I Let AI Stress-Test My Retirement Plan.

It Found Mistakes My Advisor Never Caught.

I’m not a financial advisor. I’m not a tax expert. I’m just a guy in his fifties who spent a Saturday asking an AI to poke holes in his retirement math, and learned things his actual advisors never told him.

I’m in my early fifties. Two kids heading to college. A mortgage. A spouse. The question I’d been dodging for years finally cornered me: Am I saving enough to retire?

Not in the motivational-poster sense. In the “I need actual numbers” sense.

I have advisors. My tax person does my returns every spring, and he’s good at it, but he’s focused on this year. I don’t feel right taking up his time asking “hey, if I retire at 62 instead of 67, how does that change my lifetime tax burden across forty market scenarios?” My wealth advisor has a broader view, but I always feel slightly out of place in their office. I’m not their typical client. Every time I want to ask a basic question, like “what exactly happens to my Social Security if I claim at 62 versus 70,” I worry I’m wasting their time.

So the real questions, the “what if” questions, never quite got asked. Not because my advisors couldn’t answer them, but because the social dynamics of those relationships got in the way. That’s the gap AI filled.

I sat down with an AI assistant called Claude Code and started asking questions. What followed was the most useful financial conversation I’ve had in years, not because AI is smarter than my advisor, but because I wasn’t embarrassed to ask it dumb questions, challenge its answers, and say “that number doesn’t smell right” over and over. I asked the same question forty different ways. The AI never sighed. Never glanced at the clock.

Let me be clear up front. I’m sharing the prompts I used and what I learned, not giving financial advice. My numbers aren’t your numbers. I used AI as a thinking partner, the way you might use a calculator or a spreadsheet. The AI doesn’t know your tax bracket, your health, your marriage, or your state laws. A licensed professional does, and a real advisor would catch things AI never could, like whether you’ll actually stick to the plan when the market drops 40%. What AI is remarkably good at is helping you prepare for that conversation with better questions.

The Problem With “Average” Returns

Here’s the pitch you’ve heard: The stock market averages 10% per year. Save consistently, and you’ll be fine.

That 10% is nominal. Before inflation, it’s closer to 7% in real purchasing power. But the deeper problem isn’t the number. It’s the word “average.”

The market doesn’t give you 10% every year. It gives you +33% one year, -37% the next. And when the bad years happen matters enormously.

Two retirees. Same $1 million. Same $50,000 annual withdrawals. Same returns over 30 years, just in different order. Retiree A gets the crashes early: runs out of money at 78. Retiree B gets them late: dies at 95 with $2 million.

Same average. Completely different lives.

Financial planners call this sequence-of-returns risk. It’s the reason single-number retirement projections, like “you need $1.5 million,” are essentially useless.

A plan that works “on average” is like a bridge that holds “on average.”

What Ten Thousand Simulations Reveal

Instead of calculating one future, the simulator runs ten thousand of them.

Each one draws random annual returns from patterns calibrated to real market history, including the inconvenient fact that crashes happen more often than textbook statistics predict, and that in a crisis, everything tends to fall together. Your stocks, your international funds, your real estate.

The output isn’t a single number. It’s a percentage: What fraction of the time do I run out of money? Financial planners call this the “probability of ruin,” which is exactly as grim as it sounds.

That percentage became the only number I cared about.

The Conversation: How I Actually Used AI

Here’s the thing about AI assistants: you don’t need to know the right terminology. You just talk to them like you’d talk to a friend who happens to be good with numbers. Here are the prompts I used (simplified for this article), so you can try them yourself.

Getting started, I just described my life. I typed in my actual income, savings, mortgage, family situation, and asked it to tell me how often I run out of money. Plain English, no jargon. The AI asked some follow-up questions about Social Security and investment mix, then built a working simulator. I didn’t write a single line of code.

When the numbers looked too good to be true:

The simulation says I'll have $65 million by age 100.
That's VERY unlikely. Something is wrong. Can you find
the bug?

This is important: I pushed back. The AI had made an error, counting investment income twice. My retirement had looked $20 million rosier than reality. The AI found and fixed the mistake in one pass. But it only found it because I said “that doesn’t smell right.” AI is a powerful tool, but you are the quality control.

The prompt that changed everything:

Create a council of advisors to challenge my plan. I want
a wealth management advisor, a tax specialist, and a
retirement planning expert. Have each one review every
assumption in the simulation and write me a memo telling
me what I'm getting wrong.

Three memos came back. Each one challenged specific numbers in my plan. The wealth advisor explained why the 10% stock market return I’d been assuming was too optimistic. Current stock valuations are high by historical measures, and forward-looking returns for U.S. equities are more likely in the 7–8% range. Neither number is a guarantee. The point is that your results are only as good as your assumptions.

I asked the AI to update that one number and rerun everything. My probability of ruin jumped from 20% to over 50%.

One assumption. One number. The difference between “on track” and “in serious trouble.”

Making it actionable:

OK, that's scary. What can I actually do about it? What
are the biggest levers I can pull to improve my chances?
Implement the advisors' recommendations and show me what
changes the most.

The answer surprised me: the most powerful lever wasn’t any clever financial strategy. It was simply spending less in retirement. Cutting planned spending by a third dropped my ruin probability more than any investment trick, tax strategy, or fancy withdrawal algorithm.

Getting a reality check on the reality check:

Now get those same three advisors to review everything
again. And add a newspaper editor to check whether
I'm explaining this clearly. Tell me what's still wrong.

This is the loop: build, challenge, fix, repeat. Each round caught something the previous round missed. The tax advisor flagged that my withdrawal math didn’t account for taxes on 401(k) withdrawals. The retirement planner pointed out that healthcare costs before Medicare at 65 are a massive blind spot. The editor said I was burying the most interesting findings under too much jargon.

The most valuable thing about AI isn’t the answers. It’s that you can ask the same question twenty different ways without anyone getting annoyed.

What the Math Actually Says

My specific numbers aren’t what matters here. This article is about the approach, not my case. So to illustrate what the simulator revealed, I’m going to walk through the results using the median American household: about $150,000 in total net worth and roughly $75,000 per year in income, according to the Federal Reserve’s Survey of Consumer Finances. That’s the family right in the middle, half of Americans above, half below. (One note: net worth includes home equity. Actual retirement account balances near retirement are typically lower, $65,000-$90,000. For this illustration, I’m assuming steady saving over time.)

The patterns I found in my own data showed up here too. The math doesn’t care how much you make. It cares about the ratio between what you’ve saved and what you spend.

So: assume this median household saves $10,000 a year for 30 years at 7% real returns. By 65, they’ve accumulated roughly $1.1 million in pre-tax retirement savings. That’s an optimistic target. Sustaining $10,000 a year over three decades is harder than it sounds. Job losses, medical bills, and life happen. At a more modest $5,000 a year, the number is closer to $600,000.

Now the question that matters: how much can they spend? Remember that withdrawals from a traditional 401(k) are taxable, so you’ll need roughly 10–15% more than your spending target to cover the tax bill. Here’s what ten thousand simulations show for a 30-year retirement:

Annual Spending | Withdrawal Rate | Probability of Ruin $35,000 | 3.2% | 8% $40,000 | 3.6% | 15% $45,000 | 4.1% | 30% $50,000 | 4.5% | 45% $55,000 | 5.0% | 55% $60,000 | 5.5% | 65%

Going from $40,000 to $55,000, about $1,250 a month, takes your ruin probability from “manageable” to “coin flip.” That’s not a gentle slope. It’s a cliff.

Beyond spending, four other levers matter, but none as much. Working longer is a double win: each additional year adds savings and removes a year of drawdown. Even part-time work of $10,000-$15,000 a year in early retirement dramatically improves survival. Delaying Social Security from 62 to 70 increases your benefit by roughly 77% per SSA projections, guaranteed and inflation-adjusted, though delaying isn’t right for everyone, especially if your health is poor or you have no income to bridge the gap years. Healthcare before Medicare at 65 can run $15,000-$25,000 a year for a couple on the ACA marketplace, a risk concentrated exactly when your portfolio is most vulnerable. And there are tax moves most people miss: a window in early retirement when your income drops and you can convert traditional retirement accounts to Roth at low rates. That’s the kind of thing that’s hard to figure out alone but easy to explore with an AI:

I'm retiring at 63. My Social Security starts at 67.
During those 4 years my income will be almost zero.
Can you figure out how much of my traditional IRA I
should convert to Roth each year to stay in a low
tax bracket?

This is exactly the kind of question I’d never bring to my tax guy in April. It’s not about this year’s return. It’s about a multi-year strategy that spans a decade. But it took the AI about thirty seconds to model.

The Spending Cliff

Here’s the most important pattern in the data.

There’s a specific spending level where your plan goes from “probably works” to “probably doesn’t.” The transition is sharp.

Sticking with our median American household example, with Social Security of roughly $25,000-$30,000 per year:

Below about $42,000 a year: Social Security covers most of your needs. The portfolio only fills a small gap. Even a 40% market crash barely dents your plan.

Above about $52,000 a year: You’re pulling $22,000+ from the portfolio beyond what Social Security covers. A bad first decade can start a death spiral.

Between $42,000 and $52,000: A bad market year, an unexpected medical bill, or a year of high inflation can push you from one side to the other.

Social Security is your floor. Your portfolio covers the gap above it. The size of that gap is everything.

This is why spending dominates every other lever. You can’t control the stock market. You can barely change your Social Security benefit. But you can choose whether to spend $42,000 or $52,000 a year. That $800-a-month decision determines whether your retirement works.

The Human Side

So you know the math. You know the cliff. But knowing where the cliff is doesn’t help if you panic and jump off it.

Here’s what no simulation captures: what it feels like to watch your portfolio drop 40%.

In the model, a crash is one of ten thousand data points. In life, it’s the 3 AM ceiling-staring.

The most dangerous risk in retirement isn’t a market crash. It’s your reaction to one. Selling at the bottom turns a temporary loss into a permanent one.

This is why I asked the AI to build “guardrails,” rules I wrote when I was calm that I’ll follow when I’m not:

Add spending guardrails to the simulation. If my portfolio
is doing well and I'm withdrawing less than 3.2% per year,
let me give myself a 10% raise. If the market crashes and
I'm withdrawing more than 5.5%, cut my spending by 10%.
But never let me go below 80% of my original budget.

The simulations showed that these rules, just the willingness to adjust spending based on how the portfolio is doing, reduced the probability of ruin significantly. Not because the market returns changed. Because the behavior changed.

One caveat: guardrails assume you have spending flexibility. If your baseline already barely covers essentials, there may be nowhere to cut. These rules work best when there’s a meaningful gap between what you need and what you spend.

Try This Yourself

You don’t need software. Start with one number.

Take your planned annual retirement spending. Subtract your expected Social Security. Divide what’s left by your savings. That’s your real withdrawal rate, the gap your portfolio has to fill.

Example: $45,000 spending minus $26,000 Social Security = $19,000 gap. Savings of $500,000. Real withdrawal rate: 3.8%.

• Below 3%: Sleep well.
• 3–4.5%: Workable with discipline.
• Above 4.5%: Something needs to change.

If you want to go deeper, try this with any AI assistant. Just plug in your own numbers:

Help me build a retirement simulator. I'm [age], my
spouse is [age]. I make about [income], we have [amount]
saved. We own a house worth [amount] with a [amount]
mortgage. I want to know what percent of the time I
actually run out of money. Try different retirement ages
and spending levels.

Then the magic follow-up:

Now challenge every assumption you just made. What did you
get wrong? What would a skeptical financial advisor say
about these numbers?

The AI will find its own mistakes if you ask it to. That’s the superpower, not the initial answer, but the iterative pressure-testing. Build, challenge, fix, repeat.

A word of caution: AI models can hallucinate numbers that look authoritative and are completely wrong. They have no fiduciary duty, no license, and no accountability. Treat everything an AI tells you about your finances the way you’d treat advice from a smart friend who happens to be good with spreadsheets: worth hearing, worth checking, and absolutely not the last word. A licensed financial advisor knows things AI cannot: your state’s specific tax rules, the fine print in your employer’s retirement plan, and how to structure advice around your actual life, not a statistical model of it.

What AI Is (and Isn’t) Good For

Here’s how I think about it now. I still have my tax guy. I still have my wealth advisor. I value both of those relationships and I’m not replacing either one.

What I have now that I didn’t have before is a third pair of eyes. One that’s endlessly patient, never offended by follow-up questions, and perfectly willing to argue with itself when asked. One where I can ask “what if I retire two years earlier?” without feeling like I’m wasting a professional’s time. One that helped me walk into my next advisor meeting with specific questions instead of vague anxiety.

The best use of an afternoon with AI isn’t getting answers. It’s getting better questions to bring to the people who actually know your situation.

The math doesn’t care about your dreams. But if you understand the math, you can shape your dreams around what’s actually possible.

I’m not a financial advisor, tax expert, or retirement specialist. This article describes my personal experience using AI to explore retirement math. It is not financial advice. Every number depends on assumptions that may not apply to your situation, including your earnings history, health, state of residence, tax filing status, and a hundred other variables that only you and a qualified professional can assess. Social Security projections are based on current SSA estimates and may change. Please consult a licensed financial advisor before making retirement decisions. AI is a tool for exploration, not a substitute for professional guidance.