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Dylan Patel is the founder of SemiAnalysis, a research & analysis company specializing in semiconductors, GPUs, CPUs, and AI hardware. Nathan Lambert is a research scientist at the Allen Institute for AI (Ai2) and the author of a blog on AI called Interconnects. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep459-sb See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. *Transcript:* https://lexfridman.com/deepseek-dylan-patel-nathan-lambert-transcript *CONTACT LEX:* *Feedback* - give feedback to Lex: https://lexfridman.com/survey *AMA* - submit questions, videos or call-in: https://lexfridman.com/ama *Hiring* - join our team: https://lexfridman.com/hiring *Other* - other ways to get in touch: https://lexfridman.com/contact *EPISODE LINKS:* Dylan's X: https://x.com/dylan522p SemiAnalysis: https://semianalysis.com/ Nathan's X: https://x.com/natolambert Nathan's Blog: https://www.interconnects.ai/ Nathan's Podcast: https://www.interconnects.ai/podcast Nathan's Website: https://www.natolambert.com/ Nathan's YouTube: https://youtube.com/@natolambert Nathan's Book: https://rlhfbook.com/ *SPONSORS:* To support this podcast, check out our sponsors & get discounts: *Invideo AI:* AI video generator. Go to https://lexfridman.com/s/invideoai-ep459-sb *GitHub:* Developer platform and AI code editor. Go to https://lexfridman.com/s/github-ep459-sb *Shopify:* Sell stuff online. Go to https://lexfridman.com/s/shopify-ep459-sb *NetSuite:* Business management software. Go to https://lexfridman.com/s/netsuite-ep459-sb *AG1:* All-in-one daily nutrition drinks. Go to https://lexfridman.com/s/ag1-ep459-sb *OUTLINE:* 0:00 - Introduction 3:33 - DeepSeek-R1 and DeepSeek-V3 25:07 - Low cost of training 51:25 - DeepSeek compute cluster 58:57 - Export controls on GPUs to China 1:09:16 - AGI timeline 1:18:41 - China's manufacturing capacity 1:26:36 - Cold war with China 1:31:05 - TSMC and Taiwan 1:54:44 - Best GPUs for AI 2:09:36 - Why DeepSeek is so cheap 2:22:55 - Espionage 2:31:57 - Censorship 2:44:52 - Andrej Karpathy and magic of RL 2:55:23 - OpenAI o3-mini vs DeepSeek r1 3:14:31 - NVIDIA 3:18:58 - GPU smuggling 3:25:36 - DeepSeek training on OpenAI data 3:36:04 - AI megaclusters 4:11:26 - Who wins the race to AGI? 4:21:39 - AI agents 4:30:21 - Programming and AI 4:37:49 - Open source 4:47:01 - Stargate 4:54:30 - Future of AI *PODCAST LINKS:* - Podcast Website: https://lexfridman.com/podcast - Apple Podcasts: https://apple.co/2lwqZIr - Spotify: https://spoti.fi/2nEwCF8 - RSS: https://lexfridman.com/feed/podcast/ - Podcast Playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 - Clips Channel: https://www.youtube.com/lexclips *SOCIAL LINKS:* - X: https://x.com/lexfridman - Instagram: https://instagram.com/lexfridman - TikTok: https://tiktok.com/@lexfridman - LinkedIn: https://linkedin.com/in/lexfridman - Facebook: https://facebook.com/lexfridman - Patreon: https://patreon.com/lexfridman - Telegram: https://t.me/lexfridman - Reddit: https://reddit.com/r/lexfridman
Lex Fridman introduces Dylan Patel and Nathan Lambert, highlighting their expertise in AI and semiconductors. He sets the stage for a deep dive into the current state of AI, discussing major players like OpenAI, Google, and NVIDIA, and the geopolitical implications surrounding AI technology.
"the following is a conversation with Dylan Patel and Nathan Lambert Dylan runs semi analysis A well respected research and Analysis company that specializes in semiconductors gpus CPUs and AI Hardware..."
The conversation shifts to the significance of the 'DeepSeek moment' in AI history. Lex and his guests discuss the implications of this event, particularly in relation to geopolitical tensions and advancements in AI technology, emphasizing its potential long-term impact.
"conversation is a deep dive into many critical aspects of the AI industry while it does get super technical we try to make sure that it's still accessible to folks outside of the AI field by defining ..."
Nathan Lambert explains the differences between DeepSeek V3 and DeepSeek R1 models. He discusses their training processes, the significance of open weights, and how these models fit into the broader landscape of AI development.
"reveals its Chain of Thought reasoning which O3 mini does not it only shows a summary of the reasoning plus R1 is open weight and uh 03 mini is not by the way I got a chance to play with uh O3 mini an..."
The discussion delves into the concept of open weights in AI models. Nathan clarifies what it means for model weights to be open and the implications for accessibility and usage in the AI community, highlighting the importance of transparency in AI development.
"they will continue to shift the cost curve but the quote deep seek moment is indeed real I think it will still be remembered 5 years from now as a pivotal event in Tech History due in part to the geop..."
Nathan elaborates on the training techniques used for DeepSeek models, including pre-training and post-training methods. He explains how these processes contribute to the models' performance and the challenges faced in communicating these concepts within the AI industry.
"we'll get into largely this is a open weight model and it's a instruction model like what you would use in chat GPT um they also release what is called the base model which is before these techniques ..."
The segment discusses the various licenses associated with AI models, particularly focusing on DeepSeek's permissive licensing. Nathan compares it to other models like Llama, emphasizing the importance of licensing in determining how AI technologies can be used and shared.
"each of them there's so many places we can go here but maybe let's go to open weights first what does it mean for model to be open weights and what are the different flavors of Open Source in general ..."
The conversation continues with a focus on the implications of open weights for data privacy and security. Nathan explains how open weights allow users to maintain control over their data, contrasting it with traditional API usage where data may be stored and used by companies.
"the software and what that means for AI is still being defined so uh for what I do I work at the Allen Institute for AI we're a nonprofit We want to make AI open for everybody and we try to lead on wh..."
Lex and Nathan discuss the user experience differences between DeepSeek V3 and R1. They explore how each model responds to user queries, highlighting the unique features of R1's reasoning capabilities and how it enhances the interaction with users.
"probably one of the more open models out of the frontier models so like in this full spectrum where probably the fullest open source like you said open code open data open weights this is not open cod..."
Nathan breaks down the concepts of pre-training and post-training in AI models. He explains the significance of these processes in developing effective language models and the various techniques used to enhance their performance.
"for llama one of the most red p PDFs of the year last year is the Llama 3 paper but in some ways it's slightly less actionable it has less details on the training specifics like less plots um and so o..."
The segment focuses on the evolution of training techniques in AI, particularly the introduction of reinforcement learning from human feedback. Nathan discusses how these advancements are shaping the future of AI model training and performance.
"chips I have never worked there myself and there are a few people in the world that do that very well and some of them are at Deep seek and these types of people are at Deep seek and leading American ..."
Lex and Nathan explore the practical user experience of interacting with DeepSeek models. They discuss how users can expect responses from these models, emphasizing the importance of clarity and reasoning in generating answers.
"Open Source so it's not the model that steals your data it's clovers hosting the model which could be China if you're using the Deep seek app or it could be perplexity uh you know you're trusting them..."
Dylan Patel discusses the capabilities of DeepSeek R1's reasoning models, highlighting how they break down complex problems through a chain of thought process. He explains the model's ability to generate a large number of tokens quickly, showcasing its reasoning capabilities and how it summarizes its thought process to provide answers. This segment emphasizes the significance of reasoning in AI and how DeepSeek's approach stands out in the AI community.
"that if you're an expert things that are close to The Fringe of knowledge they will still be fairly good at I think Cutting Edge AI topics that I do research on these models are capable for study Aid ..."
In this segment, the discussion shifts to the unique meta-emotions of humans, where individuals feel emotions about their own emotions. This recursive emotional layering creates complex motivational drives that are absent in other animals. The conversation explores how DeepSeek's reasoning model challenges itself to provide novel insights about human emotions, revealing profound thoughts on cognitive dissonance and the nature of human societal constructs.
"open AI maybe it's useful here to go through like an example of a deep seek R1 reasoning yeah so the if if you're looking at the screen here what you'll see is a screenshot of the deep seek chat app a..."
Dylan Patel elaborates on a striking insight generated by DeepSeek R1: humans instinctively convert selfish desires into cooperative systems through shared abstract rules. This segment delves into the philosophical implications of this insight, suggesting that societal constructs like money and laws are collective hallucinations that facilitate cooperation. The discussion highlights the innovative capabilities of AI in generating profound societal insights.
"it's like it's reasoning through how humans feel emotions it's reasoning about meta emotions going to have pages and Pages this it's almost too much to actually read but it's nice to skim as it's comi..."
The conversation reflects on the eloquence and depth of text produced by reasoning models like DeepSeek R1. Dylan Patel discusses how these models can generate compelling narratives that capture public imagination, emphasizing the importance of reasoning in AI development. This segment underscores the potential of AI to produce not just answers, but also thought-provoking insights.
"pretty profound I mean you know this is AAL digression but a lot of people have found that these reasoning models can sometimes produce much more eloquent text that a at least interesting example I th..."
Dylan Patel explains the innovative techniques behind DeepSeek's training efficiency, focusing on the mixture of experts model and the new MLA latent attention technique. This segment discusses how these methods significantly reduce training and inference costs, allowing for a more efficient use of computational resources. The conversation highlights the importance of architectural innovations in advancing AI capabilities.
"more how are they able to achieve such low cost on the training and the inference maybe you could talk the training first yeah so there's there's two main techniques that they implemented that are pro..."
In this segment, the discussion dives deeper into the mixture of experts model, explaining how it mimics human cognitive processes by activating only relevant portions of the model for specific tasks. Dylan Patel elaborates on the benefits of this approach, including reduced computational costs and improved efficiency in training and inference. The segment emphasizes the significance of this architecture in the evolution of AI models.
"and inference cost because now you're you know if you think about the parameter count as the sort of total embedding space for all of this knowledge that you're compressing down during training when y..."
Dylan Patel discusses the architecture of Transformers and their role in deep learning. He explains how Transformers utilize attention mechanisms and dense networks to achieve high performance. This segment highlights the importance of understanding different neural network architectures and their implications for training efficiency and model performance.
"the Transformer is that useful let's go let's go into the Transformer the Transformer is a thing that is talked about a lot and we will not cover every detail uh essentially the Transformer is built o..."
The conversation shifts to scaling laws in deep learning, emphasizing the relationship between model size and performance. Dylan Patel discusses how larger models tend to perform better across various tasks, highlighting the importance of scaling in AI development. This segment underscores the ongoing evolution of deep learning and the need for efficient training methods.
"tasks a mixture of experts is one of the ones at training time even if you don't consider the inference benefits which are also big at training time your efficiency with your gpus is dramatically impr..."
Dylan Patel explains the low-level programming innovations that DeepSeek employs to optimize their models. He discusses the challenges of using Nvidia's communication libraries and how DeepSeek has developed its own scheduling methods to enhance efficiency. This segment highlights the technical complexities involved in training large AI models and the innovative solutions that arise from necessity.
"get into the details with this latent attention it's one of those things I look at it's like okay this they're doing really complex implementations because there's other parts of language model such a..."
In this segment, the discussion focuses on DeepSeek's custom communication protocols for training their models. Dylan Patel explains how they manage GPU resources and optimize communication between layers to improve training efficiency. This segment emphasizes the importance of tailored solutions in overcoming hardware limitations and achieving high performance in AI training.
"builds this Library called nickel right uh in which you know when you're training a model you have all these communications between every single layer of the model and you may have over 100 layers wha..."
Dylan Patel discusses the complexities involved in running sparse mixture of experts models. He explains the challenges of load balancing and scheduling communications between different GPU resources. This segment highlights the intricacies of managing large AI models and the innovative strategies that DeepSeek employs to ensure efficient training and inference.
"the mother of innovation and they had to do this whereas uh in the casa you know open AI has people that do this sort of stuff anthropic Etc uh but you know deep seek certainly did it publicly and the..."
The conversation concludes with a reflection on the 'bitter lesson' of AI training, emphasizing the importance of scalable methods that avoid human biases. Dylan Patel discusses how enabling deep learning systems to work efficiently is crucial for tackling larger problems in the future. This segment encapsulates the overarching philosophy of AI development and the lessons learned from past experiences.
"efficiency gains you get are not worth it but deep seeks implementation is so complex right especially with their mixture of experts right um people have done mixture of experts but they're generally ..."
In this segment, the discussion revolves around the complexities of running sparse mixture of experts models in deep learning. The speakers highlight the challenges of load balancing GPU resources when data routes predominantly to one part of the model, leading to inefficiencies. They explore the implications of this routing on training networks and the need for effective communication scheduling among GPU resources.
"happens when a to you know this set of data that you get hey all of it looks like this one way and all of it should route to one part of my you know model right um so so when all of it rout routes to ..."
The conversation shifts to the 'bitter lesson' in deep learning, emphasizing that scalable training methods will prevail. The speakers discuss how researchers often seek clever short-term solutions that yield minor gains, while the long-term success lies in enabling deep learning systems to operate efficiently without human biases. They reflect on the importance of simplicity in model training and the potential for significant advancements through scalable learning.
"which is this kind of lowlevel optimization or is this a shortterm thing where the biggest gains will be more on the algorithmic high level side of like posttraining is is this like a short-term leap ..."
This segment delves into the stress associated with initiating training for large-scale models. The speakers describe the meticulous monitoring required during training runs, including the importance of tracking loss metrics and addressing issues that arise. They discuss the challenges of debugging and the emotional toll on researchers as they navigate the complexities of training, particularly when financial stakes are high.
"continue to drive success and therefore we were talking about relatively small implementation changes to the mixture of experts model and therefore it's like okay like we will need a few more years to..."
The concept of 'YOLO runs' is introduced, where researchers commit all available resources to a training run based on prior small-scale experiments. The speakers discuss the inherent risks and uncertainties of scaling up from small tests to large-scale training, emphasizing the need for intuition and experience in making these critical decisions. They highlight the balance between methodical research and the instinctive leap of faith required in AI development.
"readable code I think there is one aspect to note though right is that there is the general General ability for that to transfer across different types of runs right you may make really really high qu..."
In this segment, the speakers explore the phenomenon of loss spikes during model training, discussing the different types of spikes and their implications. They share anecdotes about past experiences with unexpected loss behaviors and the strategies employed to manage these challenges. The conversation highlights the importance of understanding model behavior and the need for adaptive strategies in response to training dynamics.
"reality especially with more complicated stuff likee the biggest problem with it or FPA training which is another Innovation you know going to a lower Precision number format I.E less accurate is that..."
The discussion focuses on the critical role of research in driving advancements in AI. The speakers emphasize that successful training runs often rely on extensive research and experimentation, which can lead to breakthroughs in model performance. They reflect on the iterative nature of AI development, where each research effort contributes to the overall improvement of models and systems.
"Durk grenal has a theory A2 that's like Fast spikes and slow spikes where there are sometimes where you're looking at the loss and there other parameters you can see it start to creep up and then blow..."
This segment provides insights into DeepSeek's GPU infrastructure and its evolution over time. The speakers discuss the company's history in quantitative trading and how it has leveraged its resources for AI model training. They highlight the significance of DeepSeek's GPU clusters and the strategic decisions made by its leadership to enhance AI capabilities.
"but how do you get if you're deep seek how do you get to a place where holy there's a successful combination of hyper parameters a lot of small failed runs and so so rapid uh iteration through failed ..."
The speakers elaborate on the YOLO run strategy within AI labs, discussing how researchers transition from small-scale experiments to large-scale training. They emphasize the importance of selecting the right parameters and the inherent risks involved in this approach. The conversation touches on the balance between methodical research and the instinctive decision-making required for successful AI training.
"more no more around right uh no more screwing around everyone take all the resources we have let's pick what we think will work and just go for it right YOLO and this is where that sort of stress come..."
In this segment, the speakers analyze OpenAI's strategic decisions in 2022, particularly their commitment to a new architecture despite skepticism. They discuss the implications of such bold moves in the context of AI development and the competitive landscape. The conversation highlights the significance of risk-taking in achieving breakthroughs and the lessons learned from OpenAI's approach.
"the amount of computing time you have is is very low and you're you're you have to hit release schedules you have to not get blown past by everyone otherwise you know what happened with deep seek you ..."
The segment focuses on the leadership of DeepSeek and its CEO, Leon Fang. The speakers discuss Fang's vision for AI and his commitment to building a robust ecosystem in China. They reflect on his approach to open-source development and the strategic direction of DeepSeek as it navigates the competitive AI landscape.
"big Winners throughout human history are the ones who are willing to do yellow at some point okay uh what do we understand about the hardware it's been trained on deep seek deep seek is very interesti..."
The conversation concludes with a discussion on DeepSeek's aspirations for the future of AI. The speakers emphasize the importance of building a sustainable AI ecosystem in China and the potential impact of DeepSeek's innovations. They reflect on the broader implications of AI development and the role of leadership in shaping the future of technology.
"and papers saying like Hey we're the first company in China with an a100 cluster this large those 10,000 a100 gpus right this is this is in 2021 now this wasn't all for training you know large languag..."
In this segment, the discussion revolves around the compute requirements for training AI models, emphasizing the significant amount of compute needed for research and experimentation. The speakers highlight that notable advancements in AI models often require 2 to 4 times the compute for research compared to the actual training run, illustrating the importance of research in driving efficiency and breakthroughs in AI.
"zoning in right like what do you call your training run right do you count all of the research and ablations that you ran right picking all the stuff because yes you can do a YOLO run but at some leve..."
The conversation shifts to NVIDIA's Hopper architecture, specifically the differences between the H100 and H800 GPUs. The speakers discuss how export controls have impacted the availability of these GPUs in China, detailing the restrictions based on floating point operations and interconnect bandwidth. They explain how DeepSeek has adapted to these constraints to maximize GPU utilization despite limitations.
"believe they actually have something closer to 50,000 gpus right now this is this is split across many tasks right again the fund um research in ablations for ballpark how much would open AI or anthro..."
This segment delves into the philosophical and geopolitical motivations behind export controls on AI technology. The speakers discuss the potential military advantages of super powerful AI and the desire to maintain a unipolar world dominated by democratic nations. They argue that export controls aim to slow down China's AI advancements to prevent them from achieving significant breakthroughs in AGI.
"interconnect we can do all this fancy stuff to figure out how to use the GPU fully anyways right and and so that was back in October 2022 but uh later in 2023 end of 2023 implemented in 2024 the US go..."
The discussion continues with an analysis of the current AI ecosystem and the implications of export controls on China's ability to train AI models. The speakers emphasize that while DeepSeek can still operate with a limited number of GPUs, strong export controls could significantly reduce the overall AI capabilities in China, affecting the market and the development of AI technologies.
"think this can be the goal of how some people describe export controls is this super powerful AI there's and you touched on the training run idea there's not many worlds where China cannot train AI mo..."
In this segment, the speakers explore the timeline for achieving AGI and its potential economic impact. They discuss the challenges of deploying powerful AI technologies at scale and the significant compute resources required for advanced AI tasks. The conversation highlights the disparity between capabilities and the practical limitations of infrastructure, particularly in the context of geopolitical competition.
"much easier goal to achieve than trying to debate on what AGI is and if you have these extremely intelligent autonomous AIS and data centers like those are the things that could be running in these GP..."
The speakers reflect on the definition of AGI and the expectations surrounding its development. They discuss the rapid advancements in AI and the potential for breakthroughs that could redefine capabilities. The conversation touches on the implications of AGI for society, military power, and the global balance of power, emphasizing the need for careful consideration of how these technologies are developed and deployed.
"where like AI is T is like measured in the power of in like how much power is delivered to compute right or how much uh is being you know that's sort of a way of thinking about what's the economic out..."
This segment addresses the geopolitical tensions surrounding AI development, particularly between the US and China. The speakers discuss the implications of AI technologies on military capabilities and the potential for misinformation and social engineering. They highlight the importance of understanding the broader context of AI advancements and the need for responsible governance in the face of rapid technological change.
"take off in the US Open AI needs a ton of gpus on inference to capture this they have this um open AI chat gbt Pro subscription which is $200 a month which Sam said they're losing money on which means..."
In this segment, the discussion revolves around the high operational costs associated with deploying AGI capabilities at scale. The speakers highlight the disparity between the costs of simple queries and complex AGI tasks, emphasizing that while AGI capabilities may be available, the infrastructure and financial resources required to implement them widely are currently lacking.
"the cost of actually operating that capability yeah this is going to be my point so so extreme that no one can actually deploy it at scale and Mass to actually completely revolutionize the economy on ..."
The conversation shifts to the geopolitical implications of AI development, particularly focusing on China's ability to rapidly integrate AGI into military applications. The speakers express concerns that while the US may excel in commercial AGI, China's military could leverage AGI technologies more effectively, especially in asymmetric warfare scenarios like drone technology.
"dollars of GPU time and there just won't be enough power gpus infrastructure to operate this and therefore shift everything in the world on the snap the finger but at that moment who gets to constu co..."
This segment discusses the current limitations of autonomous military systems, particularly in the context of drone warfare. The speakers argue that despite advancements in AI, human operators still outperform AI systems in critical military applications, raising questions about the timeline for fully autonomous military operations.
"everything I've seen uh people's intuition seems to fail on robotics so you have this kind of General optimism I've seen this on self-driving cars people think it's much easier problem than it is simi..."
The discussion focuses on the impact of export controls on AI technologies and their implications for global power dynamics. The speakers argue that if the US continues to restrict China's access to cutting-edge AI technologies, it may inadvertently push China towards more aggressive military actions, particularly regarding Taiwan.
"there could be cyber War cyber War type of technologies that uh from social engineering to actually just swarms of robots that find attack vectors in our code bases and shut down P grids that kind of ..."
In this segment, the speakers analyze China's industrial capabilities, particularly in semiconductor manufacturing and data center construction. They emphasize that China's ability to build large-scale data centers and produce chips could give it a significant advantage in the AI race, especially if export controls limit US capabilities.
"compute because Talent is not really something that's constraining right China arguably has more Talent right more stem graduates more programmers the US can draw upon the world's people which it does..."
The conversation shifts to TSMC's pivotal role in the semiconductor industry. The speakers explain how TSMC's foundry model has transformed chip manufacturing, allowing companies to outsource production and focus on design, which has become increasingly important as the costs of building fabs continue to rise.
"exactly to the the manufactur stuff so why why so longterm they're going to be manufacturing chips there chips are a little bit more specialized I'm specifically referring to the data centers right ch..."
This segment discusses the recent developments in US-China relations regarding AI and technology. The speakers highlight China's recent AI subsidies and the implications of these investments for the global tech landscape, suggesting that the competition between the two nations is intensifying.
"medium short term right um and so that's that's the big unlocker there um and even even today right if xingping decided to get you know quote unquote scale pilled right uh I.E decide that scaling laws..."
The speakers explore the potential future scenarios of AI in military strategy, discussing how the balance of power could shift based on technological advancements. They express concerns about the implications of AI on global stability and the potential for conflict as nations race to develop superior technologies.
"they only just released a subsidy of a trillion R&B uh you know roughly $160 billion um which is close to the spending of like Microsoft and meta and Google combined right for this year so it's like t..."
Dylan Patel discusses the evolution of semiconductor manufacturing, focusing on TSMC's innovative foundry model that revolutionized the industry. He explains how TSMC's approach allowed companies like NVIDIA to thrive while traditional chip manufacturers struggled under the high costs of building fabs. This segment highlights the shift from vertical integration to a more collaborative manufacturing ecosystem.
"so you look at a Leading Edge Fab that is going to be profitable today that's building you know three nanometer chips or two nanometer chips in the future that's going to cost north of 3040 billion ri..."
In this segment, Patel elaborates on the increasing complexity and costs associated with semiconductor manufacturing. He emphasizes the need for specialization and innovation in chip design, as traditional methods become less viable. The discussion includes the impact of Moore's Law and the necessity for companies to adapt to survive in a competitive landscape.
"right including Intel their latest PC chip uses tsmc chips right it also uses some Intel chips but it uses tsmc process can you explain why the foundry model is so successful for these companies why w..."
Patel highlights the critical role of research and development in maintaining a competitive edge in semiconductor technology. He contrasts the R&D capabilities of leading companies with those of smaller players, illustrating how a strong focus on innovation is essential for success in the industry. This segment underscores the significance of advanced manufacturing processes and the challenges faced by companies in keeping up with technological advancements.
"types of chips you're not going to can have the demand to pay back the cost of the Fab whereas Nvidia can have many different customers and aggregate all this demand into one place and then they're th..."
This segment explores the unique aspects of Taiwan's semiconductor workforce, including the high level of education and dedication among employees at TSMC. Patel discusses the cultural factors that contribute to the success of Taiwan's semiconductor industry, such as work ethic and the commitment of top graduates to the field. He compares this to the challenges faced by semiconductor companies in the United States.
"they've just been the best right they are so good at it right they're customer focused they make it easy for you to fabricate your chips they take all of that complexity and like kind of try and Abstr..."
Patel discusses the potential for the United States to regain its leadership in semiconductor manufacturing. He outlines the challenges and investments required to build a robust semiconductor ecosystem in the US, emphasizing the need for a cultural shift and government support. This segment provides insights into the competitive landscape and the importance of attracting talent to the industry.
"of the world right um so so there is there is a large dichotomy of like what is the top 1% of the society doing and where are they headed because of economic reasons right Intel never paid that crazy ..."
In this segment, Patel reflects on Intel's historical dominance in semiconductor manufacturing and the factors that led to its decline. He discusses the company's mismanagement and the impact of cultural shifts within the organization. This analysis provides a backdrop for understanding the current state of the semiconductor industry and the lessons learned from Intel's experience.
"when you talk about hey you have all these people that are super specialized they will work you know 80 hours a week in a factory right in a Fab and if anything goes wrong they'll go show up in the mi..."
Patel examines the global dynamics of the semiconductor supply chain, focusing on the interdependencies between countries and companies. He discusses the implications of geopolitical tensions on semiconductor production and the importance of maintaining a stable supply chain. This segment highlights the critical role of R&D centers and the potential vulnerabilities in the semiconductor ecosystem.
"better than everyone the tool guys were like oh I don't think that this this is mature enough and they're like ah you just don't know we know right this sort of stuff would happen um and so can the US..."
This segment delves into China's efforts to enhance its semiconductor capabilities, including its Five-Year Plan for domestic production. Patel discusses the challenges China faces in achieving its goals and the implications for global semiconductor markets. He highlights the importance of R&D and the potential for China to catch up in certain areas while remaining behind in advanced technologies.
"really is fundamentally about R&D and it is all about tsmc huh and so tsmc you know you cannot purchase a vehicle without tsmc chips right you cannot purchase a fridge without tsmc chips you cannot yo..."
Patel analyzes the effects of US export controls on China's semiconductor industry, discussing how these restrictions have influenced China's technological development. He explains the strategic importance of these controls and their potential long-term consequences for both countries. This segment provides a nuanced view of the geopolitical landscape surrounding semiconductor technology.
"nanm power IC or analog IC or you know random chip in my keyboard right that kind of stuff so so there is an angle of like the US's actions have been so from these export you know from the angle of th..."
In this concluding segment, Patel speculates on the future of US-China relations in the context of semiconductor technology and AI. He outlines various potential scenarios, from cooperation to conflict, and discusses the implications for global economic integration. This segment emphasizes the importance of strategic decision-making in navigating the complex landscape of international technology competition.
"dollars right and so like the amount of money that the US is spending on the semiconductor industry is is nothing right um whereas all these other countries have uh structural advantages in terms of l..."
The conversation shifts to the future of US-China relations, exploring potential trajectories ranging from cooperation to conflict. Dylan Patel outlines the implications of export controls on technology and the growing divide between the two nations, suggesting that the current geopolitical climate could lead to increased instability or even military confrontation.
"the equation for tsmc building more Fabs in the US that's what he's sort of positing right so can you lay out the so we laid out the importance by the way it's incredible how much you know about so mu..."
Patel elaborates on the implications of the US's intention to control AI technology and its impact on global economic integration. He discusses how the separation of US and Chinese technology markets is becoming more pronounced, with both sides implementing restrictions that hinder collaboration and increase tensions.
"of AI I mean ultimately the export controls are pointing towards a separate future economy I think the US has made it clear to Chinese leaders that we intend to control this technology at whatever cos..."
In this segment, Patel reflects on historical patterns of global hegemony, noting that periods of peace often coincide with the dominance of a single power. He warns that the rise of China as a competitor to the US could lead to instability, drawing parallels with historical empires and their declines.
"I have zero idea and I would love if we Kum we could all hold hands and sing Kumbaya but like I have zero idea how that could possibly happen is the Divergence good or bad for avoiding war is it possi..."
The discussion turns to the role of AI in maintaining US global dominance. Patel argues that if the US can lead in AI development, it may secure its position in the world order. He acknowledges the potential negative impacts on other nations, particularly China, as the US seeks to leverage AI for geopolitical advantage.
"right the last hand you know decades now we we've sort of seen things start to slide right with Russia Ukraine with what's going on in the Middle East and you know Taiwan risk all these different thin..."
Patel explains the complexities of export controls on GPU technology, detailing how these regulations have evolved. He discusses the implications of these controls for companies like NVIDIA and the impact on AI development in China, emphasizing the strategic importance of these technologies in the global landscape.
"position and therefore I I hope that works and and and as an American like you know kind of like okay I guess that's going to lead to peace peace for us uh now obviously other people around the world ..."
In this segment, the focus shifts to the technical aspects of AI hardware, particularly the significance of memory bandwidth and capacity. Patel discusses how these factors influence AI performance and the ongoing developments in GPU technology that aim to enhance reasoning capabilities in AI models.
"h20s promising yeah so this goes uh and I think we'd have to like we need to dive really deep into the reasoning aspect and what's going on there but the H20 you know the US has gone through multiple ..."
Patel delves into the performance metrics of AI models, explaining the importance of floating-point operations (FLOPs) and how they relate to AI training. He highlights the evolving nature of AI architectures and the need for continuous innovation to keep pace with increasing demands for computational power.
"lot of compute they involve a lot of moving memory around uh whether it be to memory or two other chips right and so these three vectors um the US initially had a multi you know had two of these vecto..."
The conversation explores the emerging focus on reasoning within AI systems. Patel discusses how advancements in AI are shifting towards models that prioritize reasoning capabilities, and the implications this has for future AI applications and their performance.
"we at our research we cut nvidia's production for H20 for this year down drastically they were going to make another 2 million of those this year but they just canel all the orders a couple weeks ago ..."
Patel and Lambert discuss the challenges associated with processing long contexts in AI models. They explain how memory constraints affect the ability to serve multiple users and the implications for AI performance, particularly in reasoning tasks that require extensive context.
"right we talk about models in terms of like how many flops they are right uh so so like you know we talk about oh gp4 is 2 e25 right two to the uh two to the 25 fth uh you know 25 Z right flop right f..."
In this segment, the discussion centers on the economics of AI inference, particularly the cost implications of serving AI models. Patel explains how the pricing structure for input and output tokens affects operational costs and the strategies companies use to optimize their AI services.
"of this humongous revolution in the last handful of years is the Transformer right and the attention mechanism attention mechanism is that the model understands the relationships between all the words..."
This segment explores the shift in AI models from simple document retrieval to complex reasoning tasks. It discusses how the sequence length and memory requirements increase dramatically with reasoning models, impacting user capacity and serving costs. The conversation highlights the challenges of maintaining performance while generating extensive outputs, emphasizing the importance of memory management in AI applications.
"context length were like let me put a ton of documents in and then get an answer out right and it's a it's a single you know pre-fill compute a lot in parallel and then output a little bit now with re..."
Dylan Patel and Nathan Lambert discuss DeepSeek's recent success, including its rise to the top of the App Store and the launch of its API product. They analyze the factors contributing to DeepSeek's low inference costs compared to competitors like OpenAI, focusing on architectural innovations and the implications of open model weights. The segment underscores the competitive landscape in AI and the significance of cost efficiency in model deployment.
"right whereas with reasoning I'm now generating tens of thousands of tokens in in sequence right and so this this memory this KV cach has to stay resident you have to keep loading it you have to keep ..."
This segment delves into the reasons behind DeepSeek's significantly lower costs for model inference compared to OpenAI. The discussion covers the architectural innovations in DeepSeek's models, particularly the new attention mechanisms that reduce memory pressure. The hosts compare pricing structures, revealing how DeepSeek's approach allows for more affordable AI services while maintaining performance.
"cheap talk about why it's so cheap on the inference it works well and it's cheap why is R1 so damn cheap so I think there's a couple factors here right one is that they do have model architecture Inno..."
In this segment, the conversation shifts to the financial aspects of AI companies, particularly the revenue models of OpenAI and DeepSeek. The hosts discuss the implications of high profit margins for OpenAI and the challenges faced by DeepSeek in scaling its services. They explore the potential for DeepSeek to operate at a loss while leveraging its hedge fund backing, raising questions about sustainability and market competition.
"relative to Prior forms all right that's the memory pressure I should say in case people don't know R1 is 27 times cheaper than 01 we think that open AI had a large margin built in okay so that's ther..."
Dylan and Nathan discuss the relationship between DeepSeek and the Chinese government, examining whether government support plays a role in its operations. They analyze the structure of AI labs in China and the implications for innovation and competition in the global AI landscape. The segment raises questions about the influence of government funding on technological advancements and market dynamics.
"don't actually think so um and part of that is this chart right look at all the other providers right together AI fireworks AI are very highend companies right xmeta together AI is treow and the inven..."
This segment contrasts the rapid release cycles of DeepSeek with the more cautious approaches of American AI companies like Anthropic. The hosts discuss the implications of prioritizing speed over safety in AI development, referencing historical parallels to the Space Race. They explore how different cultural attitudes towards risk affect the pace of innovation and the potential consequences for AI safety.
"some of the interviews there's discussion on how like doing this is a recruiting tool you see this at the American companies too it's like having gpus recruiting tool being at The Cutting Edge of AI r..."
Dylan and Nathan delve into the cultural ramifications of AI models, discussing how language models can reflect and influence societal values. They explore the risks of embedding biases within AI systems and the potential for cultural backdoors in open-source models. The conversation highlights the importance of understanding the broader implications of AI technology on society and the need for responsible development.
"why anthropic is not open sourcing things that's their claims but there's reviews internally anthropic um ra mentions things to International governments there's been news of how anthropic has done pr..."
In this concluding segment, the hosts discuss the future of open-source AI in light of recent developments. They reflect on the potential for international standards in AI and the importance of maintaining American leadership in the field. The conversation emphasizes the need for vigilance in AI development to ensure ethical practices and the prevention of unintended consequences in a rapidly evolving technological landscape.
"the US companies right this is something that Dario talks about is like that's the situation that Dario wants to avoid is Dario talks to about the difference between race to the bottom and race to the..."
The discussion revolves around the potential cultural biases embedded in AI models, particularly those with open weights. The speakers express concerns about how American and Chinese models might unintentionally embed cultural norms and biases, leading to significant implications for language and societal perceptions.
"this publicly in an instruct model that's open weights this can then proliferate right but as these systems get more and more capable what you can embed deep down in the model is not as clear right um..."
This segment highlights the dangers of intentional alignment in AI models, where underlying biases could be deliberately embedded. The conversation touches on the implications of such alignments, especially in the context of models developed by companies with potential government influences, raising concerns about manipulation and control.
"boil down into like very very important topics like hey you know sub you know subverting people right uh you know chat Bots right character AI has shown that they can like you know talk to kids and or..."
The speakers discuss the idea that superhuman persuasion capabilities in AI may emerge before true superhuman intelligence. They explore the implications of this phenomenon, suggesting that AI could be used to influence opinions and behaviors in ways that are not immediately apparent.
"people very we don't know the extent that which people can be impacted by that so there there could be this is one this is an actual concern with a Chinese company that is providing open weights model..."
This segment delves into a dystopian vision where individuals become overly reliant on AI systems for social interaction, potentially losing their ability to think independently. The speakers reflect on the psychological effects of constant engagement with AI-driven platforms and the risks of being manipulated by algorithms.
"poisoned the pre-training data I don't think like as of now I don't think anybody in a production system is trying to do anything like this I think it's mostly anthropic is doing very direct work and ..."
The conversation shifts to personal experiences of disconnecting from the internet and social media. The speakers share insights on how time spent away from digital distractions can lead to a clearer mind and a sense of control over one's thoughts, contrasting this with the pervasive influence of online algorithms.
"more more on these kinds of systems I mean we've already seen this with recommendation systems yeah recommendation systems hack the the dopamine induced reward circuit but the brain is a lot more comp..."
This segment addresses the challenges of censorship and alignment in AI models, using examples like the Tiananmen Square incident. The speakers discuss how factual knowledge is embedded in models and the complexities of filtering out sensitive information during the training process.
"be not other people but algorithms or other people presented to me via algorithms there I mean there are already tons of AI bots on the internet and every so right now it's not frequent but every so o..."
The discussion continues on the intricacies of model training, emphasizing the difficulty of auditing facts embedded in AI systems. The speakers highlight how pre-training data selection can influence model behavior and the challenges of ensuring unbiased outputs.
"companies that deploy them so one case when we've seen that and maybe censorship is one word alignment maybe via rhf or some other way is another word so that we we saw that with black Nazi image gene..."
This segment explores the importance of human input in AI training, particularly in preference tuning. The speakers discuss how human comparisons and feedback are crucial for refining AI behavior, while also acknowledging the growing capabilities of AI to generate content independently.
"by the way removing facts has such an ominous dark feel to it almost think it's practically impossible because you effectively have to remove them from the internet you're you're taking on a did did d..."
The conversation highlights the emergence of reasoning behaviors in AI models through reinforcement learning. The speakers discuss how these behaviors can develop naturally from training on questions and answers, showcasing the potential for advanced reasoning capabilities without direct human intervention.
"always have some TDS Trum derangement syndrome because it's trained so much it'll have the ability to express it but what if what if you there's a there's a wide representation in the data this is wha..."
This segment addresses the challenges faced during the execution of AI models, particularly in relation to prompt rewriting and user query handling. The speakers reflect on the implications of these challenges for the reliability and accuracy of AI outputs.
"we can go through multiple examples and what happened llama 2 was a launch that the phrase like too much rhf or like too much safety was a lot it's just that was the whole narrative after llama 2's ch..."
The discussion concludes with insights into the future of AI preferences and performance optimization. The speakers emphasize the need for ongoing refinement in AI training techniques to balance safety and performance, highlighting the evolving landscape of AI development.
"rhf that it makes the models dumb and it stigmatized the word it did in AI culture and as the techniques have evolved that's no longer the case where all of these Labs have very fine grain control ove..."
This segment discusses the emergence of reasoning behaviors in AI models, particularly in the context of the DeepSeek R1 paper. It highlights how reinforcement learning can lead to the development of complex reasoning chains without explicit human input, showcasing the potential of AI to generate insightful responses through training on verifiable tasks.
"comparing I would say highest cost and highest total usage so a lot of money has gone to these Wiz comparisons where you have two model outputs and a human is comparing between the two of them in earl..."
The conversation contrasts two major types of learning in AI: imitation learning and reinforcement learning. The speakers use AlphaGo and AlphaZero as examples to illustrate how reinforcement learning can lead to more powerful and surprising outcomes compared to traditional imitation methods, emphasizing the significance of trial and error in AI development.
"this might be a good place to uh to mention the uh the eloquent and the insightful tweet of the Great and The Powerful Andre kathi uh I think he had a bunch of thoughts but one of them last thought no..."
This segment explores how self-play contributes to learning in AI, drawing parallels to how humans learn through exploration and interaction with their environment. The discussion emphasizes the importance of verifiable tasks in training AI models and how this approach can lead to significant advancements in AI capabilities.
"of the process was learning from humans where they had they started the first this is the first expert level go player or chess player in Deep Mind series of models where they had some human data and ..."
The speakers address the challenges of creating verifiable tasks for AI training, particularly in math and coding. They discuss the potential for reinforcement learning to improve AI performance in these areas while acknowledging the complexities involved in setting up effective verification domains.
"efficient that is because of the self-play right how does a baby learn what its body is is it sticks its foot in its mouth and it says oh this is my body right it sticks its hand in its mouth and it c..."
In this segment, the discussion shifts to the future of AI, particularly regarding reinforcement learning and its applications in various domains. The speakers speculate on how AI could evolve to perform complex tasks autonomously, including business operations and creative endeavors, highlighting the potential for AI to generate real-world value.
"this is where I think the like aha moment of computer use or robotics will come in because now you have a Sandbox or a playground that is infinitely verifiable right did you you know messing around on..."
The conversation compares different AI models, focusing on DeepSeek R1 and Gemini Flash 2.0. The speakers evaluate their performance in generating novel insights and reasoning capabilities, discussing the implications of their training methodologies and the significance of human-like reasoning in AI outputs.
"approach math with language models just by increasing the number of samples so you can just try again and again and again and you look at the amount of times that the language models get it right and ..."
This segment reflects on the aesthetic and intellectual appeal of observing AI reasoning processes. The speakers share their experiences with various AI models, emphasizing the value of transparency in AI reasoning and how it enhances our understanding of intelligent systems.
"deeps paper detailed in this R1 paper which for me is one of the big open questions on how do you do this is that they did reasoning heavy but very standard post trining techniques after the large sca..."
Lex Fridman shares his reflections on the capabilities of various AI models, including OpenAI's 01 Pro and 03 Mini. He discusses how these models respond to open-ended philosophical questions, emphasizing the novelty and brilliance of their insights. Lex compares the performance of these models, highlighting the strengths and weaknesses of each in generating profound thoughts about human nature.
"yeah it's cool and it's revealing uh the reasoning it's it's magical it's magical like this is really powerful hello everyone this is Lex with a quick intermission recorded after the podcast since we ..."
In this segment, Lex Fridman delves into the reasoning processes of AI models, particularly focusing on DeepSeek R1. He appreciates the Chain of Thought tokens that reveal how AI systems deliberate on complex questions. Lex draws parallels between AI reasoning and human cognitive processes, discussing how shared hallucinations like money and laws facilitate cooperation among humans.
"element for each of these models is how the reasoning is presented deep seek R1 shows the full Chain of Thought tokens which I personally just love for these open-ended philosophical questions it's re..."
Lex reviews the unique insights generated by OpenAI's models, particularly noting how they articulate the human experience. He highlights the idea that humans uniquely transform raw materials into symbolic resources, creating a feedback loop between meaning and matter. This segment emphasizes the poetic nature of AI-generated insights about human identity and narrative.
"domestication by choice is a really interesting angle again it's one of those things when somebody presents a different angle on a seemingly obvious thing it just makes me smile and the same with deep..."
Lex Fridman discusses the comparative performance of various AI models, including OpenAI's 03 Mini and R1. He notes the strengths and weaknesses of each model in generating insightful responses, particularly in philosophical contexts. Lex emphasizes the importance of understanding the nuances in AI reasoning and how different models approach problem-solving.
"an intrinsic cognitive process that acts like an internal error correction system it allows us to adapt our identities and values over time in response to new experiences challenges and social context..."
In this segment, Lex explores the evolving landscape of AI inference techniques, discussing the implications of parallel sampling and Chain of Thought methodologies. He highlights the potential for improved reasoning capabilities in AI models and the importance of understanding how these advancements will shape the future of artificial intelligence.
"and then and and and what o1 offers and then open AI has 01 Pro and what they did with 03 which is like also very unique is that they stacked search on top of Chain of Thought right um and so Chain of..."
Lex Fridman addresses the cost dynamics associated with AI model training and inference. He discusses the significant reduction in costs over recent years and how this trend impacts the development of AI technologies. Lex emphasizes that as costs decrease, the potential for more advanced AI systems increases, paving the way for breakthroughs in intelligence.
"more in multiple choice is what they're doing or if it's something more complex where they Chang the training and they know that the inference mode is going to be different so we're talking about 01 P..."
Lex analyzes the market response to DeepSeek's advancements in AI technology, particularly its cost-effectiveness. He discusses the implications for major tech companies and Nvidia's stock performance in light of these developments. This segment highlights the complexities of market reactions and the broader context of AI advancements.
"they're not below the trend line first of all and at least for gpt3 right uh they are the first to hit it right which is which is a big deal um but they're not below the trend line as far as gpt3 now ..."
In this segment, Lex Fridman and his guest discuss Nvidia's market position amidst the rise of new AI technologies. They explore the factors influencing Nvidia's stock performance and the competitive landscape of AI hardware. Lex emphasizes the importance of understanding the broader economic implications of AI advancements on established companies.
"that is Nvidia stock plummeted uh can you explain what happened I mean and also just explain this moment and whether you know if Nvidia is going to keep winning we're both Nvidia Bulls here I would sa..."
Lex and his guest delve into the topic of GPU smuggling, particularly in relation to China's access to AI hardware. They discuss the methods used by companies like ByteDance to acquire GPUs and the implications of export controls. This segment sheds light on the complexities of global AI hardware distribution and the challenges posed by regulatory measures.
"there's also an element of Nvidia has just been a straight line up right and and there's been so many different narratives that have been trying to push down Nvidia not I don't say push down Nvidia st..."
In this concluding segment, Lex Fridman discusses the economics of acquiring AI hardware, particularly in the context of smuggling and legal procurement. He highlights the challenges faced by companies in navigating export controls and the implications for AI development in China. Lex emphasizes the ongoing evolution of the AI landscape and the strategies companies employ to secure necessary resources.
"that does everything reliably right now because it's not like an Nvidia competitor arose it's it's another company that's using Nvidia who historically has been a large Nvidia customer customer yeah a..."
The conversation continues with an analysis of the economic scale of GPU smuggling, estimating that Nvidia has shipped a significant number of GPUs to China despite restrictions. The speakers discuss the implications of these actions on the AI landscape and the competitive edge it provides to Chinese companies. This segment emphasizes the ongoing battle between regulation and market demand.
"smuggling most of the large scale smuggling is like companies in Singapore and Malaysia like routing them around or renting gpus completely legally I want to jump in how much do the scale I think ther..."
This segment explores the legal avenues available for Chinese companies to access GPUs, including renting GPU clusters under specific conditions. The speakers discuss how recent diffusion rules have changed the landscape but still allow for some legal transactions. This highlights the complexities of international trade in AI hardware and the ongoing adjustments companies must make.
"massive network of like companies to get the materials they need after they were banned in like 2018 so it's not like otherworldly uh but I agree right n Nathan's point is like hey you can't smuggle A..."
The discussion shifts to DeepSeek's operational challenges, particularly its inability to serve its model due to a lack of available GPUs. The speakers detail how this shortage affects user experience and the company's growth potential. This segment underscores the critical role of GPU availability in the AI industry and the competitive pressures faced by companies like DeepSeek.
"smuggle but yeah it's not you know as the numbers grow right uh you know 100 something billion dollars of revenue for NVIDIA last year 200 something billion this year right and if next year or you kno..."
In this segment, the speakers delve into the ethical considerations surrounding AI model training, particularly regarding the use of outputs from models like OpenAI's. They discuss the blurred lines between legal and ethical practices in AI development, raising questions about the implications of using publicly available data. This segment is crucial for understanding the moral landscape of AI research.
"would be fascinating to watch the smuggling cuz I mean there's drug smuggling right that's a that's a market there's weapons smuggling and gpus will surpass that at some points are highest value per k..."
The conversation provides an overview of the distillation process in AI, where models are trained on outputs from more powerful models. The speakers explain how this practice is common in the industry and discuss its implications for model development. This segment is essential for grasping the technical aspects of AI training and the strategies employed by researchers.
"have some completions what the model is trying to learn to imitate and what you do there is instead of a human data or instead of the model you're currently training you take completions from a differ..."
The speakers continue to explore the ethical dilemmas in AI training, particularly the challenges posed by copyright and data usage. They discuss the potential for legal repercussions and the need for clearer guidelines in the industry. This segment highlights the ongoing debate about intellectual property in the context of AI development.
"right so there's a bit of a hypocrisy because sort of open Ai and potentially most of the companies trained on the internet's text without permission there's also a clear loophole which is that uh I g..."
This segment addresses the issue of industrial espionage in the tech industry, particularly in AI. The speakers discuss how ideas are often shared informally and the challenges of protecting intellectual property. This conversation sheds light on the competitive nature of the AI field and the risks associated with idea theft.
"chbt oh I guess I guess one of the ways to do that is like a system prompt or something like that like if you're serving it to say that you're that's what that's what we do like if we host the demo yo..."
The discussion shifts to the unprecedented scale of AI mega clusters being built by major companies. The speakers analyze the implications of these developments for data center power consumption and the future of AI infrastructure. This segment is vital for understanding the evolution of AI capabilities and the resources required to support them.
"the last couple days we've seen a lot of people distill deep seeks model into llama models because because the Deep seek models are kind of complicated to run inference on because their mixture of exp..."
In this segment, the discussion revolves around the transformation of data centers from traditional distributed systems to modern AI-focused architectures. The speakers highlight the shift from database access to inference and training tasks, emphasizing the increasing scale of GPU usage in data centers and the implications for AI model training.
"clusters and what's a GPU and what's a computer and what kid not that far back but yeah so like what do we mean by the Clusters I thought I was about to do the Apple ad right what's a computer so so t..."
The conversation delves into the historical significance of GPU scaling in AI, from early models like AlexNet to the massive GPU requirements of GPT-3 and GPT-4. The speakers discuss the unprecedented scale of GPU usage, the costs involved, and the impact of scaling laws on AI performance.
"millions of gpus but the scale of the uh largest cluster is also really important right um when we look back at history right like you know or through through the age of AI right like it was a really ..."
This segment focuses on the power consumption of large-scale GPU clusters, detailing the energy requirements for training AI models. The speakers compare the power needs of GPUs to everyday appliances and discuss the challenges of cooling and energy efficiency in data centers.
"toaster toaster is like a similar power consumption to an a100 Right h100 comes around they increase the power from like 400 to 700 watts and that's just per GPU and then there's all the associated st..."
The discussion shifts to Elon Musk's ambitious plans for a massive data center in Memphis, which aims to house 200,000 GPUs. The speakers explore the infrastructure developments, including power generation and cooling systems, that are necessary to support such a large-scale operation.
"know think about 100,000 gpus um with roughly 1,400 Watts a piece that's that's that's 140 megawatts 150 megawatts right for 128 right so you're talking about you've jumped from 15 to megawatts to 10x..."
In this segment, the speakers discuss the future of AI clusters and the competition among tech giants to build the largest and most efficient data centers. They highlight the plans of companies like Meta, Amazon, and Google to develop multi-gigawatt data centers and the implications for AI research and development.
"right like all all the hypers scalers have done this now the next scale is is is something that's even bigger right and so you know Elon just to stick on the topic he's he's building his own natural g..."
The conversation addresses the challenges faced by the power grid in supporting the rapid growth of AI data centers. The speakers discuss the regulatory environment, the need for new power plants, and the implications of energy consumption on sustainability efforts in the tech industry.
"right they're building two natural gas plants massive ones uh and they're and then they're building this massive data center um Amazon has like plans for this scale uh Google has plans for this scale ..."
This segment explores the innovations in cooling technology for data centers, particularly the shift from air cooling to liquid cooling. The speakers discuss the advantages of water cooling systems and how they contribute to efficiency and performance in large-scale AI operations.
"in some parts of the US like in Virginia it cost more to transmit power than it cost to generate it which is like you know there's there's all sorts of like second order effects that are insane here c..."
The speakers discuss the ongoing competition to build the largest GPU clusters, highlighting Elon Musk's current lead with 200,000 GPUs. They also mention the plans of other companies to scale up their GPU resources and the implications for AI training and performance.
"couple hopes right like one is you know and Elon what he's doing in Memphis is like you know to the extreme they're not just using dual combine cycle gas which is like super efficient he's also just u..."
In this segment, the discussion focuses on the challenges of managing power spikes during AI training. The speakers share insights on how companies like Meta are innovating to prevent power surges and ensure efficient operation of their data centers.
"lose we you know that's way worse right I should say that uh I got a chance to visit um the Memphis data center oh wow and it's uh kind of incredible I mean I visited with with Elon just the team them..."
The conversation highlights the critical role of cooling systems in AI data centers, particularly in the context of high-performance GPUs. The speakers discuss the complexities of maintaining optimal temperatures and the innovations being implemented to enhance cooling efficiency.
"the unsung heroes are the cooling and electrical systems which are just glossed over um but I think like like one story that maybe is like exemplifies how insane this stuff is is uh when you're traini..."
The segment concludes with projections for the future of GPU clusters, discussing the potential for reaching one million GPUs in a single data center. The speakers explore the implications of this growth for AI research and the technological advancements required to support it.
"power doesn't Spike but that just tells you how much power you're working with I mean it's insane it's insane people should just go Google like scale like what does X watts do and go through all the s..."
In this segment, the discussion centers around the current state of GPU clusters, highlighting Elon Musk's impressive 200,000 GPU cluster in Memphis. The conversation compares this with clusters from Meta and OpenAI, emphasizing the importance of tightly connected GPUs for training efficiency. The segment also touches on future projections for GPU cluster sizes and power consumption, indicating a significant increase in both metrics.
"section called cluster measuring contest so uh there's another word there but I won't say it you know uh what who's who's who's got the biggest now and who's going to have the big today individual lar..."
This segment explores the potential future utilization of massive GPU clusters, particularly in training AI models. The speakers discuss the balance between training and inference, emphasizing that mega clusters are primarily for training due to their high-speed networking capabilities. The conversation also delves into the evolving landscape of AI training, where post-training methods may become more prominent, shifting the focus from traditional pre-training techniques.
"right you know that's he's going to surprise us so what's the idea with these clusters if you have a million gpus what percentage in uh let's say two three years is used for uh training and what perce..."
The discussion shifts to Google's TPU infrastructure, which is noted for its advanced design and efficiency. The speakers compare Google's data center strategy with that of Nvidia, highlighting the unique challenges Google faces due to its multi-site data center approach. They also discuss the implications of Google's TPU architecture on AI model training and the competitive landscape in AI hardware.
"pre-training is when you increase the context length for these models and we've talked earlier in the conversation about how the context length when you have a long input is much easier to manage than..."
In this segment, the conversation addresses why Google has not pursued selling TPUs despite their advanced capabilities. The speakers analyze Google's focus on internal workloads and the complexities of their organizational structure, which complicates the potential for external sales. They also discuss the broader implications of Google's TPU strategy on the AI market and competition with Nvidia.
"to have the biggest cluster fully connected right because it's all in one building yeah right and he's completely right on that right Google has the biggest cluster but you have to spread over three s..."
The segment discusses the current AI race, focusing on the competitive landscape among major players like Google, OpenAI, and others. The speakers evaluate who is currently leading in AI development and revenue generation, emphasizing OpenAI's position. They also touch on the financial aspects of AI development, including the costs associated with research and the sustainability of current business models in the AI sector.
"I mean like you're always going to make more money on Services than than than I mean like yeah like you like to be clear like today people are spending a lot more on Hardware than they are the service..."
This segment highlights the often-overlooked costs associated with AI model development, including research and manpower. The speakers discuss the financial implications of training AI models and the ongoing need for research to improve efficiency and capabilities. They emphasize that understanding these costs is crucial for evaluating the viability of AI projects and the future of AI technology.
"Nvidia it should be said as a truly special company like I mean they the whole the culture of everything they're really optimized for that kind of thing speaking of which is there somebody that can ev..."
The conversation shifts to the sustainability of AI companies like OpenAI and Anthropic. The speakers speculate on the potential for these companies to thrive or fail based on their ability to innovate and adapt. They discuss the competitive landscape, suggesting that multiple companies can coexist and benefit from AI advancements, rather than a winner-takes-all scenario.
"think I think the you know people focus on the payback question but it's really easy to like just be like well like you know GDP is humans and Industrial Capital right and if you can make intelligence..."
In this segment, the speakers discuss the gradual nature of AI advancements, emphasizing that the development of super powerful AI will not happen overnight. They highlight the importance of incremental improvements and the various features that will emerge over time, suggesting that many companies will find ways to leverage AI without necessarily being the leaders in model training.
"okay so it's not uh let's not call it AGI whatever it's like a single day it's it's a gradual thingi super powerful AI but it's it's a gradually increasing set of features that are useful and uh make ..."
The discussion focuses on how companies like Meta and Tesla can benefit from AI without directly training the best models. The speakers explore how AI can enhance existing products and services, leading to increased revenue per user. They also touch on the potential for personalized AI applications, such as robots in homes, and the vast market opportunities that could arise.
"already sell so whether that's the recommendation system or for Elon who's been talking about Optimus the robot potentially the intelligence of the robot and then you have personalized robots in the h..."
This segment addresses the challenges of integrating AI into existing business frameworks. The speakers discuss the complexities of creating AI agents that can operate effectively in the messy real world, highlighting the difficulties faced by companies in making their systems user-friendly. They emphasize the need for AI to navigate various tasks and environments seamlessly.
"brand is in Chachi PT and there is actually not that for most users there's not that much of a reason that they need open AI to be spending billions and billions of dollars on the next best model when..."
The conversation shifts to the potential of AI agents and their role in transforming industries. The speakers speculate on the future capabilities of AI agents, discussing their ability to perform tasks autonomously and adapt to new challenges. They highlight the importance of developing robust AI systems that can handle real-world complexities.
"the only use case it's like these reasoning code agents computer use all this stuff is where opena has to actually go to make money in the future otherwise they're kaputs but X Google and meta have th..."
In this segment, the speakers explore the intersection of AI and software engineering, discussing how AI tools like ChatGPT are already enhancing productivity for programmers. They analyze the current landscape of AI in coding, noting the rapid improvements in AI capabilities and the implications for future software development.
"of discussions as it's the next compute layer you you have to believe that and and you there's a lot of discussions that tokens and tokenomics and llm apis are the next compute layer or or the next Pa..."
The discussion focuses on the evolution of AI benchmarks and their significance in measuring AI performance. The speakers reflect on recent advancements in AI models and their ability to tackle complex programming tasks, highlighting the progress made in a short time frame and the implications for the future of AI in software development.
"commodity right deeps V3 shows this but also the gpt3 3 chart earlier C chart showed this right llama 3B is 1 1200X cheaper than gpt3 any gpt3 like anyone whose business model was gpt3 level capabilit..."
This segment addresses the potential for AI to enhance human interaction in various domains. The speakers discuss the challenges of creating AI systems that can effectively communicate and collaborate with humans, emphasizing the need for continued research and development to bridge the gap between AI capabilities and real-world applications.
"is totally untapped and it's not clear technically how it is done yeah that is I mean the sort of the AdSense Innovation that Google did the one day you'll have in GPT output an ad and that's going to..."
The conversation concludes with reflections on the broader AI landscape and the various players involved. The speakers discuss the importance of adaptability and innovation in the face of rapid technological advancements, suggesting that companies must remain agile to thrive in the evolving AI ecosystem.
"AGI yeah agents and AGI and if I build AGI I can make tons of money right or I can spend pay for everything right and this is this is It's just predicated like back on the like export control thing ri..."
In this segment, the discussion revolves around the existing AI training sandboxes and how they have evolved. The speakers highlight the importance of these environments in research, comparing them to the robotics teams at DeepMind. They explore the transition from isolated tasks to more generalized models in natural language processing (NLP) and the challenges of determining the point of effective generalization.
"sandboxes already exist in research there are people who have built clones of all the most popular websites of Google Amazon blah blah blah to make it so that there's and I mean open AI probably has t..."
The conversation shifts to the impact of AI on software engineering, particularly how tools like ChatGPT and code completion systems are transforming the field. The speakers discuss the productivity gains in programming and the evolving role of software engineers as they adapt to AI technologies. They also touch on the benchmarks for evaluating AI's performance in coding tasks.
"think about the programming context so software engineering that you know that's where I personally and I know a lot of people um interact with AI the most there's a lot of fear and angst too from cur..."
This segment delves into the cost dynamics of software engineering, particularly in the U.S. versus China. The speakers analyze how lower engineering costs in China lead to different market behaviors, such as the prevalence of custom-built solutions over platform SaaS. They discuss the implications of these trends for software development efficiency and the potential for rapid innovation.
"because it is a verifiable domain um you can always like unit test or compile um and and and there's many different regions of like it can inspect the whole code base at once which no no engineer real..."
The discussion continues with a focus on the future of programming in the context of AI advancements. The speakers emphasize the need for programmers to embrace AI as a collaborative tool rather than a replacement. They explore the importance of human oversight in AI-driven coding processes and the potential for AI to enhance software engineering practices.
"all of these things can go happen fast I think software and then and then the other domain is like industrial chemical mechanical engineers suck at coding right uh just generally and like their tools ..."
In this segment, the speakers discuss the intersection of AI and domain expertise, particularly in fields like aerospace and chemical engineering. They highlight the challenges faced by domain experts in utilizing outdated tools and the potential for AI to modernize these industries. The conversation underscores the importance of integrating advanced software engineering skills into specialized fields.
"the nature of what it means to be a programmer and what kind of jobs programmers do changes because I think there needs to be a human in the loop of everything you've talked about there's a really imp..."
The speakers transition to the topic of open source AI, discussing the implications of releasing models like Tulu. They explore the challenges of open sourcing AI, including the need for accessible training data and the complexities of building on existing models. The conversation emphasizes the ideological motivations behind open source initiatives and the necessity for a supportive ecosystem.
"else oh yeah there's so many lwh hanging fruit everywhere in terms of where software can like help automate a thing or digitize the thing in in the legal system I mean that's why doge is exciting you ..."
This segment focuses on the evaluation of AI models, particularly the benchmarks used to assess their performance. The speakers discuss the importance of safety metrics and how they influence the overall evaluation of models like DeepSeek and Tulu. They highlight the need for comprehensive evaluation suites that reflect the broader community's concerns about AI safety.
"available but it's like posttraining is much more accessible at this time it's still pretty cheap and you can do it and the thing is like how high can we push this number where people have access to a..."
The conversation wraps up with a discussion on the future of open source AI and its potential to reshape the industry. The speakers reflect on the need for truly open models and the challenges posed by restrictive licenses. They emphasize the importance of fostering an environment where open source AI can thrive and contribute to technological advancements.
"model and this model we released today we saw the same thing is we're at ai2 we don't have a ton of compute we can't train 405b models all the time so we just did a few runs and they tend to work and ..."
In this segment, the speakers discuss Stargate, a significant initiative aimed at enhancing AI infrastructure in the U.S. They analyze the implications of recent executive actions that facilitate the construction of data centers and the potential impact on AI development. The conversation highlights the complexities surrounding funding and the ambitious goals associated with Stargate.
"release a model later we have more time to learn new techniques like this RL Technique we had started this in the fall it's now really popular reasoning models the next thing to do for open open sourc..."
In this segment, the discussion revolves around Stargate, a joint venture involving significant investments in AI infrastructure. The speakers analyze the ambitious $500 billion figure associated with Stargate, questioning its feasibility and the implications of recent executive actions that facilitate faster construction of data centers. They highlight the role of the Trump administration in reducing regulatory barriers, enabling quicker development of AI capabilities.
"terabytes of files it's like I I I don't know what I'm going to find in there but that's what that's what we need to do as an ecosystem if people want open source AI to be financially useful we didn't..."
This segment delves into the financial aspects of building AI infrastructure, particularly focusing on the costs associated with the Stargate project. The speakers break down the projected expenses, including server costs and operational expenditures, and discuss the funding challenges faced by OpenAI and its partners. They emphasize the need for substantial investments and the uncertainty surrounding the actual financial backing for these ambitious AI initiatives.
"predicated and this is why that whole show happened now how they came up with a $500 billion number is beyond me how they came up with a hundred billion dollar number makes sense to some extent right ..."
In this segment, the conversation shifts to the key investors involved in AI projects, particularly OpenAI and Stargate. The speakers discuss the potential contributions from major players like SoftBank and Oracle, and the implications of their investments on the future of AI development. They also touch on the challenges of securing funding and the strategic importance of these investments in the rapidly evolving AI landscape.
"furthermore it's not $100 billion it's $50 billion of spend right and then like $50 billion of operational cost power Etc um rental pricing Etc um because they're renting it from opening eyes is renti..."
This segment explores the future of AI clusters and the technological breakthroughs anticipated in the coming years. The speakers express excitement about advancements in networking and data center capabilities, discussing the potential for multi-data center training and the innovations that could arise from improved interconnectivity. They highlight the importance of tracking supply chains and the strategic decisions that will shape the AI landscape.
"have to do with anything what does Trump have to do with everything he's just a hype man Trump is he's reducing the regulation so they can build it faster right um and he's allowing them to do it righ..."
In the concluding segment, the speakers reflect on the broader implications of AI for humanity's future. They discuss the potential for AI to enhance human capabilities while also expressing concerns about the risks associated with powerful technologies. The conversation emphasizes the need for inclusivity in AI development and the importance of ensuring that advancements benefit society as a whole. They conclude with a hopeful outlook on the trajectory of human civilization in the context of AI.
"could be very specific technical things like breakthroughs on post post training or it could be just size big yeah I mean it's impressive clusters I really I really enjoy tracking supply chain and lik..."