 | Tuesday, July 28, 2026 | | Open or closed, nobody in AI is turning a profit right now — they're just choosing how to lose money | .jpg) | Raul Ariano/Bloomberg via Getty Images | U.S. chip stocks just took a tumble, and a Chinese startup most people had never heard of is the reason why.
Moonshot AI put out an open-weight model called Kimi K3 earlier this month that seemed to match what Anthropic and OpenAI charge a premium for. The semiconductor index slid into a bear market in response. If this sounds familiar, it’s because this happened in 2025 with DeepSeek, when a different Chinese lab showed up with a cheaper model that worked and hit the chip stocks the same way. By last week, some analysts were walking the DeepSeek moment
2.0 back, pointing out that a model Kimi's size still needs a lot of compute to run properly, making it less far ahead than it appeared at first blush. But the instinct to reach for that comparison, even a slightly premature one, says something about how fragile the AI story has gotten. | | Sponsored |  | Your next hire might not need to be a hire. | Every growing company wants faster customer support, better sales coverage, and more responsive service—but adding headcount isn't always the answer.
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| Closed in the West, Open in the EastRight now there are basically two models on offer.
American labs and tech companies building frontier models are mostly staying closed, and they are doing it out of both business logic and government pressure.
But Mark Zuckerberg, talking
about democratizing AI, first spent around $100 billion trying to build Llama into a genuinely open alternative to what Anthropic and OpenAI were charging for — intelligence anyone could download and run themselves, no subscription required.
China's read on this moment is the opposite. Xi Jinping recently told an audience that his country should treat "open source, openness, collaboration and sharing" as a matter of national strategy in AI, not a side preference, and the labs have followed that lead even when
it costs them.
None of these companies are making money doing this yet, but the payoff
was never supposed to be the balance sheet. This is a bet on influence and reach that plays out over years, not quarters or IPO dates.
It's already working by that measure. Roughly a third of all global AI usage now runs on Chinese open source models, and in markets like Nigeria, Malaysia and Brazil, developers can build on them for more than 90 percent less than it would cost to build on OpenAI.
For Washington, that adoption curve looks less like a business problem and more like the next space race, one where influence and infrastructure matter as much as who has the best model. The Trump administration has reportedly
considered banning Chinese models outright to slow it down.
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| | A race to the bottomNone of this is cheap to run, open or closed.
That's a great deal for anyone building on top of these models. It's a much tougher one for the labs building them, most of which are still losing money hand over fist.
This is starting to look like the kind of race to the bottom that plays out in any commodity business, oil, airlines, chips. Prices fall until only the lowest cost producer survives, and everyone else prays
for a shakeout. There is no moat in AI right now, after all.
Neither side has actually solved for that. Charging a premium only works if
there's something durable behind the price, and open source is proving there isn't. Giving models away only works if the costs eventually come down enough to stop bleeding money, and they haven't, not yet, not close. Both bets assume the other problem gets solved on someone else's timeline.
That's the part the flag waving skips over. No moat and no cheap intelligence, at the same time, is not a business model. It's just where this industry happens to be
standing right now.
—Jackie Snow, Contributing Editor | | Your ultimate guide to the future of tech. | Semafor Technology decodes the innovations, trends, and forces reshaping the global tech landscape. Each briefing delivers clear insights on AI, machine learning, startups, and policies driving change.
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