The Secular Trend: From Atoms to Bits to Intelligence

Across industries, a common transformation is unfolding:
– Atoms: Systems begin with physical assets and infrastructure.
– Bits: Software digitizes access, interaction, and operations.
– Intelligence: AI automates, optimizes, and increasingly takes over decision-making.

This transition from atoms → bits → intelligence is a broad, secular trend reshaping sectors from the ground up. It reflects the progression of locus of power and control from physical systems to digital platforms, and now to AI-driven automation and insight.

These shifts drive down costs, including the cost of innovating, boost efficiency, push the frontier of R&D back and unlock hyper-personalization at scale, making industries increasingly data-driven and autonomous.

Examples across industries:
– Retail: From physical stores to e-commerce platforms to AI-powered commerce and dynamic personalization.
– Pharma: From compounds to early bioinformatics to AIphafold 3.
– Media: From print and CDs to streaming platforms to AI-curated content and real-time feeds.

The quality and quantity of data changes:
– More real-time, high-definition data is captured via sensors.
Example: Waymo cars don’t just navigate—they continuously generate rich, real-time maps of their environment.
– More information, including synthetic data, is created by AI models.
Example: Waymo has generated over 30 billion kilometers of synthetic driving data to train its systems. In healthcare, synthetic data enables privacy-preserving training of diagnostic models.

But more information doesn’t mean more truth. AI identifies patterns—not meaning, ethics, or accuracy. It does not seek “truth” as traditional institutions do:
– Banks safeguard financial value,
– Courts uphold justice,
– Universities advance scientific knowledge.

AI, on the other hand, can surface correlations without context—e.g., associating loan risk with postal codes, which can reinforce systemic biases.
The challenge, then, isn’t just managing an explosion of data—but interpreting and governing it responsibly.

This post is the first of a series around AI strategy and adoption for businesses, from economic stakes to strategic moves, regulatory impacts and risk mitigation approaches.

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