[Music] I'm Ryan. I'm a founding engineer at Bespoke Labs. And today I'm going to talk to you about Open Thoughts, which is our project to create the best open-source reasoning data sets.And I'll be switching tack a little bit from our earlier discussions on reasoning and RL and focus on the reasoning part and you'll see why. So just so we're on the same page, we've talked a lot about reasoning, but what's actually going on here. So I like this graph from JSON which shows this incredible performance that's ...

Model Overview - Kimmy K2 is a state-of-the-art mixture of experts language model with 32 billion activated parameters and 1 trillion total parameters [3] - The model was pre-trained on 155% trillion tokens with zero training instability [4] - Kimmy K2 supports up to 2 million tokens in the context window [5] Performance Benchmarks - Kimmy K2 Instruct beats Deepseek, Quen, and GPT41 on SWEBench verified, coming in right behind Cloud 4 Opus [7] - On Live Codebench, Kimmy K2 beats Cloud 4 Opus [7] - Kimmy K2 tops the list on Amy 2025 for math, GPQA Diamond [8] Optimization and Training - The model is trained with the Muon optimizer [4] - Kimmy K2 achieves exceptional performance across frontier knowledge reasoning and coding tasks [4] - The training process was open source [8] Availability and Cost - Inference is available through Kimmy directly at $0.15 per million input tokens with a cache, $0.60 without a cache, and $2.50 per million output tokens [10] - Kimmy K2 is available on Open Router [13] Industry Reception - Industry experts compare Kimmy K2 to Deep Seek V3 [11] - Kimmy K2 is recognized as a potentially new leader in open LLMs [14]

Core Insights - The article discusses the evolution of AI, particularly focusing on the "trinity" of pre-training, post-training, and reasoning, and how these components are essential for achieving Artificial General Intelligence (AGI) [3][4][5] - Bob McGrew emphasizes that reasoning will be a significant focus in 2025, with many opportunities for optimization in compute usage, data utilization, and algorithm efficiency [4][5][6] - The article highlights the diminishing returns of pre-training, suggesting that while it remains important, its role is shifting towards architectural improvements rather than sheer computational power [6][8][9] Pre-training, Post-training, and Reasoning - Pre-training has reached a stage of diminishing returns, requiring exponentially more compute for marginal gains in intelligence [7][8] - Post-training focuses on enhancing the model's personality and intelligence, which can yield broad applicability across various fields [9][10] - Reasoning is seen as the "missing piece" that allows models to perform complex tasks through step-by-step thinking, which was previously lacking in models like GPT-3 [14][15] Agent Economics - The cost of AI agents is expected to approach the opportunity cost of compute usage, making it challenging for startups to maintain high pricing due to increased competition [17][18][19] - The article suggests that while AI can automate simple tasks, complex services requiring human understanding will retain their value and scarcity [19][20] Market Opportunities in Robotics - There is a growing interest in robotics, with the belief that the field is nearing commercialization due to advancements in language interfaces and visual encoding [22][25] - Companies like Skilled and Physical Intelligence are highlighted as potential leaders in the robotics space, capitalizing on existing technology and research [22][25] Proprietary Data and Its Value - Proprietary data is becoming less valuable compared to the capabilities of advanced AI models, which can replicate insights without extensive human labor [29][30] - The article discusses the importance of specific customer data that can enhance decision-making, emphasizing the need for trust in data usage [31] Programming and AI Integration - The integration of AI in programming is evolving, with a hybrid model where users engage in traditional coding while AI assists in the background [32][33] - The article notes that while AI can handle repetitive tasks, complex programming still requires human oversight and understanding [33][34] Future of AI and Human Interaction - The article explores how different generations interact with AI, suggesting that AI should empower individuals to become experts in their interests while alleviating mundane tasks [39][42] - It emphasizes the importance of fostering curiosity and problem-solving skills in the next generation, rather than merely teaching specific skills that may soon be automated [43][44]