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AI Principles

What is critical for making meaningful progress with AI as a companion in realizing potential.

tip

Data is the new oil

There are Three Pillars driving AI progress.

  1. Compute Power: Computing power acts as the accelerator of AI, providing the necessary processing capabilities to handle complex calculations. This includes specialized hardware like GPUs and TPUs that enable AI systems to process massive amounts of data efficiently. The advancement in computing power has been crucial for training larger models and handling more complex AI tasks.
  2. Algorithm Logic: Advancements come from research labs at LLM vendors and open source innovation. Algorithms function as the engine of AI, providing the intelligence and decision-making capabilities. They determine how the system learns from data and solves problems. Through algorithmic efficiency, AI systems can process information and generate outputs with increasing sophistication and accuracy.
  3. Data Processing: improvement driven by companies like Scale and Grass. Data serves as the fuel for AI systems, providing the foundation for learning and pattern recognition1. The quality, quantity, and diversity of data directly impact the accuracy and reliability of AI models. Without sufficient high-quality data, AI systems cannot effectively learn or make accurate predictions.

The industry is closing phase two of language model development, focused on scaling up models. The next phase will require more research breakthroughs.

When To Use​

Apply this three-pillar model when you scope any AI build. Ask which pillar gates your result.

Compute sets the ceiling on model size. Algorithms set what the model can learn. Data processing sets the quality of what it learns from.

  • Name the pillar that limits you today.
  • Check whether more compute, a better algorithm, or cleaner proprietary data moves the needle.
  • Invest where the gap is widest, not where the hype is loudest.

Failure Modes​

The model fails when a team over-weights one pillar. Watch for these anti-patterns:

  • Buying compute to fix a data-quality problem — the model still learns from noise.
  • Chasing frontier algorithms with no proprietary data to feed them.
  • Treating data processing as cleanup, not as the fuel that decides accuracy.

Changes my mind: if a frontier model wins on compute alone with generic public data, the "data processing is the differentiator" claim is wrong and this page is wrong.

Context​

Next question: which pillar — compute, algorithm, or data processing — is the binding constraint on your next AI result?

Questions​

Which AI principle — grounding in verifiable data, composable capabilities, or human-in-the-loop for irreversible decisions — is most frequently violated in AI products that fail to maintain user trust?

  • At what AI capability level does the "human-in-the-loop" pattern become more about liability management than genuine safety improvement?
  • How does the composable capabilities principle change AI architecture for an application that needs to reliably complete multi-step tasks?
  • Which AI principle is most likely to become a regulatory requirement in the next 5 years — and is early adoption a competitive advantage or an overhead?

Sources​