Why Readiness Beats AI in Banking
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Why do megabank earnings highlight the hidden risk of affordable AI?
your bank can spend more on ai this year and still lose the customer relationship it was supposed to protect jamie diamond explained why competition will pass much of ai's value to customers as ai becomes cheaper and easier to copy the challenge is that affordable ai will expose the operational delays your legacy back office and culture can't hide. That warning came during a quarter when five major US banks earned more than $49 billion. Most of the headlines treated it as proof that being big keeps widening the gap in our industry, even if the real signal underneath was that these tools are about to get cheap for everyone. The institutions that use this window of opportunity to shorten decision-making and rebuild existing workflows
will be ready when the new tools become table stakes. The opportunity will reach every institution at once, leaving the outcome to those institutions that are prepared for the future before it lands. This is the new definition of resilience. Let's start with where that astonishing $49 billion actually came from. The earnings bump did not come from a sudden reinvention of everyday banking. It was amplified by one-time gains on a Visa stake, record trading activity, and a surge in deal-making, the areas where most community financial institutions don't really compete.
How are the $49 billion earnings actually generated and why isn’t AI the driver?
The headlines had little to do with the new checking account opened down the street. It came from a few very unusual market opportunities, most of which you never touch, and almost none of it came from AI changing how everyday banking works. Underneath the profit, the giants are spending heavily to build AI at the frontier of banking. Bank of America told its analysts it has more than 300 AI use cases approved, with 114 live and 34 fully implemented. The distance between 300 use cases to 34 fully implemented ones shows how much work lies between permission and operational change. The frontier is also the most expensive place to build since the biggest players are paying to discover what actually works.
What is noteworthy is that once it works, the discovery does not stay with the bank that paid for it. This is not an argument against AI. It's an argument over where the real advantage lies once everyone has access to the same tools. This is why I sometimes call AI investment a survival tax.
What does Jamie Dimon say about AI’s value passing to customers?
The cost of discovery is high, but the cost of using these tools is falling faster than almost any technology in history. Stanford tracks and the price of getting an answer from an AI as capable as the early version of ChatGPT fell from about $20 to about 7 cents in under two years. McKinsey laid out the consequences plainly from an industry side. The savings from AI will not remain an advantage for long because competitors will pass them straight to the consumers. Here's the part the industry keeps missing, and it's the whole point. The cost of the tool is dropping, but the cost of changing financial institutions is not following suit. A cheap, powerful AI does not clean up your customer's records, it doesn't fix a clunky process, earn a customer's permission, or put a human in the loop when judgment is needed, or get a committee to finally make a decision.
So the helpful move here isn't complicated. Let the megabanks cover the cost of building the expensive frontier and use the same window of opportunity to prepare your own house for when these tools become affordable. The tools themselves will become available very fast. Whether you're ready to actually use them is the only thing you control.
Why is the cost of AI tools dropping faster than the cost of organizational change?
And that readiness only exists if you build it now. Now, don't take my word for any of this. Remember what Diamond said on the earnings call, that the benefit of AI flows to the customer. He told his fellow bankers that they don't uniquely benefit from AI because their competitors will have the same models. On BNY's call, CEO Robin Vance made the other half of that point, that the technology is already remarkable. So the real differentiator is not the tool, but how well a company adopts it and embeds it.
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Chapters
7 chapters
1
Why do megabank earnings highlight the hidden risk of affordable AI?
0:00–1:27
2
How are the $49 billion earnings actually generated and why isn’t AI the driver?
1:27–2:39
3
What does Jamie Dimon say about AI’s value passing to customers?
2:39–4:01
4
Why is the cost of AI tools dropping faster than the cost of organizational change?
4:01–5:09
5
What readiness fundamentals let banks absorb new AI tools faster than competitors?
5:09–6:36
6
How does a solid data‑process‑ownership framework create a sustainable competitive edge?
6:36–7:55
7
What single customer moment should smaller banks focus on to build AI‑ready foundations?
7:55–9:02