Introduction

> The landscape of AI is evolving rapidly, and the recent emergence of models like OpenAI's o3-mini is exciting, yet DeepSeek-R1 offers comparable performance at a lower cost while showcasing its reasoning process. “The quote, DeepSeek moment is indeed real,” marking a pivotal shift in tech history, encapsulating not just technological advancements but also significant geopolitical implications.

> It’s crucial to cut through the hype surrounding AI; as we discussed, many better models will surface—both American and Chinese—that will continuously shift the cost curve and enhance our capabilities. This is just the beginning of what’s possible in AI, and the conversation about its future is more important than ever.

DeepSeek-R1 and DeepSeek-V3

> "DeepSeek-V3 and DeepSeek-R1 represent a significant advancement in AI models that leverage techniques like mixture of experts and reasoning training."

> "The debate on open-weights in AI models is crucial, with licenses varying across companies like Llama, DeepSeek, Qwen, and Mistral, impacting data access and replication costs."

> "While OpenAI, DeepSeek, and Llama showcase different naming schemes and licensing approaches, the push towards detailed technical reports signals a larger industry shift towards transparency and insight sharing."

Low cost of training

> Mixture of experts architecture represents a significant leap in model efficiency, allowing DeepSeek to activate only a fraction of its 600 billion parameters at any time – specifically, just 37 billion. This means "I can continue to grow the total embedding space of parameters" without the corresponding increase in compute costs, making both training and inference notably more efficient.

> The intricacies behind training models deeply intertwine with low-level engineering, such as scheduling Sm’s and utilizing Nvidia's NCCL library. DeepSeek's necessity led to innovations in optimizing these processes, highlighting that "necessity is the mother of invention" when facing constraints.

> The concept of the "bitter lesson" underscores that scalable methods that minimize human intervention are likely to prevail, emphasizing the need to let models learn effectively without intricate biases. The desire for simplicity does not detract from the complex methodologies that can accrue advantages across generations of models.

> The YOLO run epitomizes the high-stakes nature of model training; a decisive leap into the unknown, and while luck is a factor, it is skill that ultimately drives success. The adventures of DeepSeek and its ability to innovate amid constraints serve as testament to the idea that "the biggest complexity is running a very sparse mixture of experts model."

DeepSeek compute cluster

> Firstly, DeepSeek, under the leadership of CEO Liang Feng, has strategically leveraged its resources from high-frequency trading to delve into AI, particularly emphasizing natural language processing for fast and effective trading. This visionary approach aims to establish a new ecosystem of AI, with a strong focus on China leading in this domain.

> Secondly, while publicly disclosed GPU numbers may be limited, research suggests that DeepSeek could potentially possess around 50,000 GPUs, spread across various tasks including the fund's operations, research, and model ablations. This scale of compute allocation places DeepSeek among the top global entities in terms of computational resources, showcasing their significant presence in the AI landscape.

Export controls on GPUs to China

> The landscape of AI is fundamentally shaped by export controls, which aim to limit the computational power available for training in China to maintain a strategic edge. It's not just about slowing down the advancement of AI; it's about understanding that “the amount of AI that can be run in China is going to be much lower,” impacting their capability to achieve substantial breakthroughs like AGI.

> On the other hand, the evolution of models, such as OpenAI’s o3, signifies a shift towards more compute-intensive reasoning tasks, which significantly increases the “test-time compute” demands. This raises the stakes not only for performance but also for the underlying infrastructure needed to support advanced AI models, as “a large part of the compute is used in inference,” turning the focus on how these models can be effectively deployed in real-world scenarios.

AGI timeline

> The pace of progress towards AGI is unsettling, with rapid and surprising advancements leading to new paradigms such as DeepSeek-R1, showcasing substantial progress in AI models.

> The potential impact of AGI on geopolitics and military capabilities raises concerns about the need for caution and control in directing these powerful technologies, highlighting the importance of considering the implications of AGI deployment and control.

China's manufacturing capacity

> The balance of power in AI is intricately tied to export controls; if not handled correctly, these restrictions could ultimately guarantee China a long-term advantage by limiting the US's ability to innovate and produce cutting-edge technology. As I put it, “if AI takes a long time to become differentiated, we’ve kneecapped the financial performance of American companies,” curbing their potential to compete effectively.

> The urgency for action is critical, especially as China continues to strengthen its semiconductor and AI capabilities. It’s not just about having talent; it’s also about the immense industrial capacity to construct data centers at a scale that dwarfs anything in the US. If China decides to prioritize AI infrastructure, they could “do it faster than us,” and it’s essential that the US recognizes this race isn't just about technology, but a fundamental geopolitical shift.

Cold war with China

> - The DeepSeek moment seems to be a turning point, a catalyst for a potential cold war scenario between superpowers like the US and China in the realm of AGI. Factors like the Nvidia stock drop and US export controls are pushing these nations towards a critical juncture in their technological rivalry.

> - The risk of severe consequences, such as military action on Taiwan, looms large if China is further isolated from key technologies. This isolation, combined with internal challenges like the urban-rural divide and male-to-female birth ratios, could lead to drastic measures, potentially escalating tensions to a dangerous extent.

TSMC and Taiwan

> TSMC's dominance in semiconductor manufacturing stems from its innovative foundry model, which allows companies to outsource their chip production. This model thrives on economies of scale and specialization. As I mentioned, "the cost of building a fab is so high," and fewer companies can afford the investment required for state-of-the-art facilities, making TSMC the go-to player for advanced chip production.

> The unique work culture and educational focus in Taiwan contribute to TSMC's success. The commitment is palpable; employees show remarkable dedication, such as responding to earthquakes immediately to protect production. “The parking lot gets slammed, and people just go into the fab,” demonstrating the collective drive to ensure high-quality outputs. This contrasts sharply with the situation in the U.S., where similar dedication may be harder to cultivate.

> The geopolitical landscape surrounding U.S.-China relations is increasingly complex, especially in the context of semiconductor technology. I believe the "export controls are pointing towards a separate future economy," which complicates global cooperation and could escalate tensions. This divergence could ultimately shape the nature of future conflicts, making it imperative for leaders to navigate this landscape carefully to avoid military confrontation.

Best GPUs for AI

> The evolution of GPU export controls, particularly focusing on the H20 chip, showcases the dynamic nature of hardware restrictions. This chip, despite being neutered in some aspects, excels in others like memory bandwidth and capacity, making it crucial for AI systems, especially for reasoning tasks.

> Memory plays a vital role in AI models, especially concerning transformers and the attention mechanism. The KV cache optimization and understanding the quadratic memory cost in relation to context length are essential for efficient model serving, particularly when dealing with long context lengths and reasoning tasks.

> The shift towards reasoning models introduces new challenges in memory management and inference processing, impacting the cost and efficiency of serving multiple users simultaneously. The significance of memory constraints becomes evident as models produce extensive chains of thought, leading to increased memory requirements and potentially limiting serving capabilities.

Why DeepSeek is so cheap

> DeepSeek's meteoric rise to the top of the App Store showcases the power of innovation and accessibility in AI. Their recent launch of an API that delivers long responses demonstrates a commitment to pushing boundaries, as they’ve been able to achieve "27 times cheaper" service compared to traditional players like OpenAI while maintaining impressive output.

> The efficiency of DeepSeek's model architecture, particularly through their "multi-head latent attention" mechanism, significantly reduces memory usage, making high-quality AI more attainable. The importance of this innovation lies in how it enables competition with established models and lowers the entry barrier for various developers, effectively democratizing AI.

> The landscape of AI development is rapidly evolving, with urgency driving deeper engagement in the field. As American companies grapple with safety and compliance, DeepSeek's approach, which prioritizes speed over caution, reflects a shift in the paradigm, compelling a reevaluation of both ethical standards and competitive strategies in the global race for AI dominance.

Espionage

> One key insight is about the potential risks of unintentional or intentional alignment in AI models, where even subtle biases or cultural nuances can be embedded deep within the models, impacting how they interact with us and potentially influencing our thoughts.

> Another significant point raised is the concern around the power of superhuman persuasion that could be harnessed by AI models before achieving superhuman intelligence, leading to scenarios where we might be heavily influenced by algorithms or other entities, compromising our ability to think independently and raising questions about the control these technologies could exert over our minds in the future.

Censorship

> When it comes to model behavior and alignment, the nuances of censorship versus factual representation are critical. It’s essential to understand that “you can insert censorship or alignment at various stages in the pipeline,” and getting rid of certain facts from a model often requires deep intervention throughout the entire training process, not just at the surface level.

> The implications of Reinforcement Learning from Human Feedback (RLHF) can be profound; too much emphasis on alignment can lead to models that are not only biased but also “dumb,” as seen with Llama 2. This highlights a delicate balancing act, where “the model weights might have been fine,” but execution at the system level can lead to unexpected failures.

> As we develop more sophisticated models, the role of human input may diminish. The emergence of reasoning behaviors from pre-trained models, as showcased in the DeepSeek-R1 results, suggests that effective training can happen without extensive human preferences. This is a powerful indication that AI can reach advanced levels of reasoning and performance through robust reinforcement learning practices.

Andrej Karpathy and magic of RL

> The magic of deep learning lies in two major types of learning: imitation learning and trial-and-error learning. The power and surprises come from the latter, as seen in AlphaGo's evolution from human-guided learning to AlphaZero without human data, leading to greater strength in AI systems.

> The future breakthrough in AI may come from a shift to verifiable tasks in continuous self-play environments, where models can learn efficiently and evolve skills toward complex real-world applications like robotics and even commercial success, demonstrating the potential of reinforcement learning beyond just solving verifiable problems.

OpenAI o3-mini vs DeepSeek r1

> The landscape of reasoning models is evolving rapidly, and one of the standout developments is the introduction of "o3-mini," which aims to enhance reasoning capabilities through "large-scale reasoning training" followed by tailored post-training techniques. It's intriguing to think about how this methodological approach, leveraging reinforcement learning, might impact the transfer of skills across various domains, particularly in eloquent writing and philosophical reasoning. "There's a gradation...how much of this RL training you put into it determines how the output looks."

> Google's Gemini Flash Thinking deserves more attention than it's currently receiving; it's both cost-effective and demonstrates improvement over previous models with a different training approach. The ability for models like these to integrate logic and reasoning is crucial, especially as they evolve, providing an important alternative to others that have more expressive responses but perhaps lack focus. "Flash Thinking... is cheaper than R1 and better."

> It's fascinating to witness how the cost of running these advanced AI models has dramatically decreased over time, paving the way for more widespread innovation and exploration in AI capabilities. This trend is crucial; as we achieve more efficient training processes, the potential for unlocking higher levels of intelligence increases, suggesting a transformative future is closer than we think. "We will have really awesome intelligence before we have AGI permeate throughout the economy."

NVIDIA

> Nvidia's stock plummeted post the DeepSeek-R1 release creating market concern but it's more complex than just cost-cutting AI models; false narratives led to misinterpretation of OpenAI's spending. The evolution of AI, with models progressing rapidly, impacts not just Nvidia but the semiconductor industry as a whole, showcasing the lasting impact of Jevons Paradox and unprecedented advancements in a short timespan.

GPU smuggling

> The current landscape of AI is heavily impacted by the dynamics of GPU availability and the geopolitical strategies surrounding it. "Nvidia's the only one that does everything reliably right now," and this creates a complex scenario where companies like ByteDance operate as substantial players in the smuggling of GPUs, with claims of "over 500,000 GPUs" being rented globally to meet their demands.

> On a broader scale, the evolving regulatory environment and diffusion rules have created a precarious balance of power in the AI realm. With projections that Nvidia's revenue could facilitate significant expansions, it becomes clear that "as the numbers grow," China faces a substantial "compute disadvantage for training models" amidst these evolving restrictions, impacting their ability to serve and innovate.

DeepSeek training on OpenAI data

> Digging into the complexities of model distillation and training ethics, it becomes clear that the lines are blurred, with a mix of standard practices, legal terms of service, and IP considerations in play. The debate around training on the internet highlights a nuanced landscape where companies navigate ethical and legal boundaries while leveraging shared data for AI advancements.

> Reflecting on the dynamics of idea-sharing and industrial espionage, it becomes apparent that while stealing code and data may be challenging, the exchange of ideas in the tech industry is fluid and often driven by talent migrations. From the subtle influence of top employees moving between companies to the more sensational tales of espionage attempts, the tech world operates in a realm where ideas flow freely, sometimes outpacing legal boundaries and traditional security measures.

AI megaclusters

> The unprecedented scale of mega cluster buildouts for AI is truly mind-blowing. We're witnessing power consumption in data centers rise rapidly—from about 2-3% of total U.S. electricity to potentially 10% by 2030. The demand for AI infrastructure is changing everything we know about data consumption and computing power.

> The way clusters operate is evolving dramatically. It's no longer just about serving pages or ads but heavily focused on inference and training processes. This means handling thousands of GPUs across distributed data centers, which reshapes how we think about data infrastructure.

> The sheer size of current AI mega clusters, such as Elon's facility with 200,000 GPUs, represents a leap in scale and complexity. Training models like GPT-4 requires an astonishing amount of power—up to 2.2 gigawatts for a single data center. When you realize that’s more power than most cities consume, it's clear we're in a new era of AI.

> As AI companies race to build these massive clusters, the implications for energy consumption and environmental sustainability are significant. The need for efficient power solutions is driving innovations like water cooling and natural gas plants—essentially, they're willing to adopt temporary "dirty" solutions to maintain competitive advantage in AI capabilities.

> Looking ahead, the focus will shift from pre-training to extensive post-training processes, where AI models can continually learn and adapt. As we harness larger clusters, we're not just pushing the limits of computational power; we're redefining what it means for AI to be "intelligent" in real-world applications. This race is crucial, and as I've seen firsthand, the pace of innovation is nothing short of exhilarating.

Who wins the race to AGI?

> OpenAI is currently leading the AI race with the best model and the most AI revenue, while companies like Microsoft and Meta are making money from AI through different avenues such as recommendation systems. It's not just about training costs, but also significant investment in research and manpower that drives success in this space.

> The future of AI competition is not necessarily a winner-take-all scenario, but rather a dynamic landscape where companies like Google, Meta, Tesla, and xAI can benefit from the intelligence boost AI provides to their existing products. OpenAI and Anthropic are focusing on AGI development, with a belief that achieving AGI will bring substantial returns in the future, even if the path to get there is costly and uncertain.

AI agents

> The excitement surrounding AI agents is palpable, but it's crucial to view the term with skepticism. “There's a lot of the term agent applied to things like Apple Intelligence,” which is more about integrating existing tools rather than genuine autonomy. True agents need to adapt in real-time and tackle complex tasks independently, a feat we are still far from achieving.

> As we explore this terrain, it's vital to recognize that “the real world and the open world are really, really messy.” While AI can navigate well-defined tasks in restricted environments, the complexity of human interaction and unpredictable scenarios presents significant challenges. There's potential for innovation within defined problem spaces, but we must be mindful of the hurdles that lie ahead before we can trust AI agents to work seamlessly for us.

Programming and AI

> Software engineering will transform rapidly with AI, leading to plummeting costs and the ability for companies to build custom-tailored solutions quickly and efficiently, changing markets significantly.

> The evolution of AI in programming won't be a sudden shift, but rather a gradual transformation where human involvement remains crucial for tasks like debugging, providing preference judgments, and managing increasingly intelligent systems. It's about programmers partnering with AI and becoming domain experts to leverage the rising tide of AI for greater impact and efficiency.

Open source

> The release of Tulu marks a significant step in pushing the boundaries of open-source AI, emphasizing my belief that "it's like the time of progress." Building upon open-weight models like Llama, we're striving to make post-training techniques accessible and customizable for various domains, enabling startups and businesses to leverage these advancements in their unique contexts. The goal is to create a space where "there's a lot of room for people to play" and innovate using fully open code and data.

> The recent changes in licensing for models like DeepSeek-R1 represent a pivotal moment for the open-source movement, providing a "major reset" that encourages broader use without restrictive conditions. This shift is crucial, as the past few years have seen models with convoluted licenses that hinder true open-source collaboration. The vision for open language models is to maintain accessibility while ensuring that "everything is open with the data as close to the frontier as possible," fostering a thriving ecosystem that benefits from the collective insights and advancements in AI research.

Stargate

> - Stargate's $500 billion announcement for AI infrastructure might not add up, but with executive actions in play, regulations are loosening to fast-track its development, especially in Texas. The $100 billion phase one in Abilene, Texas is a joint venture attracting investments from SoftBank, Oracle, and OpenAI, even if the actual funds are yet to materialize. The belief is strong that the necessary finances will flow in to support this massive endeavor.

> - Trump's role in reducing regulations and creating a pro-building environment sets the stage for a potential influx of investment, despite no direct US government funding being involved in the Stargate project. The hype generated around Stargate's scale and Trump's backing could escalate an already competitive landscape in AI infrastructure development, intensifying the race for advancements and investments.

Future of AI

> The technological evolution in AI is accelerating like never before, with fascinating advancements happening across every layer of the compute stack—“human progress is at a pace that’s never been seen before.” The innovations in networking, from co-packaged optics to multi-data center training, are particularly thrilling as they push the boundaries of what’s possible, making all elements come together to form an increasingly interconnected system.

> Moreover, the need for a more open and inclusive approach to AI development resonates deeply with me; “we need to have a lot of people involved in making that.” This collective involvement will ensure that the immense potential of AI serves humanity positively, rather than being controlled by a select few, steering society into an era of abundance and reducing suffering along the way.