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1/ As AI models grow more powerful, it’s becoming clear that data strategy is just as important as model architecture for advancing capabilities. The right data—and how we use it—will determine the future of AI performance. 2/ I was explaining to someone (not in AI) that neural networks don’t just “memorize” word sequences. Instead, they learn deeper patterns, allowing them to predict the next word even when they haven’t encountered that exact combination before.. 3/ Neural networks don’t store all possible word combinations. Instead, they map words to vectors in a space where similar words are grouped together. This helps them understand context and make accurate predictions even with limited data. 4/ We’ve always operated in a data-scarce environment. There are trillions of possible word combinations, but only billions of words to train on. Neural networks overcome this by learning patterns and relationships instead of relying on brute-force memorization. 5/ As AI models scale, they need vast amounts of data. However, the supply of easily accessible, high-quality text is running out. The solution lies not in simply gathering more data but in smarter methods like careful curation, curriculum learning, and generating synthetic data. 6/ For the next generation of AI, we’ll need more than just raw web data. Data that includes reasoning traces and higher-quality human outputs will be crucial for teaching models to handle more complex tasks and think critically. 7/ Synthetic data, generated by models or simulations, can fill gaps where real-world data is lacking. When paired with curriculum learning, where models are trained progressively on more complex tasks, we can enhance both the efficiency and effectiveness of training. 8/ RL can further improve models, but it must be designed to avoid issues like reward hacking. A key challenge is building generalizable reward models that can guide AI toward genuinely useful and reliable behavior across many different tasks. 9/ We’re increasingly focused on the data engine across all our portfolio companies. The future of AI depends not just on having more data but on using it better. Smarter curation, synthetic data, high-quality human data, and robust RL will drive the next wave of capability. 10/10 want to work on AI but not directly in research/engineering? Advice: work on the data engine — it’s complex, hard, creative, hugely important.