AI Platform Engineering
Data is the new oil.
Capture and label proprietary data to evolve with better proprietary AI models.
Subjects
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Scaling AI
Summary: Human Data is Key to AI
- Three Pillars of AI Progress
- AI progress boils down to compute, data, and algorithms.
- Compute is powered by companies like Nvidia, algorithmic advancements come from large labs, and data is fuelled by companies like Scale.
- The industry is closing phase two of language model development, focused on scaling up models. The next phase will require more research breakthroughs.
- The Data Wall and Frontier Data
- The industry has hit a "data wall", having leveraged all easily accessible public data.
- Generating complex "frontier data" that captures human reasoning and problem-solving will be critical to reaching the next level of AI capabilities.
- Increasing data complexity, abundance, and measurement will be key focus areas.
- Market Structure and Business Models
- Model inference pricing has fallen dramatically, indicating renting models alone may not be the best long-term business.
- Strong businesses exist in the layers above (applications) and below (chips, cloud) the model layer.
- Major labs are investing heavily in AI, driven by the risk/reward of falling behind versus gaining a significant market advantage.
- Enterprise Adoption and Challenges
- Enterprises are experimenting with AI but few proofs-of-concept have made it to production.
- The most valuable AI applications will meaningfully drive a company's stock price through cost savings, efficiency gains, and improved customer experiences.
- Enterprises face challenges in organizing and leveraging their valuable proprietary data for AI.
Education
Courses and channels to learn AI and Data.
Websites
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