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The release of the DeepSeek R1 reasoning model has caused shockwaves across the tech industry, with the most obvious sign being the sudden sell-off of major AI stocks. The advantage of well-funded AI labs such as OpenAI and Anthropic no longer seems very solid, as DeepSeek has reportedly been able to develop their o1 competitor at a fraction of the cost.
While some AI labs are currently in crisis mode, as far as the enterprise sector is concerned, it’s mostly good news.
Cheaper applications, more applications
As we had said here before, one of the trends worth watching in 2025 is the continued drop in the cost of using AI models. Enterprises should experiment and build prototypes with the latest AI models regardless of the price, knowing that the continued price reduction will enable them to eventually deploy their applications at scale.
That trendline just saw a huge step change. OpenAI o1 costs $60 per million output tokens versus $2.19 per million for DeepSeek R1. And, if you’re concerned about sending your data to Chinese servers, you can access R1 on U.S.-based providers such as Together.ai and Fireworks AI, where it is priced at $8 and $9 per million tokens, respectively — still a huge bargain in comparison to o1.
To be fair, o1 still has the edge over R1, but not so much as to justify such a huge price difference. Moreover, the capabilities of R1 will be sufficient for most enterprise applications. And, we can expect more advanced and capable models to be released in the coming months.
We can also expect second-order effects on the overall AI market. For instance, OpenAI CEO Sam Altman announced that free ChatGPT users will soon have access to o3-mini. Although he did not explicitly mention R1 as the reason, the fact that the announcement was made shortly after R1 was released is telling.
big news: the free tier of chatgpt is going to get o3-mini!
(and the plus tier will get tons of o3-mini usage)
— Sam Altman (@sama) January 23, 2025
More innovation
R1 still leaves a lot of questions unanswered — for example, there are multiple reports that DeepSeek trained the model on outputs from OpenAI large language models (LLMs). But if its paper and technical report are correct, DeepSeek was able to create a model that nearly matches the state-of-the-art while slashing costs and removing some of the technical steps that require a lot of manual labor.
If others can reproduce DeepSeek’s results, it can be good news for AI labs and companies that were sidelined by the financial barriers to innovation in the field. Enterprises can expect faster innovation and more AI products to power their applications.
Today's "DeepSeek selloff" in the stock market — attributed to DeepSeek V3/R1 disrupting the tech ecosystem — is another sign that the application layer is a great place to be. The foundation model layer being hyper-competitive is great for people building applications.
— Andrew Ng (@AndrewYNg) January 27, 2025
What will happen to the billions of dollars that big tech companies have spent on acquiring hardware accelerators? We still haven’t reached the ceiling of what is possible with AI, so leading tech companies will be able to do more with their resources. More affordable AI will, in fact, increase demand in the medium to long term.
Jevons paradox strikes again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of. https://t.co/omEcOPhdIz
— Satya Nadella (@satyanadella) January 27, 2025
But more importantly, R1 is proof that not everything is tied to bigger compute clusters and datasets. With the right engineering chops and good talent, you will be able to push the limits of what is possible.
Open source for the win
To be clear, R1 is not fully open source, as DeepSeek has only released the weights, but not the code or full details of the training data. Nonetheless, it is a big win for the open source community. Since the release of DeepSeek R1, more than 500 derivatives have been published on Hugging Face, and the model has been downloaded millions of times.
It's been released just a few days ago and already more than 500 derivative models of @deepseek_ai have been created all over the world on @huggingface with 2.5 million downloads (5x the original weights).
The power of decentralized open-source AI!
— clem 🤗 (@ClementDelangue) January 27, 2025
It will also give enterprises more flexibility over where to run their models. Aside from the full 671-billion-parameter model, there are distilled versions of R1, ranging from 1.5 billion to 70 billion parameters, enabling companies to run the model on a variety of hardware. Moreover, unlike o1, R1 reveals its full thought chain, giving developers a better understanding of the model’s behavior and the ability to steer it in the desired direction.
With open source catching up to closed models, we can hope for a renewal of the commitment to share knowledge and research so that everyone can benefit from advances in AI.
To people who think
"China is surpassing the US in AI"
the correct thought is
"Open source models are surpassing closed ones"
See ⬇️⬇️⬇️— Yann LeCun (@ylecun) January 25, 2025
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