JPMorgan Spends $700M a Year on AI. Can It Prove the Return?

From the AI ROI Series, Show Me the Math, recorded 20 October 2026. $700 million. That is what JPMorgan says it spends on AI every year. Last Tuesday on the bank’s third-quarter call, an analyst asked what that money actually returns, and CFO Jeremy Barnum said the benefits “aren’t observable in our current quarterly earnings.” He said he is really trying to move away from quantifying them, and that some of the benefit is going to be offset by token costs. $700 million a year from the number one AI bank in the world, and the return does not show up in the numbers.

So let’s do what this show does and break the math. On Friday’s episode I promised a deep dive on JPMorgan, and here is the plan: first what $700 million actually is, then what they built with it, then the math on both sides of the ledger, and the one thing that is missing, which honestly most of us are missing too. The math is directional, so check it and tell me where you read it differently.

What $700 million a year actually is

$700 million a year is about $1.9 million a day, every day, weekends included. And it used to be bigger. A year ago Jamie Dimon said, “For $2 billion of expense, we have about $2 billion of benefit.” I always wondered what he meant by $2 billion of benefit and how it was measured. The missing piece arrived last week, when the CFO said that $2 billion is not exactly right. The real AI number is something like $700 million, and the old benefit figure included classic machine learning like fraud models. That is a very different conversation, and in a way a more honest one.

Now put the $700 million against the people using it. About 150,000 JPMorgan employees use the bank’s in-house LLM Suite every week. Divide it out and that is about $4,700 per user per year, roughly $390 a month. So how much time does each person have to save to pay that back? At $100 an hour fully loaded, $700 million buys 7 million hours. Spread across 150,000 people, that is about 47 hours a year, or about one hour a week. One hour a week and the AI pays for itself. JPMorgan’s own people say they save four.

So on paper this is the best AI investment in banking: four times break-even, a very decent ROI, and the CFO says he cannot see it in the earnings. That gap is what this whole episode is about, and it is the same gap I see in almost every enterprise AI program. The one-hour-a-week break-even is easy to compute and impossible to defend, because nobody can point at the hour. That is the problem a system of record for AI exists to solve, and we will get there.

Credit where it is earned

Let me be fair, because JPMorgan earned its reputation. The Evident AI Index just ranked it number one among the world’s banks for the fifth year running. LLM Suite went from zero to 200,000 people in about eight months. The bank runs close to a thousand AI use cases, and Dimon himself said the really important ones number about 50. I love that line. It is the most honest portfolio view I have heard from a CEO. And $700 million is only about 3.5% of a $19.8 billion technology budget, so they can afford to get this right and make a few mistakes along the way. When I first covered what JPMorgan measured a year ago, the question was whether anyone could run AI at that scale. They answered it. The question now is what it is worth.

What JPMorgan built with it

A lot, and almost all of it in house. LLM Suite routes work to OpenAI and Anthropic models. There are coding tools for 65,000 technologists, with AI use now part of their performance goals, a rollout I looked at when the bank pushed agentic AI to 250,000 employees. And there is Claude Code for engineers, with a reported cap of $2,000 a month per person. That is another large company assigning a specific allowance to an employee, in this case a developer. On Friday we talked about Accenture centrally funding $29 of Claude a month per person. If your company does something similar, I would love to hear the number.

Then there is what I found in the job postings. JPMorgan is hiring principal engineers at $204,250 to $285,000 base salary to build a GenAI gateway with routing, quotas and, quote, “per-tenant cost attribution.” Translation: they are building their own AI meter. They have probably built some of it already. Here are the numbers from the episode in one place.

The value side: four hours nobody will book

Back to the math. If the four hours are real, what is the full value? 150,000 people times four hours a week is 600,000 hours a week. Over 46 working weeks, that is about 27.6 million hours a year, and at $100 an hour, roughly $2.8 billion. Four times the $700 million. So why won’t the CFO book it? Because Dimon told investors in February that those hours are, quote, “not in an NPV.” They are self-reported. Nobody can tie them to a finished piece of work.

This shows you how hard true AI ROI is. Either JPMorgan is sitting on a $2 billion return it cannot prove, or the hours are not real, and the CFO can book neither. Many of us are in exactly this position, especially at a public company where the right instinct is to be conservative. A survey is not a ledger. The hour saved only becomes value when you can say which task it came out of, what that task cost in tokens and licences, and what the finished work was worth. That is the FinOps for AI problem in one sentence: the cost side has an invoice and the value side has a questionnaire.

The cost side: measured to the penny

I promised the cost side as well. In July the CFO called token expense “a trivial number” for the first half, with meaningful acceleration expected in the second. By October it is a whole other conversation. A JPMorgan payments data executive said some employees are spending more on tokens than their salary. I have heard those stories before, and the pattern is familiar: a burst of tokenmaxxing in the first half, then the caps arrive. And those Claude Code caps are real money. 8,000 reported licences times $2,000 a month is up to $192 million a year. That is the ceiling, and the real bill is probably far lower, but the ceiling alone is more than a quarter of the whole AI budget, on one tool, for one job family.

Gartner says inference cost per agentic workflow rises more than fivefold through 2028. So here is the asymmetry: the cost side is measured to the penny, and the value side is a self-reported survey. A cap per developer is the right reactive move, the same one Uber made after its budget blowout, but a cap tells you who spent more. It does not tell you whose work came out better. For that you need budgets that alert before they bite and a cost per finished task sitting next to each one.

Build versus buy

Everything they built measures activity

I respect a bank that builds. JPMorgan can afford it, and its CISO told SaaS vendors last year that most integration models were not good enough for the bank. Fair. But look at what got built: a gateway, cost attribution, and a dashboard that tags engineers as light, heavy or non-users. Every one of those measures activity. None of them tells you whether the work that came out was worth what went in. And every internal tool is a product. It has a team, a roadmap and an on-call rotation. Fifty engineers at those salaries, fully loaded, is roughly $17 million a year before a single dollar of value gets booked.

MIT NANDA studied around 300 AI deployments in “The GenAI Divide.” Tools bought from specialist vendors succeeded about two-thirds of the time; internal builds succeeded about a third as often. Statistically speaking, building your own measurement layer is a risky game even for the best engineering bank in the world. And to be fair to the industry, one bank did put a number out. Bank of America says about $400 million of AI spend generates about $800 million of benefit, two times the cost. You can argue with the method, but it is a number the board can hold them to.

So did JPMorgan live up to the hype?

On adoption, absolutely yes. On engineering, best in the world. On proof, not yet, and the CFO said it himself: the benefits are not observable in current quarterly earnings. A meter without a value ledger is the current state, at JPMorgan and at most of the companies I talk to. This is exactly why we built Olakai, the Enterprise AI System of Record. It captures every AI interaction in your business across copilots, coding tools and agents, structures it by person, team, model and department, and ties the cost of each finished task to the outcome it produced. The four hours become evidence, and the token bill sits right next to the return. The cost side is the easier half of this industry; the finished-task side is the half that is still mostly missing, and it is the half a CFO can actually defend.

And because banks care about this one: Olakai runs in your own cloud. No standing access, no inbound path. You hold the keys and it sits behind your firewall, which is the posture every financial services customer asks us for first. You do not need 50 engineers to build it, and you do not carry the maintenance. You just need the record.

Four questions and one line for the board

Here is how I would close this quarter. Every AI leader should be able to answer four questions. What did we spend on AI, by tool and by team? That one is easy. How many finished tasks did that buy? What did each one cost, and what did it return in dollars? And which of those returns would we defend to the board? The first is a bill. The second needs visibility into every coding tool and agent. The third and fourth need cost per finished task by team and by model next to the outcome, which is where most programs, JPMorgan included, still have a blank.

And the one line I would take to the board: we know what each finished piece of AI work cost, and we book only the value we can prove. One question for the comments. If a bank analyst asked your CFO what AI did for your company last quarter, could they answer in one number? Your AI is an investment, so let’s measure it like one. If you read JPMorgan differently, challenge me. I work it all day long.