Yes, DeepSeek IS Actually a Massive Deal for AI

Today on the AI Daily Brief why Deepseek Changes Everything in AI the AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show Notes. Hello friends, Quick note before we dive in. I had originally planned to do a normal episode divided between the headlines and the news, but everything today is so much about this big R1 deep seek news that the episode ended up being much longer than normal. I decided to just focus on that. We will be back with our normal format tomorrow presumably, but for now let's just dig into what everyone is talking about, which is Deep Seek and just how big a deal it actually is. Welcome back to the AI Daily Brief. If you have spent any time online over the last few days, and I guess if you follow any AI sources, you might have seen some sentiment like this one Frank de Gods writes, I've been using deep seek for 20 minutes, but I'm pretty sure this is the best thing in AI since the original ChatGPT signal. Writes I've been running Deepseek locally for a few days and it's absolutely on par with O1 or Sonnet. I've been using it nonstop for coding and other tasks, and what would have cost me a fortune through APIs is now completely free. This feels like a total paradigm shift. Investor Nick Carter writes, Deepseek just accelerated AGI timelines by five years, so focus on the gym. Knowledge Work is obsolete. Muscles are all that's left. The Superhuman newsletter writes, Deepseeks R1 stunned Silicon Valley China's new Deepseek R1 model has shocked Silicon Valley, and many long held assumptions about Chinese innovation and AI have evaporated overnight. Some are calling it a hoax, while others are calling it a gift to humanity. So what the heck are we talking about? Well, if you've been listening closely, you've probably heard me talk about Deep SEQ before. In December, we started hearing about their models, which were performing really well at apparently a fraction of the training cost of big models from companies like OpenAI. Last Monday, the lab released their Reasoning Model R1, and while it was immediately obvious that the model was good, benchmarking at a similar standard to OpenAI's O1 and Google's Gemini 2.0. As the week progressed, it started to become clear that something bigger was happening. A post on Blind, an anonymous professional social media network, circulated on Thursday. It was entitled metagenai Organ Panic Mode. It read, engineers are moving frantically to dissect deepseek and copy anything and everything we can from it. I'm not even exaggerating. Management is worried about justifying the massive cost of Genai Org. How would they face the leadership when every single leader of the Gen Org is making more than what it cost to train Deep Seq V3? We have dozens of such leaders. Deep Seq R1 made things even scarier. So the big thing going on here and the reason that the AI industry is so freaked out is cost. Deepsea claimed that their V3LM was trained for $5.6 million over three months. Frontier model training at US labs is closer to a half billion dollars for O1 class models and likely in the billions for the next generation of training runs. We don't have solid estimates on the post training cost to create the R1 model, but it seems reasonable to think that the budget was similarly tight. Some tech executives are openly dismissive of these claims. In an interview last week, Scale AI CEO Alexander Wang said that his understanding is that DeepSeq has a cluster of 50,000 top of the line Nvidia H100 chips in breach of export controls. The V3 paper claims the model was trained on a cluster of just 2000 Nvidia H8 hundreds, the downrated version of the chip that's allowed to be exported. Earlier this month, the South China Morning Post reported that Deepseek has 10,000 Nvidia GPUs, but didn't go into specifics about the chips. And for as unbelievable as it is, there are some reasons to believe the claims about their rock bottom training costs. Quant trader Jeffrey Emanuel broke down the innovations in their training methods in a blog post. Here's a part of that explanation, although it's worth reading in its entirety. Jeffrey writes, a major innovation is their sophisticated mixed precision training framework that lets them use 8 bit floating point numbers FP8 throughout the entire training process. Most Western AI labs train using full precision 32 bit numbers. This basically specifies the number of gradations possible in describing the output of an artificial neuron. 8 bits in FP8 lets you store a much wider range of numbers than you might expect. It's not just limited to 256 different equal sized magnitudes like you get with regular integers, but instead uses clever math tricks to store both very small and very large numbers, though naturally with a lot less precision than you'd get with 32 bits. The main trade off is that while FP32 can store numbers with incredible precision across an enormous range, and FP8 sacrifices some of that precision to save memory and boost performance while still maintaining enough accuracy for many AI workloads. If that was Greek to you, don't worry. Y Combinator partner Jared Freeman writes lots of hot takes on whether it's possible that deepseek made training 45 times more efficient, but Jeffrey Emanuel wrote a very clear explanation to how they did it. Once someone breaks it down, it's not that hard to understand. Rough summary use 8 bit instead of 32 bit floating point numbers which gives massive memory savings. Compress the key value indices which eat up much of the vram. Do multi token prediction instead of single token prediction which effectively doubles inference speed. Mixture of experts model Decomposes a big model into small models that can run on consumer grade GPUs. Point being that it's not like this is a black box where we have no idea why this is going on. There's some amount of explanation of how this actually could be. Still, whatever the truth about their training cluster, Deepseek is serving the model at rock bottom prices. API access for R1 is priced at around 3% of OpenAI's O1. Over the weekend, X was filled with examples of people accessing the model at high volumes for fractions of a cent per query. Henry writes, I've made over 200,000 requests to the Deepseek API in the last few hours, zero rate limiting and the whole thing cost me like 50 cents. Over the weekend the mindshare really broke through. Deepseek's phone assistant reached number one in the app Store and the model has racked up around 150,000 downloads from hugging Face and tops the trending list. What's more, because the model is open source and has a novel design for efficient inference, it can be run on a wide range of consumer devices. AI researcher Harrison Kinsley was able to run the full model on his admittedly beefy workstation with one terabyte of ram. Others were running smaller distilled versions of the model on phones and on laptops. Now at this point, seemingly everyone in Silicon Valley has a take about what Deepseek has achieved and what it means for the AI industry. Marc Andreessen of Andreessen Horowitz writes, Deepseek R1 is one of the most amazing and impressive breakthroughs I've ever seen, and as open source, a profound gift to the world, he returned later in the weekend to declare deep seats R1 is AI sputnik moment. I imagine pretty much everyone here is familiar with the reference, but Sputnik was of course the first ever satellite. Its 1957 launch signaled that Russia was leading the US in the space race, which came as a shock to the United States and kickstarted the Apollo program. In short, it was the wake up call during the Cold War that the US can't be complacent in the technological arms race. Y Combinator president Gary Tan wrote Deep Seek search feels more sticky even after a few queries, because seeing the reasoning, even how earnest it is about what it knows and what it might not know increases user trust by quite a lot. Indeed, the ability to view chain of thought reasoning seemed to be a pretty resonant moment for a lot of users, especially for those who have never paid to access O1. Caspian on X writes the normies think Deep Seek is cute because it shares its thought process, sharing a conversation where other folks are talking about how Deep Seek is quote, so cute because it shares its thought process and talks to itself. As you might imagine, some think something nefarious is going on. Neil Khosla, CEO of Kurei and Santa Vinod Khosla, wrote, Deep Seek is a CCP state psyop in economic warfare to make American AI unprofitable. They are faking the cost was low to justify setting price low and hoping everyone switches to it to damage AI competitiveness in the us. Don't take the bait now. While many might find that a plausible theory, the one small piece of evidence that you might point to is that this is an introductory price. R1 is currently being served at an introductory rate about 1.10 the cost of O1, but next month the cost will almost triple. To be 1/4 of a 1. Still 4x cheaper is no joke. Regardless of which story you believe on Deep Seq, we've clearly entered a new era of competition in AI. There are now multiple models that are basically on par across US and Chinese labs. The biggest difference is now cost of inference and Deep Seq is serving up the cheapest on the market. While the market reaction has been, say, terrified, and we'll get to that in just a moment, the reaction from big tech has not been fear, but the thrill of opportunity. Microsoft CEO Satya Nadella writes, Jayvon's paradox strikes again. As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we can't get enough of. Jayvon's Paradox, which is a term you're going to hear a lot more about in the next couple days, refers to the phenomenon where technological progress leads to efficiency gains and cost reductions, but that rather than reducing demand, it actually leads to a massive increase in demand. As an example, think about what happened to the demand for cloud storage as the cost became negligible. As an open source project, Deepseek has fully described their method and provided their data set, so theoretically there's nothing to hide. Hugging face are currently replicating the model in their own training run, so we'll know soon enough. If successful, the methods will quickly be copied by every big tech firm and hundreds of startups. The implication of cost efficient training and extremely good models is likely that the next era of AI is all about inference. 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One argument is that Deepseek has rendered all of those US GPUs worthless. As Chinese AI proves you can do it in a totally different way without all of that expensive capex. Going back to that piece by Jeffrey Emanuel he broke down the bear case for Nvidia in that extensive post. He tackles a ton of areas where Nvidia has excelled over recent years, from software to chip networking to raw performance. The logic is that competing chip makers are catching up quickly across multiple vectors. Couple this with a massive decrease in training costs and AI chips quickly become a commodity. Nvidia is the leader in producing top of the line chips for training clusters. But if the focus shifts to being about delivering cheap inference, there are other companies that are much more competitive with Nvidia in that space, and soon to be many more. Investor Nick Carter writes, Deepseek has completely upended the conventional wisdom around AI. That conventional wisdom includes China will only do closed source and proprietary. Silicon Valley is the global nexus of AI development and has a huge head start. OpenAI has an unbeatable moat. You need to spend tens, maybe hundreds of billions for state of the art model development. Value will accrete to the model's fat Model hypothesis. Scaling hypothesis means model performance is a linear function of training, input, cost, compute data and GPUs. All of these narratives shaken if not completely undermined overnight. And yet Nick also gets at the counterpoint. In his next tweet he says all of that said, I don't worry too much about equity value in Nvidia and AI data center companies, although he points out he does have a massive bag bias here. Why? He writes, when a commodity gets cheaper, the use of that commodity increases, so inference overnight becomes vastly more abundant. Deep seeks innovations will be rapidly incorporated by other model companies so I can be embedded cheaply everywhere. This probably shifts the ratio of training to inference and AI capex in favor of the latter, but I don't believe it undermines equity value in the firms that produce the inputs for inference, GPUs, data centers, etc. Just accelerates the transition from pre AI world to fully embedded world. All of that said, the investor premise that the model companies OpenAI, Anthropic, etc. Are where equity value will accrete has a massive hole in it. Now I've always felt and have said that I thought model companies would be capital incinerators due to high quality open source models and a race to the bottom, and I think that is more true now. But overall I don't worry about the rest of the stack, whether it's the producers or the firms that are actually bundling up compute and selling it to the end user in the form of better consumer experiences. TLDR for most of you, no need to panic, although I think it will take the market some time to digest and the ride will be bumpy in the near term. Y Combinator's Gary Tan took this on as well, responding to a market analyst who said China's deep seat could represent the biggest threat to U.S. equity markets, calling into question the utility of the hundreds of billions worth of capex being poured into the industry. Gary writes, do people really believe this? If training models get cheaper, faster and easier, the demand for inference, actual real world use of AI will grow and accelerate even faster, which assures the supply of compute will be used. Ben Thompson of Strategia also makes this point. He writes, in the long run, model commoditization and cheaper inference which Deepseek has demonstrated is great for big tech. A world where Microsoft gets to provide inference to its customers for a fraction of the cost means that Microsoft has to spend less on Data Centers and GPUs, or just as likely sees dramatically higher usage given that inference is so much cheaper. Another big winner is Amazon AWS has by and large failed to make their own quality model, but that doesn't matter if there are very high quality open source models that they can serve at far lower cost than expected. Apple, and this is an interesting one to me, is also a big winner. Ben writes. Dramatically decreased memory requirements for inference make edge inference much more viable, and Apple has the best hardware for exactly that. Apple Silicon uses unified memory, which means that the cpu, GPU and NPU have access to a shared pool of memory. This means that Apple's high end hardware actually has the best consumer chip for inference. Meta, meanwhile, is the biggest winner of all. I already laid out how every aspect of Meta's business benefits from AI. A big barrier to realizing that vision is the cost of inference, which means that dramatically cheaper inference and dramatically cheaper training, given the need for Meta to stay on the cutting edge, makes that vision much more achievable. Google, he does say, is probably in worse shape. A world of decreased hardware requirements lessens the relative advantage they have from TPUs. More importantly, a world of zero cost inference increases the viability and likelihood of products that display search. Granted, Google gets lower costs as well, but any change from the status quo is probably a net negative. Still, as Ben points out, the reason the stocks are down is that quote it seems likely the market is working through the shock of R1's existence. The moment has unquestionable geopolitical ramifications as well. It's not the first time a Chinese lab has demonstrated cutting edge capabilities, but it is the first time a Chinese model has grabbed this kind of mindshare. Importantly, R1 is competing on price in the same way that Chinese industries have outcompeted their U.S. counterparts for several decades. This moment runs right up against the Trump administration's goal of USAI dominance and will kickstart a new chapter in the rivalry. Investor Chamath Palihapitiya has a long thread explaining how the chessboard has changed in his opinion. He covered the need to pivot to inference and export those chips aggressively to allies, and also warned that VCs have been asleep at the wheel and need to improve their capital discipline. He writes, the innovation from China speaks to how asleep we've been for the past 15 years we've been running towards the big money shiny object spending programs. AI is not the first, and it likely won't be the last where we, Team USA have thrown hundreds of billions of dollars at a problem versus thinking through the problem more cleverly and using resource constraints as an enabler. Let's get our act together. We need all the bumbling middle managers out of the way. Let the engineers and the brilliant folks we have actually working on this stuff to cook more spending, more meetings, more oversight, more weekly reports and the like does not equate to more innovation. Unburden our technical stars to do their magic. A more joking take came from Jordy Hayes, who said the most patriotic thing you could do right now is develop software to use so much Deep Seq inference that you bankrupt the ccp. Some say that the battle here is not really about China versus the US but about open source versus closed source. Meta's chief AI scientist Yann LeCun writes to people who see the performance of Deep SEQ and think China is surpassing the US in AI, you're reading this wrong. The correct reading is open source models are surpassing proprietary ones. Deepseek has profited from open research and open source. They came up with new ideas and built them on top of other people's work. Because their work is published in open source, everyone can profit from it. That is the power of open research and open source. Menlo Ventures Didi Das had a contrarian take. After running comparisons all weekend, he plotted R1's performance against OpenAI's O3 model and suggested it's probably better. Then again, this performance is extrapolated on massively increased inference, so who really knows. He did point out, however, quote, the China is crushing US rhetoric totally forgets about Gemini 2.0. Flash thinking likely cheaper, longer context and better on reasoning overall. What I think is unmistakable from this hold aside the geopolitical implications, hold aside the stock market implications, intelligence has just gotten massively cheaper. There is no way that this doesn't drive prices down. Professor Ethan Malik writes, I think the market will adjust to any per token cost decrease brought on by Deep Seq quite quickly. Cost for GPT4 level intelligence dropped by 1000x in the last 18 months. A 95% price drop in reasoning models seems not to be something that will break the labs. Indeed, some many in fact are reminding that this is exactly the type of situation where you want to get away from mainstream media and look more deeply with people who are closer to the news. Nvidia's Dr. Jim Fan writes an obvious we are so back moment in the AI circle somehow turned into it's so over in mainstream Unbelievable shortsightedness. The power of 01 in the palm of every coder's hand to study, explore and iterate upon ideas compound. The rate of compounding accelerates with open source the pie just got much bigger faster. We as one humanity are marching towards universal AGI sooner Zero sum game is for losers it is very tempting in AI to dismiss big headlines as hyperbole. So many of the thread boys and the youtubers are just looking for the next dopamine hit of this changes everything. But in this case, Deep Seek I think might be as big a moment as people are feeling. My guess though is that it's not the moment that the market is reacting to, but the one that Dr. Jim Fan is pointing out here. No matter what. 2025 just got a heck of a lot more interesting. So come on back as we wait for the next crazy shoe to drop. For now, that's going to do it for today's AI Daily brief. Until next time, peace.