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“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.” -Stephen Hawking told the BBC
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“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” -Larry Page (Google co-founder)
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“I don’t want to really scare you, but it was alarming how many people I talked to who are highly placed people in AI who have retreats that are sort of ‘bug out’ houses, to which they could flee if it all hits the fan.” -James Barrat, author of Our Final Invention: Artificial Intelligence and the End of the Human Era, told the Washington Post.
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Recently, I have found myself seeking AI-generated information more regularly.
For example, I was having trouble finding the square footage of our previous home. I searched several common sites, such as the local assessor’s office without success. Knowledge of the exact square footage is one of the first pieces of information needed to calculate market value. Because I couldn’t find that information, I decided to ask ChatGPT. Within seconds, ChatGPT gave me the exact original square footage of our previous house. It was the correct original footage, but not the correct total square footage, as we added square footage by enclosing our outdoor patio. This information was not publicly available. So the information that ChatGPT provided was not totally correct. I added the square footage of the improvements to the original square footage to derive the total square footage. Anyone else seeking this information would not have the correct square footage.
Since I was already in ChatGPT, I asked it for the current market value of the house. ChatGPT immediately provided a Zillow estimate. ChatGPT then asked if I wanted more precise information on comparable home sales in the same area. When I responded yes, ChatGPT provided three comparables in the same area as our previous home.
ChatGPT asked if I wanted to increase the precision of the home valuation by factoring in added upgrades. Landscaping, fences, gutter systems, and interior additions, all increase the value of the house.
ChatGPT continued this questioning, asking more precise questions to focus on a more precise value of my previous house.
Within five minutes ChatGPT provided an estimated value for our previous home that was ultimately within $10,000 of the final selling price.
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I know of two instances where different people used ChatGPT to help diagnose medical conditions. One of these was a relatively common problem, but the second was a rare, genetic anomaly that had been misdiagnosed in the original genetic testing. It was only because of the parents’ insistence on reevaluation of the diagnosis after a prolonged and thorough AI review that the genetic profile was reevaluated and the true diagnosis was ascertained.
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This got me thinking whether AI could be a useful aid in retirement planning. I asked ChatGPT about the benefits and problems with using AI for retirement planning (this may be similar to asking a fox to guard the henhouse)!
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AI Benefits
Personalized financial projections– AI can analyze large amounts of personal financial data (income, savings, spending patterns, and investments) to create highly-customized retirement projections.
Advanced scenario testing– AI can quickly run thousands of Retirement simulations, testing variables like market downturns, inflation spikes, longevity, or health care costs. This can help to determine the resiliency of a retirement plan.
Better risk management– AI can monitor portfolio risk and suggest adjustments to maintain an appropriate balance between growth and protection. For example, it can evaluate how a traditional 60/40 portfolio using the S&P 500 index, and a bond index may behave under different market environments.
Smarter withdrawal strategies– AI can help to determine optimal withdrawal rates and timing strategies, potentially reducing the risk of running out of money. AI can evaluate and provide tax-efficient withdrawal sequences using taxable accounts, IRAs, and Roth accounts.
Continuous monitoring– unlike traditional plans reviewed once a year AI systems can monitor portfolios continuously, handling alerts when adjustments may be needed due to market movements, spending changes, or life events.
Reduced behavioral mistakes– investors often make emotional decisions during market volatility. AI can provide data-driven recommendations, helping reduce impulsive decisions such as selling during downturns.
Integration of complex factors– AI can incorporate variables that are difficult to calculate manually, including longevity estimates, inflation trends, healthcare costs, Social Security timing and strategies, and tax law changes.
Accessibility and lower cost– AI tools deliver sophisticated planning analysis that previously required expensive advisory services making advanced retirement planning more accessible.
The big picture, according to AI, is that AI does not replace financial judgment or human advice, but it enhances decision-making by improving analysis, forecasting, and monitoring risks in retirement planning.
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AI Risks
Data quality risk– all models are only as good as the data they receive. If the inputs (in income assumptions, spending estimates, tax rates, or market expectations) are inaccurate, the resulting retirement projections may also be flawed.
Overconfidence in projections– AI simulations can appear extremely precise, but retirement planning still relies on uncertain variables, like longevity, inflation, and market returns. Users may place too much confidence in probability-based forecasts.
Model bias and assumptions– many AI systems rely on historical market data. If future conditions differ significantly from the past (such as changes in inflation, regimes, or bond market behavior) AI models may produce misleading conclusions.
Lack of human judgment– AI may struggle to incorporate personal values, life goals, and behavioral considerations that often shape retirement decisions. For example, deciding when to claim benefits from the Social Security Administration often involves lifestyle and health considerations beyond pure financial optimization.
Security and privacy concerns– retirement planning requires sensitive financial information. Using AI tools increases exposure to potential data breaches, cybersecurity threats, or misuse of personal financial data.
Algorithmic herding– if many investors rely on similar AI-driven strategies, portfolios may become overly concentrated in the same asset classes, increasing systemic risk during market stress.
Black box decision making– some AI systems operate with complex algorithms that are difficult to explain. Advisors and investors may not fully understand why the AI recommends certain strategies or portfolio changes.
Regulatory and compliance uncertainty– AI-driven financial advice is still evolving in terms of regulation. Firms must ensure that automated recommendations comply with fiduciary and suitability standards.
According to AI, the bottom line is that AI can greatly improve retirement planning analysis. It should be viewed as a decision support tool rather than a decision-making tool. Human oversight, skepticism, and sound financial judgment remain essential.
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My First and Biggest Question Is: Can AI Build a Better Retirement Plan Than a Human Advisor?
I posed this question to ChatGPT: Create a portfolio of 60% stocks and 40% bonds using the highest return asset classes over the last 50 years. Use 10 different asset classes in the portfolio.
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ChatGPT response:
To construct a 60/40 portfolio using the highest-returning major asset classes over roughly the last 50 years, we should overweight equity segments that historically produced the strongest long-term returns. These equities include small caps, emerging markets, and REITs. Diversification across global equities and multiple bond types should be included. Historically, stocks have significantly outperformed bonds, with large US equities around 11 to 12% nominal annual returns since the mid-1970s. Bonds generally deliver between three and 6% depending on the type.
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This is the 10-asset class portfolio maintaining 60% stocks in 40% bonds that ChatGPT generated:
60/40 Portfolio Using Top Long-Term Asset Classes
Equity Allocation (60%)
| Asset Class | Allocation | Why Included |
| U.S. Small-Cap Stocks | 12% | Historically among the highest long-term equity returns |
| U.S. Large-Cap Stocks | 12% | Core growth engine of portfolios |
| Emerging Market Stocks | 10% | High growth economies |
| International Developed Stocks | 8% | Diversifies U.S. risk |
| U.S. Mid-Cap Stocks | 8% | Historically strong risk/return balance |
| U.S. REITs (Real Estate) | 10% | High income + equity-like returns |
Total Stocks: 60%
Bond Allocation (40%)
| Asset Class | Allocation | Why Included |
| U.S. Intermediate Treasuries | 12% | Core portfolio stabilizer |
| Investment-Grade Corporate Bonds | 10% | Higher yield than Treasuries |
| High-Yield Bonds | 8% | Higher return potential |
| International Bonds | 10% | Currency and global diversification |
Total Bonds: 40%
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It’s interesting to note that this portfolio provides high-return equity segments that dominate the growth side, and bond funds that provide income, diversification, and drawdown protection. The AI-generated portfolio also provides diversification across asset classes and global diversification. It is very similar in design to my current asset class mix.
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Obviously, it is possible for AI to quickly and efficiently generate a model portfolio that closely matches actual existing portfolios. It also becomes quickly apparent that AI would be very efficient at stress testing existing retirement plans.
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Some Questions Become Readily Apparent
Having millions of investors accessing the same information, would issues such as model bias, insufficient data quality, and herd behavior create problems for AI investors?
There are also the issues of risk and trust. Is someone truly willing to risk retirement assets built over a lifetime to AI?
AI-generated portfolios? Truly transparent? How comfortable are investors in allowing algorithms to guide long-term financial decisions? When the eventual market downturn arrives, can AI truly help investors to avoid emotional decisions like panic selling?
A major benefit of AI is also one of the major concern. It is the ability of AI to be adaptive to individuals. Does this individual adaptability mean that over many iterations a portfolio ultimately represents what AI thinks you desire, or would it still represent the technically best portfolio?
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I can truthfully admit that I have been impressed with the speed and accuracy of AI generated information. I am also aware that it is easy to fall into the trap of accepting all AI-generated information as being 100% truthful. That is currently not the case, and probably never will be!
It’s also easy to see how AI-generated information and portfolios, which are both currently free, could become mainstream very quickly.
My current position is that AI is one of many available tools. AI is not the answer to every question, nor is it the ultimate solution to every problem.
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Final thoughts
I am not planning on switching to an AI-generated financial plan anytime in the near future!
I admit that I have been blown away by the speed and accuracy of information derived through AI searches.
I’m also amazed by the individual adaptability that AI exhibits.
I am concerned about the validity of the information provided, and the fact that the adaptive ability of AI may alter the validity of information because AI adapts to please the questioner. There is the fact that this information could be maliciously manipulated.
A concern of every individual should be exposing sensitive information in AI generated formats. Data breaches seem to be more common. Exposing financial information could be catastrophic. Even something as innocuous as a bank or brokerage account number could allow hackers to gain access to financial accounts.
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