Finance
JPMorgan Chase blueprint to become first fully AI-powered megabank
Deep within the bowels of JPMorgan Chase’s data centers and cloud providers, an artificial intelligence program crucial to the bank’s aspirations grows more powerful by the week.
The program, called LLM Suite, is a portal created by the bank to harness large language models from the world’s leading AI startups. It currently uses models from OpenAI and Anthropic.
Every eight weeks, LLM Suite is updated as the bank feeds it more from the vast databases and software applications of its major businesses, giving the platform more abilities, Derek Waldron, JPMorgan chief data analytics officer, told CNBC in an exclusive interview.
“The broad vision that we’re working towards is one where the JPMorgan Chase of the future is going to be a fully AI-connected enterprise,” Waldron said.
JPMorgan, the world’s largest bank by market capitalization, is being “fundamentally rewired” for the coming AI era, according to Waldron. The bank, a heavyweight across Main Street and Wall Street finance, wants to provide every employee with AI agents, automate every behind-the-scenes process and have every client experience curated with AI concierges.
If the effort succeeds, the project could have profound implications for the bank’s employees, customers and shareholders — even the nature of corporate labor itself.
Waldron, who gave CNBC the first demonstration of its AI platform seen by any outsider, showed the program creating an investment banking deck in about 30 seconds, work that would’ve previously taken a team of junior bankers hours to complete.
Out of the box
Since the arrival of OpenAI’s ChatGPT in late 2022, optimism over generative AI has driven markets higher on gains from the tech giants and chip makers closest to the trade. Underpinning their growth is the expectation that corporate clients deploying AI will either boost worker productivity or lower expenses through layoffs — or both.
But similar to how the internet story played out in the 1990s, near-term expectations for AI may have outstripped reality. Most corporations had no tangible returns yet on their AI projects despite more than $30 billion in collective investments, according to an MIT report from July.
Jamie Dimon, Chairman and Chief Executive Officer of JPMorgan Chase & Co. speaks during an event honoring local construction workers who helped build the firm’s new headquarters at 270 Park Avenue, in the Midtown area of New York City, U.S., Sept. 9, 2025.
Shannon Stapleton | Reuters
In the case of JPMorgan, even with it $18 billion annual tech budget, it will take years for the company to realize AI’s potential by stitching the cognitive power of AI models together with the bank’s proprietary data and software programs, said Waldron.
“There is a value gap between what the technology is capable of and the ability to fully capture that within an enterprise,” Waldron said.
Companies “do work in thousands of different applications, there’s a lot of work to connect those applications into an AI ecosystem and make them consumable,” he said.
If JPMorgan can beat other banks to the punch on incorporating AI, it will enjoy a period of higher margins before the rest of the industry catches up. That first-mover advantage will allow it to grow revenues faster by going after a larger slice of the addressable market in global finance — enabling the bank to pitch more middle-market companies in investment banking, for instance.
Change on the horizon
AI was a major topic at a four-day executive retreat held in July by JPMorgan CEO Jamie Dimon, according to a person who attended but declined to be identified speaking about the private event.
Among concerns discussed at the off-site meeting, held at a resort outside Nashville, was how AI-driven changes will be adopted by the bank’s 317,000-person workforce and its possible impacts to the apprenticeship model on areas including investment banking.
If JPMorgan succeeds with its AI goals, it will mean that a bank that is already the largest and most profitable in American history is set for new heights. Dimon has led the bank since 2005, guiding it through periods of upheaval to notch record profits in 7 of the last 10 years.
The end state for JPMorgan, as envisioned by Waldron, is a future in which AI is woven into the fabric of the company:
“Every employee will have their own personalized AI assistant; every process is powered by AI agents, and every client experience has an AI concierge,” he said.
JPMorgan laid the groundwork for this starting in 2023, when it gave employees access to OpenAI’s models through LLM Suite; it was essentially a corporate ChatGPT tool used to draft emails and summarize documents.
About 250,000 JPMorgan employees have access to the platform today, which is the entire workforce except for branch and call center staff, said Waldron. Half of them use it roughly every day, he said.
JPMorgan is now early in the next phase of its AI blueprint: It has begun deploying agentic AI to handle complex multistep tasks for employees, according to an internal roadmap provided by the bank.
“As those agents become increasingly powerful in terms of their AI capabilities and increasingly connected into JPMorgan,” Waldron said, “they can take on more and more responsibilities.”
Nvidia deck
Waldron, a former McKinsey partner with a Ph.D. in computational physics, recently demonstrated LLM Suite’s capabilities to CNBC.
He gave the program a prompt: “You are a technology banker at JPMorgan Chase preparing for a meeting with the CEO and CFO of Nvidia. Prepare a five-page presentation that includes the latest news, earnings and a peer comparison.”
LLM Suite created a credible-looking PowerPoint deck in about 30 seconds.
“You can imagine in the past how that would have been done; we would’ve had teams of investment banking analysts working long hours at night to do this,” said Waldron.
The bank is also training AI to draft other key investment banking documents including the “inch thick” confidential memos that JPMorgan produces for prospective M&A clients, said the person who attended the July executive meeting.
Derek Waldron, JPMorgan’s chief analytics officer.
Courtesy: JP Morgan
The prospect of collapsing work loads means that fewer junior bankers may be needed even while AI-enabled teams handle more work and pitch more companies, according to senior Wall Street executives at several firms who spoke on the condition of anonymity to provide their candid thoughts.
But to extract the full value from this new, almost magical technology, it’s not just about the tools: Changes to how employees and departments are organized may be needed.
One proposal being discussed at a major investment bank is reducing the ratio of junior bankers to senior managers from the current 6-1 to 4-1. In the new regime, half of those junior bankers would be working from cities with cheaper labor, say Bengaluru, India, and Buenos Aires, Argentina, instead of being clustered in expensive New York.
The AI-powered junior bankers could then work on deals in shifts around-the-clock, passing the baton from one time zone to the next.
With fewer bankers on the payroll, the cost structure of investment banking would fall, boosting the bottom line, said the executives.
Structural shifts
Unlike previous generations of technology, where bespoke automation tools had to be made for every distinct job, LLM Suite can service them all, from traders to wealth managers and risk officers, according to Waldron.
The implications for workers are profound. AI will empower some workers and give them more time, positioning them at the center of a team of AI agents. Others will be displaced by AI that takes over processes which no longer require human intervention.
That shift favors those who work directly with clients — a private banker with a roster of rich investors, traders who cater to hedge fund and pension managers, or investment bankers with relationships with Fortune 500 CEOs, for instance.
Those at risk of having to find new roles include operations and support staff who mainly deal in rote processes like setting up accounts, fraud detection or settling trades.
In May, JPMorgan’s consumer banking chief told investors that operations staff would fall by at least 10% in the next five years thanks to AI deployment.
“In an AI world, you’ll still have people at the top who are managing and have relationships with clients, but many, many of the processes underneath are now being done by AI systems,” Waldron said.
AI FOMO
But it’s still unwritten as to how that future will unfold; will corporations retain workers impacted by AI, retraining them for the new roles it creates? Or will they simply opt to cut their payroll?
“Without a doubt, AI technology will have changes on the construction of the workforce,” Waldron said. “That is certain, but I think it’s unclear as to exactly what those changes will look like.”
More broadly, Waldron said that workers would shift from being creators of reports or software updates, or “makers” in his terminology, to “checkers” or managers of AI agents doing that work.
The bank is closing in on another frontier: It will soon allow generative AI to interact directly with customers, Waldron said. JPMorgan will start with limited cases, like allowing it to extract information for a user, before rolling out more advanced versions, he said.
Despite market concerns that the AI trade is a brewing bubble, corporate clients are actually more worried now that if they don’t start adopting it soon, they’ll fall behind and lose share, said Avi Gesser, a Debevoise & Plimpton partner who advises corporations on issues around AI.
“People are starting to see what these tools can do,” Gesser said. “They’re sort of like, ‘Wow, if you get the workflow right, implement it properly and have the right guardrails, I could see how that would save you a lot of time and a lot of money and deliver a better product.”
Cybersecurity and enterprise software stocks have been market dogs in 2026, with fears that AI will wipe out a wide range of companies in the enterprise space dominating the narrative. But they snapped a brutal losing streak this past week, joining in the broader market rally that saw all losses from the U.S.-Iran war regained by the Dow Jones Industrial Average and S&P 500.
Cybersecurity has been “a victim of some of the AI-related headlines,” Christian Magoon, Amplify ETFs CEO, said on this week’s “ETF Edge.”
It wasn’t just niche cybersecurity names. Take Microsoft, for example, which was recently down close to 20% for the year. Its shares surged last week by 13%.
A big driver of the pummeling in software stocks was a rotation within tech by investors to AI infrastructure and semiconductors and some other names in large-cap tech, Magoon said, and since cybersecurity stocks and ETFs are heavily weighted towards software companies, they were left behind even as those businesses continue to grow on a fundamental basis.
But Wall Street now has become more bullish with the stocks at lower levels. Brent Thill, Jefferies tech analyst, said last week that the worst may be over for software stocks. “I think that this concept that software is dead, and then Anthropic and OpenAI are going to kill the entire industry, is just over-exaggerated,” he said on CNBC’s “Money Movers” on Wednesday.
“Big Short” investor Michael Burry wrote in a Substack post on Wednesday that he is becoming bullish about software stocks after the recent selloff. “Software stocks remain interesting because of accelerated extreme declines last week arising from a reflexive positive feedback loop between falling software stocks and changes in the market for their bank debt,” he wrote.
The Global X Cybersecurity ETF (BUG), is down about 12% since the beginning of the year, with top holdings including Palo Alto Networks, Fortinet, Akamai Technologies and CrowdStrike. But BUG was up 12% last week. The First Trust NASDAQ Cybersecurity ETF (CIBR) is down 6% for the year, but up 9% in the past week.
Piper Sandler analyst Rob Owens reiterated an “overweight” rating on Palo Alto Networks which helped the stock pop 7% — it is now down roughly 6% on the year. Its peers saw similar moves, including CrowdStrike.
Performance of Global X cybersecurity ETF versus S&P 500 over past one-year period.
Magoon said expectations may have become too high in cybersecurity, and with a crowding effect among investors, solid results were not enough to to push stocks higher. But the down-and-then-back-up 2026 for the sector is also a reminder that when stocks fall sharply in a short period of time, opportunity may knock.
“Once you’re down over 10% in some of these subsectors, you start to see the contrarians start to say, ‘well, maybe I’ll take a look at this,'” Magoon said.
He said AI does add both opportunity and uncertainty to the cybersecurity equation, increasing demand but also introducing new competition. But he added, “I think the dip is good to buy in an AI-driven world,” specifically because the risks to companies may lead to more M&A in cyber names that benefits the stocks.
For now, investors may look for opportunity on the margins rather than rush back into beaten-up tech names. “I think investors are still going to remain underweight software,” Thill said.
But Magoon advises investors to at least take the reminder to keep an eye on niches in the market during pronounced downturns. “The best-performing are often the least bought and do the best over the next 12 months versus late-in-the-game piling on,” he said.
While that may have been a mindset that worked against the last investors into cybersecurity and enterprise software in mid-2025 when the negative sentiment started building, at least for now, it’s started working for the stocks in the sector again.
Meanwhile, this year’s biggest winner is also a good example of what can be an extended trade in either a bullish or bearish direction. Last year, institutional ownership of energy was at multi-year lows, Magoon said, referencing Bank of America data. “Reverse sentiment can be a great indicator,” he said.
But he cautioned that any selective buying of stocks that have dipped does have to contend with the risk that there is a potentially bigger drawdown in the market yet to come in 2026. That is because midterm election years historically have been marked by large drawdowns. “If you think it is bad right now, it could get a lot worse,” Magoon said. But he added that there’s a silver-lining in that data, too, for the patient investor. The market has posted very strong 12-month returns after midterm election drawdowns end. So, for investors with a longer-term time horizon and no need for short-term liquidity, Magoon said, “stick in there.”
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New innovation in the exchange-traded fund industry could come at a cost to investors during extreme conditions.
According to MFS Investment Management’s Jamie Harrison, ETFs involved in increasingly complex derivatives and less transparent markets may be in uncharted territory when it comes to violent downturns.
“Those would be something that you’d want to keep an eye on as volatility ramps up,” the firm’s head of ETF capital markets told CNBC’s “ETF Edge” this week. “As innovation continues to increase at a rapid pace within the ETF wrapper, [it’s] definitely something that we advise our clients to be really front-footed about… Lack of transparency could absolutely be an issue if we’re going to start seeing some deep sell-offs.”
His firm has been around since 1924 and is known for inventing the open-end mutual fund. Last year, ETF.com named MFS Investment Management as the best new ETF issuer.
“It’s important to do due diligence on the portfolio,” he said. “Having a firm that has deep partnerships, deep bench of subject matter experts that plays with the A-team in terms of the Street and liquidity providers available [are] super important.”
Liquidity as the real issue?
Harrison suggested the real issue is liquidity, particularly during a steep sell-off.
“We’ve all seen the news and the headlines around potential private credit ETFs. That picture becomes much more murky,” he added. “It’s up to advisors, to investors [and] to clients to really dig in and look under the hood and engage with their issuers.”
He noted investors will have to ask some tough questions.
“What does this look like in a 20% drawdown? How does this liquidity facility work? Am I going to be able to get in? Am I going to be able to get out? And if I’m able to get out, am I able to get out at a price that’s tight to NAV [net asset value], and what’s the infrastructure at your shop in terms of managing that consideration for me,” said Harrison.
Amplify ETFs’ Christian Magoon is also concerned about these newer ETF strategies could weather a monster drawdown. He listed private credit as a red flag.
“If your ETF owns private credit, I think it’s worth taking a look at, kind of what the standards are around liquidity and how that ETF is trading, because that should be a bit of a mismatch between the trading pace of ETFs and the underlying asset,” the firm’s CEO said in the same interview.
Magoon also highlighted potential issues surrounding equity-linked notes. The notes provide fixed income security while offering potentially higher returns linked to stocks or equity indexes.
“Those could potentially be in stress due to redemptions and the underlying credit risk. That’s another kind of unique derivative,” Magoon said. “I would very closely look at any ETF that has equity-linked notes should we get into a major drawdown or there be a contagion in private credit or something related to the banking system.”
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