The technology supercycle: AI, sensors, and biotech converging

Executive overview

Three general-purpose technologies — AI, advanced sensors, and biotechnology — are converging in ways most businesses and governments aren't tracking. Improvements in each one accelerate the others, creating a compounding flywheel effect. Most organisations see these as separate domains; the strategic risk lies in that blind spot.

The convergence of AI, sensors, and biotech is the signal most organisations are missing.

The three general-purpose technologies

• General-purpose technologies (GPTs) have profound, lasting economic impact — like the steam engine, electricity, and the internet
• AI qualifies as a GPT; so do advanced sensor technology and biotechnology
• Advanced sensors provide behavioral and real-world data that LLMs alone can't access — enabling large action models that predict human behavior
• Synthetic and generative biology lets you prompt for molecular structures or biological functions, just as ChatGPT generates text
• Improvements in one GPT beget improvements in the others — the convergence is the insight

Why convergence matters for every industry

• An investment banker or media company is as exposed to biotech disruption as a pharma company
• Cultured meat (grown from a 2g tissue sample in a bioreactor) is already on sale in Singapore — geo-economic implications ripple across supply chains, protein access, and cold chains
• Nitrile gloves, synthetic materials, weight-loss drugs: all products of AI-biotech convergence
• Sensor data unlocks behavioral layers unavailable to public-data-trained models — and is mostly held by OEMs and tech companies
• "Living intelligence" is the term Webb's team uses for what comes next after this convergence matures

Making foresight actionable

• Being aware of the supercycle isn't enough — organisations must build strategic foresight capabilities
• Track signals and distinguish them from trends; map uncertainties (what you can't know) separately
• Use scenarios to navigate uncertainty — not probabilistic tables, but vivid, concrete stories
• Herman Kahn's nuclear aftermath stories worked because they were visceral and immediately relevant to decision-makers
• Data-backed scenarios create the confidence leaders need to act; passion without data doesn't pass muster

On regulation and governance

• Heavy-handed regulation produces lawsuits, not compliance — the EU is already demonstrating this
• Regulation is inherently reactive; it can't keep pace with technologies whose trajectories are still uncertain
• Biology can't be geofenced — enforcement mechanisms for generative biology are structurally harder than for software
• Webb's alternative: a data trust model where participants share data and benefit collectively; non-participants are economically excluded
• Look outside your competitive set — near-peers and adjacent industries almost always have analogous frameworks worth adapting

Founder and leader implications

• Startups should study convergence points — that's where new businesses emerge
• Healthcare is the most vulnerable sector to disruption from this supercycle and the most urgent to rethink
• Founders fixated on exit make decisions misaligned with building something great
• The future isn't fixed — strategic moves made now compound into advantage within a year