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Why Gary Marcus Says OpenAI Could Be AI’s WeWork—and What It Could Mean for Nvidia
Marcus’s warning is a downside scenario: if AI companies struggle to turn heavy spending into durable profits, investor confidence and compute demand could weaken. A potential link to Nvidia is indirect: customers facing weaker returns or tighter financing could scale back compute purchases, even if Nvidia’s chips r...
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Key takeaways
- Marcus’s warning is a downside scenario: if AI companies struggle to turn heavy spending into durable profits, investor confidence and compute demand could weaken.
- A potential link to Nvidia is indirect: customers facing weaker returns or tighter financing could scale back compute purchases, even if Nvidia’s chips remain strong.
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openai.comCreate a landscape editorial hero image for this Studio Global article: Why did AI critic and NYU professor emeritus Gary Marcus warn investors on CNBC’s “Fast Money” on November 6 that OpenAI could become the “W. Article summary: Marcus’s warning was about the financial sustainability of the AI boom: impressive technology does not necessarily produce profitable customers or justify enormous valuations and infrastructure spending. His “WeWork of A. Topic tags: general, general web, user generated. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts with fa
Gary Marcus’s “WeWork of AI” comparison is a warning about the business and financing assumptions behind the AI boom—not a prediction that OpenAI is certain to collapse. The concern is that enthusiasm for AI could outpace evidence that companies can turn enormous investment into lasting profits. If that confidence fades, the effects could reach chipmakers such as Nvidia through customers’ spending decisions. 6
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Why Marcus invoked WeWork
On CNBC’s Fast Money, Marcus pointed to a contrast: WeWork never completed its IPO, while OpenAI has repeatedly pushed its own listing back. 6
9 Reporting places OpenAI’s expected IPO in 2027, though timing remains uncertain.
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The comparison is a caution about what a delayed public listing can signal to investors: the business may face questions about its economics, its ability to raise capital, or the price public-market investors would accept. It is not evidence that OpenAI shares WeWork’s fate. An IPO delay alone does not establish why a company postponed a listing or whether it will eventually succeed.
What the reported funding figures do—and don’t—show
Reports say OpenAI has received $11 billion in a funding round at a $1.25 trillion pre-money valuation. The same reporting says the company initially sought a larger amount at a $1.4 trillion valuation, but notes that the reported $30 billion target came from media coverage, not a figure confirmed by OpenAI’s finance chief. 5
That distinction matters. The $11 billion figure is not, by itself, proof that a full funding round failed or that OpenAI could not raise more. It does add to scrutiny of the gap between reported fundraising ambitions and the money reported so far. And a valuation attached to private financing is not the same as a price established by public-market trading.
OpenAI President Greg Brockman was reported as saying the company could be ready for an IPO in 2027. 1 That reported outlook doesn’t settle the separate questions investors would ask about profitability, cash flow, or the timing of a listing. The available reporting also doesn’t establish that AI safety was the reason for the postponement, so that explanation should not be treated as confirmed here.
Why Anthropic’s IPO plans matter—but aren’t a verdict
Anthropic’s proposed IPO has also drawn attention to how investors value AI companies. Reporting describes discussion of a valuation around $1.5 trillion after earlier expectations were higher, and notes investor questions about the cost of AI expansion. These are discussions about a planned offering, not a completed public-market valuation or a demonstrated loss in value. 1
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A listing could offer investors another point of comparison for AI companies’ costs, growth prospects and valuations. But until an offering is priced and trades publicly, speculation about its valuation cannot settle whether the economics of the wider AI sector work.
How a confidence shock could reach Nvidia
The connection Marcus raises is indirect. If AI companies or their customers have trouble financing expansion or earning satisfactory returns, they could slow purchases of computing capacity. That could weaken expectations for future chip demand and weigh on Nvidia’s growth outlook, even if nothing about its chips or management had changed. Marcus’s reported comments link a discounted OpenAI sale to Microsoft with potential damage to the wider AI trade and Nvidia. 3
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A discounted sale is a hypothetical scenario, not an established outcome. Its broader significance would depend on how investors interpreted it: as a company-specific setback, or as evidence that AI valuations and spending plans across the sector had been too optimistic.
Financing conditions could amplify that risk. If compute providers face higher borrowing costs or difficulty raising capital, they may have less room to expand and could pull back on infrastructure spending. The available reporting identifies rising financing costs and monetization concerns as part of the broader risk Marcus discussed, but it does not establish that these pressures have already caused Nvidia customers to reduce orders. 3
“The end of an era” may mean tougher investor tests
The phrase need not mean that AI adoption is ending. It can describe a shift in what investors reward: less emphasis on projected growth and more demand for evidence of durable profits and measurable productivity benefits.
The Bank of England has noted uncertainty about both the scale and timing of AI-related productivity gains and companies’ ability to monetize them. That supports the broader point that adoption alone does not guarantee financial returns. It does not substantiate the specific claim of a Bank study of roughly 10,000 decision-makers finding limited gains; the provided Bank material describes uncertainty rather than that finding. 29
The bottom line
Marcus’s warning is best read as a financing-and-confidence scenario. Delayed listings, disputed fundraising targets and scrutiny of AI spending can increase investor questions, but none proves OpenAI will fail. For Nvidia, the risk would come if those questions lead customers to slow compute spending—not simply because one AI company faces a setback. 3
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Marcus’s warning is a downside scenario: if AI companies struggle to turn heavy spending into durable profits, investor confidence and compute demand could weaken.
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Marcus’s warning is a downside scenario: if AI companies struggle to turn heavy spending into durable profits, investor confidence and compute demand could weaken. A potential link to Nvidia is indirect: customers facing weaker returns or tighter financing could scale back compute purchases, even if Nvidia’s chips remain strong.