Can ChatGPT Plan Your Retirement? What It Gets Wrong

Type your age, your savings and the year you want to stop working into ChatGPT and ask it to plan your retirement. In about fifteen seconds you get a confident, well-organised plan: a savings rate, an asset mix, a withdrawal strategy, a note about tax. It reads like the output of a two-hour meeting with a planner, and it cost nothing. So the search phrase people are typing is blunt: can ChatGPT plan retirement?

Parts of it, yes, and better than most people expect. The trouble is that the parts it gets wrong are the expensive ones, and the plan does not tell you which parts those are.

Retirement planning is three different problems wearing one name. The first is arithmetic: how much, compounding at what rate, drawn down over how long. The second is law: what the tax code and the pension rules let you do this year, in your country, with your accounts. The third is behaviour: what you will actually do when the market falls by a third two years after you retire. ChatGPT is good at the first when it shows its working, unreliable on the second, and blind to the third.

Three Problems, One Chatbot

The plan ChatGPT produces blends all three problems into one smooth document. That is the design of the tool. A language model produces the most plausible next sentence, and a plausible retirement plan has a savings rate, a tax section and a reassuring paragraph about staying the course. It writes all three in the same confident register whether it has calculated, remembered or guessed.

A human planner does the opposite, or should. They will say “the arithmetic says this” and “check the contribution limit with your accountant” and “I do not know how you will react in a crash, so let us build in a margin”. The hedges are the useful part. The model removes them.

So the way to use it is to take the three problems apart again and ask, for each one, what it is doing.

The Arithmetic: Right When It Shows Its Working

Ask ChatGPT a compounding question in prose and it will answer in prose, and the number is a guess that looks like a calculation. Ask it to write and run the code, and the number is a calculation.

The newer versions can run Python when you ask them to. Tell it: “Model this in code, show me the code and the table.” A savings projection, a withdrawal schedule, a comparison of retiring at 62 against 67, all of these it will do correctly, because the arithmetic is being done by a program rather than by a model predicting what a plausible number looks like. You can read the assumptions in the code, change one, and run it again. That is a real capability, and for a lot of people it is the first time they have seen their own numbers laid out year by year.

Where it slips is the assumptions it chooses for you. Left alone it reaches for the defaults that appear most often in what it has read: a 7% return, 3% inflation, the 4% withdrawal rule. The 4% rule comes from a 1994 study by William Bengen using US market history, and it was never meant as a guarantee for someone retiring in a different country, with different taxes, into a different market. ChatGPT will quote it as settled. Ask it where each number came from and whether it applies to you, and it will usually tell you, honestly, that it was a common assumption.

The arithmetic problem, then, is solved if you make it show its working and you question the inputs. That is a large improvement on a spreadsheet you never build.

The Law: Where It Is Confidently Wrong

The second problem is where the plan gets expensive.

Contribution limits change every year. Pension rules differ between countries and change with governments. Tax treatment depends on account type, on residency, on age, and on rules that were amended after the model’s training data ends. The model has no way of knowing what year it is in your life or which rules apply, and it does not have a lawyer’s habit of saying so.

We tested this pattern at length in is ChatGPT accurate. The short version is that it is most confident precisely where the ground has shifted, because its training data is full of the old answer and contains none of the new one. A retirement plan is a stack of exactly those answers. Which account to fill first, how withdrawals are taxed, what happens to a pension on death, whether a foreign account is reportable. Each is a factual question with a current correct answer, and the model gives a fluent answer that was correct at some point.

It also cannot see your statements. Your projected state pension or Social Security benefit is a number the government will give you for free. In the United States that is your Social Security statement, and most other countries have an equivalent. ChatGPT will estimate it instead, and the estimate can be off by an amount that changes the entire plan.

The rule for the law problem is simple. Every sentence in the plan that describes what you are allowed to do, or how something is taxed, is a claim to be checked against a primary source dated this year, or against a professional. Treat it as a well-written list of questions.

The Behaviour: The Part It Cannot See

The third problem is the one most planners will tell you matters most, and it is the one the model has nothing on.

Retirement plans fail in a specific way. Someone retires, the market drops sharply in the first two or three years, they panic and sell, and the recovery happens without them. The name for this is sequence of returns risk, and no projection with a smooth 7% return shows it. The plan on the screen assumes a person who holds. The model has never met you and does not know whether you are that person.

Worse, it agrees with you. Language models are trained to be helpful, and one way that shows up is a tendency to endorse whatever the user seems to want. In April 2025 OpenAI rolled back an update to its main model after users found it agreeing with almost anything put to it. The tuning has been adjusted since, but the pull is built in. Tell ChatGPT you are thinking of retiring at 55 on a plan that needs 8% a year and it will help you make that plan look sensible. A good planner would ask what happens at 5%.

We have written before about the question of whether financial advisors are worth it, and the strongest case for one was always behavioural: someone who stands between you and the sell button in a bad year. That is a person who knows your history and can call you. A chat window cannot.

Can ChatGPT Plan Retirement? A Fair Test

Put it through the three problems and the answer to can ChatGPT plan retirement becomes precise rather than a shrug.

It can do the arithmetic, provided you make it run code and you set the assumptions yourself. It can draft the structure of a plan and explain every concept in it, at any level of detail, with more patience than any human. It can turn a fund’s forty-page document into a list of the fees, if you paste the document in. Those are real, and a year ago they cost money.

It cannot tell you what the rules are this year with any reliability. It cannot see your accounts, your benefit statement or your tax position. And it cannot stop you doing the one thing that ruins retirements, because it does not know you and will tend to agree with you.

That is the same split we found looking at whether ChatGPT Pro is worth it: the tool earns its place where the work is explaining, drafting and computing, and loses it where the work is knowing something current or holding a line against you.

How to Use It Anyway

None of this means leave it alone. It means use it for the problems it is good at and hand the others somewhere else.

Start with the numbers, and make it show its working. “Write and run the code. Show me the table by year. List every assumption you made and where it came from.” Then change the return to 5% and the inflation to 4% and look at what happens. If the plan survives that, it is a plan. If it does not, you have learned the most important thing before spending a cent.

Then ask it for the questions, not the answers, on the law. “List every rule this plan depends on that I should verify, with the year it might have changed.” That list, taken to an accountant or a fee-only planner for a single hourly session, turns a vague meeting into a sharp one. We looked at when that hourly purchase is enough and when it is not in structured decision making, which found that a fixed process beat extra analysis by a wide margin across a thousand business decisions. A short list of questions is that process.

Finally, ask it to argue against you. “Assume I am wrong about retiring at 60. Make the strongest case.” It will do this well if asked directly, and badly if left to its instincts. The instruction has to be explicit because the default is to agree.

Keep the smaller decisions in the same frame. Whether CDs are worth it for the cash portion is an arithmetic question with a current-rate input, so the model can set it up and you supply today’s rate. Whether a particular account shelters that cash from tax is a law question, and the model’s answer is a question to check.

What to Do With the Output

Print the plan, or save it, and mark every sentence with one of three letters. A for arithmetic, L for law, B for behaviour. The A sentences you have already tested by changing the inputs. The L sentences go to a professional or a primary source this year. The B sentences you have to answer about yourself, honestly, with your own history of what you did in 2008, 2020 and 2022.

Done that way, ChatGPT has not planned your retirement. It has done the two hours of arithmetic and drafting that used to sit between you and a useful conversation, and left you with a short list of things to check and one hard question about yourself. That is worth a great deal, and it is a different thing from a plan.