Are DeepSeek Moments Now the New Normal?

(Bloomberg Opinion) — A little-known Chinese AI company recently released an open-source reasoning model that challenged Western dominance and was developed at a fraction of the cost. And no, it’s not DeepSeek.

When Moonshot AI, a Beijing-based lab, launched Kimi K2 Thinking earlier this month, it went viral in tech circles. A partner at prominent Silicon Valley venture capitalist firm Menlo Ventures called it a “turning point in AI.” The model now ranks second on Artificial Analysis’ intelligence index, behind only OpenAI’s GPT 5.1 — and ahead of the latest offerings from Alibaba Group Holding Ltd. and DeepSeek, as well as US titans like X.AI and Anthropic. Looking at another benchmark measuring more complex, problem-solving “agentic” tasks, it even outperformed OpenAI.

This time around, however, markets barely batted an eye. As Bloomberg Economics Michael Deng noted, “The contrast with January’s DeepSeek panic, which wiped almost $600 billion off Nvidia in a single day, reveals how quickly investors have internalized that Chinese labs can match frontier capabilities at lower cost.” Have we already reached the point where matching the best in AI on a shoestring budget is no longer a shock? 

It’s true that it has become increasingly hard to judge model performance based on benchmarks alone. Moonshot’s latest release joins an especially crowded domestic market. Launches and updates from Alibaba, Zhipu and MiniMax have come at a frenetic pace this year. The competition fuels innovation, even if it makes it difficult for one firm to stand out and gain a viable competitive edge — and the path to monetization seems elusive.

At the same time, the cost gap with the West is striking. Citing a source familiar with the matter, CNBC reported that Kimi K2 Thinking cost $4.6 million to train. A member of the Moonshot team later said in a Reddit Ask Me Anything session that this wasn’t “an official number.” But the representative did cheekily nod to the major spending differences in response to a question on when the next-generation model will be released, saying it will come “before Sam’s trillion-dollar data center is built.”

Silicon Valley has taken notice. I’ve written before that more US startups seem to be quietly building on Chinese AI models, including Moonshot’s. (Even before the latest update, venture capitalist Chamath Palihapitaya said that a company he worked with has switched over to Kimi K2.) It’s a trend that’s harder to quantify because few firms want to get caught up in the geopolitical crosshairs of US-China AI competition.

Fears of Communist Party censorship get a lot of attention, but testers have pointed out that this becomes less of an issue when you’re downloading and deploying the models locally. As a Moonshot representative put it in the Reddit AMA, “open-sourcing the model is hopefully a good step” to ease concerns about Chinese origins. 

Part of the reason for their popularity is China’s low-cost, open-source approach. And while this allows developers to download and build on top of these models, doing so at scale will still require some amount of AI infrastructure. It means that for Nvidia Corp. and other chipmakers the threat isn’t as existential as some imagined after the DeepSeek-triggered selloff. This may partially explain some of the muted market reaction to the torrent of highly capable models from China.

But something still doesn’t quite add up looking at the valuation gulf. Despite its latest model’s performance coming very close to OpenAI’s, Moonshot’s most-recent valuation of some $3.3 billion is a rounding error next to the US titan’s $500 billion. Even the nine-month-old AI startup from former OpenAI executive Mira Murati is reportedly in funding talks pegging it at $50 billion. It makes increasing fears of an AI bubble seem valid.

Jefferies analysts noted last week that Chinese hyperscalers’ combined capital expenditures between 2023 and 2025 were 82% lower than US peers. But the performance gap between their two best models, based on various analysis, is now razor-thin. Even with inferior quality chips and high competition, the significantly lower spending points to a clearer path to return on investment emerging in China. 

After the Kimi K2 update release, the co-founder of Hugging Face, Thomas Wolf, flagged its advancements in a social media post and asked: “Is this another DeepSeek moment?” He quickly followed that up with: “Should we expect this every couple [of] months now?”

The answer increasingly looks to be a resounding yes. We’ve normalized the idea that Chinese AI labs can seemingly come out of nowhere to close the performance gap with Silicon Valley despite chip constraints and significantly smaller budgets. For US tech giants, the question now isn’t whether they can stay ahead — it’s whether their massive spending outlays will actually translate into better commercial returns.

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This column reflects the personal views of the author and does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

Catherine Thorbecke is a Bloomberg Opinion columnist covering Asia tech. Previously she was a tech reporter at CNN and ABC News.

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