A data-driven analysis of $500B valuation, $9B annual losses, and $1.4 trillion in infrastructure commitments
Note: Financial figures for OpenAI are estimates based on reporting from The Information, Financial Times, Fortune, and other sources. OpenAI does not publicly disclose audited financials.
OpenAI is widely viewed as the next Google—a transformative technology company that will define the AI era and justify its $500 billion valuation through sustained dominance and profitability. But the financial data tells a different story, one that more closely resembles WeWork's trajectory than Google's.
The parallels are striking: a company that lost $9 billion in 2025 despite generating $20 billion in revenue, revenue growth that masks deteriorating unit economics amid fierce competition, and an explicit strategy of "leveraging other people's balance sheets" to fund expansion. Most concerning, OpenAI has committed to approximately $1.4 trillion in data center infrastructure over the next eight years while not expecting to be cash-flow positive until 2030.
This isn't speculation or hot takes. It's what the numbers reveal when examined closely.
In 2019, WeWork was valued at $47 billion despite losing $2 billion annually on $1.8 billion in revenue.[1] The company had grandiose visions of "elevating the world's consciousness," a charismatic founder who believed his own hype, a governance structure that made Enron look transparent, and investors who suspended disbelief because everyone else was doing it. By September 2019, the company imploded so spectacularly that its IPO was pulled, its valuation collapsed by 90%, and its CEO was forced out.
Today, OpenAI is valued at $500 billion following an October 2025 secondary sale.[2] The company expects to generate over $20 billion in revenue in 2025 while losing approximately $9 billion.[11] That's a 45% loss-to-revenue ratio—better than WeWork's 111%, but still deeply unprofitable. Like WeWork, OpenAI has a visionary narrative ("artificial general intelligence will transform humanity"), a governance crisis that exploded in November 2023,[8] a corporate structure designed to prioritize mission over profit that's been unwound into a for-profit entity, and a strategy of "leveraging other people's balance sheets" to fund growth.
The comparison isn't perfect—AI has more transformative potential than office subletting, and OpenAI's unit economics are improving. But the patterns remain concerning: massive future commitments ($1.4 trillion in infrastructure), intensifying competition eroding pricing power, and a valuation that requires near-perfect execution across multiple uncertain dimensions.
WeWork at its peak in early 2019 was valued at $47 billion on $1.8 billion in revenue while losing approximately $2 billion. By mid-2019, as the company prepared for its ill-fated IPO, investors finally read the S-1 filing and the valuation collapsed to $8 billion within months.
OpenAI's valuation has tripled in just over a year: from $157 billion in October 2024 to $500 billion in October 2025.[2] The company reported $4.3 billion in revenue in the first half of 2025 while burning $2.5 billion in cash, and expects to generate over $20 billion for the full year while losing approximately $9 billion.[11] That's a 45% loss-to-revenue ratio—a significant improvement from earlier projections, but still deeply unprofitable.
The improvement in unit economics is real, but context matters: OpenAI has committed to approximately $1.4 trillion in data center infrastructure over the next eight years and projects cumulative cash burn of $115 billion through 2029.[11] The absolute dollar losses remain 4.5x larger than WeWork's ever were.
WeWork's defenders pointed to rapid revenue growth—from $886 million in 2017 to $1.8 billion in 2018, a 103% increase. OpenAI's defenders make similar arguments. The company's ARR grew from $5.5 billion in 2024 to over $20 billion in 2025—an 82% year-over-year increase driven by 800 million weekly active users and 5 million paying business customers.
But both growth rates obscure deteriorating unit economics and, more importantly, unsustainable liability structures. WeWork's revenue growth came from signing long-term leases and offering short-term memberships—a structural mismatch that meant each new customer increased long-term liabilities faster than short-term revenue.
OpenAI's strategy is remarkably similar, just with different balance sheets being leveraged. The company has committed to approximately $1.4 trillion in data center infrastructure over the next eight years, including major partnerships with Oracle, Nvidia, AMD, and Broadcom.[11] (Note: These commitments include a mix of binding contracts and partnership frameworks.) The company's explicit strategy, per a senior OpenAI executive, is to "leverage other people's balance sheets" to give OpenAI "time to build the business."
This is WeWork's playbook in different clothing. WeWork signed long-term lease obligations and hoped short-term revenue would eventually cover them. OpenAI is signing long-term infrastructure commitments (26GW requires power equivalent to 20 nuclear reactors) and hoping revenue growth will eventually justify them. In both cases, the company is taking on massive future obligations that dwarf current cash flows, betting that growth will solve the structural mismatch.
| Metric | WeWork (2018-2019) | OpenAI (2025) |
|---|---|---|
| Peak Valuation | $47B | $500B |
| Annual Revenue | $1.8B | $20B+ (ARR) |
| Annual Loss | ~$2B | ~$9B |
| Loss-to-Revenue Ratio | 111% | 45% |
| Revenue Multiple | 26x revenue | 25x revenue |
| Future Commitments | ~$47B in lease obligations[1] | $1.4T infrastructure (8 years) |
WeWork's pitch to investors was that profitability was just around the corner—once they reached sufficient scale, unit economics would flip positive. The S-1 filing revealed this was fantasy. Each new location required massive upfront capital, long-term lease commitments, and buildout costs, while revenue per member was actually declining as competition increased and the company offered more aggressive discounts to hit growth targets.
OpenAI's path to profitability is now clearer—but extremely long. According to financial documents reported by Fortune, the company expects to lose money through 2028 and not turn cash-flow positive until 2029 or 2030.[11] OpenAI projects it will reach about $200 billion in annual revenue by 2030, but getting there requires cumulative cash burn of $115 billion through 2029. That's a massive bet on sustained hypergrowth in a rapidly commodifying market.
OpenAI's October 2024 funding round included provisions requiring the company to complete its for-profit restructure within two years or face significant consequences[5]—similar to WeWork's desperation financing rounds when SoftBank's commitment came with increasingly onerous terms. That restructure is now complete, removing one overhang but also eliminating the nonprofit mission that once differentiated OpenAI.
OpenAI's revenue growth looks impressive on the surface: from approximately $5.5 billion in 2024 to over $20 billion in 2025—an 82% year-over-year increase.[3][11] But underneath that headline number, several concerning trends are emerging.
API revenue is OpenAI's largest business segment, with consumer subscriptions (ChatGPT) accounting for 70% of the $13 billion total, suggesting API and enterprise combined represent approximately 30% or roughly $4 billion.[11] But API pricing has collapsed under competitive pressure.
GPT-4's initial pricing in March 2023 was $30 per million input tokens and $60 per million output tokens.[12] By May 2024, GPT-4o launched at $5/$15—an 83% reduction in just 14 months.[13] GPT-4 Turbo pricing dropped from $10/$30 to match GPT-4o's levels.
This wasn't strategic pricing to gain market share; it was defensive pricing to maintain market share against competitors:
For OpenAI, this pricing pressure creates a vicious cycle: lower prices mean lower revenue per API call, which means needing more volume to hit revenue targets, which means more compute costs, which means worse margins, which means more pressure to cut prices further.
Every tech platform business fears commodification: when competitors offer equivalent products at lower prices, eroding margins and differentiation. OpenAI is experiencing this in real-time.
GPT-4's initial advantage was significant—noticeably better than GPT-3.5 on complex reasoning tasks. But by December 2025, the competitive landscape has fundamentally shifted:
The moat has effectively disappeared. GPT-5's August 2025 release delivered measurable improvements but received mixed user reception—many called it "sterile" and "lifeless." GPT-5.1 (November 2025) improved coding performance but competitors have matched or exceeded it within weeks on key benchmarks. OpenAI retains advantages in API reliability and ecosystem maturity, but the technical differentiation that justified premium pricing has evaporated.
This is particularly problematic for OpenAI's valuation, which assumes sustained pricing power and margin expansion. If LLMs commodify into low-margin infrastructure (like cloud storage or compute), the $500 billion valuation becomes indefensible.
Tech defenders often point to switching costs as a protective moat. In theory, once enterprise customers integrate OpenAI's APIs into their infrastructure, the costs of switching to competitors—rewriting code, retraining employees, migrating data—create stickiness that justifies premium pricing.
This argument works for cloud providers. Migrating from AWS to Google Cloud or Azure is genuinely painful: you're moving compute instances, databases, storage buckets, networking configurations, identity management systems, and potentially thousands of internal tools that assume AWS-specific services. The switching costs are measured in millions of dollars and months of engineering time. This creates the vendor lock-in that allows AWS to maintain 30%+ operating margins despite competition.
But LLM switching costs are fundamentally different—and much lower:
OpenAI's enterprise business is also relatively immature. The company doesn't disclose exact numbers, but industry estimates suggest enterprise revenue accounts for 10-20% of total revenue—meaning the vast majority comes from API developers and consumer subscriptions, both of which have minimal switching costs.
Compare this to Salesforce (85% of revenue from multi-year enterprise contracts with high switching costs due to deep CRM integration), or Oracle databases (switching costs so high that customers pay maintenance fees they despise rather than migrate), or even Microsoft Office (institutional inertia and file format lock-in create massive friction).
OpenAI's position is closer to Dropbox in 2015: useful, popular, but facing commodification from Google Drive, Microsoft OneDrive, and Box, all offering similar functionality at similar or lower prices. Dropbox's stock price has languished because switching costs weren't high enough to prevent customer churn when competitors offered equivalent products cheaper.
This is why the API pricing collapse (83% in 14 months) is so concerning—it's not a strategic choice, it's what happens when you lack a moat and competitors force your hand. And it's why OpenAI's burn rate matters: the company is spending billions to acquire customers who could leave for a 20% discount.
ChatGPT has 800 million weekly active users as of late 2025,[11] with over 5 million paying business customers. The consumer subscription business remains strong, but competitive pressure is intensifying. Meanwhile, Google offers Gemini Advanced at $19.99/month, Anthropic offers Claude Pro at $20/month, and pricing competition is eroding per-user revenue across the industry.
The good news: OpenAI's user growth has been exceptional, with weekly active users growing from 500 million in March to 800 million by October. The bad news: converting free users to paid subscribers remains challenging across all AI providers, and competitors are investing heavily in their own consumer products.
In June 2024, Sequoia Capital published a widely-cited analysis titled "AI's $600 Billion Question."[7] The math was straightforward and damning:
The AI industry (led by Nvidia, cloud providers, and AI companies) was spending approximately $150 billion annually on AI infrastructure—data centers, GPUs, networking, power. For this to make economic sense, the industry needs to generate roughly $600 billion in revenue (assuming 25% profit margins). But actual AI revenue was estimated at $100-150 billion, creating a $450-500 billion gap.
As Sequoia partner David Cahn wrote: "The model is simultaneously too hot and too cold. Too hot because the infrastructure investment is premature for the revenue being generated. Too cold because current AI capabilities aren't yet good enough to drive the revenue needed to justify the investment."
OpenAI sits at the center of this paradox. The company has raised over $20 billion total and is now valued at $500 billion following its October 2025 secondary sale. That valuation assumes OpenAI will capture a substantial portion of the eventual AI market. But:
OpenAI is valued at 25x its 2025 annual revenue ($500B / $20B)—down from higher multiples earlier, but still elevated for a company that won't be profitable until 2029-2030. For comparison:
| Company | Revenue Multiple at Peak | Outcome |
|---|---|---|
| WeWork (2019) | 26x | Collapsed 90%, CEO ousted |
| Pets.com (2000) | 73x | Bankrupt within 9 months of IPO |
| Webvan (2000) | 55x | Bankrupt 18 months post-IPO |
| Google (2004 IPO) | 7x | Success |
| Amazon (1997 IPO) | 8x | Success |
| Microsoft (1986 IPO) | 7x | Success |
| OpenAI (2025) | 25x | TBD |
High revenue multiples aren't inherently disqualifying—Amazon and Netflix traded at stratospheric multiples for years while delivering extraordinary returns. But 25x revenue with $1.4 trillion in future infrastructure commitments and no profitability expected until 2029-2030 puts OpenAI in rarefied territory where execution must be nearly flawless.
OpenAI has raised over $20 billion and expects to burn $115 billion cumulatively through 2029 to reach profitability. The company projects $200 billion in annual revenue by 2030, but getting there requires sustained hypergrowth in a market where competitors are catching up. For comparison, Google at its 2004 IPO was valued at ~$23B on ~$3.2B trailing revenue with positive and growing profits.
OpenAI's funding rounds have included unusual terms: mandatory for-profit restructure requirements, valuation conditions tied to revenue growth, and secondary sales restrictions. The October 2025 secondary sale at $500 billion—up from $300 billion in March 2025's SoftBank-led Series F—suggests continued FOMO. SoftBank's heavy involvement is particularly telling—they led WeWork's funding at $47B, then rescued it at $8B six months later. Some investors are already expressing concerns about the valuation amid GPT-5's mixed reception.
Perhaps the most telling sign of unsustainable financing is the circular nature of OpenAI's recent deals. In October 2025, the company announced infrastructure partnerships with Nvidia, AMD, and Broadcom worth billions in combined commitments.[11] But these aren't traditional vendor relationships—they're structured as investments where the hardware companies fund OpenAI, which then uses that funding to buy those same companies' chips.
As one senior OpenAI executive explained, the strategy is to "come up with creative financing strategies" to signal "we're good for the debt."[11] The circularity mirrors dot-com era telecom companies investing in startups that would then buy their equipment—inflating both companies' figures until the music stopped.
The scale is concerning: announced infrastructure partnerships totaling over $1 trillion over the next decade—26+ gigawatts requiring power equivalent to 20 nuclear reactors.[11] While not all of these are binding in the same way WeWork's leases were, they represent long-term strategic bets backed by the assumption that revenue will eventually materialize.
For OpenAI's $500 billion valuation to make sense, the company needs to eventually generate $100+ billion in annual revenue with 30%+ profit margins (implying $30+ billion in annual profit, which at a 15-20x multiple justifies the valuation). OpenAI projects $200 billion revenue by 2030—achievable but requiring:
Each of these individually is challenging. Achieving all simultaneously is possible but requires near-perfect execution—which the product confusion, governance chaos, and talent exodus suggest is unlikely.
Fairness requires examining the strongest arguments against this analysis. Here are the best cases for why OpenAI might actually be the next Google:
LLMs represent a genuine platform shift. ChatGPT reached 100+ million users faster than any previous consumer product. If AI's impact matches the internet, Google's market cap grew from $23B at IPO to $1T+, suggesting OpenAI could be undervalued.
Response: VCs made similar arguments about WeWork ("real estate is the next platform"), Pets.com ("e-commerce is the future"), and Webvan. They were right about trends, wrong about companies. The question isn't whether AI is transformative—it is. The question is whether OpenAI will capture the value, or whether it accrues to infrastructure providers (Nvidia), application developers, or more focused competitors.
Amazon and Netflix lost money for years prioritizing market position over profitability. ChatGPT has become synonymous with AI like "Google" means search.
Response: Amazon's losses funded warehouses and logistics—tangible moats. Netflix's burn funded content libraries with lasting value. OpenAI's burn funds model training with uncertain differentiation and customer acquisition with low retention. Critically: Amazon and Netflix had positive unit economics that improved over time. OpenAI's unit economics are deteriorating (pricing collapse, margin compression), not improving.
Microsoft invested $13 billion and integrated ChatGPT into Windows, Office, and Azure. Even if OpenAI struggles, Microsoft could acquire the company and fold it into Azure AI.
Response: The terms reportedly give Microsoft 75% of profit until recouped and exclusive cloud rights. If Microsoft believes the technology is replicable, it has strong incentives to develop its own models (already doing with Phi-3) rather than pay OpenAI's premium. SoftBank's relationship with WeWork started supportive and became predatory—Microsoft could follow a similar path.
Let's return to the numbers, because they're what matter:
Individually, any of these could be explained away. A high valuation might reflect transformative potential. Losses might be strategic investment. Pricing pressure might be temporary. Product proliferation might be rapid iteration. Executive departures might be natural in a fast-growing company. Corporate restructuring might be necessary for scale.
But collectively, they paint a concerning picture: a company valued at half a trillion dollars that won't be profitable for another 4-5 years, facing intensifying competition from well-funded rivals who are matching or exceeding its capabilities, and committed to $1.4 trillion in infrastructure spending that must be justified by revenue that doesn't yet exist.
This is not the profile of the next Google. Google at IPO in 2004 had clear product focus (search and search advertising), improving unit economics (margins expanding as revenue scaled), technical moats (PageRank and infrastructure advantages), and a path to profitability that was visible and credible. Its $23 billion IPO valuation was approximately 7x trailing revenue on growing profits—aggressive but defensible.
OpenAI's profile has improved significantly from earlier in 2025—unit economics are better, revenue is growing faster than losses, and the for-profit restructure removes governance uncertainty. But the $500 billion valuation requires extraordinary assumptions: 10x revenue growth, sustained pricing power in a commodifying market, and perfect execution across $1.4 trillion in infrastructure commitments. The margin for error is razor-thin.
Predictions should be falsifiable. Here's what evidence would demonstrate this analysis is incorrect:
Conversely, what would confirm this analysis:
OpenAI's trajectory matters beyond the company itself. The firm has become the public face of AI progress, the benchmark against which other companies are measured, and the template for "AI startup" strategies. If OpenAI succeeds despite warning signs, it validates "growth at any cost" approaches in AI. If it stumbles, it could trigger broader AI investment retrenchment.
The $600 billion question that Sequoia posed in 2024 remains partially answered: the AI industry is investing far more capital than it's generating in revenue, but revenue is growing faster than expected. Some companies will successfully bridge that gap. Others will become cautionary tales. The evidence on OpenAI is genuinely mixed—improving unit economics but massive future commitments, strong revenue growth but intensifying competition.
WeWork convinced investors that it was a "technology company" transforming real estate. It was actually a real estate company with unsustainable unit economics and a charismatic founder who believed his own narrative. OpenAI is building something more substantive—real AI capabilities that are genuinely useful. But the $500 billion valuation requires it to be not just good, but dominant, in a market where dominance is proving elusive.
The difference between "very good AI company" and "dominant platform that justifies $500B valuation" is worth hundreds of billions of dollars. The data suggests the former is more likely than the latter.
Only time will tell whether this analysis ages like "Google will never make money" or "Pets.com is the future of retail." But in investing and business analysis, you make decisions based on available evidence, not unknown futures. And the evidence, as of December 2025, shows a company that's executing well operationally but facing structural headwinds: commodification, competition, and commitments that require near-perfect execution to justify.
[1] WeWork S-1 Filing (August 2019). SEC.gov (Note: WeWork disclosed $47.2 billion in future minimum lease payments over the life of all leases, per S-1 p. F-30)
[2] "OpenAI is the world's most valuable private company after private stock sale" TechCrunch (October 2025). Secondary sale valued company at $500B. Link
[3] Woo, A. & Nix, N. "OpenAI Projects $11.6 Billion Revenue Next Year" The Information (September 2024). Link
[4] Lunden, I. "OpenAI reportedly burning through $8.5B per year" TechCrunch (September 2024). Link
[5] Woo, A. "OpenAI Funding Deal Pressures Company to Restructure" The Information (October 2024). Link
[6] Metz, C. & Griffith, E. "How Microsoft's Satya Nadella Became Tech's Steely-Eyed A.I. Gambler" New York Times (June 2023). Link
[7] Cahn, D. et al. "AI's $600B Question" Sequoia Capital (June 2024). Link
[8] "OpenAI's board attempted to fire Sam Altman" The Verge, New York Times, and multiple outlets (November 2023). Coverage of the November 2023 board crisis that resulted in Altman's brief firing and reinstatement.
[9] Leike, J. "Why I'm leaving OpenAI" Personal blog post (May 2024). Link
[10] Patel, D. "GPT-4 Architecture, Datasets, Costs and More Leaked" SemiAnalysis (July 2023). Industry analysis of compute costs.
[11] "OpenAI says it plans to report stunning annual losses through 2028" Fortune (November 2025). Details on $115B cumulative burn, $200B 2030 revenue target, 2029-2030 profitability. Link; Also: "Sam Altman says OpenAI will top $20 billion in annualized revenue this year" CNBC (November 2025). Link
[12] "OpenAI GPT-4 API Pricing" Nebuly (2023). Historical pricing documentation for GPT-4 launch. Link
[13] Ng, A. "After a recent price reduction by OpenAI, GPT-4o tokens now cost $4 per million tokens" X/Twitter (August 2024). Analysis of GPT-4o pricing trends. Link
[14] "Introducing Claude Opus 4.5" Anthropic (November 2025). First model to exceed 80% on SWE-bench Verified (80.9%). Link
[15] "Gemini 3: Introducing the latest Gemini AI model from Google" Google Blog (November 2025). 95% AIME, 1501 Elo on LMArena. Link
[16] "Grok 3 Beta — The Age of Reasoning Agents" xAI (February 2025); Grok 4 released July 2025. Link
[17] "All You Need to Know about Inference Cost" Primitiva (2024). Analysis of GPT-4 inference costs and margin compression. Link
Additional data sources: OpenAI official blog and announcements, Microsoft earnings calls (Q3-Q4 2024), Anthropic pricing documentation, Google Cloud AI pricing, Meta AI Research announcements, Bloomberg Technology coverage, Financial Times tech reporting, Reuters business news, TechCrunch funding coverage.
Note on estimates: OpenAI is a private company and does not publicly disclose financial statements. Revenue figures, burn rates, and operational costs cited in this article are based on reporting from The Information, Financial Times, New York Times, and other business publications citing company documents, investor presentations, and sources familiar with the company's finances. Where figures are estimates or projections, this is explicitly noted in the text.