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  • Ken Griffin on AI, the Golden Age of Entrepreneurs, and the Taiwan Chip Risk That Would Cut US GDP 8 Percent: Inside the Citadel Founder’s Goldman Sachs Great Investors Interview

    Ken Griffin, founder and CEO of Citadel, sat down with Goldman Sachs’ Raj Mahajan at the firm’s Apex Symposium (recorded June 2, 2026) for this episode of Goldman Sachs Exchanges: Great Investors. It is their third public conversation in seven years, and Griffin is unusually candid: about the Friday he went home “shocked and depressed” over AI, the agentic system inside Citadel that compresses six weeks of PhD-level work into two hours, why a Chinese move on Taiwan would throw the US into a depression within six months, and the one question every hedge fund investor should ask their GP.

    TLDW

    Griffin names his two proudest leadership calls: dragging Citadel back to the office five days a week before it was acceptable (citing Fed research that remote work has hurt young Americans’ employment more than AI has), and Citadel’s pandemic role, from getting the FDA to approve experimental COVID drug trials in 72 hours to shaping the incentive design behind Operation Warp Speed, which he credits with saving roughly half a million American lives. On markets, he explains why the S&P sits at all-time highs despite wars in the Middle East and Europe: US energy insulation, stunning Chinese oil demand destruction, and record corporate earnings. On AI, he distinguishes hype from reality (a dinner of multinational CEOs gave him five stories of “AI transformation,” none of which were actually AI), then describes the internal breakthrough that changed his mind: an agentic system that reads, reproduces, and out-of-sample-tests academic finance papers in 2 to 3 hours instead of 6 to 8 weeks. The consequences: no layoffs at Citadel, but competitive moats across the economy are being filled in at lightning speed, setting up a golden age of entrepreneurship. He covers the compute market (all available compute is utilized all the time; market makers now spend hundreds of millions a year), China’s lead in roughly 67 of 74 critical technologies, the Taiwan scenario in which losing TSMC chips cuts US GDP 8 percent in six months, an energy doctrine built on nuclear, natural gas, and building data centers (with their own generation) in America, his stress-test approach to tail risk (definable, tolerable, still in business), and hedge fund economics: the industry’s cost of capital is roughly risk-free plus 4 percent, which is why Citadel has returned $25 to 30 billion to its LPs.

    Thoughts

    The most useful thing in this conversation is Griffin’s two-sided read on AI, because he refuses to pick a lane. The paper-replication story is the cleanest documented example yet of AI eating not just white-collar work but masters-and-PhD-level work, from the man whose firm profits from that labor. Yet in the same breath he reports zero headcount reduction, because Citadel has more problems to attack than people to attack them. Both things are true at once, and he names the synthesis honestly: the individual firm gets more productive while every firm’s moat gets shallower. Most commentary picks either the doom frame or the productivity frame. Griffin holds both, and his conclusion (a golden age of entrepreneurship, startups running on a few AI systems instead of 30 to 40 employees) is the actionable part.

    His dinner-party anecdote deserves to be a standard reference. Five global CEOs effusing about AI transformation, and every single story was actually machine learning, optimization, or plain digitization. The C-suite cannot tell AI from technology at large, which means a meaningful slice of the “AI is transforming our business” narrative priced into the S&P is really a decade-old digital revolution wearing a new label. That is not a bearish observation, since the earnings are real either way, but it matters for anyone trying to figure out which companies actually have AI leverage and which have rebranded their IT budget.

    The Taiwan section is the starkest risk framing you will hear from someone who runs both a hedge fund and one of the world’s largest market makers. An 8 percent GDP contraction in six months is not a market correction, it is Boeing halting production, new cars stopping, and consumer electronics freezing simultaneously, because TSMC chips are in every high-end product made. What makes his version distinctive is the second-order point: in a Taiwan blockade, he does not expect unified Western sanctions. Europe’s membership on “team USA” is less clear than it was two years ago, and the Middle East will play Switzerland because China buys its oil. Investors should notice that his answer to “how do you hedge this?” is not clever derivatives, it is his stress-test doctrine: know the worst case, size exposures so the loss is definable and tolerable, and stay in business to fight back.

    Finally, the small structural details are where the conversation earns its Great Investors billing. Compute has become a commodity input like jet fuel, fully utilized at all times and allocated purely by willingness to pay, which quietly favors high-margin businesses and squeezes everyone else. Alternative data made the present transparent, so the remaining edge in stock picking is multi-year vision about which companies are building transformative products. And the hedge fund test he closes with is one any allocator can use tomorrow: is your GP in the asset management business or the performance business? Citadel returning $25 to 30 billion to LPs is what the performance answer looks like in practice.

    Key Takeaways

    • Griffin’s proudest leadership call was bringing everyone back to the office five days a week, extremely early and against the culture, because humans are social creatures who learn through apprenticeship and mentorship.
    • He cites a Fed paper on reduced employment among workers under 30: remote work turns out to be a more important factor in diminished opportunities for young Americans than AI.
    • At the start of the pandemic, a hospital-system CEO called Griffin because he could not get FDA approval for drug trials on ventilated COVID patients; Citadel’s team got experimental trials approved in about 72 hours.
    • The key insight behind Operation Warp Speed, which Griffin discussed at length with Jared Kushner, was an incentives fix: the US government paid pharma to manufacture vaccines before FDA results existed, collapsing time-to-market from months to days.
    • By his math, the country spent a few billion dollars on that risk, saved a few trillion dollars of GDP, and saved roughly half a million American lives.
    • The S&P is at all-time highs despite a Middle East war, a still-raging war in Europe, and a potential skirmish over Cuba, because the US is relatively shielded from the energy shock.
    • China’s oil demand elasticity stunned even Citadel’s commodities business, one of the largest in the world; that demand destruction plus episodic oil flows out of the region has kept crude near the low $100s instead of the nearly $200 most models predicted if the straits closed.
    • Citadel has been a huge user of machine learning since TensorFlow arrived roughly a decade ago; the current wave is an acceleration of a digital revolution already underway, not a clean break.
    • At a dinner two years ago, Griffin asked global multinational leaders to share how AI was transforming their businesses: he got four or five great productivity stories and not one actually involved AI. They were machine learning, optimization, and digitization.
    • In the C-suite the nuance between AI and technology at large gets lost, but bigger budgets and CEO enthusiasm are pushing through real projects with real bottom-line impact; US corporate earnings are at all-time highs and multiples have actually come down as a result.
    • The use case that sent Griffin home shocked and depressed: a Citadel team member built an agentic AI system that reads an academic finance paper, reproduces it, verifies the published results, and tests them out of sample in 2 to 3 hours on average.
    • That same replication work previously took a legion of young masters and PhD hires roughly six to eight weeks per paper; Citadel finds a few tradeable ideas a year this way, and a few ideas can be worth a lot of money.
    • The point he stresses: this is not just a white-collar job being automated, it is a master’s or PhD-level job, and AI is now cracking problems (like the 80-year-old math problem OpenAI solved) that seemed beyond its reach two or three years ago.
    • Despite the breakthrough there has been no reduction in headcount at Citadel: the firm has more problems to attack than people, so Griffin takes every productivity gain he can get.
    • The flip side is that competitive moats across corporate America are being filled in at breathtaking speed, which Griffin expects to produce a golden age of entrepreneurial activity.
    • His example: a startup that would traditionally need 30 or 40 employees now runs with just a few AI systems, letting entrepreneurs take on incumbents in ways impossible 5, 10, or 20 years ago.
    • Some workers face genuinely hard transitions (his example is English-to-German translators), and the country needs to figure out how higher education can retrain these people quickly.
    • Stock picking remains a timeless business with a similar skill set, but the market will increasingly reward multi-year vision about which companies are creating transformative products rather than skill at calling quarterly earnings beats.
    • Alternative data (Citadel has access to the credit card spending of millions of Americans) made the here-and-now transparent a decade ago; AI plus bright people now triage the present almost instantly, so relative value accrues to those who can see years ahead.
    • At Citadel Securities, transformer models continue a decade of ML-driven improvement in pricing and risk management, and the same is true at other leading market-making firms.
    • For all intents and purposes, all available compute in the world is utilized all the time; access is decided by who will pay the most, and the per-unit price has risen beyond what anyone reasonably projected two or three years ago.
    • Large market-making firms now spend hundreds of millions of dollars a year on compute; Griffin compares compute inflation to jet fuel and egg prices, a cost that high-margin businesses can bear and low-margin businesses cannot.
    • China leads in roughly 67 or 68 of the 74 or 75 most important technologies in the world, including solar, EV batteries, and multiple quantum fields, and has pulled ahead in published academic papers.
    • The drivers are structural: 1.4 billion people, an extraordinarily strong educational culture, and far more STEM graduates, producing exactly the human talent needed to win in a high-IP world.
    • China is no longer relegated to producing low-margin products designed in America, and Griffin calls that shift a threat to the American way of life; the answer is not tariffs but educating US youth to out-compete, out-innovate, and out-problem-solve.
    • If China takes Taiwan and the US loses access to Taiwanese semiconductors, the rough estimate is US GDP falls 8 percent in six months: a great depression in the blink of an eye, unlike any before.
    • The mechanism is concrete: Boeing stops making planes within six months, most new cars stop being manufactured, consumer electronics production freezes, because TSMC chips are in every high-end product made.
    • There are no winners in a Taiwan escalation: tanking the US economy would have draconian knock-on effects for China given America’s importance as an export market.
    • In a Taiwan blockade Griffin does not expect unified global sanctions against China: where you sit determines your exposure, Europe’s place on team USA is less clear than two years ago, and the oil-exporting Middle East will play Switzerland.
    • On energy, the US must re-embrace nuclear, with small modular reactors a big part of the story: nuclear has effectively no carbon footprint and one of the lowest mortality rates of any energy source ever used (hydro has killed magnitudes more people).
    • He punctures the clean-energy veneer: solar cells are often made in western China by burning coal, with roughly a seven-year energy payback, and carbon fiber wind turbine blades last 20 years then fill landfills because they do not break down. No truly clean solution exists until fusion or broader nuclear.
    • Until then, natural gas is America’s huge asset: decades of cheap supply, and one of the few things that has actually brought down US carbon emissions.
    • Data centers are going to get built somewhere, and Griffin argues it would be inane for America to end up dependent on foreign countries for them; his fix for NIMBY politics is to require data center builders to construct corresponding power generation, tied to the grid for reliability, rather than pushing costs onto consumers.
    • His hedging doctrine for complicated risks: run stress tests, know exactly how much you lose and where in the worst case, and keep exposures sized so the loss is definable, tolerable, and leaves you still in business and able to fight back. You will never hedge every tail event.
    • Hedge fund industry economics: the long-run cost of capital is roughly the risk-free rate plus 4 percent; underperform and capital flows out, outperform and it flows in, and inflows dilute alpha because alpha capacity is finite.
    • Citadel has returned $25 to 30 billion to its limited partners to keep return on equity high: Griffin’s job is to grow annual alpha capacity, and any capital beyond what the portfolio needs goes back to LPs.
    • The alignment test for allocators: the biggest investor in Citadel’s funds is Griffin and his partners, and every LP should ask whether their GP is in the asset management business or the performance business.

    Detailed Summary

    Return to Office and the Cost of Remote Work

    Asked what he is most proud of beyond the numbers, Griffin starts with Citadel’s early, countercultural demand that everyone return to the office five days a week. He frames it as a human capital decision, not a control decision: people learn through apprenticeship, mentors are critical to development, and the underdevelopment of talent from remote work has damaged the broader economy. He points to recent Fed research on falling employment among under-30s: remote work turns out to matter more than AI in diminishing opportunities for young Americans. Citadel not only brought its team back but publicly extolled the virtues of doing so, and Griffin believes history will be on his side.

    72 Hours to FDA Approval and the Warp Speed Incentive Design

    His second point of pride is Citadel’s pandemic chapter. As the first US COVID cases appeared, a former partner running a major New York hospital system called: he could not get FDA approval for experimental drug trials on ventilated patients facing imminent death, and believed only Griffin could make it happen. Citadel’s team, with decades of government experience, got approvals moving in about 72 hours. The second act was Operation Warp Speed, whose core idea Griffin discussed at length with Jared Kushner: pay pharmaceutical companies to manufacture vaccines before FDA results, so a positive result means days to market instead of the standard sequence losing three to six months. No company would spend billions producing vaccines that might be flushed down the sewer, so the US government took the manufacturing risk on unproven efficacy. A few billion dollars spent, a few trillion in GDP saved, and roughly half a million American lives.

    All-Time Highs in a World at War

    Griffin’s market picture is unsentimental: there is a war in the Middle East, a still-raging war in Europe, potential trouble in Cuba, and the peace both men grew up with is off the table. Yet the S&P sits at record highs. His explanation: America is relatively shielded from the war-driven energy crisis. China has curtailed oil demand with an elasticity that stunned even Citadel’s commodity desk, and episodic oil and LNG flows keep leaving the region, holding crude around the low $100s when most estimates had a strait closure producing nearly $200 a barrel. Meanwhile corporate earnings are at all-time highs, enough that multiples have actually compressed over the last 12 months.

    The AI Story CEOs Tell Versus the One That Is True

    Citadel has used machine learning heavily since TensorFlow arrived a decade ago, powering everything from radiology reads to self-driving cars across the economy, so Griffin sees today’s AI wave as an acceleration of an ongoing digital revolution. His favorite corrective: at a dinner with global multinational leaders two years ago, everyone was effusive about AI transforming their businesses, so he asked them to go around the table with specifics. Four or five genuinely impressive productivity stories emerged, and not one involved AI: they were machine learning, optimization, digitization, technology at large. The C-suite blurs the distinction, but the enthusiasm has unlocked bigger technology budgets and real bottom-line projects, which is part of why earnings are at records.

    The Agentic System That Shocked Him

    Then comes the story behind the famous “shocked and depressed” Friday. Citadel employs legions of young masters and PhD graduates to replicate academic finance papers: read the hypothesis, judge the work, reproduce results, and test whether the effect persists out of sample (does buyback activity predict outperformance, for example). Each paper takes six to eight weeks, and the process surfaces a few valuable ideas a year. A colleague built an agentic AI system that does the entire pipeline (read, reproduce, verify, out-of-sample test) in two to three hours on average. Griffin’s emphasis: this is not routine white-collar work, it is master’s and PhD-level work, and paired with OpenAI solving a math problem open for 80 years, it shows AI cracking problems considered out of reach two or three years ago. Notably, Citadel cut zero headcount on the back of the breakthrough; the firm has more problems worth attacking than people to attack them, so every productivity gain gets absorbed.

    Filled-In Moats and a Golden Age of Entrepreneurs

    The macro consequence Griffin draws is double-edged. Hold two thoughts at once: AI is reaching very high-level work in the job market, with some workers (translators, for instance) facing hard transitions that demand fast retraining through higher education. And simultaneously, the competitive moats of corporate America are being filled in at breathtaking rates. That means entrepreneurs can launch businesses at speeds impossible 5, 10, or 20 years ago: he mentions a startup running on a few AI systems where 30 or 40 employees would once have been required. He expects a wave of these stories over the next couple of years as founders use the technology to take on incumbents.

    The Future of the Stock Picker

    Griffin has called stock picking a timeless business, and he still sees a similar skill set for the portfolio manager of the future, with one shift in emphasis. Predicting quarterly earnings beats has gotten far harder over a decade as alternative data (credit card panels covering millions of Americans, telegraphing Starbucks and McDonald’s revenues) made the present transparent. Now bright people plus good AI triage the here-and-now almost instantly. The scarce, rewarded skill becomes vision: identifying which companies are building genuinely transformative products years before the market fully prices it.

    Compute Is the New Jet Fuel

    At Citadel Securities, which holds double-digit market share across equities, futures, and treasuries, transformer models extend a decade of machine learning gains in pricing and risk. The compute market backdrop is what Griffin calls breathtaking: essentially all available compute on Earth is utilized all the time, so access reduces to who will pay the most. Per-unit compute prices exceed what anyone reasonably projected two or three years ago, and large market makers now spend hundreds of millions of dollars annually. He treats it as straightforward input inflation, like jet fuel or eggs: high-margin businesses can bear it, low-margin ones cannot.

    China’s Technology Lead and the Taiwan Equilibrium

    Griffin states the cold reality: China is one of the most innovative, fastest-growing economies in the world, leading in roughly 67 or 68 of the 74 or 75 most important technologies (solar, EV batteries, several quantum fields) and now ahead in published academic papers. The foundation is 1.4 billion people, a culture with an extraordinary emphasis on education, and far more STEM graduates. China is no longer relegated to manufacturing low-margin products designed in America, and Griffin calls that a threat to the American way of life. His prescription is pointed: not tariffs, but educating American youth to out-compete, out-innovate, and out-problem-solve. Taiwan is the painful pressure point with no winner. If China takes Taiwan and the US loses TSMC chips, GDP falls an estimated 8 percent in six months: Boeing stops making planes, most new car production halts, consumer electronics freeze, a great depression in the blink of an eye. China would suffer draconian knock-on effects too. As an investor he thinks about position: sanctions in a Taiwan blockade would not be unified, Europe’s place on team USA is a genuine question mark now, and the oil-exporting Middle East would play Switzerland since China is its biggest customer.

    Energy Realism: Nuclear, Gas, and American Data Centers

    On powering AI, Griffin wants America to lead again in nuclear, with small modular reactors central: no meaningful carbon footprint and one of the lowest mortality rates of any energy source ever deployed (hydro has killed magnitudes more people). He challenges the superficial cleanliness of renewables: solar cells are often made in western China with coal power, requiring about seven years of energy capture to break even against the coal burned making them, and 20-year-old carbon fiber wind turbine blades do not break down and are already filling landfills. Until fusion or expanded nuclear, America’s real asset is natural gas: decades of cheap supply that has actually driven US emissions down. His data center position is blunt: they will get built somewhere, and depending on foreign countries for them would be inane, so build them in America. His answer to NIMBY politics: require data center developers to build corresponding power generation, tied to the grid for reliability, so the cost never lands on the American consumer.

    Tail Risk, Tolerable Losses, and Hedge Fund Alignment

    On hedging complicated risks, Griffin’s method is stress testing: if this happens, how much do we lose and where, and is that loss tolerable? You can never manage a portfolio for every possible tail event, but you can keep exposures sized so the worst case is definable and tolerable, leaving you still in business and positioned to fight back. On industry returns, he pegs the hedge fund cost of capital at roughly the risk-free rate plus 4 percent as the long-run equilibrium: underperformance drains capital, outperformance attracts it, and since recent outperformance keeps pulling money in, growing assets dilute alpha. That is why Citadel has returned $25 to 30 billion to LPs: alpha capacity is finite, Griffin’s job is to grow it, and excess capital goes back to investors to keep return on equity high. The closing advice is an alignment test: Citadel’s biggest investor is Griffin and his partners, and every allocator should ask whether their GP is in the asset management business or the performance business.

    Notable Quotes

    “Turns out that remote working is a more important factor to diminished employment opportunities for young Americans than AI.”

    Ken Griffin, citing Fed research on under-30 employment

    “We spent a few billion dollars as a country. We saved a few trillion dollars in GDP. We saved roughly half a million American lives.”

    Ken Griffin, on Operation Warp Speed’s incentive design

    “I got four or five incredible stories of how companies were achieving meaningful productivity gains. Not one involved AI.”

    Ken Griffin, on his dinner with global multinational CEOs

    “My colleague built an agentic AI system that would read a paper, reproduce it, verify the results that were published in the paper, produce the results out of sample, and do all this work in about on average 2 to three hours.”

    Ken Griffin, on the breakthrough that replaced six to eight weeks of PhD-level work

    “We’re likely to see a golden age of entrepreneur activity. Like entrepreneurs will be able to launch new businesses at breathtaking speeds and will be able to take on incumbents in ways that you just couldn’t do 5, 10, 15, 20 years ago.”

    Ken Griffin, on AI filling in competitive moats

    “All the available compute today is more or less utilized all the time. So the question is who’s willing to pay the most for it?”

    Ken Griffin, on the global compute market

    “The US loses access to Taiwanese semiconductor chips, our GDP falls by 8% in 6 months. Simply put, we go into a great depression in the blink of an eye unlike any we’ve seen before.”

    Ken Griffin, on the Taiwan scenario

    “We better damn well build the data centers in America because they’re going to get built somewhere in the world.”

    Ken Griffin, on energy policy and AI infrastructure

    “Definable, tolerable, still in business, still in a position to fight back from that point.”

    Ken Griffin, summarizing his approach to hedging tail risk

    “Are they in the asset management business or are they in the performance business?”

    Ken Griffin, on the question every hedge fund investor should ask their GP

    Watch the full conversation here: Ken Griffin on Goldman Sachs Exchanges: Great Investors.

    Related Reading

  • OpenAI’s Leaked 2025 Financials: $34 Billion in Spending, a $38.5 Billion Net Loss, and a $17 Billion Microsoft Bill Ahead of Its IPO

    Infographic summarizing OpenAI leaked 2025 financials: $13.07B revenue, $34B total costs, $20.92B operating loss, $38.53B net loss, where the $34B went, the $17.2B paid to Microsoft versus $303M paid back, inference costs, and IPO valuation context

    OpenAI’s audited 2025 financials leaked this week, and they are the clearest picture yet of what it actually costs to run the company behind ChatGPT. Independent journalist Ed Zitron first published the documents, and the Financial Times independently confirmed them. The headline: OpenAI spent $34 billion last year, booked $13.07 billion in revenue, and reported a net loss attributable to the company of $38.5 billion. The disclosure lands just days after OpenAI confidentially filed for an IPO that could value it north of $1 trillion.

    TLDR

    OpenAI’s audited 2025 numbers, leaked by Ed Zitron and confirmed by the Financial Times, show revenue tripling to $13.07 billion while total costs reached $34 billion, producing a $20.92 billion operating loss and a $38.53 billion net loss attributable to the company. The much larger net loss is inflated by a one-time $41.55 billion non-cash charge tied to OpenAI’s October 2025 conversion from a nonprofit to a public benefit corporation; strip the non-cash items and the loss is closer to $8 billion. R&D alone was $19.18 billion, cost of revenue (inference) was $7.5 billion, and sales and marketing ballooned to $5.73 billion. OpenAI paid Microsoft $17.2 billion in 2025 while Microsoft paid OpenAI only $303 million, exposing a deep Azure dependency. The company burned $1.60 for every dollar of revenue, down from $2.37 in 2024, and gross margin slipped from roughly 40% to 33% as more capable models consumed more compute per query. The leak arrives as OpenAI files a confidential S-1, targets a listing as early as September 2026 at up to a $1 trillion valuation, and races rival Anthropic, which is more valuable on paper and claims it is already turning an operating profit.

    Thoughts

    The most important thing to understand about these numbers is that there are two loss figures and the press will conflate them. The $38.53 billion net loss is the scary headline, but $41.55 billion of it is a non-cash accounting charge from converting investor convertible interests into equity during the for-profit restructuring. That charge is real on the audited statement and it will show up in the eventual S-1, but it is a one-time artifact of OpenAI’s unusual corporate history, not money that left the building. The number that describes the actual business is the $20.92 billion operating loss. That is the one to watch, and it is still enormous.

    The genuinely encouraging line in the whole release is the loss-per-dollar ratio. In 2024 OpenAI spent $2.37 to generate a dollar of revenue. In 2025 that fell to $1.60. A company that is still losing $1.60 on every dollar is not a healthy business, but a company whose efficiency improved by a third in a single year while tripling its top line is at least pointed in a defensible direction. The bull case for OpenAI lives entirely in the slope of that line. If it keeps improving at that rate, the math eventually crosses over. If it stalls, the valuation is a fantasy.

    The Microsoft relationship is the single most revealing disclosure, and it is wildly asymmetric. OpenAI paid Microsoft $17.2 billion in 2025. Microsoft paid OpenAI $303 million. That is a 56-to-1 ratio, and it reframes the partnership: Microsoft is not really a peer or even just an investor, it is OpenAI’s landlord and primary supplier, collecting rent on every model trained and every query answered. The April 2026 renegotiation that capped revenue-share payments at $38 billion through 2030, down from a projected $135 billion, suddenly looks less like a favor and more like OpenAI desperately trying to lower its single largest cost. The dependency cuts both ways, but right now Microsoft holds the better hand.

    The structural problem hiding inside the cost of revenue line is inference. Training a model is a fixed, one-time cost. Serving it is a recurring cost that scales with every one of ChatGPT’s roughly 800 million weekly users. OpenAI spent $5.02 billion on Azure inference in the first half of 2025 alone, and the more capable its reasoning models get, the more compute each answer burns. That is why gross margin went down even as revenue went up. It is the opposite of how software is supposed to work, where the marginal cost of one more user trends toward zero. OpenAI’s marginal cost is real, large, and growing. The counterargument is that per-token inference costs have been falling roughly tenfold a year, so the unit economics could still flip. That is the entire wager.

    Finally, the timing matters more than the numbers. OpenAI’s confidential S-1 means these audited figures were going to become public regardless, since the SEC requires the full prospectus at least 15 days before a roadshow. What the leak changes is who gets to study them first. Prospective IPO buyers, enterprise customers signing multi-year API contracts, and competitors now have the audited books weeks or months early, and they are reading them against Anthropic, which filed at a higher valuation and claims an operating profit. For a company asking the public markets to underwrite a $1 trillion bet on a monopoly outcome that does not yet exist, losing control of the narrative this early is not a small thing.

    Key Takeaways

    • OpenAI’s audited 2025 financials were first published by independent journalist Ed Zitron and independently confirmed by the Financial Times, the first verified look at the company’s books before its planned IPO.
    • Revenue grew from $3.7 billion in 2024 to $13.07 billion in 2025, more than tripling year over year, making OpenAI one of the fastest-growing businesses in history.
    • By the end of 2025 OpenAI was generating roughly $2 billion in monthly revenue, up from about $1 billion a quarter at the end of 2024.
    • Total costs and expenses hit $34 billion in 2025, up from $12.48 billion in 2024.
    • Research and development was the single largest expense at $19.18 billion, up from $7.81 billion, and exceeded total revenue on its own.
    • Of that R&D spend, $10.59 billion went to Microsoft, almost certainly the GPU compute cost of training frontier models on Azure.
    • Cost of revenue, the expense of serving ChatGPT responses (inference), rose from $2.65 billion to $7.5 billion.
    • Sales and marketing jumped from $1.11 billion to $5.73 billion, a 418% increase.
    • General and administrative costs rose from $907 million to $1.57 billion.
    • The operating loss, the truest measure of day-to-day economics, grew from $8.78 billion to $20.92 billion.
    • The net loss attributable to OpenAI was $38.53 billion, up nearly eightfold from $5.09 billion in 2024.
    • The bulk of that jump was a one-time, non-cash $41.55 billion charge from OpenAI’s October 28, 2025 conversion to a public benefit corporation, reflecting the changing fair value of convertible interests and warrant liabilities.
    • Stripping out the restructuring charge and other non-cash items such as stock-based compensation and Microsoft computing credits, the underlying loss was about $8 billion.
    • Including all factors, gross net loss reached $60.35 billion, lowered to the $38.53 billion attributable figure by removing $21.82 billion attributed to noncontrolling and redeemable noncontrolling interests.
    • OpenAI burned $1.60 for every $1 of revenue in 2025, an improvement from $2.37 in 2024, the clearest data point in the bull case.
    • Measured as a percentage of revenue, the operating loss improved from 237% in 2024 to 160% in 2025.
    • In total, OpenAI paid Microsoft $17.2 billion in 2025: $10.59 billion in R&D fees, $6.047 billion in cost of revenue, $527 million in sales and marketing, and $42 million in G&A.
    • Microsoft paid OpenAI just $303 million in the same year, a 56-to-1 imbalance underscoring OpenAI’s Azure dependency.
    • SoftBank paid OpenAI $867 million in 2025.
    • At year-end OpenAI carried $3.64 billion in outstanding payables to Microsoft, plus tens of millions more in accrued and non-current liabilities.
    • OpenAI spent $5.02 billion on Azure inference in just the first half of 2025; Azure inference from 2024 through Q3 2025 totaled $12.43 billion.
    • ChatGPT serves roughly 800 million weekly users, meaning billions of queries a week, each one burning GPU time at Azure’s pricing of about $6.98 per H100 GPU-hour.
    • Gross margin fell from roughly 40% in 2024 to 33% in 2025, because more capable reasoning models consume more compute per query.
    • Research firm Sacra estimates OpenAI’s inference costs reached $8.4 billion in 2025 and will rise to $14.1 billion in 2026, a 68% increase.
    • At year-end OpenAI held just over $50 billion in assets, with almost half in cash.
    • The April 2026 Microsoft renegotiation ended exclusivity and capped revenue-share payments at $38 billion through 2030, down from a projected $135 billion, potentially saving OpenAI up to $97 billion over five years.
    • OpenAI filed a confidential draft S-1 with the SEC around May 22, 2026 and confirmed it publicly on June 8, naming Goldman Sachs and Morgan Stanley as underwriters.
    • The company is targeting a listing as early as September 2026 at a valuation that could exceed $1 trillion, though Sam Altman has said a public offering “may be a while.”
    • OpenAI raised $122 billion earlier in 2026 at a $730 billion pre-money valuation, putting its post-money value around $852 billion.
    • At an $852 billion valuation, OpenAI trades at roughly 65 times its 2025 revenue.
    • Rival Anthropic also filed IPO paperwork this month after raising $65 billion at a $900-$965 billion valuation, making it more valuable on paper than OpenAI, and says it expects to report an operating profit of $559 million in the June quarter.
    • HSBC analysts estimate OpenAI may need more than $207 billion in additional capital through 2030 even under optimistic projections.
    • OpenAI projects profitability by 2029 or 2030; independent analysts put the more likely date at 2031 or later.
    • Bridgewater partner Greg Jensen reportedly told clients the implied revenue multiples price OpenAI for “a monopoly outcome that does not yet exist.”
    • Zitron separately reported OpenAI had a negative 122% non-GAAP operating margin in Q1 2026 and that ChatGPT growth has stalled, with the company projecting paid ChatGPT Plus subscriptions to fall from 44 million in 2025 toward cheaper tiers in 2026.

    Detailed Summary

    How the leak happened and why it matters now

    The audited documents were obtained and first published by Ed Zitron on his newsletter Where’s Your Ed At, then independently verified by the Financial Times, which reviewed the same materials. That dual sourcing matters: this is not a rumor or a model, it is OpenAI’s actual audited financial statement. The timing is the story. OpenAI filed a confidential draft S-1 with the SEC around May 22, 2026 and confirmed it publicly on June 8. Under SEC rules the full prospectus must be released at least 15 days before an investor roadshow, so the 2025 numbers were going to be public soon regardless. The leak simply moved that disclosure forward, handing prospective investors, enterprise customers, and competitors an early look at the books.

    Revenue tripled, costs grew faster

    OpenAI’s revenue rose from $3.7 billion in 2024 to $13.07 billion in 2025, and monthly revenue reached nearly $2 billion by year-end. By almost any normal standard that is spectacular growth. The problem is that costs grew faster, reaching $34 billion against $12.48 billion the year before. The gap between what OpenAI earns and what it spends has widened every year since its founding, and 2025 is the starkest example yet. Revenue alone was outpaced by research and development as a single line item in both of the last two years.

    Two loss numbers, and why both matter

    There are two figures that get cited interchangeably and should not be. The operating loss of $20.92 billion is what the business spent beyond what it earned from operations: training models, serving ChatGPT, paying engineers, running marketing. The net loss attributable to OpenAI of $38.53 billion is far larger because 2025 was the year OpenAI completed its conversion from a nonprofit to a for-profit public benefit corporation, finalized on October 28, 2025. That restructuring triggered a $41.55 billion non-cash charge reflecting the changing fair value of convertible equity interests and warrant liabilities. Before the conversion, investors held convertible interest rights treated as liabilities under US accounting rules and revalued upward as OpenAI’s valuation climbed, creating the charge. It is not expected to recur. Including all minor items, gross net loss reached $60.35 billion, reduced to the $38.53 billion attributable figure after removing $21.82 billion tied to noncontrolling and redeemable noncontrolling interests, primarily the OpenAI Foundation’s stake. Strip the non-cash noise and the underlying loss was about $8 billion.

    Where the $34 billion went

    The spending breaks into four lines. Research and development was $19.18 billion, the largest category, with $10.59 billion of it flowing to Microsoft for training compute. Cost of revenue, the expense of serving responses to users, was $7.5 billion and captures inference, the compute consumed every time someone prompts ChatGPT or calls the API. Sales and marketing reached $5.73 billion, up 418% year over year, a striking jump for a product that grew largely by word of mouth. General and administrative costs added $1.57 billion. The shape of the spending tells you OpenAI is simultaneously racing to build better models, serve a massive and growing user base, and aggressively defend market share through marketing.

    The Microsoft dependency

    The most striking single disclosure is the scale of the Microsoft relationship. OpenAI paid Microsoft $17.2 billion in 2025: $10.59 billion in R&D fees for model training, $6.047 billion in cost-of-revenue for inference serving, $527 million in sales and marketing, and $42 million in G&A. Microsoft paid OpenAI just $303 million the same year. SoftBank paid OpenAI $867 million. The 56-to-1 ratio between what OpenAI pays Microsoft and what Microsoft pays back makes the structural reality plain: Microsoft is OpenAI’s largest landlord. The dynamic began shifting in April 2026, when the two renegotiated, ending Microsoft’s exclusivity and capping revenue-share payments at $38 billion through 2030, down from a projected $135 billion. That could save OpenAI up to $97 billion over five years, though Microsoft keeps its IP license through 2032 and remains the primary cloud partner.

    Why inference is the core problem

    Training happens once. Serving happens billions of times a day. When OpenAI releases a model it spends months and billions on training compute, a fixed cost that falls away when training ends. Inference is the opposite: every ChatGPT message runs through the model on Azure GPU hardware, consuming electricity and compute to generate a response. With roughly 800 million weekly users, that is billions of queries a week, each burning GPU time at roughly $6.98 per H100 GPU-hour on demand. OpenAI spent $5.02 billion on Azure inference in the first six months of 2025 alone. Sacra estimates full-year inference costs of $8.4 billion in 2025, rising to $14.1 billion in 2026. This is why gross margin fell from about 40% to 33% even as revenue tripled: more capable reasoning models consume far more compute per query, and revenue has not kept pace with the cost growth that capability generates.

    What it means for the IPO and the race with Anthropic

    OpenAI was last valued around $852 billion post-money after raising $122 billion in early 2026, which puts it at roughly 65 times 2025 revenue. It has named Goldman Sachs and Morgan Stanley as underwriters and is targeting a listing as early as September 2026 at up to a $1 trillion valuation, though Altman has hedged that it “may be a while” and that staying private might be the better course. HSBC estimates the company may need more than $207 billion in additional capital through 2030. The race is with Anthropic, which filed paperwork the same month after raising $65 billion at a $900-$965 billion valuation, making it more valuable on paper, and which says it expects a $559 million operating profit in the June quarter. The contrast is sharp: the two leading AI labs heading toward public markets at the same time, one bleeding cash at scale, the other claiming profitability, both asking investors to bet on a future that has not arrived.

    Notable Quotes

    “The financial condition of OpenAI is deeply concerning. $38.53 billion in losses are astronomical, and far higher than most believed it would be. Losses also appear to be mounting year-over-year at a dramatic rate, and I’m not sure how this company finds a way toward any kind of sustainability or profitability.”

    Ed Zitron, the independent journalist who published the leaked audited financials

    “It’s unclear what this means, nor how OpenAI reconciled the removal of $3.74 billion in costs. I will not speculate further.”

    Ed Zitron, on a discrepancy he found in the restated 2024 figures

    “OpenAI’s two biggest expenses are R&D and marketing. Budget cuts there, coupled with an ability to raise prices or win new sources of revenue, could see the company move into the black over time. Cutting R&D would be the most difficult part of that, given that AI companies can only hold onto their customers by generating the best-performing models.”

    Jim Edwards, Fortune, on whether OpenAI has a realistic path to profitability

    “What the audited documents make impossible to argue is that the path to profitability is short, clear, or cheap.”

    TechTimes analysis of the leaked OpenAI financials

    The implied revenue multiples price OpenAI for “a monopoly outcome that does not yet exist.”

    Bridgewater partner Greg Jensen, reportedly telling clients how to read OpenAI’s valuation

    “OpenAI spent $34bn last year as the ChatGPT maker poured money into a race to dominate the fast-growing AI market ahead of a planned stock market listing.”

    George Hammond and Bryce Elder, Financial Times, framing the audited 2025 spend

    Read Ed Zitron’s original reporting with the full breakdown here, and the Financial Times confirmation here.

    Related Reading

    • Ed Zitron, Where’s Your Ed At the primary source that broke the audited 2025 financials with the full line-by-line breakdown.
    • OpenAI (Wikipedia) background on the company’s history, structure, and the nonprofit-to-for-profit conversion that drives the non-cash charge.
    • Inference (Wikipedia) on the recurring compute cost that explains why OpenAI’s gross margin shrinks as usage grows.
    • Anthropic the rival lab that filed IPO paperwork the same month at a higher valuation and claims it is already operating at a profit.
    • SEC on confidential filings context for why OpenAI’s audited numbers were headed for public disclosure regardless of the leak.