In 1990, the managing director of an insurance company had a suspicion that his operation was inefficient. He didn’t know the scale. He brought in a cost accountant, dug into the numbers, and found that errors, rework, and waste were costing the business £170 million a year. Not in a rounding-error sense. In a “how is this company still operating” sense.

The group CFO launched a global review across all sixty countries where the firm ran. The findings were worse than the UK number. The CEO started an improvement programme that ran for years.

None of that £170 million had ever appeared on a financial report. It was sitting inside salary lines, inside overhead, inside the cost of everyone who had a job because the work wasn’t getting done right the first time.

The story comes from a paper by Rowan Jackson on what he calls ERW: errors, rework, and waste. He’s been working on this problem for decades. His working figure for how much of operating expenditure it consumes across industries is 20 to 45 per cent. In construction, the Get It Right Initiative (GIRI), which has done the most serious UK research on this, puts avoidable error at around 21 per cent of project turnover, and total costs including latent defects and indirect effects at up to a quarter of project spend. Rowan uses 30 per cent as a working figure once you include the waste that never gets counted. That’s roughly £10 to £25 billion a year in the UK construction sector alone.

Here’s the part that matters. None of those costs appear in the P&L in a way that lets anyone see them clearly. They’re absorbed. They’re the three people doing the work of two because the system needs redundancy. They’re the fourth draft of a document because the first three were wrong. They’re the brick wall pulled down and rebuilt because it was set five centimetres out of place. They’re the customer who doesn’t come back.

If this sounds familiar, it’s because it’s the same mechanism that makes AI productivity claims so misleading. And that’s the part worth sitting with.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Why this isn’t a construction problem

The reason I’m writing about ERW in a series about AI displacement is that I kept running into a gap when I was arguing the Next Rung thesis. The gap was this: if AI delivers genuine productivity gains, displaced workers might at least benefit from cheaper goods and services as consumers, even if they can’t find work as producers. That’s the standard economist’s consolation. Output goes up, prices come down, everyone’s real income rises. The Luddites were wrong because this happened, repeatedly, for two hundred years.

The problem with applying that logic to AI is that nobody is measuring the productivity gains net of ERW. They’re measuring them gross.

A developer using an AI coding tool ships a feature in half the time. Productivity doubled. But if that feature has a bug that takes two developers a day to find and fix in production, the gross number is a lie. The fix is in a different budget line, probably in a different quarter, possibly attributed to someone else. The productivity number stays up. The ERW cost gets absorbed somewhere the accountants don’t look.

METR ran a randomised controlled trial in early 2025 on experienced open-source developers using frontier AI tools. The developers predicted the tools would make them 24 per cent faster. After using them, they reported feeling 20 per cent faster. The actual measurement, from screen recordings of the work, showed they were 19 per cent slower.

Let that sit. A 40-point gap between perceived and actual productivity, in a field where the gains are supposed to be obvious and everyone has strong priors in favour of the technology. The participants weren’t lying and they weren’t stupid. They were doing what humans do when the rework happens invisibly, gradually, at a lower cognitive register than the work itself. It disappears into the background.

And that’s in code, where the feedback loop is tight. The code either runs or it doesn’t, eventually. In knowledge work where the outputs are documents, reports, summaries, and decisions, the feedback loop is much longer. Sometimes it never closes.

Rowan’s NHS moment

Rowan told an NHS orthopaedic consultant about the 30 per cent construction figure. The consultant laughed and said the rework rate in the NHS is closer to 60 per cent.

I don’t know if 60 per cent is right. Neither does the consultant, really, because nobody is measuring it at that granularity. But think about what a number in that range would mean. NHS England’s resource spending is running at around £187 billion a year (2024-25). 30 per cent of that is £56 billion. 60 per cent is £112 billion. The UK defence budget for 2025-26 is £62.2 billion.

Put it another way. If NHS rework is anywhere near that range, the hidden cost of getting things done twice is large enough to run the country’s entire defence capability. It sits inside the budget lines nobody argues about in Parliament because nobody can point to it.

This isn’t an NHS-specific problem. Matthew Syed’s Black Box Thinking puts preventable medical accident deaths in the US at around 1,000 a day, which would be the equivalent of two fully loaded Boeing 747s crashing every morning. Aviation responds to that kind of failure rate by grounding fleets and rewriting procedures. Healthcare treats it as background noise, because the individual deaths are absorbed into individual case files, and the system-level number never makes it onto any report.

The through-line from insurance to construction to healthcare is the same. When errors and rework are invisible to the accounting system, they get baked into the cost of doing business, and the only way anyone finds them is by going looking.

AI is about to make this much worse, because AI gives you a tool that generates output at a speed that outruns your ability to verify it.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The productivity lie in plain sight

Here’s the trap. The way AI is being sold into the economy right now is through gross productivity claims. GitHub Copilot makes developers 55 per cent faster. McKinsey estimates 30 per cent of US work hours automatable by 2030. Klarna’s AI customer service handled 2.3 million conversations in 35 languages, the equivalent of 700 full-time agents.

Every one of those numbers is a gross figure. None of them is net of rework, error, or the work required to correct what the AI produced. And when you go looking for the net figure, what you find is sobering.

Klarna’s story makes this cleaner than most. The company replaced 700 customer service staff with AI in 2023. It reported aggressive cost savings. By spring 2025, customer satisfaction had dropped enough that the CEO, Sebastian Siemiatkowski, admitted publicly that “we focused too much on efficiency and cost. The result was lower quality, and that’s not sustainable.” Klarna started hiring human agents back. The company now runs a hybrid model, with AI handling routine queries and humans handling escalations and complex cases.

Notice what’s missing from Klarna’s original productivity claims and every similar announcement. The rework cost. The complaint volume. The customers who walked away. The brand damage. The eventual rehiring budget. These are real costs, but they appear in different lines of the P&L from the “AI savings” line, and often in later periods. The initial productivity claim gets made, quoted, and celebrated. The correction happens quietly, if it happens at all.

This is the ERW pattern, just applied to cognitive work instead of factory floors. And it’s doing the same thing it did in manufacturing: letting leadership celebrate gains that don’t net out, while the real costs accumulate somewhere the quarterly reporting doesn’t cover.

What the next rung argument needs

My original post on The Next Rung made the case that AI is breaking the three mechanisms that have absorbed displaced workers in every previous technological transition: new task creation, demand expansion, and skill transferability. Rowan’s ERW framework sharpens the demand-expansion argument in a way I hadn’t fully worked through.

The standard story goes: productivity gains lower prices, lower prices expand markets, expanded markets create new work. It assumes the productivity gains are real and durable, not gross numbers waiting to be eroded by rework.

If ERW runs at 20 to 45 per cent of operating expenses across the economy, and AI deployments are being sold on gross productivity claims that don’t account for it, then the demand-expansion mechanism is weaker than the optimists claim, because the actual cost reduction is smaller than reported. Some of what looks like productivity is just cost-shifting: work that used to be done by one person with a salary line is now done by an AI plus a different person doing corrections, often across different budget centres.

That matters for displacement. If the AI deployment delivers real net productivity, displaced workers at least benefit as consumers. If it delivers gross productivity that gets eaten by rework, the savings don’t materialise, the consumer doesn’t benefit, and the displaced worker has neither a job nor cheaper goods to buy.

This is the economic equivalent of a sealed box. Capital captures the announced gains. Labour loses the jobs. Consumers don’t get the price cut because the rework ate it. The only people who come out ahead are the ones selling the AI.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

What to do about it

The honest version of the AI productivity conversation has to start by measuring net, not gross. Every organisation deploying AI into a workflow should be running a rework ledger alongside the productivity dashboard. How many outputs had to be corrected? How many downstream errors traced back to AI-generated content? How many hours of human review does each AI-assisted task actually consume? What was the quality score before and after, on the customer-facing side?

Rowan’s ERW work, Harrington’s process improvement methodology from the early nineties, the Toyota Production System, Danaher’s operating model, these aren’t legacy industrial frameworks. They’re the only serious methods the business world has ever developed for measuring what’s actually happening in a workflow, rather than what leadership wants to be happening. They apply to AI deployments the same way they apply to car plants.

If you’re running an AI rollout and you’re not tracking rework, you’re not running a productivity programme. You’re running a perception programme. They’re different things, and only one of them shows up on the balance sheet in the end.

The next piece in this series goes further on what that looks like in practice. For now, the point is simpler. The productivity gains you can’t measure are the ones you shouldn’t claim. And the costs that don’t appear on your financial report are the ones that will eventually eat it.

With thanks to Rowan Jackson, whose paper on Errors, Rework, and Waste (ERW) provided the framework and several of the examples in this piece, including the £170 million insurance case, the NHS consultant’s 60 per cent figure, and the broader observation that errors, rework, and waste are the silent killers of organisational performance.

Sources: Get It Right Initiative (GIRI) construction error research; METR 2025 study on developer productivity with AI tools; Moffatt v Air Canada 2024 BCCRT 149; Klarna AI customer service reversal coverage, 2025; NHS England financial directions 2025-26; UK defence budget data from House of Commons Library; Matthew Syed, Black Box Thinking; H.J. Harrington, Business Process Improvement (1991).

I’ve seen five of them in the last 24 hours. Different people, different parts of the country, a couple in the US. None of them know each other. None of them would describe what’s happened to them as part of anything.

They’re all posting the open-to-work card. The green ring on the profile photo, the “this isn’t a post I expected to write,” the bit at the end asking for likes and shares to help it reach the right people. Which is fair enough. As an individual move it’s one of the boxes you check. Everyone else does it, so you do it too, and you’ve got as much chance as the next person. I’m not knocking anyone for posting one. If I were in their position I’d probably do the same.

But here’s what struck me, and it took five of them in a day for it to land.

Every single one is looking for a job in the exact thing they’ve just been made redundant from.

The senior marketing manager wants another senior marketing role. The operations lead wants another operations lead role. And in the normal logic of a career, that’s the sensible thing. You’ve got ten years of doing the thing, so you go and find someone who needs the thing done. That logic has held for every previous generation of worker who lost a job. The role still existed somewhere. You just had to go and find the version of it with your name on it.

The logic only works if you’re the one being displaced.

If the role itself is being made redundant, going to find the same role somewhere else is going back to the same edge of the same cliff, just at a different company. You’re not unlucky. You’re early. And the thing you’re applying for is the thing that’s quietly being removed from org charts in the same month you’re applying for it.

That’s the part that nobody’s saying.

Go and read the comments under any of these posts. I did. They’re full of decent people being kind. “That’s a shame.” “Good luck, all the best.” “Can’t believe it’s happened to you.” Genuine, warm, well meant. And not one of them says the actual thing.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Nobody says: this happened because you’re in a senior marketing role, and a senior marketing role is one of the easiest things in the building to displace, because an AI can do most of the day-to-day now. Nobody says: the reason it’s hard to find another one is that the people hiring have worked that out too. Nobody says: the gap between the role you want and the roles that exist is going to keep widening, and the next one might not last either.

They don’t say it for one of three reasons. Either they don’t know it. Or they know it and don’t want to be the person who says it under a post where someone’s already having the worst week of their year. Or, and this is the one that sits with me, they half-know it and saying it out loud would make it real. As long as nobody names it, it stays a run of individual bad luck. A few people who didn’t quite land the next thing. The moment somebody names it, it becomes a pattern, and the pattern has a direction, and the direction includes the person reading the comment.

So everyone keeps it as bad luck. It’s easier. It costs nothing to type “good luck, all the best.” It costs a lot to type what’s actually happening.

This is what the slow version of a crisis looks like. Not a crash. Not a headline. Not a number on the six o’clock news. Just a steady drip of individually-explainable exits, each one with a perfectly reasonable story attached, each one absorbed and moved past, each one followed by a sympathetic comment and a scroll to the next thing in the feed.

There’s a phrase for the way societies live inside a situation everyone can feel and nobody will name. Everything stays the same until it suddenly doesn’t. You keep finding ways to read the evidence as normal, right up until you can’t.

The redundancies are real. They’re happening now, this week, to people you’re connected to. But they don’t arrive as displacement. They arrive as a green ring on a profile photo and a brave caption and 188 likes. The wave is already here. It just doesn’t look like a wave from inside it. It looks like a coffee request.

The displacement will not be televised.

It’ll be posted, one open-to-work card at a time, and we’ll all wish them luck, and we’ll scroll on.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The timeline is phased. The first phase is quiet and already underway. The second phase breaks. The third phase reshapes. You need to understand the shape of all three because what you do now determines your position in all three.

Phase One, 2026-2028, is the quiet squeeze. Hiring freezes in knowledge work sectors. Contractor positions disappearing. Quiet restructuring that doesn’t show up in headlines because HR departments know the labour market is watching. Real unemployment rises while official figures stay flat because people leave the job market entirely rather than accept precarious work. The people in exposed roles start to notice that the stability they expected isn’t coming. The political class hasn’t developed a coherent response yet because the crisis doesn’t feel urgent if you’re not the one being squeezed. This phase feels like a normal downturn. It isn’t.

Phase Two, 2028-2032, is the breaking point. Enterprise AI adoption reaches critical mass. Competitive pressure forces the holdout companies to adopt. The pace of displacement accelerates past the pace at which retraining or natural job transition can absorb it. Housing markets face pressure as people who expected to service 25-year mortgages lose income. Political systems strain. The gap between “this is a technical transition” and “this is a systemic crisis” becomes impossible to deny. Unemployment peaks. Political systems are forced to choose between managed transition or something worse. This is the phase where institutional responses are determined. The choices made here, whether to tax AI gains or let them concentrate, whether to fund retraining or assume the market will sort it, whether to expand the social safety net or leave it where it is, those choices determine which scenario unfolds.

Phase Three, 2032-2040, is the reshaping. Whatever institutional response emerged in Phase Two plays out at scale. Managed Transition looks like the post-war settlement being rebuilt at higher velocity. Neo-Feudalism looks like the slow consolidation of the extractive model. Fragmentation looks like separate regions pursuing incompatible strategies producing unexpected interactions. The black swan, if it hits, makes everything unpredictable. But by 2032, the main direction is set.

The window for influencing this is open now and closes around 2032. Not absolutely. Politics can surprise. Crises can force rapid reversals. But the window for voluntary transition, for policy made in calm rather than panic, for solutions chosen rather than imposed, that window is roughly six years wide. After that, the structural decisions have been made and the space for modification narrows sharply.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Here is what I think we do with that window.

First, demonstrate that managed transition works at scale. The evidence from pilots is clear: UBI doesn’t cause work participation to collapse, retraining works when funded properly and taken seriously, people respond to genuine opportunity. But pilots don’t move political systems because pilots are small and expensive and can be dismissed as unrepresentative. What moves political systems is scale demonstration: a city, a region, implementing genuine managed transition policies and showing outcomes. If even one major economy implements something close to managed transition and the results are decent, the probability of other economies adopting increases. If none do, the path toward neo-feudalism becomes harder to contest.

Second, rebuild the connection between productivity and wages. For 60 years after the Second World War, productivity growth and wage growth tracked together. Since the 1980s, they’ve diverged. Productivity up, wages flat. That disconnect is not a law of nature. It’s a policy choice, or more accurately, a policy non-choice: the absence of mechanisms that would force productivity gains to translate into wage gains. The mechanism exists. It’s called taxation and redistribution. Implementing it is not economically impossible. It’s politically contested. Demonstrating that it’s possible, that you can have high productivity and shared gains simultaneously, is the necessary precondition for the policies that enabled it post-war to seem non-radical now.

Third, protect the people in the blast radius through the transition period. The displacement isn’t happening to everyone simultaneously. It’s happening to orchestration-heavy roles first: coordinators, analysts, junior consultants, middle managers, marketers focused on process rather than strategy. These are the people with mortgages, kids in school, the lowest tolerance for income shock and the highest political voice. The people who can articulate what’s happening and demand response. If these people are left to fend for themselves, the political response will be chaos. If they’re supported through the transition, the response can be managed. The difference is not charity. It’s structural stability. You don’t let the politically articulate middle class fall into precarity without consequences. Those consequences are bad for everyone.

Fourth, invest in the capabilities that survive. AI automates pattern-based, documentation-heavy, coordination-heavy work. It doesn’t automate trust, genuine problem-solving, human relationships at scale, creative work where originality is the product, skilled trades with physical presence, care work. The education and training infrastructure should reflect this. Teaching people to code is not the solution because most of those coders are not going to be hired; the competitive supply of code-writers will exceed demand. Teaching people genuine problem-solving, communication, the ability to navigate ambiguity, relationships and trust-building, those investments do translate into resilience. The system that measures this is Enterprise Skills at K12 level, but the principle applies across the lifespan. Invest in human capabilities that an algorithm can’t replace. Don’t invest in the illusion that tech skills will save you.

Fifth, maintain optionality. The scenarios are not precise. Nobody knows exactly how this plays out. The people and institutions that survive are the ones with enough flexibility to respond when the actual future arrives. Don’t optimise for one scenario. Build resilience. Diversify income, skills, relationships, assets. Have multiple plans, not one plan with contingencies. The worst position you can be in is locked into one future with no ability to adapt. The best position is flexible across scenarios.

Those five aren’t complete. They’re the places where the window is open and closing fastest. They’re also the places where individual action and institutional action reinforce each other. If you build a diversified, resilient working life, you create demand for education systems that teach resilience. If you invest in trust networks and community, you create the social infrastructure that managed transition needs. If institutions demonstrate that managed transition works, they create permission for individuals to position themselves differently. The system and the individual aren’t separate. They feedback.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Here’s what I know and what I don’t.

I know the displacement is real and its scale is comparable to the largest labour market disruptions in modern history. I know the current trajectory, without policy intervention, trends toward concentration. I know the window for voluntary, policy-driven response is open and closing. I know the precedent: when managed transition has been implemented, it works.

I don’t know if the political will exists to implement it. I don’t know if the pace of AI development will exceed the pace of institutional response. I don’t know if the black swan is 5% or 50% in reality. I don’t know which scenario we end up in. That’s why I’m not betting on one outcome. That’s why I’m suggesting moves that make sense across all four.

The book is the full argument: what’s happening, why it matters, what the futures look like, what you do now. This Substack is where the argument lives and where it develops as the evidence changes. I’ve said enough in these posts that you know roughly where I’m standing and roughly what I think matters. The rest is the details and the lived experience of what this transition actually feels like, which is what the book explores.

The window is open. Not for much longer. What you build in the next six years shapes what you’re positioned in when 2032 arrives.

There’s a concept in nuclear physics called critical mass. It’s the minimum amount of fissile material needed to sustain a chain reaction. Below that threshold, individual atoms split but the energy dissipates. Nothing much happens. Above it, each reaction triggers others, and the thing becomes self-sustaining.

Enterprise AI adoption crosses that line somewhere around 2028.

This is Chapter 7 from Part Two. It’s the chapter where the gradual pressure of Part One becomes unmistakable. Where hiring freezes and contractor squeeze-outs give way to explicit redundancy programmes that cite AI as the reason. Where the denial stops being possible.

The mechanism matters because it explains why individual skill or excellence becomes insufficient protection. Your job isn’t disappearing because you’re bad at it. It’s disappearing because your employer is losing the competitive race against companies that have already rebuilt their operations around AI.

Here’s the frame: by 2028, 88 percent of organisations are using AI in at least one business function. That sounds like the revolution has already happened. The number that actually mattered was different. Thirty-one percent of use cases had reached full production in 2025, and that number was doubling annually. Full production means the AI isn’t being tested. It’s doing the work. Decisions are flowing through it. People who used to do those tasks are now doing something else, or nothing at all.

Run the maths. If 31 percent of use cases hit production in 2025 and the doubling rate held, 60 percent were in production by 2027. By 2028, the question wasn’t whether your company used AI. It was whether your company had been redesigned around it.

The productivity gap became impossible to ignore. Top AI-native startups were generating average revenue per employee of $2.5 million. Traditional companies averaged $200,000. That’s not a competitive advantage. That’s a structural difference so large it operates on different economics entirely.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The decision that killed companies was choosing between two bad strategies. Adding AI to a legacy structure is like putting a jet engine on a horse cart. You get noise and expense and the cart falls apart. The companies that pulled ahead were the ones that built new carts. But rebuilding from scratch required capital, will, and organisational design changes that most mid-market firms couldn’t execute while maintaining business-as-usual.

So they tried to manage decline instead. Cutting costs. Consolidating departments. Reducing headcount by attrition. These measures saved money in the quarterly numbers. They didn’t change the underlying economics. Every quarter the gap widened. Every quarter the catch-up cost increased. After a certain point, catching up became impossible.

This pattern, technology historians call the incumbent’s dilemma. The incumbents weren’t stupid. They were rational actors making decisions within a framework that no longer applied. Improve incrementally. Protect margins. Retain talent. All sensible advice in stable competitive environments. All catastrophically insufficient in one undergoing phase transition.

The adoption curve was the fastest in human history. The telegraph took 80 years to peak. Television took 15. Generative AI achieved 78 percent organisational adoption within a single year. The reason is structural. Previous technologies required hardware. AI required an internet connection and will.

This compression of the adoption curve had a specific consequence. Previous technology transitions gave industries decades to adjust. The cloud transition gave them fifteen years. AI gave them roughly five, from mainstream availability in 2023 to restructuring necessity by 2028. That’s not enough time for labour markets to absorb displaced workers. It’s not enough time for retraining programmes to scale. It’s not enough time for most people to fully understand what’s happening.

The tipping point hit different sectors on different timescales, but the sequence was consistent. Professional services went first. Legal services felt it when major firms deployed AI contract review systems that processed in hours what paralegals had processed in weeks. The entry-level pipeline, the three years of document review that had always been the route into partnership-track careers, was the exact pipeline AI automated first. Junior associate hiring dropped 20-30 percent at the major firms between 2026 and 2028. Revenue held steady. Headcount didn’t. Revenue per partner increased, which looked like success on a balance sheet and looked like a closed door to law graduates who’d spent three years and six figures earning a qualification for roles that no longer existed.

Consulting followed with a six-month lag. Accounting had been on notice longest. For the professional class, critical mass meant something very specific. It meant the economic viability of their roles was no longer a matter of individual performance. It was a function of their employer’s competitive position. And their employer’s competitive position was deteriorating.

This is where the abstraction meets the mortgage payment. You can be excellent at your job. Skilled, diligent, well-regarded. And if your employer is on the wrong side of the AI transformation curve, your excellence is irrelevant. The company isn’t failing because you’re bad at what you do. It’s failing because a competitor is doing what you do with a quarter of the people.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The professional class had never experienced this particular form of economic threat. Manufacturing workers knew it. They’d lived through plant closures where individual performance didn’t matter because the whole facility was uneconomic. But professionals, the people with degrees and certifications and career paths that climbed through predictable stages, had always operated on the assumption that competence provided security.

The system stopped providing it. Not because professionals had done anything wrong. Because the system had changed state.

The quiet squeeze became loud in the most ordinary possible way. Not riots. Not protests. Emails. The email from HR. The restructuring announcement. The meeting request from your manager with no agenda attached. The job listing for your role, rewritten around AI capabilities, posted while you still held the title.

Between 2025 and 2028, displacement felt like bad weather: unpleasant, temporary, someone else’s problem. By 2028, the weather had become climate. When your company announces its third restructuring in eighteen months and the new job descriptions all include AI proficiency as a core requirement, and the roles being eliminated are the ones that look exactly like yours, the denial architecture starts to crumble.

The cascade didn’t stop at companies. It moved through supply chains, client relationships, entire industries. A company going AI-native didn’t just reduce its own headcount. It changed the economics for everyone connected to it. The outsourcing firm that provided back-office support. The staffing agency that placed contractors. The training company that ran professional development. The landlord who leased the office space. The sandwich shop on the ground floor.

Commercial real estate felt it early. Office vacancy rates in professional services districts climbed steadily from 2027 onward. The recruitment industry was hit with particular force. Staffing agencies found their market contracting from both directions. Fewer roles to fill because companies needed fewer people. Fewer candidates to place because displaced professionals were competing for a shrinking pool.

The professional training industry collapsed. The companies that ran leadership courses, project management certifications, and professional development workshops found that their customer base was cutting spend at unprecedented rates. Only 13 percent of firms intended to invest more in training in 2025, down from 32 percent the year before. The professional development industry had existed in the space between what workers could do and what employers needed them to do. When AI closed that gap, the industry lost its reason for being.

By 2029, the question had changed. In 2025, people asked: will AI take my job. By 2029, they asked: how long before it takes mine. The shift from if to when was the psychological critical mass, the moment denial became impossible.

What changed wasn’t the information. The data had been available for years. What changed was the inability to maintain the story that it wouldn’t happen to people like you. When the person sitting next to you at a dinner party, someone with the same degree, the same career trajectory, the same kind of mortgage, tells you they’ve been made redundant because an AI system now does what their department used to do, the abstract becomes concrete.

Critical mass wasn’t a theory any more. It was happening in real time.

The full chapter walks through the mechanism in detail. It shows you how the competitive cascade works, what board-level AI governance looks like, and why the traditional strategies for managing technological disruption failed. It explains the incumbent’s dilemma and why some companies became uncompetitive not because they were poorly managed, but because they made rational decisions within a framework that no longer applied.

It’s not comforting. It’s not designed to be. It’s designed to explain what you’re seeing, and to help you position before the wave hits your particular door.

Right, it’s real. The Next Rung is written, it’s with the publisher, and as of this week you can pre-order it. It’s out on 19 January 2027, and you can reserve your copy now.

If you’ve been reading along here, you already know the shape of it, though I’m not allowed to say “shape” anymore, so let’s say you know what it’s about. AI is doing something to knowledge work that doesn’t match the story we keep getting told from the top. It isn’t quietly automating a few tasks and leaving everyone to get on with the interesting parts. It’s taking out the orchestration layer, the coordinating and interpreting and deck-building that turned out to be most of what a lot of us actually do all day. And the mechanism that carried displaced workers up to the next rung last time, the thing that moved factory workers into services and services into knowledge work, is breaking at the same time. There isn’t an obvious next rung now. That’s the bit the book is trying to name, and then do something useful about.

I wrote it from an awkward spot. I build the tools that do some of this. I advise the business owners making the decisions about how to structure their teams, and the honest conclusion keeps being that they need fewer people. And I educate some of the kids who’ll inherit whatever’s left. I don’t think that contradiction disqualifies me from writing about it. I think it’s the reason to.

Why pre-order, and why now rather than in January

Pre-orders do more than they look like they do. They tell bookshops there’s demand before the book is even on a shelf, which is most of what decides whether it gets stocked properly or buried in a back room. And every pre-order, whenever you place it, counts toward the launch-week numbers, because the order lands when the book ships. So if you’re going to buy it anyway, buying it now is genuinely more useful to me than waiting. Same money, better timing.

Where to pre-order

I’d point you at Waterstones first.

Pre-order on Waterstones — £14.99

Two reasons. If enough of you order through Waterstones, it makes the case for them to put it in actual shops, which is the thing I’m really after. And supporting a bookseller that still has physical shops feels like the right call for a book that’s partly about what happens to the high street when the orchestration economy hollows out. Free UK delivery over £25, so if there’s a second book you’ve been meaning to get, this is your excuse.

If Waterstones doesn’t suit, it’s also on Amazon and direct from the publisher. The print version is the one that helps most, so if you’ve no strong preference, go print.

That’s the whole ask

No grand pitch. If the idea up top is one you’ve been circling without quite having the words for it, the book is my attempt at the words. Pre-order it, and if you know one person who’d recognise themselves in it, send them this. I’ll see a good few of you at the launch in January.

Thanks for reading this far. Genuinely.

Chris

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

There is a memorandum. It exists in a folder somewhere in the Retail Banking Division of a major UK lender. It’s marked preliminary. It has a date-stamp and a distribution list that stops at Group Risk Committee. It contains numbers that are large enough to require board-level briefing. The memo is about you, if you’re a professional-class homeowner with a mortgage in the London or South East commuter belt. It’s about what happens to the housing market when a thousand people a week stop earning what they’re supposed to earn.

The memo uses language designed to make frightening things sound manageable. “Emerging correlated default risk” means a lot of people in the same neighbourhood are going to miss their mortgage payments at the same time. “Income volatility patterns” means people are losing their jobs. “Structurally invalidated income assumptions” means the salary that justified the loan doesn’t exist any more.

Here’s what the memo says:

UK professional-class homeowners with mortgages: approximately 1.2 to 1.5 million households.

Average London professional-class mortgage: £450,000 to £500,000.

Total exposure in this segment: £540 billion to £750 billion.

Current mortgage balances in arrears nationally: £20.6 billion (1.2% of total).

Modelled default rate under simultaneous income shock: 15 to 20% of the professional-class portfolio.

Problem mortgages under that scenario: £81 billion to £150 billion.

Which is a 4 to 7 times increase in arrears across the entire national system.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The memo notes that this pattern is not how the existing stress tests work. The existing stress tests assume idiosyncratic defaults, meaning individual borrowers experiencing individual hardship, spread across different regions and sectors. The scenario the memo describes is correlated defaults, meaning thousands of people in the same profession, in the same region, losing income simultaneously. The existing stress tests were calibrated for the 2008 financial crisis, which was a credit supply failure. This scenario is a labour market shock. Different shape. Different trigger. Different contagion pathway.

The memo recommends dedicated review of portfolio concentration risk. Scenario modelling for 10%, 15%, and 20% default rates. Early engagement with the PRA. Board-level briefing on findings.

The thing about a memo like that is that by the time it reaches action, it’s already obsolete. The lead time for policy response is shorter than the speed of the trigger. By the time the pattern is confirmed in arrears data, the provisioning window has already passed.

But let me tell you what the memo doesn’t say.

Tom and Priya bought a house in 2024. Three bed, semi-detached, Zone 4 London, excellent transport links, which means you can hear the 07:42 from the kitchen. £575,000. Tom in financial services compliance, £68,000. Priya a paralegal in commercial property conveyancing, £42,000. Combined: £110,000. Mortgage: £460,000. Loan-to-income: 4.2 times. Within the constraints. Within the rules. The numbers worked on paper.

The numbers always work on paper. Paper doesn’t lose its job.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Priya’s firm restructured first. Not dramatically. A memo using the words “operational efficiency” and “right-sizing.” Eight paralegals becoming two. The work she’d done for four years, reviewing title documents, compiling completion bundles, could now be done by an AI system that processed in an afternoon what the team processed in a week. The system needed oversight, the firm was keen to emphasise this, but oversight by two people, not ten.

Tom’s income alone was 38% of net pay to mortgage. Manageable technically. Not comfortably. Not with council tax and energy and car payment and nursery fees. Not for more than a month or two.

Priya looked for work. Legal recruitment in London was what recruiters, speaking off the record, called “challenging.” Which meant: hundreds of paralegals, handful of jobs, all the mid-tier firms restructuring, all using the same tools, all shedding the same roles. The positions that existed were either senior, which required experience she didn’t have, or junior, which paid £28,000 and left her earning nothing after childcare costs.

She signed up for Universal Credit. Six weeks processing. When it arrived, it covered approximately one-third of the mortgage payment.

Tom’s employer announced an operational transformation. The compliance team of twelve became six. Tom was one of the six who didn’t survive the restructuring. Redundancy: twelve weeks at full salary. £13,600. Which covered 3.2 months of household expenses at current burn rate.

The household had savings. £8,200 in an ISA. £1,100 in current account. £9,300 total.

The calculation: redundancy pays for three months. Savings cover another two months. Universal Credit covers a third of the mortgage. They applied for a payment holiday. Interest accrues. It gets added to the debt. They applied for forbearance. Deferred payments defer but don’t disappear. They accrue at the mortgage. They have the structure of relief and the mathematics of debt accumulation.

Eight months after Priya lost her job, Tom took a facilities coordinator role at £38,000. A 56% reduction from his previous salary. They stopped talking about mortgages at the kitchen table. They stopped talking about most things. The mortgage statement is still in the drawer. They both know the number.

This is where the abstract memo meets the actual mathematics of a household trying to maintain a lifestyle built on income assumptions that no longer exist.

The stress tests assume distributed risk. Individual defaults spread across time, sector, geography. The system has capacity to absorb individual shocks because individual shocks happen individually. But when the shock is correlated, when it’s thousands of professional-class households in the same postcodes losing income in the same quarter because their employers adopted the same technology, the system’s absorption capacity breaks.

The mortgage system works on the assumption that income is durable. You earn more as your career progresses. The property appreciates. You’re less exposed twenty years into the mortgage than twenty months in. That model holds when the income is actually durable. When the job doesn’t disappear because the role is being automated, it holds.

But what happens when the assumptions break?

For Tom and Priya’s cohort, the professionals in London and the South East with mortgages at four times income, the assumption broke in 2025. It will break for the next cohort in 2026. And the cohort after that in 2027. Not because anyone made a reckless decision. Because the architecture of UK mortgages is built on durability assumptions that are no longer true.

The mortgage broker who approved a £460,000 mortgage on £110,000 income was following the rules. The rules say you can lend up to 4.5 times income, with stress testing for rate rises and modest employment disruption. The broker stress-tested for a rate spike. For house price falls. For one income dropping temporarily. He didn’t stress-test for structural, correlated, simultaneous income loss across a demographic because that wasn’t something the historical data suggested would happen.

The historical data was wrong.

So the memo sits in the risk committee folder. The defaults accumulate in arrears. And households like Tom and Priya’s navigate the space between “we have a mortgage” and “we can no longer afford this mortgage” by making decisions that feel like success even though they’re compression: moving the child out of nursery, one parent taking a lower-paid role, holidays cancelled, saving stopped, the slow withdrawal into the minimum viable life that the mortgage allows.

For 1.2 to 1.5 million professional-class homeowners, the salary assumption that justified the lending no longer holds. Not because they’re bad at their jobs. Because the jobs are being automated away. And the system has no plan for what happens when that assumption fails at scale.

The dual-income mortgage trap is that it only works if both incomes are resilient. If Priya had worked in teaching, or nursing, or a trades business, Tom’s job loss would have been survived through her income plus benefits. Instead, both incomes were in sectors experiencing correlated automation shock. The paralegal work was automated. The compliance work was automated. The system had compressed them from two professional salaries to one reduced salary and Universal Credit, and there’s no recovery trajectory from that except time and luck.

This is not a moral failing. It’s how the system built itself. Dual professionals in the same economic corridor are cheaper to lend to than single earners because they appear more resilient. But when the correlation is professional sectors, not just household income levels, the resilience disappears.

The mortgage system was designed for individual risk. It’s being tested by correlated risk. And the households currently in the middle of that test are the ones learning, at the kitchen table, at 3 a.m., what it means when the system’s assumptions fail and your life is the mathematics that remains.

The first thing you notice when you’ve shown Orca to a hundred people is that the reactions follow three patterns. Not variations on one pattern. Three distinct sequences that almost never cross.

The second thing is that the objections, while they sound different in each conversation, sort into three archetypes with three honest answers.

The third thing is that there’s one question, usually asked quietly about halfway through, that tells you whether the person’s going to buy or not. Once you know what that question is, you can read the demo outcome almost before you get to the end.

Let me describe what I’ve seen.

The three reactions

Relief. This is the one that comes first. The person’s sitting in the demo. They watch a meeting brief generate in under a minute. They see follow-up emails draft in their voice. They watch the inbox reduce from chaos to thirty actionable items. And something in their shoulders drops.

You can actually see it happen. The weight they’ve been carrying, the thing they’ve been managing since they woke up this morning and probably yesterday and probably the day before that, has a path off. Not gone. Not solved. But there’s a direction now. Someone’s built a thing that handles the part of their job that’s been eating their day.

The relief is real and it’s immediate. Some people cry. Not dramatically. Just that thing where your eyes get wet because you’ve been living in scarcity and someone just showed you abundance.

Guilt. This comes next, usually about fifteen minutes in, when the person starts doing the maths. A PA who would cost them two or three grand a month is being replaced by a tool that costs two hundred and fifty pounds. A coordinator role that’s been taking someone eight hours a day is now taking an hour. The back-of-napkin arithmetic is obvious.

And with that arithmetic comes the uncomfortable recognition: I could have let that person go. Or I don’t need to hire that person. Or the person I’m about to hire won’t actually be necessary. The relief about time opens directly into discomfort about people.

This is where some demos stall. The person who was excited about the capability five minutes ago is now quiet. They’re working through something that’s not technical.

Maths. If the person moves past the guilt phase (which some do, some don’t), they usually land in a more calculating frame. Okay, the tool works. Okay, the economics are obvious. Okay, there’s a capability gain. What does this do to my business? What does it let me do? What new advantages do I have?

This is usually when the conversation shifts from “this is nice” to “how much does this cost me to implement, what’s the rollout, when can I start.” The person’s moved from ethical discomfort into operational pragmatism. They’ve decided to do it or they’re pricing the decision.

Here’s the important bit: relief and guilt don’t predict conversion. People can feel both and still not buy. But when someone moves to the maths phase, they usually do. The maths phase is where the ethics gets integrated into the business decision.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The three objection archetypes

The objections I hear sound different but they fall into three categories. And each one has an answer that actually addresses what’s being asked, rather than what’s being said.

The integration objection. “How much work is it to get this into our systems?” Or “does this connect with our CRM?” Or “what if we use a different email client?” This sounds like a technical question. What’s really being asked is: is this real or is this a demo? Can it actually work in my actual business, or will it turn out to be a theoretical capability?

The honest answer: integration is the difference between demo and product. Orca connects to what you actually use (Gmail, Slack, Outlook, Salesforce, HubSpot). It doesn’t require APIs. It watches your calendar, reads your emails, understands your CRM context. Setup is usually a weekend. After that, it’s running.

Show them the integration. Walk through it. Let them see that it’s not vapour.

The accuracy objection. “Will it get it right?” This isn’t really about accuracy. It’s about risk. What happens if the tool is wrong or presents bad information?

The honest answer: it’s accurate enough that it saves time. It’s not accurate enough that you stop thinking. You read the output, catch what’s misunderstood, fix it in two minutes. You’ve still saved forty minutes. The demo shows this. Show where Orca gets something slightly wrong. Show the fix. Show that the person catches it. That’s what matters. Not perfect accuracy. Accurate enough that the human layer is the final check.

The displacement objection. This is the Sarah question, but asked with a different edge. “Isn’t this going to put people out of work?” Or “aren’t you destroying jobs?” Or the version that comes from someone who’s thinking about being on the receiving end: “will I still have something to do?”

The honest answer depends on who’s asking. If they’re asking from the position of running a company, the answer is: yes, probably, you should think about what to do about that. If they’re asking from the position of being in a coordination role themselves, the answer is: probably, but not immediately, and you should start thinking now about what you want to do next.

Neither of these is comforting. But both of them are true. And I’ve found that people respect honesty about the hard part more than they respect being offered a false comfort.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The conversion question

About forty minutes into a demo, usually while we’re looking at the follow-up email generation, someone will ask a question. It’s asked quietly, often apologetically. And once I know what question gets asked, I can usually predict whether they’re buying.

The question is some version of: “So what does this actually give me back?”

Not the feature. The actual hours. The actual days. The thing they get to do instead.

A founder asking this is usually about to convert. A director asking this is usually about to convert. Even someone who’s been quiet the whole time, if they ask this question, they’re thinking about implementation. They’re mapping what they’d do with the time.

The people who don’t ask this question, who stay focused on capability and features and integration, those people often don’t buy. Or they buy and don’t use it. Because they never moved from “what can this do” to “what can I do with the time it frees up.”

The question matters because it reveals whether someone’s thinking about the tool or thinking about their life. Thinking about their life is the thing that makes someone actually adopt something.

What we’ve changed

In a hundred demos, we’ve made three changes based on what we’ve seen.

First, we spend less time on accuracy. Showing perfect output doesn’t predict adoption. Showing realistic output with human judgment does. We deliberately show where Orca gets something slightly wrong and how the user catches and fixes it in thirty seconds. People watch that and see themselves in it.

Second, we moved the pricing conversation. It used to be at the end. Now it’s upfront. Two hundred and fifty pounds a month. Not hidden. Not positioned as “investment”. Just: this costs this much. The people who flinch and leave are the people who were never going to adopt it anyway. The people who say “okay, what does it do” are usually the people who buy. It filters much better.

Third, we’ve simplified the setup conversation. We used to walk people through all the integrations, all the options, all the configuration possibilities. Now we say: connect your calendar, your email, your CRM. That’s it. We handle the rest. Want more later? We’ll add it. But the door is lower. More people walk through.

They’re just: we stopped showing what we thought was impressive and started showing what moves someone toward deciding.

The field journal part

The people who convert usually already know they need something like this. They’re drowning. Exhausted by coordination work, not excited about automation.

They usually ask about privacy and security early. People who care about that detail usually care about things in general, which predicts they’ll use the tool properly. They usually want to talk to someone else before they buy. Not impulse. Thinking it through.

The ones who don’t convert usually say “I’ll think about it” and then don’t. Without a two-week buying decision, it usually means no.

The ones who do buy usually say “What’s the first step?” Right now. In this conversation.

The difference is whether the person’s operating from scarcity or choice. Scarcity, they see the tool as a solution to a pain they’re living in. Choice, they see it as an option to evaluate. Scarcity converts. Choice doesn’t.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Here is a fact that does not get enough attention in professional development circles: the half-life of technical skills has shortened to about two and a half years. That means half of what you learned in a specialist technical domain will be outdated, superseded, or rendered less relevant in two and a half years.

This is not a new observation. It has been tracked in engineering, IT, and data science for about a decade. But the implications have not been absorbed into how people actually think about their careers.

The standard response to skill decay has always been Continuing Professional Development. Take courses. Attend conferences. Read white papers. Keep up. This was sensible when skill half-lives ran at ten to fifteen years. You could take a course in 2010 on a technical framework and still be deploying that framework competently in 2020. The investment had a decade-long tail.

When the half-life shortens to two and a half years, the economics change entirely.

If you take a course on a specific technical skill today, half of what you learn will be outdated in thirty months. That is not a long enough runway to build career capital. It is not a long enough runway to move from learner to practitioner to expert. It is barely long enough to move from learner to functional.

Consider the practical timeline: you take a course on technology A. Three months to move from competent student to functional practitioner. Six months to deploy it in a real system where the theory meets the constraints of actual infrastructure. Twelve months to develop genuine expertise. Eighteen months to be the person people call when it breaks.

At two years, you are finally at the point where the time investment has paid off and you can add value by teaching someone else. At two and a half years, the technology is becoming obsolete. The pattern that worked for you is being replaced by a new pattern.

You can learn the new pattern, of course. But you are now starting over. The opportunity cost of the first two and a half years was learning something that is becoming obsolete. You are not building on the previous foundation. You are discarding it.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

This is the hidden cost of accelerating technical change. It looks like increased opportunity. More skills available to learn, more technologies emerging, more certifications available. What it actually is: a shortened useful lifespan for every piece of technical knowledge you acquire.

The response from the education industry has been to suggest learning faster. Do more courses. Do micro-credentials. Do bootcamps. Stack up the certifications. Build a portfolio. The arithmetic is seductive: if skills are becoming obsolete faster, just learn more skills faster.

This misses the fundamental problem. It is not a problem of speed. It is a problem of half-life.

If you learn skills at a rate of one major technical area per two years, and the half-life of each skill is two and a half years, you are running on a treadmill that is accelerating. You are always in catch-up mode. You are always behind. You cannot build genuine expertise because by the time you reach it, the technology has moved on.

The micro-credential industry is built on this dynamic. The promise is that you can keep up by doing smaller, more frequent learning. But if the half-life is two and a half years, and you are spending three months per micro-credential doing an updated certificate, you are still in catch-up. You are just doing it with more frenetic energy and less depth.

Professionals in these fields often describe it as a treadmill feeling. Constant learning, constant motion, constant awareness that you are falling behind. Because you are. The half-life is not something you can out-learn. It is a structural property of how fast the technology is changing relative to how fast humans can acquire mastery.

The second-order problem is the one that matters more. When the half-life becomes this short, you cannot build stable long-term career capital in technical depth anymore. You can build it in proximity to the technology - in understanding patterns, in knowing which person to call, in having the context to learn the new version when it arrives. But you cannot build it in the technology itself.

This is why the most valuable people in rapidly-changing technical fields are not the ones with the deepest knowledge of the current framework. They are the ones who have seen the previous three generations of frameworks and understand the pattern underneath. They can move to the new technology faster because they have learned how to learn at the architecture level, not the implementation level.

This requires a shift in how people think about professional development. The standard approach is to keep the technical knowledge current. That is increasingly a losing game. The alternative approach is to stop trying to keep technical knowledge current and instead build meta-skills: learning how to learn new technologies, understanding the patterns across generations of frameworks, developing judgment about which framework to apply in which context.

Those things do not become obsolete in two and a half years. They compound.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

But CPD is not structured that way. CPD is structured on the assumption that you can accumulate specific knowledge and that knowledge will remain valuable. Courses are built on that assumption. Certifications are built on that assumption. Continuing Professional Development hours are mandated on that assumption.

When the half-life of the knowledge being taught is two and a half years, mandating forty hours of CPD per year is a ritual, not a hedge. You are going through the motions of staying current while the very concept of current is becoming a moving target.

The honest position is this: if your career depends on technical knowledge, you have a problem. The half-life of that knowledge is shorter than the time it takes to move from competent to expert. You cannot build a career on expertise in a specific technology anymore. You have to build a career on the ability to move between technologies.

That is a different skill. It is a harder skill. It is not something you learn in a course. It is something you learn by doing it repeatedly, by seeing the patterns across multiple generations, by developing judgment about architecture and principle rather than memorising implementation.

For people in rapidly-changing fields, the choice is: spend energy trying to keep technical knowledge current and always feel behind. Or accept that technical knowledge will decay and build your career on being the person who can navigate that decay with judgment and speed.

CPD is comforting because it feels like you are doing something. But if the underlying knowledge is decaying faster than you can update it, the action is not hedging the risk. It is performing the illusion of risk management while the actual risk accumulates in the background.

People don’t read books straight any more. They skim. They skip the bits that don’t seem to apply. They read the first paragraph and the conclusion and skip the middle. Some of them read nothing except the bold-faced sentences and the headers. That’s not laziness. It’s time management. The people whose jobs are most exposed to AI displacement, the ones this book is written for, are almost by definition the people with the least time to read a full-length manuscript.

So I built the argument twice.

Once, in full. Chapters with analysis, observation, composite characters, the texture that makes something feel like a real story. The second, in bold. Every chapter has a bold layer: the summary written in the densest possible prose. Argument without elaboration. Claim without evidence. Just the frame.

This is deliberate constraint writing. You can’t include every nuance in five thousand words across an entire book. So you have to ruthlessly eliminate the supporting detail and keep only the skeleton of the argument. The claim that cannot be removed without the whole thing collapsing.

The process was iterative and difficult in unexpected ways. I’d write a full chapter, thirty or forty pages on something like the task-level exposure audit. Then I’d ask: if I had to reduce this to four or five pages of pure argument, what’s load-bearing? What can disappear? The process strips away the writing. What remains is the shape.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Chapter 16, “The Honest Audit,” becomes: Your job is not one thing. It’s a bundle of tasks with different exposure levels. Map your week. Score each task high, moderate, or low. If 60 per cent of your day is high-exposure work, your role is in trouble even if the remainder is distinctly human. Your employer is paying for the whole day. Beyond 40 per cent high-exposure, the maths stop working. Include a skills currency check. Include household risk assessment. Include the dual-income concentration problem. The audit tells you where you stand. That’s all it does. That’s all it needs to do.

That’s roughly one-fifteenth of the chapter. Every other detail, every composite character, every conversation, every worked example that made it feel human and grounded and real, all of that is construction. Load-bearing is: the framework plus the honesty about what the framework means.

The reason this matters is structural. The people time-poor enough to be in the highest-exposure roles are almost by definition the people least able to read a fourteen-chapter manuscript. They’re working sixty-hour weeks. They’re managing teams. They’re billing hours to clients. They’re rebuilding their home offices while managing school pickups. The last thing they have is an afternoon.

So if the argument matters enough to be read, it matters enough to be skimmable. And the only way to write something skimmable without producing something superficial is to build the scaffolding explicitly. Here’s what’s essential. Here’s what’s supporting. Read the bold. Trust that the rest is there if you need it. But you might not. The bold layer should stand alone.

The risk of this approach is obvious: it looks like you’re not trusting your argument. Like you think people need cliffs notes. The opposite is true. You’re assuming your readers are smart and time-constrained. You’re treating them as adults capable of deciding how deep to go. You’re saying: the frame is here. If you want the texture, it’s there too. If you don’t have time, don’t read it.

The secondary benefit is something I didn’t anticipate when I started. Writing the bold layer forced me to test whether every claim was actually necessary. Did this character serve the argument or was I just telling a story I liked? Did this example illuminate the point or was I just padding? The constraint made the argument sharper. Not shorter. Sharper. Necessary.

The practical result is something between a book and a primer. Read only the bold. You’ve read the book. Really it’s five thousand words of dense argument. You understand the displacement logic, the scenarios, the positioning framework, the household risks. You don’t have all the detail. You have the spine. Read the full text around the bold. You have the spine plus the texture. The evidence, the characters, the ethical grounding, the sense that someone has thought about this in detail and it’s not a thesis. It’s a pattern from advisory work that the analysis confirms.

This is what I wish someone had done for me in 2019. I was reading three research papers a day trying to understand the AI displacement pattern. An economist’s paper on labour market disruption. A tech analyst’s report on enterprise adoption curves. An academic study on the skills half-life problem. If I’d had a book where the bold layer gave me the frame and the full text let me verify it was grounded, I’d have saved probably a hundred hours. I’d have understood the pattern faster. I’d have positioned differently.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

There’s a philosophy underneath this. Which is: accessibility is not a reduction. Good writing is writing that works at multiple depths. A sentence can be clear as journalism and precise as an academic claim simultaneously. You don’t have to choose. You’re just choosing which reader you’re optimising for on any given sentence.

The bold layer optimises for the reader with forty minutes and a paper, who needs the frame. The full text optimises for the reader who has time but wants grounding. Between the two, there’s a book that works for how people actually read.

One more thing. The bold layer made the argument testable in a way the full text alone wouldn’t. I could print the bold layer separately, five pages stapled together, and say: does this frame feel like it accounts for what you’re seeing? Does the scenario list feel complete? Are the positioning principles missing anything? People who would never read a 80,000-word manuscript would read five pages and give feedback. That feedback was how I caught gaps. How the argument sharpened. How I confirmed that the pattern Chris I was seeing in advisory work matched the pattern the research predicted.

If the bold layer lands, you’ll read the book. You’ll want the detail. You’ll want the characters and the composite examples and the ethical reasoning underneath the claim that we’re likely heading toward neo-feudalism at almost fifty per cent probability rather than managed transition.

If it doesn’t land, if the frame doesn’t feel like it accounts for what’s actually happening in your industry or your household, then you haven’t wasted an afternoon. You’ve spent forty minutes figuring out that this book isn’t written for where you’re standing.

That’s honest. And it respects your time. Which, if you’re in a high-exposure role, is the scarcest resource you have.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Scenario planning is not prediction. It’s decision-making under uncertainty. The goal is not to guess which future arrives. The goal is to identify the actions that make sense across multiple futures, so you’re not betting everything on one outcome being correct.

Here are the four futures described in the book, with rough probabilities. The probabilities are directional, not precise. Think of them as: this scenario is roughly twice as likely as that one, not as a weather forecast.

Scenario A: Managed Transition, 30% probability. Governments implement progressive taxation targeting AI productivity gains. Universal basic income or significantly enhanced welfare systems provide a genuine floor. Retraining programmes, funded jointly by government and the companies profiting from automation, enable displaced workers to move into care work, creative work, community roles, human-trust professions. Trade unions retain or rebuild negotiating power. The social safety net expands rather than fragments. This is the outcome that works if political will emerges. The evidence suggests it’s technically feasible. Every pilot programme shows it works when implemented. The historical precedent exists: post-war welfare state construction, Danish flexicurity, South Korean retraining at scale. The politics are weak. Which is why the probability isn’t higher.

Scenario B: Neo-Feudalism, 45% probability. This is the path of least resistance. Capital owners capture most AI productivity gains. A small elite persists in relationship roles, creative roles, oversight roles. Everyone else works in gig fragments: content moderation, delivery shifts, platform work with 35% commission and no protections. Housing becomes extractive. You rent from corporate landlords. Political systems become performative: elections happen, but the legislation that shapes daily life is written by people who profit from things staying as they are. This scenario doesn’t need to be chosen. It just needs to not be prevented. Every current trend, wealth concentration, platform consolidation, labour fragmentation, institutional decay, points here.

Scenario C: Fragmentation, 20% probability. No coherent national or supranational response emerges. Different regions adopt radically different models. Some go authoritarian and restrict AI to protect employment. Some go libertarian and accelerate fully. Some collapse into instability. International cooperation breaks down. Trade relationships fragment. Supply chains diverge. Information spaces split into separate realities. The precedent is sobering: the 1930s, Brexit, the Visegrad Group’s divergence from EU consensus. Fragmentation’s danger isn’t chaos in one country. It’s that dozens of countries pursue incompatible strategies simultaneously, and the interactions between those strategies produce consequences none of them planned for.

Scenario D: Black Swan, 5% probability. A cascading systemic failure collapses existing institutions. The trigger could be financial, biological, environmental, or military. It could be technological: an AI system failure at critical infrastructure scale. By definition, you can’t plan for what you can’t anticipate. The precedents are real but partial: the 1918 pandemic, the 2008 financial crisis, COVID-19. Each showed the fragility of systems we thought were solid. The black swan breaks every assumption of gradual transition, but it also makes political will possible. Major crises have historically produced the change that prosperity never would. The New Deal followed the Depression. The welfare state followed the war. The question isn’t whether to plan for the black swan. You can’t. The question is whether you have enough flexibility, enough reserves, enough adaptability, to respond when everything changes.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

None of these futures is guaranteed. All of them are plausible. Several are probable. The rough weighting reflects current trajectories: what happens if we do what we’re doing, with minor variations. If someone somewhere demonstrates the political capacity to redirect the distribution of AI gains, even partially, the weightings shift. If the situation deteriorates faster than expected, or institutional capacity proves weaker than modelled, they shift again.

Which one should you prepare for. All of them. The moves that make sense under Managed Transition are not the same as the moves that make sense under Neo-Feudalism. But there are moves that make sense under all four.

Positioning over predicting. Build a working life that isn’t dependent on any single employer or any single income stream. The people who survived the 2008 crisis weren’t the ones who predicted it. They were the ones with multiple income sources, low fixed costs, and professional skills that remained valuable when the sector they worked in contracted. Positioning applies across all four scenarios. In Managed Transition, your diversification is a hedge. In Neo-Feudalism, it’s survival. In Fragmentation, it’s adaptability. In Black Swan, it’s optionality.

Portfolio over paycheque. Build a collection of skills, certifications, relationships, and demonstrated capabilities rather than relying on a job title to signal your value. This matters in Managed Transition because the new economy will value demonstrated capability over formal credentials. It matters in Neo-Feudalism because credentials will have been commodified into meaninglessness. It matters in Fragmentation because your credentials may not transfer across regional boundaries. It matters in Black Swan because portable capabilities are the only thing that survives when institutions collapse.

Trust networks over institutional trust. Institutions are becoming less reliable: employers restructure without notice, platforms change terms unilaterally, government support systems are means-tested into ineffectiveness. Trust networks are not romantic. They’re practical. The people who will help you in a crisis are the people you’ve actually helped before, not the institutions that claim to represent you. Build relationships with people you respect, work with people you trust on projects that matter, create value for communities you’re part of. This applies across all scenarios. In Managed Transition, networks are how you access the new opportunities. In Neo-Feudalism, networks are your only buffer against isolation. In Fragmentation, networks are how you navigate incompatible systems. In Black Swan, networks are what sustains you when institutions fail.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Owned assets over rented access. This is the most contentious one because it’s expensive and time-dependent, but it matters. A house you own, even partially, is different from a room you rent. A business you own, even if it’s small, is different from an employee relationship. A skill set that’s yours because you built it is different from training provided by an employer. Owned assets are the ones they can’t take away through a policy change or a corporate decision. In Managed Transition, owned assets are the foundation of economic security. In Neo-Feudalism, they’re the difference between participation and extraction. In Fragmentation, they’re portable across regional boundaries. In Black Swan, they’re what enables rapid adaptation.

Skills with human irreplaceability. AI automates pattern-based, documentation-heavy, coordination-heavy work. It doesn’t automate trust. Doesn’t automate genuine problem-solving where the problem is unique and ambiguous. Doesn’t automate relationships where the human element is not a bug but the core of the service. Care work, creative work at the level where originality matters, mediation, skilled trades with physical presence, teaching when it’s about developing people not delivering content. These don’t guarantee security under any scenario, but they provide better odds than orchestration-heavy work. The people displaced first are the coordinators. The people who survive longest are the ones doing work that an algorithm can’t evaluate on a scorecard.

You don’t need to believe any particular scenario to act on these. You need to recognise that uncertainty is genuine and the decisions you make now shape which scenario you’ll be positioned in when 2035 arrives. The time horizon is roughly eight years. The window for voluntary transition is wider than the window for forced transition. The moves listed above work across all four futures. Some of them take time. Positioning a career takes months or years. Building a trust network takes years. Acquiring a owned asset takes years or decades. Starting now, with incomplete certainty, beats waiting for clarity that may not arrive in time.

The scenario you actually live in will be determined by thousands of individual decisions and institutional choices, most of them not visible at the time they’re made. But your position within that scenario is shaped largely by you. Choose accordingly.

Every demo, eventually, someone asks it. Not always with that name. Usually it’s phrased more carefully. “What about my team.” Or “What happens to the person in this role?” Or sometimes just a long pause followed by an uncomfortable question that starts with: “So…”

But it’s always the same question underneath. Sarah is a composite. She’s a PA who’s been managing meeting coordination for six years. She’s an account coordinator who spent the last three years thinking this role was the path to account management. She’s a research analyst who’s good at finding signals in data and who’s watched that signal-finding task get automated every quarter. She’s whoever is doing work that Orca now does.

The question, carefully phrased, is: what happens to her?

I’ve been asked this about a hundred times. I’ve given different versions of the answer each time, because I was figuring it out. This is the version I give now, because I think it’s honest.

The first part: what doesn’t work

Let me start with what definitely doesn’t work, because I see it get suggested all the time and it’s important to be clear about why.

“Retrain into AI skills.” This is the response that everyone seems to reach for. She’ll learn prompt engineering. She’ll become an AI specialist. She’ll level up into a completely different career.

Here’s what happens in practice. Sarah has five years of pattern recognition from her coordination role. She knows how to catch the thing that’s slightly wrong in a dataset. She knows how to sense that a client is not saying what they mean. She knows the client environment and the political dynamics and why the CRM is organised the way it is. Those patterns don’t transfer to prompt engineering.

Prompt engineering at a valuable level is a completely different cognitive architecture from coordination work. It’s technical, iterative, requires real experimentation. By the time Sarah finishes an eight-week course, the prompt engineering itself is being automated. The tools are getting better at figuring out their own prompts faster than humans can learn to write them.

Sarah doesn’t need to become a prompt engineer. What might need more people is knowing what problems are worth solving with AI, checking whether solutions actually work, making sure output’s being checked properly. That’s the meta-layer above prompt engineering. But Sarah gets there through doing something adjacent to her current work, at a different scale. Not through training.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The second part: what Sarah already understands

Sarah understands how work flows between systems and people. She’s built relationships with clients or her team. She’s developed judgment about what matters. She’s got three to six years of track record, which says: this person can stick with something difficult.

None of this means her old job continues. But it’s raw material for something else.

The third part: the positioning work

Here’s where Sarah’s choice actually matters.

She’s got roughly three to six months before the tool is fully rolled out in her company and the coordination work isn’t really a role anymore. In that window, she can do something or nothing.

Nothing: she waits, she hopes the automation doesn’t happen as fast as expected, she sends out a CV to ten companies doing the same coordination work at scale, she hopes the next company hasn’t automated this yet. Statistically, this is what most people do. Statistically, it doesn’t work out well.

Something: she stops waiting and starts thinking about what problem her judgment and her relationship networks actually solve that doesn’t depend on her doing coordination work.

For someone with Sarah’s background, this usually looks like one of three things. Not retraining. Just repositioning.

First, the client relationship angle. Sarah knows clients and what they actually want. Companies often need someone to hold that client relationship at a different level than transaction management. Account director, new business development, or internal operations role that understands client needs. It’s different from her current role but uses the fact that she knows clients and they know her.

Second, the operations angle. As AI handles routine workflows, someone has to ensure the infrastructure’s right. Is the tool being used correctly? Is the output being checked? Is there a human in the loop where needed? These aren’t flashy roles, but they’re durable. Sarah’s five years understanding workflow is a real advantage.

Third, the something-adjacent angle. What does her client actually need that she delivered as part of coordination work, but that isn’t coordination? Strategy, audit, analysis, or just continuity because the client trusts her. There’s usually something in the existing work that was always interesting but got obscured by coordination overhead.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

None of these are guaranteed. Some Sarah’s won’t find a slot because there isn’t one in their company. But the point is: Sarah’s choice determines whether she’s in the 30 per cent who get landed somewhere else, or the 70 per cent who get displaced and never find their footing again.

The fourth part: what the company has to do

The company’s choice is stark. Automate and keep headcount unchanged (Sarah eventually leaves), or automate and restructure (find her a new role, or have an honest conversation about it).

The honest conversation is the one that doesn’t usually happen. “The coordination work is being automated. That’s not something we can stop. But we need someone in the client relationship role, or the operations role, or the analysis role. Do you want that job, or do you want to look elsewhere?”

This doesn’t pretend automation isn’t happening. It doesn’t offer a fake job. It acknowledges change and offers a choice.

Some Sarahs will say yes. Some will say no. Both are better than pretending the old role exists. Companies that handle this well survive with culture intact. The ones pretending the old structure exists while Sarah does something new without support are where culture breaks.

The fifth part: the realism about timing

Here’s the timeline as I see it.

Sarah’s got about six months. Maybe a year if she’s lucky and her company’s slow to move. In that window, the tool is being deployed, the efficiency is becoming obvious, the leadership is doing the maths about headcount.

If Sarah’s done her positioning work, there’s a decent chance she lands somewhere. Not the job she expected. Not the trajectory she planned for three years ago. But a job that uses what she knows and pays something like what she’s earning now.

If she hasn’t done that work, the window closes and she’s looking for work in a contracting market. She’s looking for jobs in coordination at companies that haven’t automated yet, which is rapidly becoming a list that only includes companies in serious trouble. She’s competing with people who have six months more experience than her in roles that are being squeezed right now.

This isn’t a lecture about personal responsibility. This is describing the structural reality Sarah’s facing. Some of it is in her control. Some of it isn’t. The companies that handle this reasonably well are multiplying slowly. Some of it is chance.

But doing nothing, waiting, hoping the change doesn’t happen as fast as everyone’s predicting, that’s the one choice that’s almost certainly worse.

The sixth part: what I actually tell Sarah

When Sarah asks me the question directly (which happens, sometimes), here’s what I say:

Your job as it existed is being automated. That’s not my prediction or my preference. That’s what the tools do. Your company will probably automate this. If they’re smart, they’ll restructure so you have somewhere to go. If they’re not smart, they’ll pretend the automation doesn’t require any structure change and you’ll eventually leave frustrated.

Your best move right now is to figure out what about your current role you actually want to keep doing, and go talk to your company about whether there’s a place for that. Or start looking for a place that needs what you know.

Don’t retrain. Don’t become a prompt engineer. Don’t assume the role will come back. Don’t wait for someone to tell you what to do.

Figure out what problem your judgment and your relationships solve. Find a company that has that problem. Go solve it.

That’s the answer I give. Not comfortable. Not “don’t worry, everything will be fine.” Not “learn new skills and you’ll be fine.” Just: here’s what’s actually happening, here’s what you can control, here’s what you should do about it.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The question is older than economics. Older than the nation state. Who is the system for. Who benefits. Who pays. Who gets left behind. Every stable period in human history is built on an answer to that question. Every period of upheaval is built on the answer changing.

We’ve mostly been allowed to ignore it in the UK for the past 60 years. The post-war settlement, the one built by frightened people in the aftermath of the Depression and a world war, answered the question in a way that held. The economy was, roughly, for working people. Not perfectly. The distribution was never equal. But the productivity gains from each generation flowed broadly enough to broadly enough people that the political consensus held. You worked. You expected to earn more than your parents. Your children would earn more than you. The system wasn’t perfect but it moved in the right direction often enough to feel like progress.

That arrangement is now under stress. The data is clear on this. Real wages for median-income workers in the UK have been flat since 2008. Younger cohorts expect to earn less, in real terms, than their parents did at the same life stage. Home ownership is generationally stratified: people who bought before 2008 accumulated wealth; people who haven’t bought by 2026 mostly can’t. Pension income sources have shifted from defined benefit schemes that the employer funded to defined contribution schemes where the worker bears the risk. The intergenerational compact, the implicit bargain that each generation would be better off than the last, is visibly breaking down.

AI displacement doesn’t create this problem. It accelerates it and concentrates it. When productivity gains flow entirely to capital owners and there’s no political mechanism to redirect them, the question that was background noise becomes foreground text. What is the economy actually for. Not what we say it’s for. What is it actually for, given the way it distributes outcomes and concentrates gains.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The question is political economy, not tech. This is crucial and worth dwelling on because the tech industry has a vested interest in framing labour displacement as a technical problem with technical solutions. Retraining. Upskilling. New job categories. The narrative holds together until you look at the precedent. The Industrial Revolution created enormous new job categories. It took 60 years for wages for ordinary people to stop declining. The transitions that worked, the ones that avoided social collapse, were always political interventions: unions, regulations, tax structures, welfare systems. The countries that managed transitions well, Denmark, South Korea, post-war Germany, were the ones where political institutions did something deliberate.

The countries where political institutions did nothing, or where the political consensus was that “this is how markets work and we don’t interfere,” produced the 1930s. Concentrated wealth. Rising authoritarianism. Collapsed democracies. A 70-year cold war that could have become a hot one at any moment.

That’s not destiny. It’s precedent. And the reason it matters is that we’re entering a period where that question, who is the economy for, has to be answered again. Not because we’re virtuous. Because the previous answer is visibly breaking down and the gap between “the economy is for everyone” and “the economy is increasingly for capital owners” is becoming politically dangerous.

The timing is not flexible. The window for addressing this through policy is roughly now through 2032. After that, the structure calcifies. Capital gains compound. Wealth concentration becomes structural rather than reversible. The people who miss the transition period face either multi-decade waits for generational turnover to change the structure, or politically unstable outcomes as the excluded majority challenges the arrangement.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

In the next five years, societies around the world will make decisions about AI deployment, taxation of productivity gains, platform regulation, the status of gig workers, the funding of retraining. Those decisions will be made quietly, in technocratic conversations and corporate boardrooms and parliamentary committees where attendance is sparse. They won’t feel momentous at the time. But they will determine whether the next 50 years look like a managed adjustment or a neo-feudal extraction.

The uncomfortable truth is that nobody is asking this question at scale. The tech industry has no incentive to ask it. The financial sector has no incentive to ask it. The political class is fragmented and lacking the consensus to ask it. Which means it will be answered by default, which is the same as being answered badly.

Women in Swindon running repair workshops out of converted office blocks are asking it, implicitly, by building something other than platform work. Former accountants teaching financial literacy in Tower Hamlets are asking it. 24-year-olds in Leeds building working lives from multiple income streams and demonstrated skills are asking it. They’re not waiting for permission. They’re just building the world they’re willing to live in.

That’s how institutional change always starts. Not with a master plan. Not with a political mandate. With people deciding quietly that the current answer to “who is the economy for” is not acceptable and building something different.

The question is old. The answer changes. The window for influencing that change is open now. In a few years, it closes. And then the question gets answered without you.

Here is the video if you want to see it.

Then on Wednesday the government's own review landed and said the quiet part out loud.

Alan Milburn, former health secretary, running a government-commissioned review into youth unemployment. The interim report came out on the 28th. The number everyone quoted was £125bn a year, which is what the crisis costs the country once you add up lost taxes and higher health and welfare spend. That's more than England spends on education. It's a big, attention-grabbing figure and it did its job, it got the headlines.

But the number isn't the bit that mattered to me. The bit that mattered was why he says it's happening.

Milburn put it down to a shortage of entry-level jobs and, his words, a failure of a system stuck in the past. A NEET figure of just over a million 16-to-24-year-olds, highest in twelve years, one in eight of that age group. And the cause he names first, before anything else, is that the bottom of the ladder isn't there any more.

That's the thing I've been banging on about. So have a lot of other people, to be fair, I'm not claiming I spotted it. What's new is that it's now in a government-commissioned review rather than a LinkedIn post or a bloke talking over BBC clips. When the system commissions a report and the report says the system is broken, that's worth a pause.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Now, I want to be straight about what the report actually says, because it would be easy to grab the half that suits me and run. Milburn doesn't say AI ate the jobs. He says young people are now more likely to be economically inactive than unemployed, and he links a lot of that inactivity to anxiety, depression, and neurodevelopmental conditions. Health is central to his story. So it's not a clean "the robots did it" line, and anyone who tells you it is hasn't read past the headline.

Here's how I'd hold both at once. The entry-level rung was already thinning, that's the structural bit, and AI is accelerating it. And when the first rung goes, you don't just lose a job. You lose the place where an eighteen-year-old learns to turn up, take feedback, be part of something, get a bit of confidence. Take that away and of course the health numbers get worse. The two things aren't competing explanations. They're the same wound from two angles. The floor goes, and then the people who'd have been standing on it start to struggle in every other way too.

Which brings me to the part I find genuinely uncomfortable, and I'd be a fraud if I skipped it.

I build the tools that do some of this. I run a company that automates the coordinating-and-admin layer of a business, the stuff a junior used to cut their teeth on. I sell it because it works and because the maths for the business owner is obvious. And then I write things like this, arguing that we're hollowing out the rung that teaches people how to work. Both of those are true at the same time and I haven't found a way to make them stop being true. I'm not going to pretend the tension resolves. It doesn't. The honest position is to keep naming it rather than pick the comfortable half.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

So what do I actually think we do.

I don't think it's interview training, which is what a couple of people suggested when I posted the video. Teaching someone to present better is a fine answer to "two candidates, one job." It's no answer at all to "the job's gone." You can coach somebody brilliantly for a role that no longer exists and they still don't get it. The polish helps at the margin. The margin isn't the problem.

And I don't think it's the thing politicians reach for either, which is another funding pot and another scheme. Milburn's report nods at organisations that used to do this work and got defunded, and someone pointed me at The Challenge, which folded in 2019. That matters. But pouring money into reintegration after the fact is treating the symptom. The thing that's broken is upstream: we've stopped building the first step, and we're surprised people can't climb.

If I'm honest I don't have the tidy answer to put here. I've got a direction, which is that you can't fix the top of a ladder when the bottom's missing, and most of what's being proposed is top-of-the-ladder stuff. That's about as far as I've got. I'll keep working on it out loud.

Last thing, on why the video did well. It wasn't my commentary. It was the BBC clips. The reach belongs to the topic, not to me, and it's worth being clear-eyed about that. But the topic is getting reach because a lot of people can feel this even if they can't name it yet. A government review naming it is one more sign the ground's already moved. The naming usually comes a good while after the moving.

Most people already know their job is at risk. They just haven’t said it out loud yet. There’s a specific kind of knowing that sits below the surface of daily routine. You feel it in the meeting where the AI demo does your Thursday in forty minutes. You feel it when the graduate scheme gets paused for the second year running. You feel it in the silence after your manager says “we’re looking at efficiencies,” a phrase that has never once meant anything good for anyone in the room.

Knowing and auditing are different things. Knowing is a feeling. An audit is a number. And the distance between the two is where most people hide.

This chapter is about closing that gap. Not with false comfort, not with panic, but with honest arithmetic.

Your job is not one thing. It’s a bundle of tasks, and each task has a different exposure level. This is the most important shift in the whole audit. Nobody’s role is entirely safe or entirely doomed. It’s a mix, and the proportions matter.

Take a marketing manager. On a given day, they might pull data from analytics platforms and build a report. High exposure, pattern-based, AI does this now. Write a strategy brief from a template. High exposure, documentation-heavy, AI generates these. Sit in a meeting with a client and read the room when the conversation gets tense. Low exposure, requires emotional context and relationship history. Manage three agency relationships through email coordination. High exposure, pure orchestration. Make a judgment call about whether to shift budget mid-campaign based on instinct and data. Moderate exposure, judgment component matters, but AI-augmented decisions are coming.

That’s five tasks. Three are highly exposed. One is moderately exposed. One is relatively safe. If 60% of your day is spent on the high-exposure tasks, your role is in trouble even if the remaining 40% is distinctly human. Because your employer is paying you for the full day, and if three-fifths of that day can be done by software at a fraction of the cost, the maths stops working in your favour.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

The question isn’t whether your job disappears. It’s whether enough of your job disappears that the remaining tasks don’t justify your salary.

Here’s how to do the task-level audit. Take a piece of paper and a typical week. List everything you do. Mark each task: high, moderate, or low exposure. Be honest. Nobody else is going to see this list. If you catch yourself writing “strategic leadership” when what you actually do is chair a weekly standup and forward the notes to your manager, fix it. The audit only works if it’s honest.

A business analyst I know ran this exercise over a weekend. Her weekly hours broke down roughly like this: twelve hours on data extraction and report building, high exposure. Six hours on stakeholder meetings and relationship management, low exposure. Eight hours on email coordination between departments, high exposure. Five hours on genuine strategic thinking, connecting patterns the data revealed to business decisions nobody else had noticed, low exposure. And nine hours on documentation, meeting notes, status updates, process paperwork, very high exposure.

That’s twenty-nine hours of high or very high exposure out of forty. Seventy-two per cent. The eleven hours of lower-exposure work, the relationship management and the strategic thinking, were real. They were valuable. They were also not enough to justify a full-time salary when the other twenty-nine hours could be done by a language model and an automation workflow.

She didn’t like the number. Nobody does. But honest numbers are the ones you can plan around.

One thing the task-level audit misses: velocity. Your exposure score today is a snapshot. The more important question is how fast it’s changing. AI capability is not advancing linearly. It’s compounding. The tools available in 2024 handled basic reporting and content generation. The tools available in 2026 handle multi-step reasoning, cross-platform coordination, relationship tracking, and contextual judgment. The gap between “AI can theoretically do this task” and “AI does this task better and cheaper than a human” is closing faster than most people’s career plans account for.

A role that scores 2.5 on task-level exposure today might score 3.5 by next year. Not because the role changed. Because the tools improved. The audit needs to be dynamic, revisited quarterly, adjusted as capabilities evolve. A score you’re comfortable with in March might be uncomfortable by September.

This isn’t meant to create panic. It’s meant to create urgency. The difference between the two is that panic produces poor decisions and urgency produces timely ones.

Now, the household audit. Individual risk is one thing. Household risk is what actually matters, because bills don’t care whose income pays them. Most discussions about career risk focus on the individual. But people don’t live as individuals. They live in households. Shared expenses, shared debts, shared dependants, shared assumptions about how the mortgage gets paid.

Ask uncomfortable questions. Are both earners in high-exposure sectors? If you’re a marketing manager married to a business analyst, you’ve got correlated risk. Both roles share the same exposure profile: orchestration, text-based output, professional services. If your employer automates your role and your partner’s employer does the same thing six months later, you’re not dealing with a career setback. You’re dealing with a household crisis.

What’s your financial runway? The FCA surveys are consistent: one in four UK adults has low financial resilience. A third have less than five hundred pounds in emergency savings. Eleven million Britons have less than a thousand pounds to draw on. If your household is in these categories and both incomes are exposed, the audit produces a number that should alarm you. Not because alarm is useful on its own. Because it should accelerate the timeline on the strategies that come next.

The average UK adult under 55 has about ten thousand pounds in savings. For most households, that’s two to three months of expenses. If your role disappears and you’re job-hunting for six months, the savings don’t cover it.

What are your fixed costs? Mortgage or rent, childcare, school fees, car finance, insurance, utilities. These don’t flex when income drops. A household spending 40% or more of income on housing, which describes a large chunk of UK households in major cities, has almost no margin for income disruption. The gap between “uncomfortable” and “crisis” is about three months.

Is there a fallback? Family resources, geographic flexibility, skills that transfer to lower-risk sectors. Not nice-to-haves. The difference between a managed transition and a panic.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Dual incomes create the illusion of resilience. The reality depends on sector diversification. Two incomes are better than one. Obvious. But two incomes in the same sector, or in sectors with correlated risk profiles, create a different kind of vulnerability. Not twice as safe. Twice as exposed to the same shock.

A household where one partner works in healthcare and the other in digital marketing has genuine diversification. If the marketing role gets automated, the healthcare income continues. The household absorbs the shock. A household where both partners work in professional services, one in consulting and one in accounting, has concentration risk. The same AI adoption wave that restructures the consulting firm will restructure the accounting firm. Not necessarily on the same day. But within the same window, driven by the same competitive pressure, producing the same result: fewer roles, lower pay, or both.

The audit question for dual-income households is simple: write down both roles, assess both for task-level exposure, and ask whether the risks are correlated. If they are, your household risk is significantly higher than either individual risk suggests. And the practical follow-through: if the risks are correlated, one partner should be actively positioning toward a lower-risk sector. Not both simultaneously. That creates chaos. But a deliberate, planned diversification where one household income moves to a less exposed area while the other maintains the current trajectory. Household-level portfolio management. It’s as important as anything in a pension statement.

The skills half-life problem sits underneath all of this. The skills that got you here may not get you through what’s coming. And the speed at which skills depreciate is accelerating. Skills half-life, the time it takes for half of what you know to become outdated, is now estimated at under two and a half years in technical fields. Half of what a professional learned in a training course eighteen months ago is already losing relevance.

For the honest audit, this means something uncomfortable: the skills on your CV, the ones you list under Core Competencies or Areas of Expertise, may already be past their peak market value. Not because you’re bad at them. Because the market’s need for a human to perform them is declining.

The audit should include a skills currency check. When did you last learn something actually new, not a refresher, not an update, but a capability you didn’t have before? If the answer is more than two years ago, your skills portfolio is depreciating faster than you’re replenishing it. That’s a position you can change. But you need the honest assessment first.

The honest audit doesn’t tell you what to do. That’s the next part. It tells you where you stand. And where you stand determines what’s available to you.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

Mrs Okafor was standing at the photocopier at 7:48 on a Wednesday morning, printing out a worksheet about accounting pathways, when the thought hit her that the worksheet was already obsolete. Not in the usual way that teaching materials age, not stale and a year behind, but fundamentally wrong. The map she was producing no longer matched the territory the students would walk into.

She is the careers lead at a comprehensive school in south London. She teaches business studies, manages the careers programme, coordinates employer encounters, tracks students against the Gatsby Benchmarks, does the work that gets framed as supporting student futures while operating inside an education system and a labour market that are both moving faster than she can keep pace with. When her predecessor left on maternity, there was no handover, just a cupboard full of expired Unifrog logins and a laminated poster from 2019 saying “Your Future Starts Here” with a stock photo of a woman in a hard hat.

The problem with being a careers lead in 2026 is that the landmarks have moved. Accountancy used to mean something. Professional services used to be a destination. University still looks like a safe bet to people who got good results and don’t know what else to do. But the Big Four firms deployed AI in 2024 and 2025 that now handles audit work, tax compliance, financial analysis. The entry-level route into accountancy, the grinding three-year spell doing spreadsheet work while studying for ACCA exams, is the exact pathway most vulnerable to automation. Mrs Okafor knows this because she reads the CBI Employment Trends Survey. She knows that 23% of firms are cutting training investment, double the previous year’s figure. She knows the employers are retreating.

And then she came across Enterprise Skills.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

She almost filed it in the mental folder where every EdTech pitch goes, somewhere between “interesting in theory” and “when would I find the time.” Except the platform didn’t claim to replace careers guidance or to reinvent the classroom. It did something smaller and more useful. It measured what students could actually do, not what they could memorise and reproduce under exam conditions. A student scoring 46 out of 100 across eight capabilities, with clear visibility into where they were strong and where they needed work, had something honest. In a system where assessment is usually blunt, a tool that said “you’re at 46, here’s what you’re good at, here’s what needs work” was refreshing.

What caught her attention most was the universality. Fifty-six per cent of the students using the platform weren’t business studies students. They were geographers, PE students, computer science students, art students like Reece. Students who would never take a business qualification but who needed to understand how workplaces actually function, how decisions get made, how money moves, how teams work, how data informs action. The capabilities the platform measured were the eight things every employer said they wanted but nobody was systematically measuring: commercial awareness, decision-making, problem solving, financial literacy, adaptability, data analysis, team collaboration, and leadership.

Mrs Okafor ran a pilot with her Year 10 business studies class. Then she expanded it. Then she opened it to any student willing to use it outside lesson time.

Reece is in Year 11. Third row from the back in the assembly, far enough away to think his own thoughts. He’s not a business studies student. Geography, PE, computer science, art. He wants to be a graphic designer, or possibly something in gaming, or possibly something with animation, or possibly something else entirely. He’s sixteen. Uncertainty is developmentally appropriate. But his mum wants him to be an accountant. Her brother is an accountant who drives a BMW and has a utility room and an en suite, which in the family’s mental geography occupies a specific position: the destination where hard work leads.

Reece had been watching YouTube videos about AI and accounting. The ones made by people who seem to know what they’re talking about suggested the Big Four were deploying AI systems that do audit work, tax compliance, financial analysis faster and more consistently than junior accountants. The entry-level route his mum was pointing at, three years grinding through ACCA exams doing spreadsheet work, is the pathway most vulnerable to automation. He didn’t say this to anyone because he’s sixteen and knows that saying “I saw it on YouTube” to an adult is roughly as persuasive as saying “I read it in a dream.” Adults need credentials before they’ll accept information as valid, which is ironic given what’s happening to the value of credentials.

He did one simulation on the platform during break. Then another. Then three more that week, off his own back, outside lesson time. The simulations put him inside contexts where he had to make decisions based on incomplete data, where he had to understand what commercial decisions actually meant, where he could see the consequence of his choices in real time. Not a game. Simulation. Close to real, consequences real, feedback immediate.

Mrs Okafor ran into him in the corridor after lunch.

“I’ve been doing the simulations,” he said.

“I know. You’re doing well.”

“The thing is, I’m quite good at deciding when I don’t know what to do. But I’m bad at maths and I don’t care about accounting, so I don’t think it’s a safe bet to do what my mum wants, because if the safe bet involves things I’m actually bad at, it’s not really a safe bet.”

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

That, Mrs Okafor thought, was more career insight than most Year 11 students articulate in a year of formal careers provision. Not the answer. Just clarity about the question. Self-knowledge expressed through evidence rather than aspiration. Not “I want to be X because it sounds good” but “I know what I’m good at and what I’m not, so I can start narrowing the field intelligently.”

His mum came into school for parents’ evening in November. Mrs Okafor walked her through Reece’s Workplace Readiness Score. Explained what each capability meant in workplace terms, translating from the education dialect to the parental dialect, which are similar but not identical languages.

“Accountancy is changing very fast,” Mrs Okafor said, choosing her words with the care of someone who has to tell the truth without terrifying anyone, and who has learned through experience that parents’ evenings are not the place for dystopian predictions about the labour market, however well-sourced. “The skills that will matter in any career, including accountancy, are adaptability, decision-making, problem solving. Reece has those. The specific subject matters less than the capabilities underneath it. Whatever he ends up doing, knowing what he’s good at and being able to prove it will matter more than the name on the certificate.”

Which, standing in the hall after the last parent had left, coffee cold in her hand again, was exactly how Mrs Okafor felt about the whole situation. The old map was familiar. The old landmarks were visible. Accountancy, law, medicine, engineering, the professions that parents could name and trust and point their children toward with confidence. The new map had different contours. The landmarks had moved or dissolved or hadn’t been built yet. And the gap between the map the parents held and the territory their children were walking into was growing wider every term.

The kids who figure this out early are not the majority. Most teenagers believe what their parents believe and what their school implies: that if you get good grades, go to university, and choose a respectable profession, you’ve done the thing correctly. The kids who figure it out, who sit down with evidence about what they’re good at and what the labour market actually rewards, who understand that the safe bet route their mum is pointing at might not be safe, those kids are rare.

Reece is not exceptional. He’s just willing to look at the evidence rather than accept the inheritance assumption. And in a labour market that’s changing faster than the advice system can adapt to, that willingness makes a difference. A kid in Year 11 who understands their own capabilities and who understands that the traditional path is riskier than it looks has twelve months to make a different choice than the one the system is pointing him toward.

The school system is built around qualifications. The parents want their children to “do well,” which means good grades and university. But the most useful thing Mrs Okafor can do is show them something different. Not “forget university,” but “what you choose to study matters less than what you’re actually good at, and you need to be honest about both.”

That’s what the kids are doing when they figure it out early. They’re being honest about what they’re good at and building plans around that evidence rather than around the prestige of the destination. Reece is going to study something else and keep the doors open. His capabilities (adaptability, decision-making, problem solving) will work in multiple contexts. He won’t be trapped in the accountancy pipeline the way he would have been if he’d believed the safe bet story.

The honest version of careers advice in 2026 is this: the old landmarks don’t point where they used to. The professions that looked safe five years ago are contracting. The credentials that used to open doors are opening them more slowly. What actually works is understanding what you’re good at, building proof of that capability, staying adaptable, and not getting locked into a single path that assumes a labour market that no longer exists.

Most schools aren’t there yet. Most careers advice still points at accountancy and law and the old destinations. But the kids who have access to honest information about what they’re good at, the ones with evidence instead of assumption, are making different choices. And those choices, made at sixteen, compound across the whole of their working life.

Mrs Okafor opened the Enterprise Skills dashboard after the parents’ evening. Looked at this week’s session data. Reece had done another simulation. Adaptability score up to 62. Data analysis still at 34. The old Mrs Okafor, the one before she understood what was actually happening in the labour market, would have seen that profile and advised Reece to focus on the weak area, to aim for balance, to become generally competent. The new Mrs Okafor, the one who understands that the labour market is moving, sees a profile and thinks about the trajectory. Adaptability is going up. That matters more. The student who can adapt, who can solve problems without complete information, who can make decisions under uncertainty, is the one who survives the transition that’s already underway.

The kids figuring it out first are doing something harder than following the map. They’re learning to navigate without one. And the teachers who are seeing that clearly, like Mrs Okafor, are starting to teach something different.

I have two jobs that don’t fit together cleanly. Some weeks they actively conflict.

One job is advising agency owners. Mid-market firms mostly, ten to eighty people, some are ones who’ve found a way to do good work but haven’t solved the problem of scaling without losing quality. I help them figure out how to grow the business while keeping the work interesting and the margins real. That’s OMG Center. That’s where I spend a lot of oxygen.

The other job is selling them a tool that automates the work their account managers do for a living.

The geometry doesn’t work. I can feel it when I’m in advisory mode with a client, talking about headcount decisions and margins and what work actually moves the needle, and then I switch into sales mode and I’m showing them how Orca can do that work. The looks I get are complicated.

Let me describe what this actually looks like in practice.

The advisory conversation

The conversation with an agency owner usually starts with a specific pressure. They’ve got a team that’s growing. Revenue is growing. But they can’t seem to hire fast enough or good enough to keep up. The economics are getting tight. They’re spending too much time on client management. Their team is spending too much time on admin. Some of the junior people they hired are undershooting on output. They’re wondering whether they’ve hit their natural ceiling, or whether they’re just not managing the operation efficiently.

This is where I usually start asking questions about what’s actually taking time. Two to three days a week on meeting prep. Another day on follow-up emails and CRM management. Hours every week on inbox triage and making sure nothing falls through the cracks. Hours on research intelligence and competitive briefings. Account managers spending a fifth to a quarter of their time on social media and content updates.

That’s basically the role. Account management is orchestration work. Knowing what happened in the last meeting, what the client’s actually worried about, what they said they wanted versus what they actually need, what the competitive context is, and then bringing all of that together into a coherent strategy recommendation or a status update.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

So I ask them: what do you think that orchestration work is actually worth to your business?

They usually say some version of: not as much as we’re paying for it. Or: most of it could be automated but we need someone there who knows the client. Or: they’re good at this part (pointing to strategy or relationship management) but we’re paying for them to do the other part too.

This is where I lean in and say something like: your business would get better if you had fewer people doing orchestration and more people doing the stuff that only they can do. Strategy. Real relationship management. Creative direction. The work that actually moves the needle. But you need different people for the orchestration part. You need tools.

And then they usually say: yeah, we know, we’ve tried some stuff, it’s messy, and also aren’t the tools going to put people out of work. Which is fair. That’s the real question.

What I’m advising now

Here’s what I tell them these days. And this is where the two jobs collide in a way that matters.

Build headcount for what can’t be automated. Let Orca do the orchestration. Measure the difference in what your account managers can actually produce when they’re not doing coordination work all day. Then restructure around that output.

That’s the honest advice. Not “keep your current structure and add a tool.” Not “add tools but keep the headcount.” But actually: the work that AI can do, let it do. Free up the humans for the work only they can do. Measure the result. Restructure accordingly.

Because if you don’t, your competitor will. And then you’re paying the same cost for orchestration work that they’ve automated away. You’re losing margin. You’re losing the ability to compete.

When I say this to a client, they usually nod. They know it’s true. They’ve probably already started running the numbers. And then they ask: so what do we do with the people who are only doing orchestration work? And I tell them the truth: that’s the hard part. That’s the part no advisory can solve for you. That’s the part you have to handle as a leader. But the answer isn’t to pretend the tools won’t work. It’s to manage the transition with eyes open.

The conflict visible

Here’s where it gets uncomfortable.

Some of the people I’m advising are also in conversations with me about Orca. I show them the tool. They see what it does. I show them what it costs: two hundred and fifty pounds a month. I show them the math: if one of their account managers spends a quarter to a third of their time on orchestration, and a tool costs that little, then the economics are obvious.

And I can see them work through the reasoning. They came to me for advisory. I told them to automate orchestration. Now I’m selling them the thing that does it. The two roles have collapsed into the same transaction.

I don’t pretend this doesn’t matter. I lay it out. I tell them: I’m advising you to do this, and I also have a commercial interest in you buying this from me. You should price that into how you interpret what I’m saying. But I also think the advice is real. The automation is real. And the longer you wait to do this, the more margin you’re leaving on the table while your competitors figure it out.

Some of them buy. Some of them don’t. The ones who don’t usually go and build something internal or they use a different tool. That’s fine. The structure of my business doesn’t require every advisory client to buy Orca. What it does require is that I’m honest about the conflict.

The Next Rung is a book on how AI is quietly dismantling the middle of knowledge work, and what you can do about it before the market decides for you: pre-order it before it publishes in January.

Pre-Order The Next Rung

What’s actually different about this

I could use the language most tech companies use: “amplifying your team”, “helping your people be more productive”. That language is comforting. It’s also dishonest.

Orca doesn’t amplify the orchestration work. It absorbs it. When a meeting brief that used to require someone half a day now takes a minute fully automated, the work isn’t being augmented. It’s being replaced.

The question is what you do about it as a leader. You acknowledge what’s happening, measure the impact, and structure your business around the new reality instead of pretending the old one still exists.

For agencies, that means: more headcount in strategic roles, less in coordination roles. Account managers doing account work, not AM admin. Planners planning, not planning then turning plans into decks then writing them down for the CRM.

The hard part isn’t the tool. The hard part is the conversation with the people whose jobs were mostly orchestration. That’s where the real work is. That’s where the leadership maturity shows up or doesn’t.

Why I can hold both roles

Someone asked me recently whether I felt conflicted about selling a tool that displaces people while advising the owners who might buy it. I said: only if I pretend the tool wouldn’t work, or the displacement isn’t real, or the maths are different than they are.

But I don’t pretend any of those things. I name the displacement. I explain the maths. I advise people to handle the transition with eyes open. Then I offer them a tool to do the work better and cheaper.

That’s not corruption of the advisory relationship. That’s completion of it. I’m advising them to do something that’s structurally necessary, and offering them a tool that makes it feasible.

Do I benefit when they buy Orca? Yes. Does that mean the advice is wrong? No. It means the advice happens to align with my commercial interests, which is worth disclosing, which I do.

I also stay visible. I don’t hide in the business model. I show up in demos. I’m in the advisory conversations. I’m subject to the feedback about what actually happens when these transitions occur. If the displacement is worse than I’m predicting, I see it. I can’t pretend it didn’t happen.