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AI has a big problem when it comes to financial advice: MIT professor

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The financial capability of artificial intelligence platforms is improving to the extent that it will likely be able to replace human financial advisors in the future, according to finance experts.

However, AI has a major drawback relative to human advisors: a lack of fiduciary duty, they said. And a resolution to that legal gray area doesn’t seem near at hand, they said.

A fiduciary duty is a legal obligation that many financial advisors — and professionals in other fields, such as lawyers and doctors — owe their clients. It essentially means they will put their clients’ best interest ahead of their own.

“The problem that we have to solve is not whether AI has enough expertise,” said Andrew Lo, a finance professor and director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. “The answer right now is, clearly, AI has the [financial] expertise.”

“What they don’t have is that fiduciary duty,” Lo said. “They don’t have the ability to suffer consequences if they make a mistake to the same degree that a human advisor does.”

An advisor who violates their fiduciary responsibility can be subject to fairly serious consequences, including regulatory penalties, civil liabilities and criminal charges, Lo said.

The notion of putting a client’s interest ahead of yours “has no teeth” without responsibility or legal liability, he said.

An ‘unresolved’ legal question

About 85% of respondents who have used GenAI for financial advice acted on the recommendations provided, according to the survey, which polled 1,019 adults.

“People are looking to these services for all sorts of advice, and they’re getting it, and it seems to be a big open regulatory question,” said Sebastian Benthall, a senior research fellow at New York University School of Law’s Information Law Institute.

“Who’s really responsible, and can people really be relying on a product to do this if it’s not being backed up by a corporation with a fiduciary duty?” Benthall said. “It’s really unresolved.”

Why you shouldn’t blindly trust AI — or humans

That said, there are some good use cases for AI in financial planning, Lo said.

AI is “really good” at providing resources online for various financial concepts that typical people don’t understand, Lo said. For example, if someone were to seek answers to basic questions about Medicare, AI can generally provide a reliable overview, he said.

While AI’s output is sophisticated in many financial respects, consumers generally shouldn’t blindly trust answers to questions about their own household finances, Lo said.

They don’t have the ability to suffer consequences if they make a mistake to the same degree that a human advisor does.

Andrew Lo

finance professor and director of the Laboratory for Financial Engineering at the MIT Sloan School of Management

James Burnham, a legal and government affairs official at Elon Musk’s xAI, said in a social media post in March that the company’s AI platform, Grok, “is not tax advice so always confirm yourself too.”

Of course, many human financial advisors provide advice to clients, and it is then up to the client to decide whether to implement it.

“I think that’s the way that I would look at LLMs: They can be very, very useful in providing different options and in describing how those options might work, but you should always remember that the advice that they can give you could be wrong,” Lo said.

“But I would argue that that’s true with human financial advisors as well,” he said.

Not all human advisors are fiduciaries

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I used an AI tool to do my taxes—here's where experts say I went wrong

Benthall, of New York University, proposed a similar legal predicament regarding AI advice: Since AI giants right now are largely U.S.-based, if an AI were to suggest that investors put their retirement savings into U.S. stocks, that advice could be viewed as self-dealing, or a financial conflict of interest.

That said, companies that provide AI services don’t appear to receive compensation for their advice to retail investors, and therefore aren’t fiduciaries, said Jiaying Jiang, an associate law professor at the University of Florida Levin College of Law who is researching AI and fiduciary duty.

Who’s really responsible, and can people really be relying on a product to do this if it’s not being backed up by a corporation with a fiduciary duty? It’s really unresolved.

Sebastian Benthall

senior research fellow at New York University School of Law’s Information Law Institute

However, financial advisors who owe a fiduciary duty to clients could violate that duty by using AI, Jiang said.

For example, if an advisor uses AI to give a certain recommendation to a client, but that recommendation isn’t in the client’s best interest, it is the advisor — and not the company backing the AI platform — that would be liable, Jiang said.

Ultimately, Lo said he thinks government policy needs to change to provide fiduciary protections for consumers who get financial advice from AI.

Until then, “we’re not going to get to the point where we can fully delegate these [financial] decisions,” Lo said.

“But I do believe that that will eventually happen,” he said.

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Personal Finance

Maximizing Returns in High Rate Climate and market uncertainty

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Maximizing Returns in High-Rate Climate

In the macroeconomic environment of late July 2026, personal cash management requires an active, structured approach to wealth preservation. With central bank benchmark rates remaining elevated to ensure long-term disinflation, conservative yield-bearing vehicles—such as short-term U.S. Treasury bills, money market funds, and high-yield savings accounts (HYSAs)—continue to offer reliable nominal returns between 4% and 5%. For individual investors, maximizing net returns requires moving beyond passive checking accounts and executing a disciplined cash laddering strategy.

Leaving substantial liquid capital in traditional bank deposits creates an invisible drag on personal net worth due to ongoing inflation. By implementing a tiered liquidity framework, individuals can split emergency cash reserves and short-term capital allocations across rolling maturities. Allocating cash into 4-week, 8-week, and 13-week Treasury bills creates a continuous cycle of maturing liquidity, allowing investors to continuously reinvest capital at prevailing market yields while maintaining immediate access to emergency funds.

Tax efficiency represents a critical dimension of high-yield cash optimization. For high-earning individuals residing in states with substantial local income taxes, direct holdings in short-term U.S. Treasury instruments often deliver superior net post-tax yields compared to standard commercial bank HYSAs. Because interest earned on federal Treasury bills is strictly exempt from state and local taxation, investors can retain a larger portion of their compounding interest gains without taking on additional credit or market risk.

Ultimately, personal wealth accumulation in mid-2026 relies on intentional capital deployment. Cash should be managed as a productive asset class that generates consistent, risk-free returns. Regularly auditing account yield terms, automating recurring transfers, and leveraging tax-advantaged fixed-income instruments ensures that personal liquidity remains fully optimized against macroeconomic fluctuations.

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Personal Finance

Algorithmic Wealth Management: Balancing Automated Financial Planning with Human Oversight

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Balancing Automated Financial Planning with Human Oversight

The personal finance industry in July 2026 is experiencing a technological evolution, driven by the wide deployment of next-generation algorithmic wealth management tools. Modern digital financial platforms have expanded far beyond basic automated index investing; today’s robo-advisors utilize real-time tax-loss harvesting, dynamic portfolio rebalancing, and hyper-personalized spending analysis to optimize retail investor outcomes. However, as these digital solutions become ubiquitous, investors face the crucial challenge of balancing automated execution with human strategic judgment.

The core advantage of automated financial planning platforms lies in their ability to remove emotional bias from investment execution. During periods of market volatility or localized sector realignments, automated algorithms systematically rebalance portfolios back to target asset allocations without falling victim to panic selling or speculative enthusiasm. Furthermore, integrated cash-flow monitoring algorithms analyze individual spending patterns in real time, automatically sweeping surplus income into designated retirement or debt-liquidation accounts to accelerate net worth accumulation.

Despite these operational advantages, algorithmic tools have inherent structural limitations when addressing complex, highly personalized financial life events. Decisions involving multi-generational estate planning, highly complex tax strategies, small business exits, or nuanced real estate transactions require contextual human judgment that software models cannot replicate. Relying solely on automated models without periodic human professional review can result in misaligned risk profiles or overlooked tax liabilities during major life transitions.

The optimal approach for personal financial planning in late 2026 is a hybrid advisory model. Individuals should utilize automated platforms for routine asset allocation, continuous tax optimization, and low-cost passive index tracking, while engaging qualified human financial advisors for periodic strategic planning, estate structuring, and qualitative risk evaluations. This dual approach ensures maximum cost efficiency and disciplined execution while retaining essential strategic guidance.

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High-Yield Optimization: Structuring Personal Cash Reserves in a Sustained Rate Environment

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Structuring Personal Cash Reserves in a Sustained Rate Environment

In the financial environment of mid-2026, personal cash management has re-emerged as a vital component of holistic wealth building. With central bank policy rates holding firm to maintain long-term price stability, yield-bearing instruments such as money market funds, high-yield savings accounts (HYSAs), and short-term Treasury bills continue to offer compounding returns around 4% to 5%. For individual investors, effectively structuring cash reserves requires shifting away from passive bank deposits toward active yield optimization.

A frequent pitfall in personal asset management is leaving substantial liquid capital in traditional checking or low-yield savings accounts, where real returns are continuously eroded by baseline inflation. By implementing a disciplined ‘cash ladder’ strategy—allocating liquid funds across tiered maturities using ultra-short Treasury instruments and FDIC-insured high-yield accounts—individuals can secure maximal yields while retaining immediate liquidity for emergency expenses or tactical investment opportunities.

Simultaneously, investors must evaluate the tax efficiency of their cash holdings based on their tax bracket and geographic location. For high earners situated in states with high local income taxes, direct holdings in short-term U.S. Treasury bills often yield a higher net post-tax return than standard high-yield savings accounts, as Treasury interest is strictly exempt from state and local taxation. Understanding these nuanced tax distinctions allows individuals to capture significant incremental gains without taking on additional market risk.

Ultimately, personal financial health in late 2026 hinges on intentional liquidity management. Cash reserves should not be viewed merely as static emergency funds, but as a dynamic asset class that contributes positively to net worth growth. Regularly auditing yield terms, automating cash transfers, and optimizing for post-tax efficiency ensures that personal capital remains fully productive across all economic conditions.

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