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AI Data Pipelines

What business strategy provides the best operating model for using a unique data footprint?

Unique Data from Deep Domain Expertise is the Point of Difference.

  • The industry has hit a "data wall", having used 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 using their valuable proprietary data for AI.

Good quality deterministic data derived from niche subject matter expertise is the most valuable asset in the world.

tip

Shit in equals shit out

Concept​

Tech Stack​

  • Data Pipelines
  • Memory Management
  • Vector Databases

When To Use​

Use this operating model when your edge is proprietary domain data, not a model you rent. Apply it when public data has run out and the next gain comes from frontier data that captures real reasoning.

  • Start from the data footprint only you own.
  • Feed deterministic, expert-verified data — shit in equals shit out.
  • Price the application layer above the model, not the model alone.

Failure Modes​

The pipeline breaks in named ways. Check the signals:

  • Renting a model with no proprietary data — you compete on nothing.
  • Feeding noisy or generic data and expecting reliable output.
  • Shipping a proof-of-concept that never reaches production because the data was never organised.

Changes my mind: if a business wins long-term by renting frontier models with generic public data, the proprietary-data thesis is wrong and this page is wrong.

Context​

Next question: which slice of your proprietary data footprint would a competitor find hardest to reproduce — and is your pipeline built on it?

Questions​

Which AI data pipeline component — ingestion, transformation, or evaluation — is most commonly the bottleneck between a model working in development and working reliably in production?

  • At what data volume does the cost of storing all training data indefinitely become prohibitive — and what's the right data retention strategy?
  • How does continuous evaluation against production data change the pipeline architecture compared to batch evaluation against a held-out test set?
  • Which AI data pipeline pattern — feature store, embedding pipeline, or retrieval-augmented generation — is most ready for production use without requiring deep ML expertise to operate?