Soaring AI Bills Are Vexing Wall Street Executives

Source: Rthvika Suvarna; Bloomberg News · 2026-10-21

No one ever wants to give a technology team a blank check. Yet for many chief financial officers, that’s exactly what they were asked to do in the early rush to embrace AI. Now, after one-too-many surprise bills, finding a way to control costs has become an imperative.

“I actually don’t think anybody in the organization has a good handle on it — if I am going to do XYZ, what is going to be my cost,” said Bhavi Mehta, a partner at Bain & Co. and its global lead for advanced analytics in financial services. “As AI moves toward more sophisticated reasoning and agentic workflows, the tokens required for a given task can vary significantly. That makes understanding cost alongside business value increasingly important as banks scale AI,” he added.

Only 12 of the 50 largest global banks tracked by AI benchmarking firm Evident have publicly attributed projected or realized returns to their adoption of AI, according to its latest report. Yet it’s a rare CEO who doesn’t talk up the potential benefits. Rising investment in the technology came up during third-quarter earnings calls at several US banks from Citigroup Inc. to Goldman Sachs Group Inc., but only a few executives had precise figures for their AI expenditures or the financial benefits they are generating.

While tokenmaxxing — the practice of engineers using AI as much as possible — has become notorious — that’s not always the cause of token usage blowout.

When Deutsche Bank AG’s monitoring flagged individual employees consuming unusually large volumes of tokens, many turned out to be doing useful work, said Christoph Rabenseifner, the bank’s chief strategy officer for technology, data and innovation. Instead of cutting them off, the bank steered that usage in a more productive direction. It also tested one task on a smaller, specially trained model instead of a large frontier model, cutting costs several times over, Rabenseifner added.

Banco Santander SA runs large frontier models only to check whether an application meets its quality bar, then shifts proven workloads to lighter, cheaper ones. The approach helped cut execution costs from about €5 ($5.61) to €0.03 per loan approval process, said Ignacio Bernal, the bank’s chief artificial intelligence officer.

“When you move AI from pilots to production across a global bank, inference cost stops being a rounding error and becomes a real operational cost line,” Bernal said, noting some processes run millions of times a year, driving up computing expenses significantly.

Canadian Imperial Bank of Commerce has found that its most powerful models, including Anthropic PBC's Opus, can cost C$3,000 ($2,130) per employee a month, a bill that could add up quickly across thousands of workers. As a cost-control measure, the bank restricted access to a few hundred employees, while capping its latest in-built workspace CAI 2.0, still in testing, at 250 users who can each spend between C$4 and C$250 a month.

“It’s too powerful to have 50,000 people just using it without control,” Jaime Tatis, CIBC Chief Data and Artificial Intelligence Officer, said in a July interview.

Agentic AI is emerging as a primary driver of rising costs. Big banks announced 19 agentic AI use cases in the first quarter of 2026, up from eight the previous quarter, according to Evident. In the second quarter, the number halved – not because banks are cooling on agents, but because the firms are asking them to take on more complex work. Wells Fargo & Co., for instance, wants agentic AI to master 1,100 steps in the mortgage process before deploying it across workflows, the Evident report said.

Unlike conventional AI tools, agentic systems autonomously execute complex, multistep tasks, which include analyzing financial statements, tracking borrowers, verifying documents and detecting money laundering activity. This processing demands significant computational resources, translating into heavier token consumption, and in turn, drives up monthly costs.

At one Asian lender after some teams burned through their allocation of tokens in the first week, the bank decided to pool tokens and build an agent that trims prompts before they reach a model, according to a senior data executive who asked not to be identified talking about internal matters.

The cost question has brought Chinese models into the conversation. A July JPMorgan Private Bank note said a GLM model from Beijing’s Z.AI performed on par with the best model available in March at more than 80% less than the priciest frontier model at the time.

Regulators have flagged some concerns. Canada’s banking watchdog recently asked staff to draft minimum standards for how lenders use frontier AI models, including on security, while the US Federal Reserve Vice Chair for Supervision Michelle Bowman has warned banks to shore up their systems against AI-driven threats.

That makes less-vetted models, particularly foreign ones, harder to justify to boards. Risk chiefs also worry that AI agents, once deployed, can behave unpredictably inside a bank's own systems, a concern that has gained traction in recent months.

During the early rush to cloud services more than a decade ago, banks took at least five years to control costs that rose with usage and bring them under a dedicated budget line, according to J.R. Storment, executive director of the Tokenomics Foundation. What’s different now is that financial chiefs are reacting to AI cost spikes within 6 to 18 months of adoption, making it difficult to put a final price tag on premium AI models, he added.

At JPMorgan Chase & Co., some employees were “spending more on tokens than their salary” and yet the company wasn’t rationing AI usage at that time, Zachery Anderson, chief data and analytics officer for the bank’s payments division, told Semafor in June. JPMorgan has since set monthly spending limits of $2,000 on Anthropic’s Claude Code for certain engineers, Business Insider reported this month. The bank’s CFO Jeremy Barnum said on the lender’s earnings call this month that its annual AI expense was about $700 million, though a precise figure is hard to pin down because the technology is now spread across the entire bank.

Alex, a senior machine-learning engineer, at a large European bank was asked to cap his team’s AI spending in February this year. The trigger was an €80,000 bill the team had piled up after building an advanced chatbot and deploying it across thousands of workflows. The sum, though hardly outrageous for a large bank, signaled to the finance chief that it might snowball into millions if left unchecked. For Alex, who asked not to be identified by his full name to avoid professional repercussions, the question quickly shifted from whether the chatbot worked to whether the bank could afford to run it at scale.

To cut costs, he reworked the chatbot so it cached answers to frequently asked questions and resurfaced them whenever a user asked again. Simpler requests went to open-source models. Only token-intensive prompts, the ones requiring deeper searches, went to premium models from platforms like ChatGPT or Claude. That brought the AI bill down by roughly a third.

Some banks are tackling the forecasting problem earlier. French lender BNP Paribas SA approves every AI project after its running cost is calculated. The group’s internal platform sets a token price, multiplies it by expected volumes and weighs the total against the value the tool should deliver, said Hugues Even, the bank’s group chief data officer.