AI Engineering
Data is the new oil.
Capturing and accurately labeling proprietary data is critical to evolving valuable AI models.
Data is the bottleneck to foundation model performance.
Subjects
Domain expertise concepts.
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Scaling AI
There are Three Pillars for driving 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.
Engineering
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