The Usool Studio.

Insights & Innovation

Vol. 2 · No. 14 · 30 Sep 2026

  • Principles in Practice
  • Industry Briefings
  • Engineering Notebook
  • Design Margin
  • Field Notes
  • The Operator's Lens

Insights & Innovation

Essays by the people doing the work. Every piece is signed, dated, peer-reviewed inside the studio, and written by the person whose name is on it.

The property launches with its first commissioned pieces this quarter. The pillars, the authors and the editorial standard below are final; the essays are being written and peer-reviewed, and none will be published under a byline that is not the author’s own.

The anchor · Principles in Practice

The second rewrite, and how to not need one

Almost every AI system we are asked to rescue is on its second build. The first one did not fail because the model was wrong — it failed because nobody could tell when the model was wrong, and by the time anyone could, the architecture had been shaped around not knowing.

[Principal]

[Principal]

4 September 2026 · 18 min read

The pattern is consistent enough to be boring. A prototype works, impresses a room, and gets a budget. The budget buys features rather than instrumentation, because features are what the room responded to. Eighteen months later the system is in production, the model has been swapped twice, and nobody can answer the only question that matters: is it better or worse than it was in March?

That question is unanswerable not because it is hard but because answering it requires a decision made at the start — that there would be a golden set, that it would be versioned, and that a drop against it would block a release. All three are cheap on day one and close to impossible to retrofit, because retrofitting them means admitting you do not know whether the last eighteen months of changes helped.

So the second rewrite is not really a rewrite of the code. It is the installation of the thing that should have been installed first, and the code change is the cost of having built around its absence.

What we do instead is boring in exactly the way that infrastructure should be. The eval set exists before the first endpoint. It is small, specific, and owned by someone in the business rather than by us — because an eval set written entirely by engineers measures the things engineers thought of. It runs in CI, it blocks, and the number of times it blocks is published internally. One hundred and thirty-seven, at the time of writing.

The uncomfortable part of this argument is that it makes the first six weeks look slower. It does make them slower. The honest framing we give clients is that we are trading six weeks at the start against the fourteen-month rebuild we have now watched happen enough times to describe its stages in advance.

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  • Engineering Notebook

    What an eval gate costs, and what it buys

    The first time our eval gate blocked a release, it cost the client four days and saved them a regression that would have reached eleven thousand policyholders.

    [Engineer]

    [Engineer]

    30 Sept 2026 · 14 min

    in review
  • Design Margin

    Designing for the clinician who has twelve seconds

    We timed it: between the patient looking up and the clinician needing to have decided, there are about twelve seconds, and every interface we had designed assumed sixty.

    [Designer]

    [Designer]

    18 Sept 2026 · 11 min

    in review
  • Principles in Practice

    The second rewrite, and how to not need one

    Almost every AI system we are asked to rescue is on its second build, and the first one failed for the same reason every time.

    [Principal]

    [Principal]

    4 Sept 2026 · 18 min

    in review
  • Engineering Notebook

    Why we moved retrieval off a managed vector service

    Nine months after launch the cost curve made our original call the wrong one, so we migrated to pgvector on the client's existing Postgres and cut inference-adjacent spend by sixty-one per cent.

    [Engineer]

    [Engineer]

    21 Aug 2026 · 16 min

    in review
  • Industry Briefings

    Classifying a system against the EU AI Act at design time

    Risk tier is an architecture decision, not a compliance task, and treating it as the latter is how teams end up rebuilding in month nine.

    [Principal]

    [Principal]

    7 Aug 2026 · 13 min

    in review
  • The Operator's Lens

    Your first AI hire is not a researcher

    Founders ask us who to hire first and are consistently surprised by the answer, which is almost always a platform engineer who has operated something in production.

    [Principal]

    [Principal]

    24 Jul 2026 · 9 min

    in review

The data

The Usool CTO & Founder AI Confidence Index.

An annual survey of 412 technology leaders technology leaders on AI investment confidence, production rates, and what actually blocked their last initiative. Original data, published in full, free to cite with attribution.

The 2026 survey is in the field. The figures below show the shape of the report and are drawn from the published literature rather than from our own responses. None of them is a Usool finding yet, and nothing here should be cited until it is.

71% say their last AI initiative stalled before production
say their last AI initiative stalled before production
18% have an eval suite that can block a release
have an eval suite that can block a release
2.4x cost overrun on the median stalled initiative
cost overrun on the median stalled initiative

Share of AI initiatives reaching production

Percentage of surveyed organisations with at least one AI system serving live traffic, by survey year.

0%10%20%30%40%12%202217%202323%202426%202529%2026
View the figures as a table
Share of AI initiatives reaching production, by survey year
Survey yearReaching production
202212%
202317%
202423%
202526%
202629%

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Notes on Foundations.

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