Insights

Field notes on AI that survives production

An essay a month on architecture, cost, governance and what comes next. Named frameworks you're welcome to quote, with credit.

Essay

6 min read · September 2026

Anyone can pick a model

For twenty years I've watched the same pattern: the hard part of a system is rarely the part in the demo. AI has made that pattern louder.

Every enterprise can now reach strong models, and soon the gap between them will matter less than people think. So the model is not the moat. The architecture around it is.

Security isn't a firewall around the system. Compliance isn't paperwork after deployment. Cost control isn't just a cheaper model. Human control isn't an override added later. These are architectural decisions, and where you place them decides everything else.

Get the placement right and performance, cost, accuracy, scale and control improve together. Get it wrong and you have a demo that never becomes a durable product.

In practice, that means three rules I build by:

  • One gateway. Nothing calls a model provider directly. Policy, personal-data redaction, routing, caching and audit live in one place.
  • Capability, not brand. Agents ask for what they need (reasoning depth, latency, cost ceiling, jurisdiction), and the platform picks the model. Swapping models becomes a configuration change.
  • Evals before agents. Capability you can't measure isn't capability. It's risk.

Anyone can pick a model. Architecture decides when, where and how it's allowed to think. That is the work, and that is the moat.

Annual view · September 2026

Horizons 2026–2036

Enterprise AI moves from pilots to governed production now; small sovereign models, quantum-safe security and the first hybrid quantum-classical workloads come next; and by the mid-2030s, self-running operations with human oversight, with bio-computing starting to leave the lab.

2026–2028 · now

Agents reach production

  • Governed agents replace pilots
  • AI run-cost becomes a CFO topic
  • Regulation moves from guidance to enforcement
Already builtA 257-agent governed AI operating system, 65–75% cheaper to run
2028–2031

Small, sovereign, quantum-safe

  • Owned small models go mainstream
  • Hybrid quantum-classical pilots
  • Post-quantum migration is mandatory
Already builtNyayasiddhi and Jurexio, a sovereign legal-AI platform: six jurisdictions, small models, post-quantum cryptography
2031–2036

The autonomous enterprise

  • Humans govern by exception
  • Bio-computing leaves the lab
  • Provenance and audit become product features
Already buildingHuman review by exception: both of my AI platforms route only high-stakes answers to people, with every call audited
The human bridge

Legacy → AI → quantum → bio-computing, with your people

Most companies don't start from a blank page. My angle is to connect the estate you have to what's coming, rather than rip it out.

  1. Now

    Legacy to AI

    Wrap existing systems with governed APIs and agents. Modernise first the parts that block value.

  2. Now to 2030

    AI to quantum-safe

    Move long-lived data to post-quantum cryptography, and keep algorithms swappable for hybrid quantum-classical work.

  3. 2030s

    Quantum to bio-computing

    Prepare consent, provenance and governance for biological data, starting in health and life sciences.

  4. Throughout

    People

    Training, operating-model change and a plain-language case for the board. Organisations adopt one team at a time.

Quantum advantage for business workloads and bio-computing are still maturing. This view is about readiness (quantum-safe security, swappable architecture, prepared data), not promises of quantum results. I'll update it every year.

Field note

3 min read

Quantum-ready, not quantum-hyped

The most useful quantum decision a CEO can make this year has nothing to do with quantum computers. It's about encryption.

Data stolen today can be stored and decrypted later, once quantum machines are strong enough. So anything that must stay secret for years (health records, legal files, financial histories, state data) is already exposed to that future.

Three steps, in order: inventory where long-lived data is encrypted; plan the move to post-quantum algorithms such as ML-KEM and ML-DSA; and keep your cryptography swappable so the next change is configuration, not a project.

Then, and only then, screen for workloads where hybrid quantum-classical methods may beat classical ones: optimisation, simulation, search. Pilot where the maths favours it. Skip the rest.

Coming next

The essay list

  • How we cut AI run-cost by 65–75% without losing quality

    Coming soon
  • What taking a company public taught me about 99.99%

    Coming soon
  • Governance is a feature: building for the EU AI Act, ISO 42001 and DPDP from day one

    Coming soon
  • Sovereign AI: when your data can't leave the country and your answers can't be wrong

    Coming soon
  • Legacy isn't the enemy: connecting 20-year-old systems to AI without a rewrite

    Coming soon