Deepseek-R1: Implications
The following prompt was supplied to a distilled instance of Deepseek-R1:32B, running on my desktop machine with a single GPU (an NVIDIA RTX A4500 with 20GB of VRAM). Its response (including the <think> prelude) follows. –jim
Write an op-ed piece for my blog based on a set of bullet points. Adopt a sober tone that is appropriate for a fellow at the Berkman Klein Center who is skeptical about AI hype, and worried about the social and geopolitical implications of AI evolution, but still impressed by Deepseek’s technical achievement. Write it as about 250 words, just paragraphs, no special section headings, but make it flow smoothly, as if I had written it myself.
The hot-take bullets on which to base the blog are:
Thanks, export controls, you created a gain of function laboratory for open source AI
The real story here is not that the deepseek platform can answer queries for cheap, it’s that they MIT-licensed the whole weight set and you can run it yourself
Even the distilled versions that run on my local GPU with 20GB VRAM (eg., deepseek-r1:32b) are clearly useful
Every ecosystem needs at least one open-licensed entrant to keep things interesting, and I didn’t think we were going to find one for “big LLM” … here it is.
Nvidia should be just fine because I would really like to have the 384GB VRAM it would take to run the whole open model privately
The hyperscalers who built AI platforms will have to work harder to differentiate from the open source competitor now in ways that matter to paying customers, rather than randos who pay nothing
Short-duration reasoning services might turn out to be like telecom circuits in the 1970s: a commodity, used in great profusion and diversity with zero vendor allegiance, plummeting costs
Building a decentralization/brokerage layer to abstract away and hide the vendors will be complex and painful. ironically, this time, the users have AI on our side to help research and negotiate against their makers
<think> Okay, I need to write an op-ed for my blog based on the provided bullet points. The tone should be sober, suitable for someone at Berkman Klein who's skeptical about AI hype but acknowledges Deepseek's technical achievement.
First, I’ll start by thanking export controls for inadvertently creating an open-source AI lab. This sets a critical yet fair tone. Then, I should highlight that the real story isn’t just about cost efficiency but the MIT-licensed weights, emphasizing accessibility and community impact.
Next, mentioning how even smaller versions run on local GPUs shows practicality and widespread usability. It’s important to note that every ecosystem needs an open entrant, and Deepseek fills that gap for large LLMs, which adds value beyond just technical prowess.
I should address Nvidia positively since they can benefit from those needing high VRAM setups, showing a balanced view. Hyperscalers like AWS or Azure will face tougher competition, pushing them to innovate more meaningfully than before. This could lead AI services to become commoditized, similar to telecom circuits in the 70s.
Lastly, discussing the complexity of creating a decentralized layer with AI’s help ties it back to user empowerment against vendors. I need to ensure each point flows smoothly into the next, maintaining coherence without section headings.
I’ll structure it paragraph by paragraph, making sure each bullet is addressed clearly and cohesively. Keeping the language professional yet accessible will be key to aligning with the intended audience at Berkman Klein. </think>
The recent developments in AI have been nothing short of remarkable, but they also raise significant concerns about the direction we’re heading. Export controls, meant to regulate the spread of advanced technologies, have inadvertently created an environment where open-source AI can thrive—effectively turning it into a “gain-of-function” laboratory for innovation. This unintended consequence is both fascinating and concerning, as it underscores how regulation can sometimes pave the way for new capabilities that were not anticipated.
The story of DeepSeek isn’t just about its ability to deliver cheap query responses; it’s fundamentally about accessibility. By MIT-licensing their entire weight set, they’ve opened up a world of possibilities for researchers, developers, and even hobbyists. This move is akin to democratizing access to advanced AI tools, allowing anyone with the right expertise—and even some without—to experiment and build upon these models. The fact that distilled versions can run on local GPUs with just 20GB of VRAM is a testament to their practicality. It’s not just about the raw power of these models but how they can be adapted and utilized in diverse settings.
Every ecosystem needs at least one open-licensed entrant to keep things interesting, and DeepSeek fills that gap for large language models (LLMs) in a way that feels almost inevitable. While this doesn’t diminish the technical achievements of companies like Nvidia or hyperscalers building AI platforms, it does shift the landscape. Nvidia will likely do just fine because there’s still a market for high-end infrastructure—like those requiring 384GB of VRAM to run models privately—but the hyperscalers will have to work harder to differentiate themselves. They can no longer rely solely on proprietary advantages; they’ll need to offer services that genuinely matter to paying customers, not just attract casual users who aren’t inclined to pay.
Looking ahead, short-duration reasoning services might follow a trajectory similar to telecom circuits in the 1970s—a commodity used widely and cheaply, with vendors vying for market share in a rapidly evolving landscape. This could lead to plummeting costs and increased diversity in how these services are deployed. But this transition won’t be smooth. Building a decentralized or brokerage layer that abstracts away vendor-specific details will be complex and painful. The irony is that users now have AI on their side, potentially making it easier to research, negotiate, and even circumvent the very systems designed to control access to these technologies.
In summary, while there’s plenty to be skeptical about in the current AI hype cycle, moments like this remind us of the transformative potential—and risks—of open-source innovation. The balance between regulation, competition, and accessibility will be key as we navigate this new frontier.