AI Data Pipelines
What business strategy provides the best operating model for leveraging a unique data footprint?
Data Differentiator
Data produced from deep work is the most valuable asset to collect and refine.
- 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.
Good quality deterministic data derived from niche subject matter expertise is the most valuable asset in the world.
Shit in equals shit out
Concept
Context
- Knowledge Schema: Context is everything.
- Data Value: Data is the new oil.
- Business Models: Explore breadth of options:
- Vertical Integration: Dig deep to drill into value.