DeepSeek updated its R1 AI model a few days ago. It performs better and it’s still cheaper than most other top models.
Did you miss it? I missed it. Or I saw the news briefly and then forgot about it. Most of the tech industry and investors greeted the launch with a giant shrug.
This is a pretty stark contrast to early 2025 when DeepSeek’s R1 model freaked everyone out. Tech stocks plunged and the generative AI spending boom was seriously questioned.
This time, DeepSeek’s rollout “came and went without a blip,” Ross Sandler, a top tech analyst at Barclays, wrote in a note to investors.
“The stock market couldn’t care less,” he added. “This tells us that the investment community’s level of understanding on the AI trade has greatly improved in just five short months.”
An unscientific DeepSeek poll
I polled my colleagues on Business Insider’s tech team on Friday, just to see if I’d been spending too much time watching Elon Musk and Donald Trump argue on social media (rather than doing my real job).
Here are some of their responses:
- One editor said they didn’t notice DeepSeek’s update, but now they feel guilty for not spotting it. (Solid thinking. Only the paranoid survive in journalism).
- Another colleague said they knew about it from their quick headline scans, but didn’t read too much into it.
- A tech reporter saw a Reddit thread about it, scanned it, and didn’t think about it again.
- Another reporter said they missed it entirely.
- Another editor: “hadn’t noticed tbh!”
So, it barely registered. And these folks are glued to tech news every second of the day.
Why does no one really care now?
DeepSeek’s latest R1 model is probably the third best in the world right now, so why isn’t it making waves like before?
Sandler, the Barclays analyst, noted that DeepSeek’s latest offering is not quite as cheap as it used to be, relatively speaking. It costs just under $1 per million tokens, which was roughly 27 times cheaper than OpenAI’s o1 model earlier this year.
Now, DeepSeek’s R1 is “only” about 17 times cheaper than the top model, according to Barclays research and data from Artificial Analysis’ AI Intelligence Index.
This illustrates a broader and more important point. Something I’ve been telling you about since last year: Most top AI models are roughly similar in performance because they’ve mostly been training on the same data from the internet.
This makes it hard to stand out from the crowd, based just on performance. When you leap ahead, your inventions and gains are incorporated quickly into everyone else’s offerings.
Price is important, yes. But distribution is becoming key. If your employer has an enterprise ChatGPT account, for instance, you’re highly likely to use OpenAI models at work. It’s just easier. If you have an Android smartphone, you’ll probably be talking to Google’s Gemini chatbot and getting responses from the search giant’s AI models.
DeepSeek doesn’t have this type of broad distribution yet, at least in the Western world.
Was the AI infrastructure freakout misplaced?
Then, there’s the realization that “reasoning” models, such as DeepSeek’s R1 and OpenAI’s o3, require a massive amount of computing power to run. This is due to their ability to break requests down into multiple “thinking” steps. Each step is a new kind of prompt that is turned into a huge number of new tokens that need to be processed.
The DeepSeek freakout in January happened mostly because the tech industry worried that the Chinese lab had developed more efficient models that didn’t need as much computing infrastructure.
In fact, this Chinese lab may have instead helped popularize these new types of reasoning models, which might require even more GPUs and other computing gear to run.
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