Companion to The Control Layer, episode: The AI Gold Mine Nobody's Digging — with Sam Parkinson, co-founder of Mettle Studio. Published 26 August 2026.
I have spent more hours than I would like to admit inside disclosure exercises on construction projects, and they all have the same texture.
Something has gone wrong — a programme slipped, a cost moved, a design did not do what somebody expected — and the parties reach for litigation to answer a question that has nothing to do with engineering. Who is to blame. Where does the liability sit. And then a room of expensive people begins hunting: through project directories nobody has organised since mobilisation, through archived mailboxes belonging to people who left two years ago, through folder trees where the same document exists in six versions and none of them is marked as the one that counted.
Thousands of pounds. Weeks of senior time. Looking for a smoking gun that, more often than not, does not exist to the depth anyone needs it to.
That is the first bill. This week’s episode is about the second one.
The same pile, seen from the other end
Sam Parkinson co-founded Mettle Studio, a design and engineering studio of fifteen that builds bespoke software for organisations with complicated problems. Somewhere around thirty-six minutes into our conversation I put to him the orthodox position — that your data is a mess, and you must clean it up before AI is worth attempting — and noted that he has argued the opposite in print. That the mess is the gold mine.
His answer was flat. “That’s not true. It’s just not true, because so many AI applications don’t even need loads of data at all.”
His example is worth having, because it is unglamorous and it is real. An engineer reviews a drawing against a contract to confirm the drawing carries what the contract requires. A model can do that with the contract and the drawing. Nothing else. No training corpus, no warehouse, no five-year cleansing programme. We are not building machine-learning engines any more; the general-purpose model arrived already knowing how to read — and that single shift moves the data question from a precondition to a preference.
So the specifications, the risk assessments, the meeting minutes, the decades of drawings — the same estate that costs a fortune to search under disclosure — is also the thing nobody has looked at. Sam’s framing: point a hundred agents at a thousand projects, pull one comparable strand out of each, and you have an insight that no human review programme was ever going to fund.
The mess bills you twice. Once in court, and once in the value you never extracted from it.
The ladder, laid out fairly
The most useful twenty minutes of the episode is a maturity ladder we built live. I gave Sam rung one and rung five and asked him to fill the middle.
Rung one is dismissal — “I’ve asked Copilot, the unpaid-for Copilot that has very limited capabilities, a few questions. It’s not helped me, so AI is not going to work.”
Rung two is shadow AI, and Sam’s description is the most quotable thing he said: “The company hasn’t bought anything for anyone… but all your employees have used AI in personal use, everyone’s doing that.” Nothing procured, nothing approved, nothing secured, everybody using it anyway.
Rung three is buying the licence properly. I want to be fair to this rung, because Sam is right about it and I have heard it dismissed by people who should know better. A paid base layer, given to everyone, with real training behind it and accountability sitting with existing business leaders rather than a new committee, does genuine work. It makes the simple things go well, and it compounds. “It’s hard to argue against, really,” Sam said. As a floor, it is.
Rung four is where you stop buying a licence and start building — automating something that could not be automated before. Sam’s phrase was the honest one. “That’s a leap of faith, isn’t it?”
Rung five is measured outcomes with governance in place, and Sam’s view is that four unlocks five.
The argument I want to make here
The ladder is a good instrument and it has one structural problem: it measures what an organisation has bought.
Read the rungs again. Rung one, nothing purchased. Rung two, nothing purchased and everyone improvising. Rung three, a licence. Rung four, a build. Rung five, the build measured. Every transition is a procurement event. Purpose does not appear anywhere on it — not as a rung, not as a gate, not as a question anyone has to answer before spending.
This is where Sam and I part company, and it is the only place in the hour where we genuinely did. He would start most organisations at rung three, with the licence, and let the appetite that shows up tell you what to build next. I would not. The organisations I have watched get real value from AI did not get there by buying capability and looking for somewhere to point it. They got there by deciding, deliberately and in advance, what outcome they were trying to create — and then working out which of their activities actually serve that outcome and which are ceremony that has survived because nobody audited it.
Buy first and the appetite that surfaces is the appetite of whoever shouts loudest. It is rarely the appetite that matters most.
This is not a position I formed in the interview. It is the founding premise of the framework we built at Arkava in February, and the sentence at the top of it reads: unlike conventional AI adoption frameworks that begin with technology selection, the Arkava Layer Approach begins with purpose. Five layers — Purpose, Control, Intelligence, Action, Value — with three rules underneath them. Purpose before technology. Control is non-negotiable. Value must be measurable.
Which gives you a different instrument, and a harder one, because you cannot buy your way up it. There is a free one-page version of it at the foot of this piece. The first layer you cannot answer honestly is where your organisation actually is, and most people who believe they are at Action are somewhere in Purpose, just with a licence.
A ladder you climb by spending is a ladder everyone can climb; that is exactly why arriving at the top of it distinguishes nobody.
The steering committee, and other British institutions
An organisation at rung two, told to do something, convenes.
“The steering committee can just be a polite way of looking busy without actually doing anything,” I said, and Sam went further: they write elaborate policy for a technology nobody in the room has used, and install blockers they did not need. My own line was that this is putting laws in place before you know what the crime is. Sir Humphrey (fictional character from the 80’s British sitcom, Yes Minister) would recognise the artefact immediately — a document whose real function is to demonstrate that the question was taken seriously. The tell is that nobody changes their behaviour when it is published, including the people who wrote it.
Sam’s signal for a fake rung four is sharper, and I would put it on a wall: full capability handed to a selected team, everyone else left on ungoverned shadow AI. One qualification he did not mention. A chosen team is not automatically theatre — if it is working a named outcome with a measure attached, it is a sound way to concentrate return. It becomes theatre when the team runs pilots rather than outcomes, and the other 180 people have been given nothing and are still pasting commercial documents into personal accounts.
Where Sam invited a fight, and got one
The strongest disagreement in the episode is one Sam opened himself.
UK construction generates genuinely sensitive material — critical national infrastructure designs, defence-adjacent documentation, energy network specifications, transport drawings — and much of it is contractually required to stay in UK jurisdiction. The tools the industry is reaching for run on US-headquartered platforms.
Sam’s position is that this is overstated. Organisations have run on Microsoft estates for decades, email is scanned by systems distributed worldwide, code sits on GitHub servers under a policy promise, and a prompt sent to a model and answered in memory is materially the same transaction. Then he said something I truly respect: “I’m going to be corrected on this and I’m going to find out I’m wrong, but I don’t know why it’s different.”
Fair enough. Here is my perspective on why it is different, and it is not the argument he may have been expecting.
The confidentiality question is the weakest one available, and Sam is broadly right about it. The one that matters is resilience and reliance. When a capability becomes critical to how your organisation operates, and that capability is supplied from a jurisdiction where you have no influence over the people who write the law, you have not made a procurement decision. You have made a dependency decision — on the continued goodwill of politicians you cannot lobby, in a legal system you cannot petition, subject to instruments you will read about after they take effect. Your costs, your availability and your security posture become a function of somebody else’s domestic politics.
The legal mechanism is the CLOUD Act, which is why the transience argument does not save you: what matters is not where the bytes rest but who can be compelled to produce them, under whose law. The practical demonstration arrived on a Friday in June, when Anthropic suspended Claude Fable 5 and Mythos 5 worldwide under a US export-control order and restored access on 1 July. Nobody’s data leaked. The service simply stopped. An organisation that had put that model inside a critical path discovered the difference between a supplier and a dependency in the length of an afternoon.
Sam half-conceded this himself, one exchange later — “when you’ve got governments that have the power to do that as well” — and then we both moved on. We should not have.
One correction, generously
Sam said, on tape, that “in America now, if an AI produces any output, you can’t say that output’s yours.”
That is not the test. The US Copyright Office set out its position in Copyright and Artificial Intelligence, Part 2: Copyright-ability in January 2025, and the question it asks is about human authorship, not tool involvement. Work made with AI assistance, where a human has contributed real creative control, can be registered. Purely machine-generated material cannot.
I flag it because his underlying instinct is right and the detail matters to anyone about to sign something. He described his own written output as produced by AI and authored by him — “the AI would never have produced that without me telling it exactly what I wanted it to produce” — which is a decent stab at the very test he thought he was failing. The UK has gone somewhere different again, and the gap between Washington and Whitehall is an article of its own. It will be next.
The economics nobody says out loud
Late in the conversation Sam raised construction’s margins and then chose the optimistic reading. Quality rises, ambition rises, the top line grows. I agreed with him on the episode. But I want to be more precise here.
The productivity gain is real and the margin improvement is available. What stops it is neither economics nor technology. The people who must authorise the change are stewards whose authority derives from having succeeded at the old method — they reached the board by delivering projects the traditional way, and they were good at it. AI arrives and asks whether the process that made them, is still the right process. That is an identity question put to the person who signs the business case, and risk aversion in that seat is not irrationality. It is self-preservation wearing a governance costume.
Which changes what the advantage actually is. The window is about who can scale and consolidate a market position before the capability becomes ubiquitous. An architecture practice produces exceptional work because it has assembled exceptional talent and given it exceptional tools — and until now that quality was a function of scale, because you needed the firm to afford the capability. A five-person practice with real talent can now produce work of comparable quality. The threat of democratisation runs upwards, towards the incumbent whose advantage was never the talent but the overhead around it.
Every company becomes a software company. Almost none of them will build the team.
Sam’s prediction, given for the record: within a five-year horizon, every company becomes a software company, building internally to automate parts of the business that were never software problems. His falsifiable signal is job adverts everywhere for developers.
The prediction is right. But I challenge, that the implied delivery model is not. Sam said as much himself and moved past it: “It’s not just as easy as hiring people. You need to hire quite a few different skills and have them working together nicely and have the right processes in place.” Those skills are scarce, expensive, and being competed for by organisations that already know how to hold them.
For most mid-market firms the realistic answer, for some years yet, is partnership — people who supply the capability without dragging the organisation’s attention off its own customers. A 200-person contractor does not need to become a software house; it needs software, delivered by people who do that, aligned to outcomes it has already defined. Which is the buy-versus-build argument Sam and I ended on, and his framing was much better than mine: buy the Lego bricks, assemble what you need, stop buying the finished set that fits nobody. For anyone old enough to remember the Meccano tin, it is the same insight in a different box.
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Predictive judgement
The prediction. The advantage available from AI adoption in professional and construction services is a consolidation window, not a permanent capability gap. By 31 August 2028, quality of output will have ceased to be a reliable proxy for firm size in UK architecture, engineering design and specialist consultancy — and the firms that converted the window into market position will have done so through client acquisition and scale, not through retained technical superiority.
The signals to watch. Small practices, under twenty people, winning framework appointments previously restricted by capability-based prequalification. Prequalification criteria themselves shifting from headcount and prior-scale tests toward demonstrated outcomes. Consolidation activity among mid-tier consultancies, which is what a closing window looks like from the inside.
What would prove me wrong. If, at 31 August 2028, capability-based prequalification thresholds are unchanged or tightened, and the share of framework appointments held by firms under twenty people has not risen materially, the advantage was never a window. It was a moat, and the incumbents were right to sit inside it.
And Sam’s, recorded for the tracker. Every company becomes a software company within a five-year horizon — test date 31 August 2031, signal: developer vacancies rising in organisations whose product is not software. I have said above why I think the in-house half of it will not survive contact with the hiring market. We will both be checkable.
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The first layer you cannot answer honestly is where your organisation is. Most people who believe they are at Action are somewhere in Purpose holding a licence.
The bottom line
There is a version of this conversation where construction is three to five years behind, the data is the reason, and the fix is a cleansing programme with a five-year run-rate and a consultancy attached. Sam spent the episode taking that story apart, and he was right to.
The story I keep returning to is smaller, and it is not about construction at all.
There are organisations today who believe they are waiting on AI. Waiting for the business case, waiting for the data, waiting for the steering committee to report. Their own staff have been using it for eighteen months. And nobody told the board.
The waiting was never the strategy. It was the last thing anybody agreed on.
Sam Parkinson is co-founder of Mettle Studio. The full conversation is on YouTube and wherever you get your podcasts.
You can follow the studio’s substack at Mettle Studio
Every guest on The Control Layer puts a prediction on the record with a date attached, so it can be checked later rather than admired now.
A separate piece on the auditability gap — proving what an autonomous agent did, in the most litigious industry in Britain — follows later this quarter. The Washington-versus-Whitehall authorship question is next.
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References
McKinsey Global Institute, Imagining construction’s digital future, June 2016 — the industry digitisation index placing construction second from bottom. Note: Amer refers to this as a 2018 report in the episode; the correct date is June 2016.
US Copyright Office, Copyright and Artificial Intelligence, Part 2: Copyrightability, January 2025. https://www.copyright.gov/ai/
Clarifying the Lawful Overseas Use of Data (CLOUD) Act, 2018, US Department of Justice. https://www.justice.gov/criminal/cloud-act
Anthropic, on the suspension and redeployment of Claude Fable 5 and Mythos 5 — US export-control order of 12 June 2026; access restored 1 July 2026.
Klarna Group plc, Q4 2025 earnings release filed with the SEC — 118 million active consumers, up 28 per cent year on year. https://www.sec.gov/Archives/edgar/data/2003292/000200329226000002/klarnaq425earningspressr.htm
Sebastian Siemiatkowski to Bloomberg, 8 May 2025, reported by CX Dive. https://www.customerexperiencedive.com/news/klarna-reinvests-human-talent-customer-service-AI-chatbot/747586/
Arkava Ltd, The Arkava Layer Approach, 22 February 2026 — internal framework document; the five layers and design philosophy are summarised here with permission.