internally or have to outsource it to OpenAI, Google or other AI firms.

As the most powerful media brand with a treasure trove of iconic characters, Disney’s deal sets the stage for other media companies sharing IP with AI companies and social media sites. Look for a series of similar agreements in early 2026.

This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.

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