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JPMorgan Chase blueprint to become first fully AI-powered megabank

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11 months ago

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JPMorgan launches AI tools: Here's what to know

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.”

Goldman Sachs tests agentic AI to automate software engineering

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Private Credit Expansion and Regulatory Oversight in 2026 Capital Markets

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5 days ago

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August 9, 2026

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The global private credit market has solidified its role as a fundamental pillar of enterprise finance, expanding rapidly across middle-market lending, asset-backed finance, and infrastructure financing. As non-bank financial institutions capture a larger share of corporate debt origination, global regulatory bodies are increasing oversight to evaluate market transparency and systemic risk interconnections.

Growth Drivers in Direct Lending
Direct lending platforms have continued to attract substantial institutional allocations from pension funds, sovereign wealth entities, and insurance companies seeking attractive risk-adjusted yields. Private debt funds offer corporate borrowers customized financing structures, faster execution timelines, and confidentiality compared to syndicated loan markets.

In 2026, private credit managers are increasingly financing larger corporate transactions, providing multi-billion-dollar credit facilities for buyout deals and corporate restructurings. The flexibility of private debt contracts—featuring unitranche pricing and tailored covenant packages—has made direct lending the preferred capital source for middle-market enterprise sponsors.

Regulatory Scrutiny and Systemic Risk Assessment
The rapid growth of non-bank intermediation has drawn heightened scrutiny from financial regulators in North America and Europe. Because private debt agreements are negotiated privately without public exchange disclosures, central banks are evaluating potential vulnerabilities related to asset valuation consistency and fund liquidity profiles.

Regulatory agencies are introducing guidelines aimed at improving reporting standards for private investment vehicles managing institutional assets. Key focus areas include monitoring leverage ratios within private credit funds and evaluating indirect credit exposures between commercial banking institutions and private debt funds.

Navigating Elevated Refinancing Costs
With benchmark interest rates remaining elevated, private debt borrowers face higher debt service obligations on floating-rate credit facilities. Financial advisory firms report an increase in proactive liability management strategies, including payment-in-kind (PIK) interest options, equity infusions from sponsors, and covenant modifications.

Private credit managers with deep operational capabilities are actively working alongside portfolio companies to optimize working capital and maintain cash flow coverage ratios during periods of higher borrowing costs.

Key Financial Takeaways
1. Mainstream Asset Class: Private credit has expanded beyond niche alternative asset status into a core corporate finance solution.
2. Enhanced Transparency Standards: Regulatory frameworks are evolving toward greater disclosure requirements for private debt managers.
3. Proactive Risk Management: Lenders and sponsors must prioritize debt sustainability and active portfolio monitoring amid high benchmark rates.

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Venture Capital and Startup Valuations in 2026: Focus on Unit Economics and Sustainable Growth

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1 week ago

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August 5, 2026

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The global venture capital (VC) ecosystem is operating under a disciplined investment framework in 2026. Following years of valuation adjustments and shifting liquidity environments, venture capital firms and private equity investors are prioritizing proven unit economics, positive cash flow pathways, and capital efficiency over rapid, unconstrained user acquisition.

The Shift Toward Disciplined Startup Valuations
Early-stage and growth-stage startup valuations have stabilized at sustainable historical averages. Venture capital partners are conducting rigorous due diligence processes before deploying capital, scrutinizing gross margins, customer acquisition costs (CAC), net revenue retention (NRR), and lifetime value (LTV) metrics.

While total capital deployed remains robust, seed and Series A funding rounds are taking longer to finalize. Founders are expected to demonstrate clear product-market fit and defensible intellectual property rather than relying on top-line revenue projections unsupported by strong underlying economics.

M&A Activity and Liquidity Solutions
The market for venture-backed exits is seeing renewed momentum through strategic mergers and acquisitions (M&A) and secondary market liquidity facilities. Established corporate enterprises are acquiring high-performing technology startups to integrate proprietary artificial intelligence models and specialized software solutions into their product ecosystems.

Simultaneously, secondary market transactions have become an essential liquidity mechanism for early employees and institutional investors. Specialized secondary funds are purchasing pre-IPO shares at discounted valuations, providing liquidity opportunities while companies remain private for longer durations.

Sector Allocation: Deep Tech, Clean Energy, and Enterprise Automation
Venture capital investment is heavily concentrated in deep technology and capital-intensive engineering sectors. High-growth investment themes include:
– Next-Generation Semiconductors: Hardware startups designing specialized AI processors and energy-efficient microchip architectures.
– Clean Technology: Battery chemistry innovations, carbon capture solutions, and grid-scale energy storage startups.
– Enterprise Process Automation: Software platforms that automate complex workflows in healthcare, financial services, and industrial logistics.

Key Insights for Entrepreneurs and Investors
1. Prioritize Capital Efficiency: Startups focused on achieving operational profitability receive higher valuation premiums from institutional investors.
2. Strategic Exit Planning: Corporate M&A is serving as a primary exit route for venture-backed startups navigating prolonged IPO windows.
3. Focus on High-Moat Technologies: Deep tech and proprietary software architectures are securing the majority of growth-stage capital allocations.

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Finance

The Evolution of Digital Payments: Cross-Border Settlement and Central Bank Digital Currencies in 2026

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1 week ago

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August 5, 2026

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The infrastructure supporting global commerce is undergoing a major technological upgrade as real-time digital payment rails, cross-border settlement solutions, and Central Bank Digital Currency (CBDC) pilot programs achieve widespread commercial adoption. Financial institutions and fintech developers are reimagining payment processing to eliminate friction, lower transaction fees, and accelerate settlement speed.

Transforming Cross-Border Settlement Infrastructure
For decades, international corporate payments relied on legacy correspondent banking networks characterized by multi-day settlement delays, opaque fee structures, and high foreign exchange markups. In 2026, modern cross-border payment networks are enabling near-instantaneous settlement for international trade transactions.

Financial technology platforms are leveraging distributed ledger technology and real-time gross settlement (RTGS) interconnections to settle transactions in seconds. International trade participants benefit from reduced working capital requirements and minimized foreign exchange volatility risks during cross-border transfers.

Commercial Expansion of Central Bank Digital Currencies
Central banks representing major global economies are advancing CBDC initiatives from research phases into active commercial deployment. Wholesale CBDCs—designed specifically for interbank settlement and financial institution clearing—are demonstrating substantial efficiency gains in domestic and international transactions.

At the retail level, several nations have introduced public digital currency options alongside existing commercial banking networks. These sovereign digital payment channels aim to expand financial inclusion, lower consumer transaction fees, and improve the efficiency of government-to-citizen financial disbursements.

Open Banking and Embedded Finance Ecosystems
Alongside settlement infrastructure upgrades, open banking regulations and embedded finance frameworks are transforming merchant-consumer interactions. Commercial businesses across retail, travel, and business-to-business (B2B) services are integrating seamless payment APIs directly into their customer software interfaces.

Through open banking frameworks, consumers can initiate secure bank-to-bank payments without relying on traditional credit card networks, significantly reducing merchant processing fees. Integrated Buy-Now-Pay-Later (BNPL) options and point-of-sale credit facilities continue to expand, driving higher conversion rates for digital commerce platforms.

Strategic Financial Takeaways
1. Treasury Optimization: Corporate treasurers should leverage instant cross-border payment platforms to minimize liquidity buffers and foreign exchange exposure.
2. CBDC Integration: Financial institutions must prepare internal core banking systems to interface with emerging wholesale CBDC payment rails.
3. Merchant Fee Reduction: Enterprise merchants can lower payment processing overhead by adopting account-to-account (A2A) open banking checkout solutions.

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