DeepSeek’s R1 model is shaking up the AI industry. It offers capabilities similar to top U.S. models at a lower cost, redefining AI development and igniting discussions on innovation, open-source tech, and geopolitics. This episode examines how DeepSeek’s cost-effective training, open-source strategy, and competitive performance challenge existing norms and reshape AI’s future.
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The headlines Blair this changes Everything but does it really today we Are talking about China's deep seek and Just how much it changes the trajectory Towards AGI 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 chat gbt signal writes I've Been running deep seek locally for a few Days and it's absolutely on par with 01 Or Sonet I've been using it non-stop 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investor Nick Carter writes deep seek just accelerated AGI timelines by 5 years so focus on the Gym knowledge work is obsolete muscles Are all that's left the Superhuman Newsletter writes deep seeks R1 stun Silicon Valley China's new deeps R1 Model has shocked Silicon Valley and Many long-held assumptions about Chinese Innovation and AI have evaporated Overnight SU 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
Seek 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 open AI 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 Open AI 01 and Google's Gemini 2.0 as The week progressed it started to become Clear that something bigger was Happening a post on blind and Anonymous Professional social media Network Circulated on Thursday it was entitled Meta gen organ panic mode it read Engineers are moving frantically to Dissect deep sea can copy anything and Everything we can from it I'm not even Exaggerating management is worried about Justifying the massive cost of gen org How would they face the leadership when Every single leader of the Gen org is Making more than what it cost to train Deeps V 3 we have dozens of such leaders Deep SE car1 made things even Scarier so the big thing going on here And the reason that the AI industry is So freaked out is cost DC claimed that Their V3 llm was trained for $5.6 Million over 3 months Frontier Model Training at us Labs is closer to a half Billion dollars for 01 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 deep seek 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 H 800s the downrated version of The chip that's allowed to be exported Earlier this month the South China Morning Post reported that deeps 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 and 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 in in said uses Clever math tricks to store both very Small and very large numbers though Naturally with a lot less Precision than You get with 32 bits the main trade-off Is that while fp32 can store numbers With Incredible Precision across an Enormous range FP 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 why Combinator partner Jared Freeman writes Lots of hot takes on whether it's Possible that deep seek 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 8bit 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 deep seek is serving the model At Rock bottom prices API access for R1 Is priced at around 3% of open ai's 01 Over the weekend X was filled with Examples of people accessing the model At high volumes for fractions of a scent Per query Henry writes I've made over 200,000 requests to the deeps API in the Last few hours zero rate limiting and The whole thing cost me like 50 over the Weekend the Mind share really broke Through deep See'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 1 tbyte 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 deep seek has achieved and What it means for the AI industry Mark
Andreon of Andre and Horowitz writes Deep seek R1 is one of the most amazing And impressive breakthroughs I've ever Seen and is open source a profound gift To the world He returned later in the weekend to Declare deep seeks 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 signal 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 wakeup call During the Cold War that the US can't be Complacent in the technological arms Race why commentated 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 01 caspan 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 kosa CEO of Kai and Son Of kosla wrote deep seek is a CCP state Scop 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 1et The cost of 01 but next month the cost Will almost triple to be 1 qu of 01 Still Forex cheaper is no joke Regardless of which story you believe on Deep seek 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 seek is serving up The cheapest on the market while the Market reaction has been let's 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 Sai Nadella Writes javon'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
Javon'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 Deep seek 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 no 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 in other words companies Are no longer just competing on the Quality of their models they're Competing to deliver them as cheaply as Possible now when it comes to Market Reactions there has definitely been a Response the S&P 500 futures Market was Down more than 3% in overnight trading And some amount of panic is absolutely Settling in the concern of course is That big Tech has sunk hundreds of Billions of dollars into AI
Infrastructure over the past few years And seems likely to spend a trillion Dollars this year one argument is that Deep seek 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 barcase 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 chipmakers 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 Deep seek 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 Open a I 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 models 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 AI can be embedded cheaply everywhere This probably shifts the ratio of Training to inference in 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 premised that the model Companies open AI anthropic Etc or where Equity value will accreate 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
Raise 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 white commentators Gary tan took This on as well responding to a market Analyst who said China's deep seat Represent the biggest threat to us 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 Strater also Makes this point he writes in the long Run model commoditization and cheaper Inference which deep seek 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 rights 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 NP pu 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 mind share importantly R1 Is competing on price in the same way That Chinese Industries have out Competed their us 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 trath Palapa has a long thread Explaining how the chess board has Changed in his opinion he covered the Need to Pivot to inference and Export Those chips aggressively to allies and Also warn 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 first then 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 Jordie Hayes who said the most patriotic Thing you could do right now is develop Software to use so much deep- seek 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 Clos Source Meta's Chief AI scientist Yan laon Writes to people who see the performance Of deepsea can think China is surpassing The US in AI you're reading this wrong The correct reading is open source Models are surpassing proprietary ones Deep seek 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 men L Ventures DD dos had a contrarian take after Running comparisons all weekend he
Plotted r1's performance against open Ai's 03 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 that 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 mik writes I think The market will adjust to any per token Cost decrease brought on by Deep seek Quite Quickly cost for GPT 4 level Intelligence dropped by 1,000x in the Last 18 months a 95% price drop in Reasoning model seems not to be Something that will break the labs Indeed some many in fact are reminding That this is ex 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 Short-sightedness 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