Profits from AI models will remain modest or nonexistent in 2025.
Resolved true The record shows it happened or was so.
Prediction: Statements about events outside the person's control, checked once they fell due: did they come true?
The second judge would not resolve this claim; the verdict shown is the first judge's.
What would count as true or false
A prediction for 2025, judged at year end. The claim: profits from AI models (the businesses of selling or serving foundation models, such as OpenAI and Anthropic, or model-provider segments) remain modest or nonexistent. True: the major model developers reported or credibly reported operating losses or negligible profits for 2025, and no major firm reported large profits specifically from AI models. Mostly true: the leading pure-play model makers were unprofitable, but some firm earned meaningful but modest model-specific profit. Mixed: the picture is split, with some major model providers substantially profitable from models and others not. Mostly false: several major providers reported substantial model-specific profits, though the industry overall was still loss-making. False: AI model businesses broadly reported large profits in 2025. Profits from chips (Nvidia) or cloud infrastructure count only if attributed to AI models themselves; the claim concerns models, not hardware.
Set before the research began.
Evidence (4)
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E1 Leaked financial docs show OpenAI is losing billions of dollars a year Ars Technica · 16 June 2026
“All told, OpenAI’s day-to-day “loss from operations” increased from $8.78 billion in 2024 to $20.92 billion in 2025, a concerning direction for a company that is telling investors it hopes to be profitable by 2030.”
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E2 Anthropic's Revenue Jumped 12-Fold in 2025, but So Did Its Losses Yahoo Finance · 10 October 2026
“Anthropic's revenue surged nearly 12-fold to about $4.6 billion in 2025. But the artificial intelligence (AI) company also posted an operating loss of $8.06 billion and a net loss of roughly $42 billion.”
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E3 Anthropic API Gross Margin Estimated Above 80% as Inference Margin Reaches Mid-60s | TokenPost TokenPost · 11 October 2026
“Anthropic’s blended inference gross margin was estimated at 38% in 2025 and negative 94% in 2024 before reaching the mid-60% range. The API business was estimated to have a gross margin above 80%.”
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E4 Deep Dive: Economics of the AI Build-Out | Contrary Research Contrary Research · 28 August 2025
“Under its 2025 operating model, OpenAI is not expected to turn a profit until 2029, though Microsoft is likely to profit off its arrangement with the company in the meantime.”
Reasoning
E1 reports, based on leaked financial documents, that OpenAI's loss from operations rose to $20.92 billion in 2025. E2 reports that Anthropic posted a 2025 operating loss of $8.06 billion and a net loss of about $42 billion. E3 gives analysts' estimates of Anthropic's gross margins, not reported profits. None of the evidence shows a major firm reporting large profits from AI models. OpenAI and Anthropic were therefore loss-making in 2025, and none of the evidence shows large profits from AI models, which meets the 'true' criterion.
Cite this claim
Gary Marcus, “Profits from AI models will remain modest or nonexistent in 2025.” (said 1 January 2025). Verdict: resolved true. Reliability API, claim c_4c7558b0b806, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_4c7558b0b806
This claim is in dataset release v1.1.2, with the same verdict.
Reply to this claim
More prediction claims by Gary Marcus
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Mostly true
“GPT-5 may be more satisfying but is likely even more expensive to train and operate.”
GPT-5 is likely to be even more expensive to train and operate than GPT-4.
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Mostly true
“Some of its advice will be head-scratchingly bad.”
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Mostly true
“GPT-5 will surely be better, a lot better than GPT-4.”
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Resolved true
“GPT-5 really is about to drop, no foolin’.”
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Resolved true
“It will still make a significant number of shake-your-head stupid errors, in ways that are hard to fully predict.”
GPT-5 will still make a significant number of obvious errors that are hard to predict.
Verdicts are made automatically; how verdicts are made.