Kimi K3 rocks the AI industry as Moonshot AI undercuts closed-source American competitors on price
After OpenAI announced that it was effectively abandoning its idea of owning first-party data centers earlier this year, the lease contracts it held with Neoclouds became more important than ever. Deals like the enormous $300 billion compute commitment with Oracle became paramount for the very existence of OpenAI's service as a company.
But if there were questions about how OpenAI would afford such a venture at the time, they're even more pronounced now. OpenAI is already losing money on its subscription-based accounts and missed key revenue targets earlier this year . And that's after losing 10s of billions in 2025, despite revenue rising consistently throughout the year.
OpenAI has committed to some $600 billion in compute spend by 2030. Even if revenue is rising, it might not be rising anywhere near quickly enough to cover these kinds of bills, and cutting the price of the most popular, affordable models suggests margins will either shrink dramatically or disappear altogether.
This may be why there's also a lot of talk of Nvidia backstopping OpenAI with a $250 billion investment . OpenAI is far from alone here, either. Google spent around nine times its cloud revenue on AI infrastructure over the past year, while Anthropic has only been able to post profits on annualized revenue recently because of a limited cut-price deal with xAI to rent its Colossus data center.
AI is not suddenly cheaper to run or cheaper to build for, and yet companies are slashing prices and making faster, more capable models available for less. On the surface, the numbers just don't add up.
The AI industry often cites the Jevons Paradox when it comes to accelerating AI adoption and mass-market use. Where in Jevons' time making more efficient coal-powered engines resulted in more coal use, rather than less of it, AI developers claim that as AI use becomes more efficient, greater uses for it will be found, leading to greater overall use.
That may be the future that the token cost-cutting may be hoping to rush us towards. If tokens are cheap, people will use more of them overall, leading to higher earnings. Throw in next-generation AI accelerators becoming more prevalent within AI data centers towards the end of the year, and we could have 10x more tokens per watt, making slimmer margins more profitable by volume.
Then there's Vera Rubin to look forward to, which Nvidia claims will deliver another 10x increase in token performance efficiency. It is certainly possible that the advantages of Blackwell and Vera Rubin platforms will make AI a more potentially profitable industry for inference servers. But even then, it's hard to imagine the big companies covering anything close to their enormous investments with direct AI earnings. Especially as increasing competition drives down token pricing.
Jon Martindale is a contributing writer for Tom's Hardware. For the past 20 years, he's been writing about PC components, emerging technologies, and the latest software advances. His deep and broad journalistic experience gives him unique insights into the most exciting technology trends of today and tomorrow. ","collapsible":{"enabled":true,"maxHeight":250,"readMoreText":"Read more","readLessText":"Read less"}}), "https://slice.vanilla.futurecdn.net/13-4-25/js/authorBio.js"); } else { console.error('%c FTE ','background: #9306F9; color: #ffffff','no lazy slice hydration function available'); } Jon Martindale Freelance Writer Jon Martindale is a contributing writer for Tom's Hardware. For the past 20 years, he's been writing about PC components, emerging technologies, and the latest software advances. His deep and broad journalistic experience gives him unique insights into the most exciting technology trends of today and tomorrow.
Key considerations
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Reference reading
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- https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-companies-are-now-racing-to-the-bottom-crashing-token-prices-and-competitive-models-push-companies-to-cut-costs#main
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