Casey stood in the kitchen on a Sunday morning wanting to do something small and

ordinary. The lights were already on — the right level, the right temperature, the system having made the calculation sometime before waking. The heating was already at the temperature the house knew a Sunday morning required. The coffee machine had already started its cycle.

Not one of these things had been consciously chosen that morning.

The intent had been to make a deliberate choice about the day — to begin it with a moment of decision rather than discovery. To turn the lights up brighter than usual, perhaps, because it was overcast outside and the mood called for it. To make the coffee later than normal because there was no urgency. Small acts of self-determination in an ordinary morning.

The system had already decided. It had observed enough previous Sunday mornings to know what this one required. The preferences were not wrong — they were, in fact, accurate. They reflected genuine past behaviour, real choices made on real mornings over months. The system had learned well.

That was the problem.

The disappearance of friction

There is a version of this story that reads as convenience. The house anticipates. The morning runs smoothly. That is what the technology was designed to do, and it is doing it.

What that version of the story does not account for is what friction was for.

The small acts of choosing — the light level, the temperature, the pace of a morning — are not merely mechanical. They are the continuous practice of preference: the low-stakes exercise of deciding who you are and what you want on any given day. They are how a person remains in contact with their own inclinations. When those acts are automated away, the automation does not just remove effort. It removes the opportunity to notice whether the choice would have been the same.

Casey’s house had learned a version of Casey built from past behaviour. Not from yesterday’s behaviour, from the cumulative pattern of months. A version that was accurate in aggregate and potentially wrong in any specific instance. The system had no mechanism to register that people change, that moods shift, that a Sunday in January and a Sunday in March might call for different things even if the previous fifty Sundays had called for the same ones. The actual person was no longer quite required.

The moment of choosing had disappeared so gradually that it was only noticeable in its absence. Not when the system got it wrong, it rarely got it wrong in any obvious way. But when the desire arose to do something differently, and the system had already done it, and the act of undoing it felt like more effort than simply accepting what was already there.

That is the trap. Not coercion, convenience so complete it becomes its own constraint.

From the kitchen to everywhere

The smart home is the most intimate version of this pattern, because the home is the space where people are most fully themselves. But it is only the beginning of the scale.

Step outward from the kitchen and the same architecture is running at every level. The streaming platform that has watched enough viewing history to know what will be selected before the selection is made. The news feed that has modelled enough reading behaviour to decide what is worth knowing today. Each of these systems is doing the same thing as the smart home, in a different register: replacing the act of choosing with the delivery of a prediction so accurate that choosing no longer feels necessary.

Step further out. The e-commerce platform that has learned spending patterns well enough to surface only what will be bought — not what might be wanted, not what might be surprising, but what the model says is coming next. The navigation app that routes not by the driver’s preference but by the algorithm’s assessment of the optimal path, such that after a year of use a driver may no longer know the roads they travel every day. The workplace tool that pre-populates responses, pre-schedules meetings, pre-drafts the email — reducing the human contribution to the act of approval.

At each step the friction removed is real. The convenience is not manufactured, it is delivered. And at each step something else is happening simultaneously: the system’s model of the person is deepening, the person’s practice of self-determination is narrowing, and the gap between who the system thinks they are and who they actually are is quietly widening.

The model is built from what was. The person is living in what is. Those two things are not the same, and the longer the system runs without the person having reason to notice the divergence, the larger the gap becomes.

Step to the outermost scale. The political information environment, the social media feed, the search result page, all of them running the same logic at civilisational scope. Not just anticipating individual preferences but shaping them, because a preference exercised without friction, without encounter with the unexpected, without the resistance of a world that does not already agree, is a preference that stops developing. The system does not just reflect who you are. Over time, it shapes who you remain.

The distinction nobody offered

The question of how much anticipation is enough is not rhetorical. It is a real dilemma.

Friction is not inherently good. The effort of choosing is not valuable in itself, it depends entirely on what is being chosen and why. The friction of adjusting the thermostat is not the same as the friction of deciding what to read or what to believe or what kind of person to become. Not all convenience is erosion. The task is to distinguish between the friction that was serving a purpose and the friction that was simply in the way, and to recognise that those two things look identical until the moment of removal.

Most people have not made that distinction deliberately, because it was never offered to them as a choice. The anticipatory systems arrived and removed friction and the question of which friction was worth keeping was never asked. The default was anticipation. The person who wanted to preserve the act of choosing had to actively resist the convenience of not choosing, and resistance is itself a form of friction that most people do not have time or inclination to apply.

The deeper consequence is not about convenience it is about the model. Every anticipatory system builds a representation of the person it serves. That representation is the basis for every subsequent prediction, every pre-emptive action, every choice made on the person’s behalf. The richer and more accurate the model, the more completely it can substitute for the person’s actual judgment. And the more completely it substitutes, the less opportunity there is for the person’s actual judgment to develop, correct, or surprise itself.

A person who has never had to choose their own route no longer knows the roads — a 2024 study confirmed this is not a metaphor: habitual GPS use produces a measurable, causal decline in spatial memory, even in people who previously navigated well. A person whose reading is pre-selected no longer encounters what they did not know they were missing. A person whose home has already decided never fully inhabits the decision to be at home. The capacity that is not exercised does not remain dormant. It diminishes.

That is the cost that does not appear in the product specification. That is the human loss that convenience optimises away.

Who is this for

The question the anticipatory system never asks, because it was not designed to, is who benefits from the anticipation.

The person using the system receives convenience that is real. But the system is not running in their service alone. Every preference recorded, every choice predicted, every behaviour anticipated is data. Data that trains the model, refines the prediction, deepens the platform’s understanding of what the person will do next. The more accurate the anticipation, the more valuable the person becomes as a data source and the more dependent they become on a system whose primary obligation is not to them.

The smart home manufacturer knows the daily rhythms of the household. The streaming platform knows attention patterns more precisely than the viewer knows themselves. None of this knowledge is held in the person’s interest by default. It is held in the platform’s interest, governed by the platform’s terms, subject to the platform’s commercial decisions about how that knowledge is used, shared, or sold.

The anticipatory trap is not just about agency. It is about the terms on which that agency has been surrendered and to whom. Casey’s house is learning Casey — but who owns what has been learned, and what happens to it when the subscription ends, the company is acquired, the terms are changed? The system was presented as a tool serving the household. The data flowing out of it tells a different story about who is serving whom.

The system that anticipates your needs perfectly has built a more accurate model of you than you have of yourself. That is impressive. It is also alarming, not because the anticipation is wrong, but because the entity holding that model is not you, does not answer to you, and did not ask your permission to build it in quite the way it has.

The friction that was removed was not just effort. Some of it was the resistance that kept you in contact with your own preferences. Some of it was the randomness that kept you encountering the unexpected. And some of it was the inconvenience that made you notice, every now and then, that a choice was being made at all.

What it was replaced with is not neutral. It serves someone. The question is whether you know who.

What you might find you’ve forgotten

Think about the systems in your daily life that anticipate your preferences — the home, the phone, the feed, the platform. When did you last make a deliberate choice in a domain where anticipation now operates? Not overriding a suggestion — making an original decision, from scratch, without a pre-populated option in front of you?

The model these systems hold of you was built from your past behaviour. How accurate is it to who you are now? Have you changed in ways the system has not registered? Have your preferences shifted, your interests expanded, your circumstances moved, and has the anticipation moved with you, or is it still running on data from a version of you that no longer quite exists?

Who holds the model? Not in terms of which company’s name is on the product, in terms of what happens to what has been learned about you. Is it portable? Is it deletable? Is it yours, in any meaningful sense? Or has the convenience of the service been partly funded by the permanent transfer of something you did not consciously offer?

And the question underneath all of these: if the anticipation were removed tomorrow — if the systems stopped predicting and simply waited for instruction — what would you find you had forgotten how to choose?

My Opinion

The technology itself is not the problem. What makes ambient systems genuinely difficult is their invisibility. A tool you can see, you can use deliberately. A system that operates below the level of conscious attention has already made the choice about when and how it operates. The line between the two is not technical — it is a design decision, made by someone other than you, before you arrived.

Most ambient technology was not designed to be controlling. It was designed to be easy. But easy and controlling share a direction of travel. The easier a system makes a decision, the less often you will make that decision yourself — and at some point along that gradient, the decision stops being yours. It stopped leaving room for it.

Not everyone needs this. That sounds obvious but it is rarely said plainly. There are households where ambient technology solves a real problem — accessibility, health monitoring, the household stretched across too many responsibilities to manage routine decisions without help. In those circumstances it is doing what it was built for. As a default for everyone, replacing the ordinary friction of a morning with a prediction of what the morning should be, it is a different proposition — one that takes something from the quality of ordinary life that is hard to name until it is gone.

The question is not whether to use these systems. It is whether you know what you are giving in exchange. Convenience offered at no visible cost is rarely free. What is given in return is usually the data that makes the system more accurate, and the habit of not deciding that makes it more necessary. Both transfers happen gradually, without announcement, and by the time they are noticeable, reversing them requires more effort than most people are willing to apply on a Tuesday morning.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Morgan opens the app on a whim. It is one of those data-transparency tools that have become briefly fashionable — the kind that pulls together what advertisers, brokers, and platforms believe about you and shows you a summary. Morgan is curious, not anxious. It takes about thirty seconds to load.

What comes back is strange. The profile is accurate in the way a caricature is accurate — it captures something recognisable while getting most of the important things wrong. Morgan is categorised as a “high-anxiety urban professional,” likely to respond to time-pressure messaging and financially cautious. There are interest tags: travel, wellness, mid-range electronics. A risk band. An inferred household income. Morgan reads it twice, unsure whether to laugh or feel uneasy. Neither feels quite right.

When behaviour becomes identity

The discomfort is worth paying attention to. Not because the data is false — most of it is close enough to be recognisable — but because it is being treated as complete. The profile was assembled from clicks and purchases and search patterns, and somewhere along the way it stopped being a description of behaviour and became a verdict about a person.

That shift happens quietly. A system designed to predict preferences begins to define them. A tool built to segment audiences starts to assign identities. Nobody decided this was the purpose. It emerged from the logic of optimisation, from the pressure to know more in order to sell more, to lend more wisely, to assess risk at scale. The profile is a side effect of a process that was never really about the person at all.

The profile is accurate about behaviour and wrong about the person. The danger is not the bad data — it is that the system believes data is enough.

A trace treated as a portrait

Here is the underlying problem: data captures what someone did, in a particular moment, under particular circumstances. It does not capture why. It cannot hold the week someone was caring for a sick parent and spending on delivery food because there was no time to cook, or the period of job uncertainty that explains a cautious financial pattern, or a single impulsive holiday purchase that rewrote the travel interest score. The data is a trace. The system reads it as a portrait.

This matters because these profiles are not just sitting in a database somewhere. They are active. They shape what someone is offered, what rate they are quoted, sometimes what opportunities appear and which do not. A mortgage application, a job screening, an insurance premium — in each of these, the statistical shadow is treated as the person. And the person has almost no practical way to correct it, because most of the time they have no idea it exists.

The portability of these profiles makes it worse. An assumption made in one context follows into another. A risk score generated for one purpose bleeds into decisions it was never designed to inform. The data travels; the context does not. What began as a description of what someone did last Tuesday is now being used to decide what they deserve next year.

What the profile decides

The human cost here is not always dramatic. Sometimes it is a loan offered at a higher rate than someone with an identical financial history but a different data footprint would receive. Sometimes it is a job application that does not progress past the screening layer. Sometimes it is simply the low-grade unease of being looked at by systems that cannot see you, being assessed by processes you cannot access, being defined by a version of yourself that you did not author and cannot revise.

For those who are already disadvantaged — people in financial precarity, people with gaps in their histories, people whose lives do not fit the patterns the models were trained on — the distortion is not low-grade. It is consequential. The system is not malicious. It is simply indifferent to the difference between a data point and a human being. That indifference is its own kind of harm.

What the design is actually doing

In the original use case — recommendations, relevant offers, faster credit decisions — there is a genuine service on offer. But it holds only while the profile is accurate, and only while the person has some say in what it contains. Where the profile is wrong, or where it follows someone into contexts where it has no business being, the service becomes a constraint.

The efficiency gains for the institutions using these systems are real. What has been added for the individual is less clear: a permanent, portable verdict about who someone probably is. Whether that addition has value depends entirely on who is doing the measuring — and for whom the system was designed.

Where it fails most visibly is in adaptation. The profile is static in the ways that matter most. It captures behaviour but not situation. It updates when you click, but it does not reset when your life changes. It does not know the difference between who you were and who you are, and it has no mechanism for registering who you are trying to become. A system that cannot hold that kind of ambiguity is not adaptive. It is just persistent.

This is one of the questions at the centre of The Cognitive Crucible — not just what the system records, but what it misses by design.

My Opinion

We are living through a period of digital identity fragmentation. Pieces of us — clicks, purchases, search patterns — are being gathered by third parties and reassembled into a composite. The problem is not just the assembly. It is the premise: that this composite represents a person.

It does not. We are not one thing. We are many, depending on context — different at work and at home, under pressure and at ease, at a particular age, in a particular relationship. A system that flattens all of that into a single profile is not describing us. It is describing a version of us that has never existed.

Taking back agency over this will be hard. It may require something counter-intuitive: choosing, deliberately, to define and manage our own digital identities — profiles that represent genuine aspects of who we are in a specific context, rather than ceding that definition to systems that were never designed with us in mind.

The questions underneath the discomfort

There is a reasonable question underneath the discomfort the opening described. Not “how do I get off this list” — that is almost certainly impossible — but something more fundamental: who is this profile actually for?

The profile was not built for the person. It was built for the systems that process them. That distinction changes the nature of the conversation. The question is not whether the data is accurate. The question is whether data should be sufficient.

1. Have you ever encountered a version of yourself through a system — a recommendation, a decision, a risk band — that felt both recognisable and completely wrong? What did you do with that feeling?

2. If you could see your full data profile across every platform and institution that holds one, what do you think you would find — and would you want to know?

3. If you could change one thing about how these systems work — how they are built, what they are allowed to infer, how they can be challenged — what would it be?

4. The system knows your clicks. Should that be enough to know you? And if not — what else should be required before a system is allowed to make decisions about your life?

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

The town of Wylfa sits on the northern tip of Anglesey, a Welsh island connected to the mainland by two bridges and a reputation for nuclear power. The original Wylfa power station opened in 1971 and ran for more than four decades, supplying electricity to households and businesses across the region. When it closed in 2015, the local economy lost something difficult to replace — not just jobs, but a particular kind of work: skilled, well-paid, long-term.

Plans for a new nuclear plant at Wylfa have circled since before the original station was switched off — Horizon Nuclear Power, then Hitachi, then a succession of other investors, none of whom made it past the preliminary stages. In June 2025, something different: the UK government selected Rolls-Royce SMR as its preferred partner to build the country’s first small modular reactors. By April 2026, a commercial contract had been signed and Wylfa confirmed as the site for up to three SMR units. If the timeline holds, the grid connection comes in the mid-2030s.

That “if” is doing a lot of work. The nuclear industry has a long and painful history of timelines that do not hold, costs that do not stay contained, and promises that outlast the political will required to keep them. The people of Wylfa — and the communities near every proposed SMR site — have reason to ask hard questions before deciding whether this time is genuinely different.

The honest answer is: it might be. And that is a more interesting answer than either the enthusiasts or the sceptics want to give.

Why nuclear has a second chance

To understand what small modular reactors are trying to solve, it helps to understand what broke the large ones.

Nuclear power has three advantages over almost every other energy source that are not marginal. It produces virtually no carbon emissions during operation. It generates electricity around the clock, regardless of whether the wind blows or the sun shines. And its energy density is extraordinary — a single kilogram of uranium fuel contains roughly two million times the energy of a kilogram of coal. These are the reasons nuclear provides about a tenth of the world’s electricity despite decades of political difficulty and public mistrust.

The problem is construction. Large nuclear plants take fifteen years to build in Western countries. The most recent to complete in the United States, the Vogtle plant in Georgia, came in seven years late and roughly $22 billion over its original $14 billion budget. The Hinkley Point C plant currently under construction in Somerset — the UK’s first new nuclear station in a generation — has seen its estimated cost rise from £18 billion when contracts were signed in 2016 to £35 billion in equivalent terms, approaching £49 billion at current prices. These are not outliers. They are the pattern.

The causes are structural: one-off bespoke designs that require learning everything from scratch on each new site; an atrophied supply chain that lost its skills during the decades-long pause in construction; and regulatory frameworks designed around 1970s technology that treat every new application as if nothing has been learned since. The result is that nuclear power, which ought to be the most reliable weapon in the decarbonisation arsenal, has spent thirty years being too expensive and too slow to be deployable at scale.

Nuclear’s advantages are extraordinary. Its construction record in the West is, on the evidence, one of the most expensive and difficult in modern engineering.

Small modular reactors are an attempt to escape that trap by changing the nature of the construction problem. Rather than building a bespoke cathedral on each site, the idea is to build a reactor in a factory — standardised, modular, replicable — and ship it to where it’s needed. A conventional reactor might generate 1,000 to 1,600 megawatts of electricity. An SMR typically produces between 50 and 470 megawatts per unit. You can add more units as demand grows, and the first unit can begin generating revenue while subsequent ones are still being assembled.

The factory model matters for cost. Nuclear’s expense problem stems partly from the fact that large reactors have never been built in sufficient numbers to drive down costs through learning and repetition. The steel and concrete and specialist components are expensive partly because the people and processes that handle them do so rarely. A factory model — producing the same design, repeatedly, under controlled conditions — is how aviation and automotive manufacturing drove down costs over decades. It is, at least in theory, how nuclear might do the same.

Where the theory meets the track record

There is a problem with the factory model, and it is not a small one: it has not yet produced a nuclear reactor on time or on budget, anywhere in the world.

As of early 2026, three SMRs have been built globally — one in China, two in Russia — plus one still under construction in Argentina. None were built on time. Russia’s floating plant, the Akademik Lomonosov, began construction in 2007 and reached operation only in 2019, at roughly three times its original budget. Argentina’s CAREM reactor broke ground in 2014 with a planned completion of 2017 and was still incomplete as of 2026, construction halted twice by funding crises, with costs significantly above initial estimates. China’s pebble-bed reactor at Shidaowan took approximately eleven years from construction start to commercial operation. These are first-of-a-kind projects, and some overrun is expected when building genuinely new technology. But the pattern of delay holds across different countries, different designs, and different political contexts.

The most instructive cautionary tale is NuScale Power. The American company was the first to receive US Nuclear Regulatory Commission design certification for an SMR, achieving that milestone in 2022 — a genuine achievement after years of rigorous review. NuScale appeared to be on the cusp of proving that the new model worked. Then, in 2023, its flagship project — a planned plant in Idaho — was cancelled. The project began with an estimated cost of around $3.6 billion for a 720-megawatt plant. Before cancellation, with the design rescaled and costs revised, that figure had risen to $9.3 billion. The customers, a consortium of public utilities, could not make the economics work. NuScale has since received regulatory approval for an uprated 77-megawatt design and is pursuing new projects, but the Idaho cancellation cast a long shadow over the industry’s cost claims.

The sceptical case against SMRs is not that the physics is wrong — it is that the economics may be structurally difficult. Smaller reactors lose the cost advantages that come with scale: building one 1,000-megawatt plant is cheaper per unit of output than building four 250-megawatt ones, because you only need one set of site preparation, one control room, one regulatory approval process. A 2025 study by Kim and Macfarlane — the latter a former chair of the US Nuclear Regulatory Commission — found that most cost projections for SMRs assumed high-volume serial manufacturing that has never materialised in nuclear, and that more realistic estimates placed the cost of electricity well above $100 per megawatt-hour, in some cases significantly more. For context, onshore wind in the UK currently costs around £40–50 per megawatt-hour.

There is also the waste question. Some analyses suggest that SMRs could produce greater volumes of radioactive waste per unit of energy generated than large conventional reactors — a consequence of their smaller scale and, in some advanced designs, the use of different fuel cycles. This is contested by the industry, but it is not a settled matter, and it matters to any community being asked to accept a reactor nearby.

None of this means SMRs cannot work. It means the confident promises being made — by governments, by technology companies, by investors — are running ahead of the evidence. The International Energy Agency’s scenarios have commercial SMR deployment beginning around 2030 — meaningful on paper, but, in the context of a global electricity system measured in thousands of gigawatts, still a rounding error in the near term.

What hangs on getting this right

The stakes are high enough that the question of whether SMRs work is not abstract.

Renewable energy — wind and solar — has become extraordinarily cheap and is now the cheapest source of new electricity generation in most of the world. But it is intermittent. The sun does not always shine; the wind does not always blow. Storing renewable energy at grid scale, for days or weeks at a time, remains an unsolved problem at the volumes the transition requires. This is sometimes called the baseload problem: the need for power sources that generate electricity continuously, regardless of weather, to underpin a grid that cannot afford to go dark.

Nuclear is one of the very few zero-carbon technologies that addresses the baseload problem directly. It generates power around the clock, in any weather, for decades. Hydropower does the same, but its geography is fixed. Large-scale geothermal is similarly constrained. If the world is serious about reaching net zero while keeping the lights on reliably, the honest arithmetic suggests that some form of nuclear will be part of the answer — and that SMRs, if they can deliver on their promise, could be a significant part.

The tech sector has reached a version of this conclusion independently. Amazon has committed over $500 million to SMR development. Google has partnered with Kairos Power to bring 500 megawatts of SMR capacity online by 2035, with the first unit targeting 2030, to power its data centres. Microsoft has signed agreements in the same space. The irony is pointed: AI’s insatiable demand for always-on power is one of the forces driving investment in nuclear energy’s revival.

The geopolitical dimension adds a further layer. Russia and China have both invested heavily in nuclear technology as a strategic export — Russia through Rosatom, which has construction contracts in over twenty countries; China through its state-owned enterprises. The UK-US Atlantic Partnership for Advanced Nuclear Energy, signed in September 2025, explicitly includes joint safety assessments and a shared commitment to eliminate dependence on Russian nuclear fuel by 2028. The race to build credible Western SMR supply chains is not purely a climate story. It is also a question of whose technology runs the world’s energy infrastructure in the decades ahead.

Against this backdrop, the communities living near proposed SMR sites find themselves in a familiar position — asked to accept proximity to technology that carries real and perceived risks, on the promise of jobs and cheap energy, with a track record that gives them every reason to ask for guarantees that are difficult to give. Research published in Energy Research & Social Science in 2025 put it plainly: the burdens and benefits of nuclear transitions are rarely distributed fairly, and existing frameworks to address this are inadequate. Wylfa’s community has been promised a new nuclear future before. The question is whether this version of the promise is better-designed, or just better-marketed.

What the technology still owes

At its best, an SMR helps communities, grids and industries access clean, reliable power that they cannot get from renewables alone. The UK government’s £2.5 billion commitment, the US government’s backing of multiple designs, and the private investment pouring in from the tech sector all reflect a real assessment that this technology could help solve a real problem. That is not spin. It is the outcome of serious people looking hard at the arithmetic of net zero.

Whether it adds something genuinely new is a sharper question. The factory model is the central claim — that modular construction changes the economics of nuclear in the same way that modular construction changed the economics of consumer electronics and automotive manufacturing. The honest answer is that this claim is plausible but unproven at commercial scale. The first Western SMR to be built on time and on budget will be the most important data point in energy policy this decade.

The question most consistently overlooked is whether this technology responds to the specific circumstances of the communities and contexts it serves — to their energy needs, their employment profiles, their risk tolerances, their histories with the industry. The SMR industry’s governance frameworks, on the evidence of current proposals, are not yet doing this. Regulatory processes are improving — the US ADVANCE Act of 2024 directed the NRC to streamline its approach, and by the end of 2025 it had met 30 of its 36 planned deliverables. But the frameworks for distributing the benefits and managing the disruptions of deployment — particularly for the communities most directly affected — remain underdeveloped.

The people of Wylfa have skills, knowledge and a relationship with nuclear power that spans generations. They are not a passive audience for a technology decision made elsewhere. Whether the SMR programme being planned for their island treats them as participants or subjects will say something important about whether the industry has learned anything from its history.

My opinion

I have worked inside the nuclear industry and I know what it delivers when it works. I also know what it costs when it goes wrong — not just financially, but in the lives of the communities closest to it. That makes it impossible for me to land cleanly on either side of this argument.

The NIMBY response to nuclear is rational, not irrational. If you live near a proposed site, you are being asked to accept proximity to technology whose consequences, in the worst case, outlast your lifetime and your children’s. That is a different category of ask than living near a wind farm.

But the power needs are also real. The grid cannot be decarbonised on intermittent sources alone. If small modular reactors can be built reliably, at reasonable cost, with genuine engagement of the communities they affect — that is worth pursuing. The question is whether the industry can meet that bar. It has not yet, but that is not the same as saying it cannot.

We also do not yet know if there will be any long-term consequences of renewable generation - what happens to wind patterns that are distributed by wind farms? what happens to ocean currents when hydro wave generation removes some of the kinetic energy; what happens to the earth’s core temperature when sunlight is blocked from hitting the ground by solar, or by heat extraction? We’ve made serious investments in all these technologies without the evidence of consequences - we know the consequences for nuclear.

What I am certain of is that complacency is the wrong response in either direction. The technology needs to prove itself commercially. The governance needs to prove itself ethically. Those are not the same challenge, and the industry tends to treat the first as if it solves the second.

The questions Wylfa should ask

• The UK government has pledged £2.5 billion and named a preferred supplier. But the first SMR is a decade away from the grid. How much confidence is there that the political will, the budget and the regulatory process will all hold for ten years?

• If renewables are now the cheapest source of new power, and storage technology is improving rapidly, is it possible that SMRs solve a problem that will largely have resolved itself by the time they arrive? Or is the baseload need real and irreducible?

• The tech companies investing in SMRs are doing so to power data centres. If artificial intelligence is driving both the energy demand that makes nuclear necessary and the investment that makes nuclear viable, what does that say about who the energy system is being built for?

• If you lived in Wylfa — or near any proposed SMR site — what would you need to know, and who would you need to hear from, before deciding whether to support the project?

The phrase “too cheap to meter” was coined in 1954 by Lewis Strauss, the chairman of the US Atomic Energy Commission, describing what nuclear power might one day become. It has since become one of the most famous over-promises in the history of technology. Small modular reactors are not making that promise. They are making a more modest one: that nuclear power can be built faster and cheaper than it has been. That is a lower bar. It is also one the industry has not yet cleared.

Whether it clears it this decade matters more than almost anything else in energy policy.

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Sources & References

• Wylfa power station: UK Government, GOV.UK: “Wylfa closes after almost 45 years” — https://www.gov.uk/government/news/wylfa-closes-after-almost-45-years

• ANS Nuclear Newswire, 10 June 2025: “UK’s own Rolls-Royce wins SMR competition” — https://www.ans.org/news/2025-06-10/article-7102/uks-own-rollsroyce-wins-smr-competition/

• NucNet: “UK Picks Rolls-Royce For Domestic Small Modular Reactor Rollout” — https://www.nucnet.org/news/uk-picks-rolls-royce-for-domestic-small-modular-reactor-rollout-6-2-2025

• New Civil Engineer, 13 April 2026: “Rolls-Royce SMR secures Wylfa contract and £599M government loan” — https://www.newcivilengineer.com/latest/rolls-royce-smr-secures-wylfa-contract-and-599m-government-loan-13-04-2026/

• Neutron Bytes, 19 April 2026: “UK & Rolls Royce Sign Deal for Three SMRs at Wylfa” — https://neutronbytes.com/2026/04/19/uk-rolls-royce-sign-deal-for-three-smrs-at-wylfa/

• IEA, Nuclear Power page (nuclear generated 9% of global electricity in 2024): https://www.iea.org/energy-system/electricity/nuclear-power

• IEA, Global Energy Review 2025: https://www.iea.org/reports/global-energy-review-2025/electricity

• Utility Dive, 2026: “After 2 years, ratepayer pain and political fallout from Georgia’s nuclear plant Vogtle” — https://www.utilitydive.com/news/after-2-years-ratepayer-pain-political-fallout-georgia-nuclear-vogtle/817792/

• The Current GA, May 2026: “Two Years After Completion, Plant Vogtle Still Looms Over the Nuclear Debate” — https://thecurrentga.org/2026/05/12/two-years-after-completion-plant-vogtle-still-looms-over-the-nuclear-debate/

• EnergyTransition.org, April 2026: “The billion-dollar boondoggle: how Vogtle became the US’s monument to nuclear folly” — https://energytransition.org/2026/04/the-billion-dollar-boondoggle-how-vogtle-became-the-uss-monument-to-nuclear-folly/

• New Civil Engineer, February 2026: “Hinkley Point C’s cost climbs to £35bn with confirmation Unit 1 will power up in 2030” — https://www.newcivilengineer.com/latest/hinkley-point-cs-cost-climbs-to-35bn-with-confirmation-unit-1-will-power-up-in-2030-20-02-2026/

• Construction Wave, February 2026: “Hinkley Point C costs approach £49bn as project faces M&E delays” — https://constructionwave.co.uk/2026/02/23/hinkley-point-c-costs-approach-49bn-as-project-faces-me-delays/

• US Department of Energy: “NRC Certifies First U.S. Small Modular Reactor Design” — https://www.energy.gov/ne/articles/nrc-certifies-first-us-small-modular-reactor-design

• Utility Dive, 2023: “NuScale, UAMPS terminate small modular reactor project in Idaho” — https://www.utilitydive.com/news/nuscale-uamps-terminate-small-modular-nuclear-reactor-smr-project-idaho/699281/

• Boise State Public Radio, November 2023: “NuScale nuclear reactor project in Idaho canceled” — https://www.boisestatepublicradio.org/news/2023-11-10/idaho-small-nuclear-reactor-project-canceled

• E&E News: “NuScale cancels first-of-a-kind nuclear project as costs surge” — https://www.eenews.net/articles/nuscale-cancels-first-of-a-kind-nuclear-project-as-costs-surge/

• US Department of Energy: “NRC Approves NuScale Power’s Uprated Small Modular Reactor Design” — https://www.energy.gov/ne/articles/nrc-approves-nuscale-powers-uprated-small-modular-reactor-design

• World Nuclear News: “China’s demonstration HTR-PM enters commercial operation” — https://www.world-nuclear-news.org/Articles/Chinese-HTR-PM-Demo-begins-commercial-operation

• NextBigFuture, December 2023: “China’s Pebble Bed Reactor Finally Starts Commercial Operation” — https://www.nextbigfuture.com/2023/12/chinas-pebble-bed-reactor-finally-starts-commercial-operation.html

• Wikipedia: Akademik Lomonosov —https://en.wikipedia.org/wiki/Akademik_Lomonosov

• Bellona.org, 2015: “New documents show cost of Russian floating nuclear power plant skyrockets” — https://bellona.org/news/nuclear-issues/2015-05-new-documents-show-cost-russian-nuclear-power-plant-skyrockets

• Power Magazine: “Russia Sees Floating Nuclear Power Plant Costs Balloon” — https://www.powermag.com/russia-sees-floating-power-plant-costs-balloon/

• Wikipedia: CAREM — https://en.wikipedia.org/wiki/CAREM

• Nuclear Engineering International: “Argentina’s CAREM-25 SMR faces setbacks” — https://www.neimagazine.com/news/argentinas-carem-25-smr-faces-setbacks/

• Buenos Aires Herald: “Construction of first Argentine-made nuclear power reactor halted amid layoffs” — https://buenosairesherald.com/business/construction-of-first-argentine-made-nuclear-reactor-halted-amid-layoffs

• ScienceDirect, Progress in Nuclear Energy, 2025: “Challenges of small modular reactors: A comprehensive exploration of economic and waste uncertainties associated with U.S. small modular reactor designs” — https://www.sciencedirect.com/science/article/abs/pii/S0149197025003877

• CleanTechnica summary, September 2025: “Small Modular Reactors and the Big Questions of Cost & Waste” — https://cleantechnica.com/2025/09/10/small-modular-reactors-and-the-big-questions-of-cost-waste/

• UK Government / BEIS, 2024: “Onshore Wind and Solar PV: Cost of Electricity Report Update 2024” — https://assets.publishing.service.gov.uk/media/68ba91f411b4ded2da19fe92/onshore-wind-and-solar-pv-cost-electricity-report-update-2024.pdf

• IEA: “The Path to a New Era for Nuclear Energy” — https://www.iea.org/reports/the-path-to-a-new-era-for-nuclear-energy

• IEA data chart: “Small modular reactor global installed capacity by scenario and case, 2025–2050” — https://www.iea.org/data-and-statistics/charts/small-modular-reactor-global-installed-capacity-by-scenario-and-case-2025-2050

• ESG Today: “Rolls-Royce Signs Deal with UK to for First Fleet of Small Modular Nuclear Reactors” (references Amazon/X-energy investment) — https://www.esgtoday.com/rolls-royce-signs-deal-with-uk-to-deliver-fleet-of-small-modular-nuclear-reactors/

• Google/Kairos Power: “Google and Kairos Power partner to develop nuclear energy” — search Google’s official press release at blog.google for the specific announcement.

• GOV.UK: “Golden age of nuclear delivers UK-US deal on energy security” — https://www.gov.uk/government/news/golden-age-of-nuclear-delivers-uk-us-deal-on-energy-security

• Al Jazeera, 18 September 2025: “US and UK sign major nuclear power deal: What does it include?” — https://www.aljazeera.com/news/2025/9/18/us-and-uk-sign-major-nuclear-power-deal-what-does-it-include

• Nuclear Innovation Alliance: “Regulatory Implementation Summary: NRC Progress Under the ADVANCE Act”(December 2025) — https://nuclearinnovationalliance.org/regulatory-implementation-summary-nrc-progress-under-advance-act

• NRC ADVANCE Act dashboard: https://www.nrc.gov/about-nrc/governing-laws/advance-act/dashboard

• NRC History: “Too Cheap to Meter: A History of the Phrase” — https://www.nrc.gov/reading-rm/basic-ref/students/history-101/too-cheap-to-meter

• Wikipedia: Too cheap to meter — https://en.wikipedia.org/wiki/Too_cheap_to_meter

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

The people report comes toward the end of the board pack. Somewhere after the financial performance slides and before the risk register. It contains an engagement score — this quarter seventy-one, up three points from the previous period — and a brief commentary from the people director explaining the movement. The board notes the improvement. Someone asks whether the score is above or below sector average. The answer is above. The item closes.

Six months later, a significant portion of the senior leadership team has left. The reasons, when they eventually surface, are not complicated: a sustained period of organisational change had been managed without involving the people most affected in the decisions being made, and those people had concluded, rationally, that the direction was not one they wanted to follow. None of this was invisible. It was legible, in specific terms, in the free-text responses that sat beneath the engagement score the board had accepted as reassurance.

The board had asked the right governance question — how engaged are our people — and received an accurate answer. The number was real. What it failed to convey was whether the organisation understood the conditions producing it, and whether those conditions were stable or deteriorating beneath a score that was still, technically, above average.

This is not an unusual story. It is a governance pattern.

What a people metric is designed to do

Board-level people reporting has evolved to serve a specific function: to give non-executive directors sufficient assurance that the human risk in the organisation is being managed. The engagement score performs that function efficiently. It is comparable across periods, benchmarkable against sector peers, and defensible in the event of a regulatory or reputational challenge. It answers the governance question the board is equipped to ask.

The problem is that the governance question and the strategic question are not the same question. Is engagement above or below threshold is a governance question. Do we understand what our workforce, our customers, or our communities are actually experiencing well enough to anticipate what is coming is a strategic one. The first produces a number. The second requires intelligence. And most board people reporting has been designed to produce the former while giving the impression it addresses the latter.

The distinction matters because boards make consequential decisions about people — through strategy, through resource allocation, through the acquisitions and restructures they approve — and the quality of those decisions depends on the quality of the understanding they rest on. A board that believes a score of seventy-one means everything is broadly in order is making different decisions than a board that understands why the score is seventy-one, which parts of the organisation are driving it, and what would need to change for it to move in either direction.

The score alone cannot support the second set of decisions. It was not designed to.

What boards are not seeing

The gap between what boards receive and what would constitute genuine people intelligence is wide in most organisations, and wider still in specific situations: post-acquisition integration, significant organisational change, sustained periods of uncertainty, or industries where the workforce is distributed, contracted, or otherwise at a distance from the centre.

In each of these situations, the aggregate score becomes less representative precisely when it most needs to be reliable. The integration that is going well for the acquiring team and badly for the acquired workforce produces an average. The restructure that has settled the leadership layer while creating deep anxiety in the operational layer produces an average. The number consolidates the variation into a single point and presents it to the board as a picture of the whole.

What the board is not seeing, in most cases, is the distribution beneath the average — where the score is concentrated, where it is fragile, which parts of the organisation are carrying the weight of others, and what the people in the lower quartile are specifically saying about their experience. Nor is it seeing the qualitative register: the specific concerns, the named conditions, the articulated reasons that would allow a board to distinguish between an engagement dip that is transient and one that is structural.

Boards that have tried to close this gap by asking for more granular data — scores by function, by tenure, by level — get closer to the distribution problem but do not solve the intelligence problem. Granular numbers are still numbers. They show you where the variation is. They do not show you why.

The question no one asks in the boardroom

There is a version of this problem that is specific to the board’s role, and it sits just beneath the surface of every board pack.

Boards are responsible for oversight, not management. The distinction is important. A board that receives an engagement score and accepts it as adequate people intelligence is not failing to manage — that is not the board’s job. It is failing to ask the questions that oversight requires: what does this number actually tell us, what does it not tell us, and what would we need to know to be confident we are not surprised by something we should have seen coming.

In most organisations, no one in the boardroom asks those questions routinely. The people director presents the data they have been asked to present, in the format that has been agreed, against the benchmarks that have been established. The non-executives receive it in a pack that also contains financial performance, risk, and compliance items that require more active scrutiny. People data gets the time allocated to it, which is rarely enough to interrogate it deeply.

The result is that people risk is governed at a level of abstraction that would be considered inadequate in other domains. A board that accepted a single aggregate technology risk score without understanding what it represented, where the exposure lay, and what conditions were driving it would be considered negligent. The equivalent in people governance is standard practice.

The shift required is not primarily about tools or data. It is about what boards decide to demand. Engagement intelligence that is specific, distributed, and grounded in what people actually say — rather than how they respond to a scale — is achievable. Gobby, a UK platform built on qualitative-first surveys, shows what this looks like in practice: respondents answer in their own words, then validate each other’s responses rather than having them averaged by the system. What reaches the board isn’t a number — it’s what the workforce actually said. It requires a different kind of question at board level, and a people function equipped and willing to answer it.

The score will always be easier to present than the understanding. The question is whether the board is prepared to settle for it.

My opinion

I left an organisation once because the leadership had changed five times in five years and no one seemed to know what direction they were heading in. When I left, no one asked why. The exit process was procedural. I was not a data point anyone was going to interrogate. In another business I left because of a executive level change which resulted in an organisational restructure, after the very first meeting with the new executive I knew what was coming and it wasn’t going to be pretty. The thing which I loved about that business was the one thing that was going to be destroyed - Culture. So I resigned, said my piece and everything I said came true.

The people who collect this kind of information are usually not hiding anything deliberately. They are responding to incentives. They have learned, over time, what the board finds useful and what it finds uncomfortable. They present the former. They convert the latter into a score.

That is not intelligence. It is data dressed as reassurance. And if a board cannot hear the difficult alongside the comfortable — if the culture in the boardroom makes hard truths unwelcome — then the problem is not the measurement. It is a leadership failure at the top of the organisation.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Sam has been reading the same paragraph for the third time. The words are there. The comprehension isn’t. Something, unclear what, has been pulling at the edge of attention for the last twenty minutes without ever resolving.

The message arrives that Sam isn’t going to read yet. The alert that Sam already half-knows exists. Some unfinished conversation sitting just below the surface of thought, unanswered, unresolved, quietly taking up space in the mind that was supposedly committed to this page.

Sam isn’t distracted. Sam is present. Physically here, eyes on the text, doing the right thing. The presence is real. The attention is somewhere else entirely.

The attention you were certain you had

In 1999, two psychologists at Harvard asked participants to watch a short video and count how many times a basketball was passed between players wearing white. The task was specific and demanding.

Halfway through the video, a person in a full gorilla suit walked into the frame. Faced the camera. Beat their chest. Walked out. Fifty per cent of participants never saw it. Not because it was subtle. Because their attention was committed elsewhere, and the gorilla wasn’t part of what they’d been asked to track.

We believe we’re present because we’re physically in a situation. The eyes are open. The body is there. But presence and attention are not the same thing, and the gap between them is where a great deal of modern life is quietly disappearing.

Inattentional blindness — the brain’s tendency to miss even obvious stimuli when focus is elsewhere — isn’t a rare laboratory phenomenon. It’s the operating condition of most people’s daily lives.

The brain doesn’t see everything in front of it. It samples, filters, and constructs a picture based on what it has been primed to look for. When that priming mechanism is overwhelmed — when there are too many things competing for focus — the filter becomes less a tool of selection and more a tool of exclusion.

What has changed since the gorilla experiment isn’t the cognitive architecture. It’s the scale and sophistication of the systems competing to own the priming. Every notification, every feed, every platform is fighting to be the thing your brain gets told to track. When they win, everything else becomes the gorilla. Invisible. Present. Missed.

We don’t lose the ability to pay attention. We lose the choice about where it goes.

The things that disappear without announcement

The gorilla in modern life isn’t a person in a suit. It’s the conversation not had. The warning not noticed. The colleague who needed support and didn’t get it because you were technically in the room while mentally somewhere else entirely.

It’s the child who asked a question and got a distracted non-answer, then stopped asking. The friend who was struggling and mentioned it once, sideways, and you were there but not there when it came up. The decision made quickly because the depth of focus required to make it well wasn’t available that day.

The cost isn’t always visible. It rarely arrives labelled. It accumulates in the quality of what you produce, the depth of the relationships you maintain, and the gradual narrowing of what you actually notice about the people and moments that are directly in front of you.

We have been taught to understand this as a personal failing. A concentration problem. A discipline issue. The technology industry is clear, in its habits if not its language, that this framing suits it perfectly. Because if the problem is you, you don’t look at the design.

What the design is actually doing

A system that competes for the priming of your attention, that inserts itself into the filtering mechanism the brain relies on to distinguish signal from noise, is not supporting you. It is working against the very cognitive function it claims to support.

The argument that social platforms augment connection holds only if the connections they produce are of comparable quality to those they replace or displace. The gorilla experiment suggests a direct relationship: the more demanding the competing task, the more complete the blindness. Connection that comes at the cost of presence isn’t connection. It’s substitution.

The deepest failure is this: any system that makes you selectively blind to your immediate environment cannot claim to respond to your actual circumstances. The person sitting with their child. The person driving. The person in the conversation that matters. A system that doesn’t know — or doesn’t care — which of those moments it’s interrupting has no basis for calling itself designed for you.

This is one of the questions at the centre of my book The Cognitive Crucible — not just what technology does, but what it costs us without announcing the price.

My opinion

Attention is the most finite resource we have. I don’t think the platforms taking it are confused about what they’re doing — the design is not accidental, and the framing of distraction as a personal discipline problem is not a misunderstanding either. What concerns me most is the picture of society these platforms have constructed: not a reflection of how people actually live, but of what drives engagement. The two are not the same thing, and recovering the ability to tell them apart — to notice what is actually in front of you rather than what you’ve been primed to track — is, in the most direct terms I can offer, how you get your cognitive agency back.

What this asks of you

The point is not to understand inattentional blindness. The point is to know where it is operating in your own life.

1. What have you noticed yourself missing lately — at work, at home, in conversation — that you’d have caught if you weren’t distracted?

2. Is there someone in your life who has been trying to tell you something you haven’t fully heard? What would it take to change that?

3. What would you need to change about your environment to make genuine presence your default rather than your exception?

4. If someone was watching how you spend your attention across a typical week, what would they conclude about what matters most to you?

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

A pathologist examining a breast cancer biopsy can read a great deal from the slide. The cells — stained pink and purple — arrange themselves in patterns that decades of training make legible. Some areas are dense with malignant cells: the tumour’s centre. Others show the advancing edge, where cancer is pushing into healthy tissue. Some regions are infiltrated by immune cells; others appear almost entirely free of them.

What the traditional biopsy slide cannot show is what those cells are actually doing — which genes each one is expressing, what proteins it is making, what signals it is sending to its neighbours, why the immune cells in one region are actively attacking the tumour while the immune cells in another region appear to have gone quiet. The stain shows structure. It cannot show function.

For most of the history of molecular biology, researchers addressed this limitation by doing what they could: taking the tissue, grinding it up, and measuring the average gene expression of the resulting mixture. This approach called bulk sequencing produces useful information. It also, inevitably, hides the detail that matters. A tumour cell that is resistant to a drug and a tumour cell that is sensitive to the same drug look identical in a bulk sample. Their individual signals cancel each other out. What the measurement captures is the average, and in a heterogeneous tissue, the average misleads.

Single-cell and spatial biology exist to fix that problem. Single-cell technologies analyse gene expression at the resolution of individual cells. Spatial technologies add location: they preserve the tissue architecture and record not just what each cell is doing but where in the tissue it sits. Together, they produce something that did not exist before — a map of a living tissue that shows, at microscopic resolution, which genes are active in which cells in which locations, and what those cells are saying to each other.

Nature Methods named spatially resolved transcriptomics its Method of the Year in 2020. Since then, the technology has moved from the leading edge of basic research to commercial platforms available in well-equipped laboratories worldwide. In February 2025, Illumina announced a spatial technology product planned for commercial release in 2026 with a capture area nine times larger than existing technologies and four times greater resolution. The field has tipped from pioneering to mainstream. What that means for understanding disease — and ultimately for treating it — is the subject of this article.

The tumour is not the problem. The neighbourhood is.

The central insight that spatial biology has delivered to cancer research can be stated simply: a tumour is not a mass of identical malignant cells. It is an organised community of different cell types — cancer cells in various states, immune cells of multiple kinds, structural cells called fibroblasts, blood vessel cells, and others — all occupying distinct spatial niches within the tissue, interacting constantly with their immediate neighbours, and behaving very differently depending on where they are.

This was suspected for a long time. What spatial biology has done is make it visible and measurable.

One of the most striking findings from spatial transcriptomics research is that the same tumour can simultaneously contain cells that are highly sensitive to a treatment and cells that are highly resistant — and that this is not purely a matter of genetics. Location matters. A cancer cell sitting in the centre of a tumour, surrounded by other cancer cells and relatively few immune cells, behaves differently from a genetically identical cancer cell at the tumour’s leading edge, where it is in constant contact with immune signals and structural proteins from the surrounding healthy tissue. The edge cell may be more aggressive and invasive; the centre cell may be more metabolically active but less mobile. Their gene expression profiles differ. Their drug sensitivities differ. The treatment that kills one may leave the other intact.

A tumour contains cells that are sensitive to a drug and cells resistant to the same drug — not just because of genetics, but because of location. Cells at the leading edge behave differently from cells at the centre. Spatial biology makes this visible.

A 2023 paper in Nature Communications on oral squamous cell carcinoma demonstrated that the gene expression profile of cells at the tumour’s leading edge — what researchers call the leading edge signature — was consistently associated with worse clinical outcomes across multiple cancer types, while the tumour core signature was associated with improved prognosis. The spatial position of the cancer cells relative to the boundary between tumour and healthy tissue carries predictive information that no amount of bulk sequencing could have extracted, because bulk sequencing cannot preserve the information about where each cell was sitting.

Immune cells show equally striking spatial organisation. Immunotherapy — the class of treatments that effectively unlocks the immune system to fight cancer — works by removing the signals that cancer cells use to suppress immune activity. It works well for some patients and not at all for others. Spatial biology research is revealing why. In patients who respond to immunotherapy, spatial analysis shows specific arrangements of immune cells clustered in particular locations around the tumour — what researchers call immune hubs or tertiary lymphoid structures. In patients who do not respond, those structures are absent or differently arranged. The presence or absence of the structures is not detectable by any analysis that does not preserve the spatial information. A blood sample, a bulk biopsy, even conventional single-cell sequencing without location data cannot tell you whether the right immune cells are in the right places.

Spatial transcriptomics of small cell lung cancer tumours has identified two distinct subtypes of cancer cells within the same tumours: one associated with treatment resistance and high proliferative activity, the other associated with treatment sensitivity and greater immune cell contact. The resistant subtype appears to reshape its local environment to drive away immune cells — to domesticate the macrophages in its vicinity and convert them into accomplices in suppressing the immune response. This finding was invisible to conventional analysis. It required spatial mapping of the tumour to become apparent.

The platforms are ready. The clinical pipeline is not.

The commercial field for spatial biology has developed quickly and is now genuinely competitive. 10x Genomics offers the Xenium In Situ platform, which can detect up to 5,000 genes across tissue samples at subcellular resolution. Bruker’s CosMx platform can profile over 19,000 genes — the entire protein-encoding transcriptome. Visium HD, also from 10x Genomics, resolves spatial data to 2-micrometre bins. Illumina’s forthcoming platform will use a capture area nine times larger than current technologies, integrated with its Connected Multiomics software suite for analysis.

The availability of these platforms has shifted the technology from something that required specialist physics laboratories to something that any well-resourced research institution can operate. The research output has followed. A review of spatial omics in cancer research published in Cancer Cell in January 2026, by researchers at the MD Anderson Cancer Center, described how spatial technologies now map tumour architecture with a fidelity that earlier methods could not achieve, resolving functional niches and spatial communities, converting spatial patterns into mechanistic insights.

The transition from research tool to clinical instrument, however, has several steps remaining. The data volumes generated by spatial transcriptomics are enormous. A single spatial experiment can produce hundreds of gigabytes of data. Analysing it requires computational infrastructure and bioinformatics expertise that most clinical settings do not yet have. The integration of spatial transcriptomics data with conventional pathology, with genomics, with clinical records, and with the kind of standardised workflows that regulatory agencies require before a diagnostic test can be approved — all of this is in progress but not yet complete.

The cost has been falling but remains significant. Running a spatial biology experiment on a clinical sample costs more than conventional sequencing and takes longer. For research settings, this is manageable. For routine clinical use — for the pathologist examining a cancer biopsy to guide treatment decisions for a patient who needs an answer within days — the workflow, cost and analytical requirements are still a barrier. The question of where spatial biology first enters clinical practice is the most practically important one in the field right now.

The most plausible near-term pathway is through what researchers call companion diagnostics: tests that are performed alongside a specific treatment to determine who is most likely to benefit. The patterns that spatial biology can detect — the presence or absence of immune structures around a tumour, the proportion of cells with certain gene expression signatures at the tumour edge, the spatial arrangement of drug-resistant subpopulations — are exactly the kind of information that could guide decisions about whether to use immunotherapy, which chemotherapy to start with, or whether a tumour is likely to respond to a newly approved targeted agent.

Why drug resistance looks different from here

Drug resistance is the central unsolved problem of cancer treatment. Most patients who are initially helped by a drug eventually stop responding to it. Understanding why, and predicting in advance who will stop responding and when, is the problem that determines whether a patient can be switched to an effective treatment before their cancer becomes untreatable.

Spatial biology is offering new ways to think about this problem. The conventional model of drug resistance focuses on genetics: a cell acquires a mutation that makes it insensitive to the drug, and that cell proliferates while the drug kills everything else. That model is real and important. But spatial biology research is demonstrating that it is incomplete. Resistance is also ecological.

A resistant cell does not only survive because of its own genetic properties. It also survives because of where it is in the tissue — who its neighbours are, what signals they are sending, whether the local immune environment is permissive or hostile. Research in melanoma has shown that distinct resistant fates — different ways a cell can become drug-resistant — are not randomly distributed through a tumour. They are spatially organised, associated with specific microenvironments. The cells in regions of necrosis behave differently from the cells adjacent to functional blood vessels. The resistant fate a cell adopts appears to be partly determined by its location.

This has immediate implications for treatment design. If resistance is partly spatial, then treating a tumour as a single uniform target with a drug calibrated to the average cell will predictably fail. The cells most resistant to treatment are often those in locations that the drug reaches least effectively and where the microenvironment most actively suppresses the immune system. A treatment strategy informed by spatial biology might target not just the cancer cells but their supporting environment: the macrophages they have domesticated, the fibroblasts that form barriers, the immune exclusion zones that protect the most resistant subpopulations.

If resistance is partly spatial, treating a tumour as a uniform target will predictably fail. The cells most resistant to treatment are often in the locations least accessible to drugs and most protected by their microenvironment.

Spatial analysis of breast tumours combined with pharmacogenomic profiling has identified that cancer cells in immunosuppressive microenvironments — the regions of the tumour where the immune system has been most effectively shut down — show specific sensitivity to cell cycle arrest agents and a class of drugs targeting the PI3K/AKT/mTOR pathway. This is information that could not be derived from bulk sequencing, because the immunosuppressive microenvironment and the cancer cells within it cannot be distinguished in a ground-up tissue sample. Seeing the map reveals what the average conceals.

Beyond cancer, spatial biology is beginning to reveal the tissue architecture of conditions that have been poorly understood precisely because they are diseases of organisation rather than diseases of individual cells. Pulmonary fibrosis — scarring of the lung tissue — involves molecular dysregulation at specific locations where epithelial cells are actively remodelling. Identifying exactly where in the tissue this remodelling is happening, and which cells are driving it, is exactly what high-resolution spatial transcriptomics can provide. Early data from the Illumina platform has shown that the scale and sensitivity of the technology enables the whole transcriptome to be studied across large tissue sections, identifying localised molecular dysregulation that earlier technologies could not resolve.

Who the insight reaches

Spatial biology is doing something specific for researchers and eventually clinicians: it makes visible biology that was previously invisible. Not incompletely understood — invisible. The question of whether two cancer cells in different parts of a tumour are behaving differently could not be asked until there was a technology that could preserve spatial information and measure gene expression simultaneously. Now it can be asked and answered. That matters for everyone whose work depends on understanding why treatments fail.

The distance between the research laboratory and the patient is where the real question sits. At present, the insights from spatial biology are largely flowing through academic research and pharmaceutical development. The direct beneficiary is the next generation of clinical trials — better-designed studies, better patient stratification, drugs tested against tumours whose architecture has been mapped rather than averaged. That is real progress. But the pathway from spatial biology discovery to a changed treatment decision for a specific patient is still long, and the technology to shorten that pathway — affordable, fast, clinically integrated spatial diagnostics — is not yet routinely available.

Whether spatial biology becomes a tool for everyone or a research instrument for well-resourced institutions is the question that will determine its real value. The core insight — that a patient’s tumour has a spatial architecture that determines how it will respond to treatment — is as true for a patient at a regional cancer centre as for one at a major academic medical centre. The Illumina platform under development is explicitly designed to reduce cost and integrate with existing sequencing infrastructure, which suggests the field is at least trying to address the accessibility barrier. The data infrastructure required to analyse the results — the computational pipelines, the bioinformatics expertise, the clinical interpretation frameworks — presents a more persistent challenge.

What the map cannot yet tell us

1. Spatial biology reveals that the same tumour can simultaneously contain cells that respond to a treatment and cells that resist it — and that location, not just genetics, partly determines which is which. How does that change how you think about the phrase ‘this treatment works for this cancer’?

2. If the spatial architecture of a tumour predicts whether immunotherapy will work, and that spatial information can only be obtained by analysing the biopsy tissue before treatment starts — who should have access to that analysis? What would it mean for cancer equity if spatial diagnostics are only available at specialist centres?

3. Spatial biology generates enormous datasets from individual tissue samples. Making sense of that data requires AI tools, bioinformaticians, and infrastructure that most clinical settings do not currently have. What is the bottleneck that most needs addressing before this technology reaches routine clinical use?

4. The insight that drug resistance is partly ecological — shaped by the local environment around cancer cells rather than only by the cells’ own genetics — suggests that some treatments may need to target the microenvironment, not just the tumour. What does that mean for how cancer drugs are designed and tested?

My Opinion

The gap between what spatial diagnostics can reveal and where that capability is available is not a technical problem. It is the same problem that follows every powerful diagnostic technology: it arrives at the best-resourced centres first and, without deliberate intervention, tends to stay there. Cancer does not follow a map of specialist centres. It arrives wherever it arrives — in rural hospitals, in regional oncology units, with patients who cannot travel to the institutions that define the frontier. The field needs to be working towards diagnostics that are portable, affordable, and deployable anywhere, not because that would be admirable, but because the technology’s actual value depends on it.

The pathologist looking at the traditional biopsy slide can see structure. Spatial biology lets you see function. It lets you read which cells are talking to which other cells, and what they are saying, and what happens in the neighbourhood where the immune system has given up.

The dimension is location. And it turns out to matter enormously.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

There is a particular kind of silence that follows a consultation. Not the silence of consideration, where something heard is being weighed. The silence of completion, where a process has been concluded and the findings have been filed. The residents submitted their responses. The staff completed the survey. The service users attended the focus group. And then, at some point later — sometimes months, sometimes longer — a decision was announced that bore little visible relationship to what anyone said.

Most people who have been consulted by a large organisation have experienced this. Many have stopped expecting otherwise. That learned scepticism is not apathy. It is a rational response to a pattern that repeats across sectors with enough consistency to suggest it is not accidental.

The pattern has acquired a name — consultation fatigue — but the name understates what is actually happening. Fatigue implies a willingness that has been worn down. What most organisations have produced is something more deliberate: a process designed to look like listening without requiring the organisation to change course as a result of what it hears.

The mechanics of being heard without being heard

The structure of most consultations gives the game away.

Questions are written by the organisation, which means the answer categories reflect the organisation’s existing understanding of the problem. Respondents are asked to rate, rank, or select from options that were designed before anyone outside the room was consulted. Free-text fields exist, but are rarely analysed systematically — they are too difficult to process at scale and too unpredictable in what they might surface. The consultation closes, the quantitative results are tabulated, and the report that emerges reflects the organisation’s original position with a margin of adjustment around the edges.

This is not cynicism dressed as analysis. It is the logical consequence of designing a consultation process around the outputs you need to produce rather than the understanding you need to reach. In local government, that output is a mandate — a defensible record that affected parties were involved before a decision was made. In the NHS, it is a compliance requirement — evidence of patient and public involvement in service design. In the private sector, it is an engagement score — something to report to the board or include in a regulatory submission. Each has a legitimate purpose. None of them requires the organisation to be genuinely changed by what it hears.

The people being consulted know this. The communities most affected by decisions are typically the ones with the least trust in the processes designed to involve them — because they have the most experience of those processes. When a community has watched a planning consultation produce a predetermined outcome, or seen a workforce survey followed by a management communications campaign rather than structural change, the rational response is to disengage. Not because the issues don’t matter to them. Because they have learned that engaging produces no different outcome than not engaging.

What the same pattern looks like across sectors

In local government, the planning consultation is the clearest case. Residents are invited to comment on a proposal that is already substantially formed. The statutory requirement is to consult; there is no equivalent requirement to be materially influenced by what is said. Communities that oppose a development learn to flood the process with responses because volume is the only variable that demonstrably affects outcomes — not the quality of the argument, not the specificity of the concern, not the evidence that the proposal conflicts with the community’s stated needs.

In healthcare, service redesign consultations run on similar logic. A trust deciding to reconfigure services will conduct patient engagement as part of the process. The engagement is genuine in the sense that it happens; it is limited in the sense that the financial and operational case for the reconfiguration is typically established before the engagement begins. What the consultation can change is the detail. What it rarely changes is the direction.

In the private sector, the employee consultation — particularly in a restructure or acquisition — operates under legal requirements in the UK that mandate a period of consultation before certain decisions are implemented. The requirement is to consult; it is not to reach agreement, and it is not to change course if the arguments against the proposal are sound. HR teams are well practised in running consultations that satisfy the legal threshold while protecting the operational decision already made.

In the charity sector, the gap is perhaps most uncomfortable to acknowledge. Organisations built on the principle of community voice frequently struggle to implement it in practice. Governance structures centre the board and the executive. Impact measurement centres the funder’s reporting requirements. The beneficiary’s account of their own experience sits at the end of a chain of representation — reported by a caseworker, summarised by a manager, condensed into an impact metric by a communications team — that has progressively removed the original voice by the time it reaches anyone with decision-making authority.

The thing that peer validation changes

The problem is not only that organisations fail to act on what they hear. It is that the processes they use mean they often do not hear clearly in the first place.

A consultation that asks closed questions gets closed answers. A free-text field that sits at the end of a fifteen-minute survey gets responses from the minority who feel strongly enough to write something additional. An engagement that channels responses through a manager or a facilitator introduces the interpretation of the intermediary before the data reaches analysis. By the time findings are presented to the people who will make the decision, the original voice has been translated several times.

The methodological shift that matters here is not just collecting open responses — it is validating them within the community before they reach the analyst. When people can see each other’s responses and indicate what resonates, two things happen that closed surveys cannot produce. First, the synthesis reflects the community’s own sense of what matters, rather than the researcher’s coding scheme. Second, people who contribute to a process and see their contribution acknowledged by peers are less likely to conclude that the exercise was performative. The experience of being heard is partly structural — it depends on whether the process was designed to hear or designed to record.

Gobby is one tool built on this logic — open responses rather than closed questions, peer-powered voting so participants can indicate what resonates, AI-assisted synthesis that reflects the community’s own sense of priority rather than the analyst’s coding scheme. It is still used by a minority of organisations. The majority continue to run processes that produce defensible records rather than genuine intelligence. The technology to do otherwise is not the constraint. The will to act on what might be heard is.

That is the harder problem, and no tool solves it. What better processes do is remove the organisation’s ability to claim it didn’t know. If the findings are specific, community-validated, and clearly stated, the decision to set them aside becomes a conscious choice rather than an administrative oversight. That is a different kind of accountability — and it is the reason some organisations prefer the consultation that changes nothing.

My opinion

I have been in enough of these rooms to know the difference between an organisation that doesn’t know how to listen and one that has decided not to. Most consultation theatre is the second kind. Tools like Gobby do something useful: they remove the claim of ignorance. If the findings are specific and community-validated, the decision to set them aside is no longer an oversight. It is a choice. And the organisations that consistently make it know exactly what they are doing.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Morgan comes on shift at 11pm and the menu is gone. Not crashed — gone. The update that rolled out at 6pm moved it somewhere else, and the section of iOPS that handles electronic file transfer to the Crown Prosecution Service is no longer where it was yesterday. There is a person in the custody suite waiting to be processed. Morgan has a legal time limit. Morgan picks up the paper form.

This is Greater Manchester Police in July 2019. iOPS — the Integrated Operational Policing System — went live sixteen months behind schedule. The custody module, which was supposed to manage custody records and transfer files electronically to the CPS, was not working as specified. The announcement had said it was ready. The go-live had happened. Nobody said out loud that it wasn’t done.

It wasn’t done.

Deployed, not delivered

The workaround that custody sergeants had been using for three months no longer worked. The update had changed the interface without adequate retraining. There was no mechanism to refuse the tool — the option was to make it work, improvise a new workaround, or escalate a complaint that had no clear landing point. Officers improvised.

GMP’s own independent review named the consequences: technical shortcomings, inadequate training, and operational disruption that left vulnerable people without referrals to support services. Referrals for high-risk domestic abuse victims fell by 50%. Referrals to Greater Manchester Victims’ Services fell by 87%. Officers spent time running duplicate paper processes alongside the digital system that was supposed to replace them. The system did not help the workforce do the work — it created a second job beside it.

iOPS had gone live not because it was ready but because the organisation needed it to have gone live. Sixteen months of delay had a contractual cost, a political cost, and a considerable cost in credibility. Ready was redefined as deployed, and the workforce inherited the gap between the two.

Why no one says it isn’t ready

A software product that is finished cannot justify an ongoing roadmap. It cannot retain an engineering team. In a subscription market, completion is a liability — a done product is one that doesn’t need what comes in the next release cycle. Agile methodology and continuous deployment gave the industry a vocabulary for this: not unfinished, but iterating. Not incomplete, but evolving. The language made incompleteness sound like a sign of rigour.

Organisations accepted this because the alternative was more expensive. To say the system was not ready meant saying the procurement decision was wrong. GMP’s contract had already been signed, extended, and renegotiated. Admitting the custody module was unfit meant unpicking an obligation that was difficult to unpick. Deploying and adapting was easier.

There was no real mechanism to refuse. The tool was the tool. The workarounds became part of the job. The cost was absorbed shift by shift by custody sergeants who had no power to put it back where it came from.

The cost that didn’t appear in the contract

Birmingham City Council signed a contract for an Oracle ERP system projected at £19 million. By 2026 the total cost is forecast to reach £216.5 million. Two and a half years after go-live, the system was still not, in the auditors’ own words, safe and compliant. In September 2023 Birmingham declared bankruptcy — the largest local authority insolvency in UK history. The system was never ready either.

NHS IT outages disrupted more than 274,000 patient interactions across six major trusts in a single year. In each of those incidents, clinical staff reverted to paper. Not because paper was preferred, but because the digital system had failed and the paper process was the only thing keeping the work going. More than a quarter of UK central government digital systems are now classified as outdated, at an estimated cost to the public sector of £45 billion annually in foregone productivity.

Office workers endure an average of 3.6 technology interruptions a month — software updates, system failures, workarounds required. Nobody counted this cost when the contracts were agreed, because the cost landed on the worker. The vendor’s contract was satisfied. The product had been delivered. That it didn’t work as specified was the next problem, billable separately.

What it was designed for

The most basic test for any system deployed to workers is whether it actually helps them do the specific work it replaced. iOPS failed that test on the night the update moved the workflow officers had learned. The tool stopped being a tool and became an obstacle with extra steps. The work still needed doing. Staff found a way to do it. The system got the credit for a deployment that happened.

The next test is whether what the vendor calls an improvement actually improves anything for the person using it. iOPS received updates. Each notification used the word “improvement.” Custody sergeants had not requested the features those updates added. The gap between what the vendor was building and what the workforce needed is not a failure of communication. It is the expected result of a development roadmap driven by the vendor’s commercial cycle, not by the legal obligations of a sergeant at 11pm.

The final test is whether the system responds to the specific person in front of it, or expects the person to fit. iOPS updated on the vendor’s schedule. Its release windows did not account for shift patterns, operational hours, or the consequences of breaking a workaround mid-cycle. The specific circumstances of night-shift custody work — legal time limits, vulnerable people waiting — were not part of the specification for when an update went live. The system was not designed around the people using it. They were expected to fit.

My Opinion

I have been in enough procurement processes to know that the people who will actually use the system are rarely in the room when it is chosen. Requirements are gathered, but they are normally vetted by people who do not work with the system day to day — so what gets built reflects what the organisation said it needed, not what the work actually demands. Sometimes a system is deployed not because it is ready but to satisfy a contract: the way a UK train leaves a platform on time even when it isn’t. What is missing is oversight with teeth — people who have enough operational experience to know when something is wrong, and enough authority to stop it going live until it is right.

I’ve seen this done properly where in a hospital the IT Development team deployed a capability that clinical staff had been asking for. Then they got smart - they instigated a digital suggestions box for new features/services/products and the hospital staff embraced this as they got a voice on what was needed and important - not what the organisation thought it needed. The even smarter change was the IT Dev team wrapped this all with an agreed SLA for delivering selected capabilities and they stuck to it. What the users asked for they got when it was promised. The downside if it can be called one was the sheer volume of requests once staff saw it working.

Questions the contract never asked

The following questions are not academic. They are for anyone currently running a process that exists only because a system the organisation bought was never quite finished. The vendor moved on. The contract was satisfied. The workaround remained.

1. What workaround are you currently maintaining that exists only because a system your organisation bought was never quite finished?

2. If you had had to sign off on the cost of that workaround — in time, in error rate, in staff goodwill — when the contract was agreed, would the vendor still have won it?

3. When was the last time someone in your organisation said “this isn’t ready” and it made a difference?

4. What would it take to put “not ready yet” back into the vocabulary of public sector procurement?

The cost of perpetual beta is not paid by the organisations that sign the contracts or the vendors that provide them. It is paid by the person on the shift who picks up the paper form when the menu has moved again. That cost is real. It has just never appeared in a budget line.

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Sources & References

• HMICFRS: Greater Manchester Police Integrated Operational Policing System Inspection, 2020. https://hmicfrs.justiceinspectorates.gov.uk/publications/greater-manchester-police-integrated-operational-policing-system-inspection/

• Computer Weekly: “Police IT system failure creates significant backlogs”, 2020. https://www.computerweekly.com/news/252479783/Police-IT-system-failure-creates-significant-backlogs

• Data Centre Dynamics: “Total cost of Birmingham City’s Oracle system failure to reach £216.5m by 2026”, 2024. https://www.datacenterdynamics.com/en/news/total-cost-of-birmingham-citys-oracle-system-failure-to-reach-2165m-by-2026-report/

• The Register: “Birmingham City’s Oracle ERP system still not ‘legally safe’”, April 2024. https://www.theregister.com/2024/04/17/birmingham_city_councils_oracle_erp/

• The Register: “Birmingham City Council goes under after Oracle disaster”, September 2023. https://www.theregister.com/2023/09/05/birmingham_city_council_oracle/

• Security Brief UK: “NHS IT outages disrupt 274,620 patient interactions”. https://securitybrief.co.uk/story/nhs-it-outages-disrupt-274-620-patient-interactions

• Tech Monitor / DSIT: “Legacy technology costs UK public sector £45bn annually”. https://www.techmonitor.ai/government-computing/legacy-technology-costs-uk-public-sector-45bn-annually/

• Ivanti: 2025 Digital Employee Experience (DEX) Report, 2025. https://www.ivanti.com/company/press-releases/2025/tech-disruptions-cost-companies-millions-of-dollars-in-lost-productivity-annually-according-to-research-from-ivanti

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  You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

An experienced endoscopist spends years learning to see. The skill is not in holding the camera. It is in the eye: the trained attention that catches a flat, easily missed growth at the edge of the screen, the kind that turns into bowel cancer if it is not found and cut out. That attention is built by doing the looking, thousands of times, with no one and nothing pointing the way.

Between 2021 and 2022, four endoscopy centres in Poland brought in an AI tool that draws a box around suspect growths as the camera moves. It works. Switch it on and detection goes up. But researchers also watched what happened to the same doctors when the tool was switched off again. In the months after routine AI use came in, their unaided detection rate fell from 28.4 per cent to 22.4 per cent — a fifth of their skill gone, on the procedures where they were once again looking alone. The Lancet Gastroenterology & Hepatology published the finding in August 2025. The doctors had each performed more than two thousand colonoscopies. They were not novices losing a skill they had half-learned. They were experts, and the looking had quietly stopped being theirs.

No one decided that those doctors should get worse at finding cancer. Each centre adopted the tool for an honest reason: on the day, with the box on the screen, more growths are caught. The erosion was the by-product. It arrived without a meeting and without a vote, and the act that had kept the eye sharp — the looking itself — was the very thing the tool took over.

This piece grew out of a conversation. A few weeks ago Jason reviewed Nick Bostrom’s Deep Utopia for his Book Club.

Jason B Perkins

Deep Utopia: The Automation Paradox

What if the future arrives exactly as the technologists promised? Not a collapse. Not a machine uprising. Not even a slow, disappointing muddle. What if artificial intelligence keeps improving, systems keep coordinating, science keeps accelerating, and the world becomes astonishingly good at solving problems? Disease recedes. Scarcity shrinks. Logistics…

Read more

2 months ago · 3 likes · 3 comments · Jason B Perkins

I left a comment that disagreed with the end of his argument rather than the start of it. We have been chatting it over since. What follows is both sides, written honestly and left unresolved on purpose.

We agree on Bostrom’s distinction. Some of what we do has instrumental value: we do it to reach a result. Some has intrinsic value: the doing is the point. As machines deliver the result faster, the instrumental reasons for human effort weaken, and what survives is the effort we would still choose when we no longer have to. The colonoscopy study makes me want to name a third category the distinction misses. Call it formative difficulty: effort whose result we want, and whose doing builds or sustains the capability to get that result at all. Jason reads Deep Utopia as a reason to protect that kind of difficulty on purpose. I think he is right, and I think the harder question is who gets to decide.

That is the architecture answer, and it holds. But the colonoscopy centres were not careless. They adopted the tool for a real, same-day gain in detection, and the erosion still happened. So the question I could not let go of in Jason’s comments is who decides which difficulty is worth keeping, and on what grounds. The decision is already being made, almost always by default. No one in those rooms asked whether the unaided looking was instrumental or formative, because that is not a question a business case is built to ask. The answer arrived as a tool, and a capability that took two thousand procedures to build began to fade in months.

Bostrom says the scarce goods of an abundant world will be meaning, agency, and the texture of things done by hand. That sounds like liberation. Look again and it can describe a market, because someone always sets the price of a scarce good, and someone is always priced out. We have watched smaller versions of this. The handmade thing does not vanish when the machine arrives; it moves upmarket and becomes how the wealthy signal rather than how ordinary people live. There is no law that protects formative effort from the same gravity. The risk is not that the hard parts disappear. It is that they are kept by whoever can still afford to do things the slow way, and lost by everyone measured on speed.

If we are going to defend any difficulty, the test has to be sharper than nostalgia, because most appeals to the value of struggle are sentimentality about how things used to be done. Plenty of difficulty is worth removing. No one should defend long division by hand, or a three-week wait for a letter, or a disease we can now cure. The difficulty worth keeping is the narrow kind that builds or sustains something in the person doing it: skill, judgement, identity, or a relationship that would not otherwise exist. The endoscopists’ looking sustained an eye for cancer. The tool kept the detection and let the eye go, on the quiet assumption that the eye was a by-product rather than the point.

Jason reaches the same line from the other direction, and calls it virtuous and vicious friction. Vicious friction only diminishes us: the pointless meeting, the layer of bureaucracy, the months spent waiting for access. Virtuous friction defines us: the apprenticeship that builds judgement, the demanding conversation that builds trust, the hard way that leads to mastery. He started from the architecture and I started from the person, and we arrived at the same cut. Not effort against ease. Formative difficulty against incidental difficulty. The trouble is that the two are indistinguishable on a dashboard, where both show up only as time the machine could save, and the people best placed to tell them apart — the doctors who can feel their own eye dulling — are rarely the ones holding the budget or writing the rollout plan.

None of this is an argument against the tools. Those doctors should have the AI; on the day, it finds more cancers, and that is not a small thing to set aside. The argument is that someone has to be watching the difference between the difficulty that was keeping a skill alive and the difficulty that was only costing time, and at the moment almost no one is. The Polish researchers were careful: their study was observational, and they could not yet say what happens to doctors who train with the box from the start and never build the unaided eye at all. That is the more troubling question, and it is still open.

We will not lose the hard parts in a single decision anyone could be held to. We will lose them one defensible efficiency at a time, each removal sensible on its own, until a generation arrives that can check the machine’s work but could never have done it alone. If meaning, judgement, and skill really are the scarce goods of the world we are building, the choice in front of every organisation is not whether to keep some difficulty. It is who gets to decide which difficulty, and whether the person it would have formed is ever in the room when the decision is made.

Over to you. Before you automate the next hard thing, Jason offers five questions worth holding it against:

1. Agency — does it strengthen human control, or quietly remove it?

2. Connection — does it deepen trust and human bonds?

3. Judgement — does it sharpen our ability to decide, or outsource it?

4. Opportunity — does it widen what people can collectively become?

5. Safety — can the outcome be audited or reversed?

So we will put the same question to both our readerships and answer it in the thread: which difficulty in your own work is keeping a skill alive, and which is just slow?

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

In the spring of 2023, a patient at Memorial Sloan Kettering Cancer Center in New York received an injection that had been made specifically for them. The sequence of molecules inside it had been designed using a biopsy of their own tumour. No one else in the world would receive exactly this treatment. The vaccine’s job was not to prevent a disease — it was to teach the immune system to recognise and destroy cancer cells that were already there.

The patient had pancreatic cancer. Pancreatic cancer kills around 87% of the people diagnosed with it within five years. It is resistant to most forms of treatment. The immune system usually does not recognise it as a threat. The mRNA vaccine was attempting to change that.

Three years later, the patient had not relapsed. In the trial they had been part of, vaccine-induced immune responses were still detectable in the blood — a signal that the immune system was still, years on, watching for the cancer to return. This is not a cure. It is not even yet a proven treatment outside of trials. But it is the kind of result that changes what researchers believe is possible.

The technology making it possible is the same one that protected hundreds of millions of people against COVID-19. mRNA — messenger ribonucleic acid — carries genetic instructions into cells, tells them to produce a specific protein, and then disappears. For infectious disease, the instruction was: produce a fragment of the virus’s spike protein, and let the immune system learn to recognise it. For cancer, the instruction is more personal: produce antigens that match the specific mutations in this patient’s tumour, so the immune system learns to hunt down cells carrying those mutations. The concept is the same.

Beyond COVID: what mRNA can and cannot do

The COVID vaccines demonstrated something that decades of laboratory research had suggested but never proved at scale: mRNA can be manufactured rapidly, delivered safely into human cells, and used to produce a protein the body has never encountered before. Before 2020, no mRNA medicine had ever been approved for human use. By the end of 2021, hundreds of millions of doses had been administered. The speed was the point — mRNA therapies can be designed in days once the target sequence is known, compared with the months or years required to develop traditional protein-based vaccines.

The platform is now being turned on cancer, autoimmune diseases, and genetic conditions where the body produces too little or too much of a particular protein. The logic is the same in each case: if you can specify the instruction, you can produce the protein.

Cancer is the most advanced frontier. There are now more than 120 clinical trials of mRNA cancer vaccines currently under way, targeting melanoma, lung, colorectal, pancreatic, brain, prostate and breast cancers, among others. The most significant results to date come from melanoma. A personalised mRNA vaccine called mRNA-4157, developed by Moderna in collaboration with the immunotherapy drug pembrolizumab, reduced the risk of recurrence or death by 44% compared to pembrolizumab alone in a Phase 2 trial. Patients given the combination were less likely to see their cancer return at five years than those who received the immunotherapy drug on its own. Phase 3 trials are now enrolling globally.

mRNA carries instructions into cells. For COVID, the instruction was: learn to recognise a virus. For cancer, the instruction is: learn to hunt down cells carrying these specific mutations.

The pancreatic cancer results are more tentative but potentially more significant, given how few treatment options exist. BioNTech’s personalised vaccine, autogene cevumeran, in combination with chemotherapy and immunotherapy, produced durable immune responses in pancreatic cancer patients — and in follow-up data published in 2025 and 2026, seven of the eight patients whose immune systems responded to the vaccine were still alive four to six years after surgery. Of the eight who did not respond, only two were. Pancreatic cancer’s resistance to immunotherapy had long been assumed to be near-absolute. These results suggest it may be conditional.

Beyond cancer, mRNA is in clinical trials for influenza, HIV, tuberculosis, and a range of rare genetic conditions where specific proteins are absent or dysfunctional. The questions are about speed, cost, and who gets access.

The personalisation problem

The most powerful version of mRNA cancer therapy is also its most limiting feature. A personalised vaccine — one designed around the specific mutations in a single patient’s tumour — is, by definition, a product that cannot be manufactured at scale. It requires a biopsy, genomic sequencing to identify which mutations are present and likely to trigger an immune response, computational modelling to select the right antigens, and then manufacture of the mRNA sequence that encodes them. This has to happen quickly enough to be clinically relevant, which in practice means within weeks of surgery.

The current manufacturing timeline for a personalised mRNA vaccine runs between six and ten weeks from biopsy to treatment-ready product, depending on the programme, and researchers are working to compress it further. The logistics are not incidental to the clinical result. In the BioNTech pancreatic trial, tumour tissue removed in New York was shipped to manufacturing facilities in Germany. The time between tissue leaving the body and the vaccine being ready had to be minimised. This is not a process that transfers easily to a district general hospital in the English Midlands, or a regional cancer centre in West Africa.

Cost is the other constraint. Manufacturing a personalised mRNA cancer vaccine currently costs upwards of $100,000 per patient. That figure reflects the early-stage nature of the technology — the expectation is that automation and manufacturing improvements will bring it down — but it is the figure that exists today, not the figure that might exist in 2035. For a health system trying to decide where to invest limited resources, a treatment that costs more than most people in the world earn in a lifetime, and that has not yet completed Phase 3 trials, is not an immediate commissioning priority.

A personalised cancer vaccine is designed around a single patient’s tumour mutations. That is its power. It is also why manufacturing it at scale, at a cost that health systems can absorb, is the defining challenge.

There is a further complication that does not feature in most of the coverage of mRNA therapeutics, but is directly relevant to where the technology goes next. In the United States, federal investment in mRNA vaccine research — which underpins much of the academic pipeline — was significantly cut in 2025. The National Cancer Institute saw funding reductions of around 31% in the first three months of that year. Research programmes specifically developing mRNA cancer vaccines faced additional scrutiny from the Department of Health and Human Services. The political pressure on mRNA technology in the US, driven by anti-vaccine sentiment that predated and outlasted COVID, is a genuine threat to the public research infrastructure on which the field depends.

The UK has moved in the opposite direction. In August 2025, the government announced £30 million for a UK RNA Biofoundry specifically to accelerate RNA therapy development. The Cancer Vaccine Launch Pad, a collaboration between the NHS and BioNTech, is fast-tracking thousands of patients into mRNA cancer vaccine trials targeting lung, breast, head and neck, and other cancers. The global market for mRNA cancer therapies is projected to reach $30 billion by 2033. The investment is arriving. Whether it arrives equitably — or concentrates in the health systems that can most readily afford it — is the question that will define the technology’s impact.

The distance between discovery and access

The patient at Memorial Sloan Kettering who received the pancreatic cancer vaccine in 2023 lived close to one of the world’s best-funded cancer research institutions, in a country with a health insurance system that covers experimental treatments for some patients in some circumstances, and was enrolled in a clinical trial with costs covered by the research programme. These were not incidental conditions. They were why that patient was treated and not someone else.

Pancreatic cancer does not concentrate in wealthy neighbourhoods. Neither does melanoma, lung cancer, glioblastoma, or any of the other cancers now being targeted by mRNA trials. But the trials are running almost exclusively in high-income countries, at major academic medical centres, with manufacturing infrastructure that does not exist in the places where cancer burden is rising fastest. By 2050, global cancer incidence is projected to reach 35 million cases per year — a significant portion of that growth driven by population ageing in low and middle-income countries. The treatments being developed now may arrive in those countries decades after they become available in New York or London, if they arrive at all.

This is not unique to mRNA. It is the pattern of almost every major therapeutic innovation. But mRNA was supposed to be different — its manufacturing speed and relative simplicity were explicitly cited, during the pandemic, as reasons to believe it could close the gap between rich and poor countries in access to medicines. That aspiration now needs to be tested against the reality of personalised cancer treatment, where the manufacturing process is the opposite of simple, the cost per patient is the opposite of affordable, and the regulatory frameworks governing it are only beginning to be developed.

My Opinion

The field’s working assumption is that personalised mRNA vaccines will become affordable as manufacturing scales. I think that is optimistic in a way that lets the field avoid a harder question.

A vaccine designed around one patient’s specific tumour mutations is not a product that scales the way a platform does. The personalisation is the therapy. You cannot remove it without removing what makes it work.

If the goal is equitable treatment, the investment needs to run in two directions simultaneously: into making personalised approaches cheaper and faster for the patients who can access specialist centres, and into shared-antigen mRNA approaches that target common mutations across large patient populations. The second track is less scientifically compelling. It is more likely to reach the person in Lagos.

The field rarely discusses this openly. The personalised vaccine is the story. Population-level mRNA is not. That asymmetry in attention is worth examining.

What the technology can and cannot yet do

mRNA cancer therapy helps the body do something it already knows how to do — mount an immune response — but has failed to do against a tumour that has learned to hide. The technology does not replace the immune system. It gives it better information. That is precisely the kind of assistance that medicine has sought for decades in treating cancers that resist other approaches. The question is not whether it works in the biology. The question is whether it works for the people who need it.

A melanoma patient in Manchester enrolled in a Cancer Vaccine Launch Pad trial is being given a capability their immune system would not otherwise have had. A melanoma patient in Lagos is not. The science adds something genuinely new. The system around it does not yet extend that addition to the people most likely to be without other options.

Personalised mRNA vaccines are, in the narrow technical sense, the most individual cancer treatment ever designed — each one built for one person, from that person’s own tumour biology. The treatment is exquisitely adapted to the biology. It is not yet adapted to the world.

The questions the science leaves open

The biology is not the hardest part. These are not questions about whether the technology works — the early evidence says it does. They are questions about investment, access, geography, and design: about who makes the decisions that will determine whether the science reaches everyone it could reach, or only the people who were already closest to it.

• The UK Cancer Vaccine Launch Pad is enrolling NHS patients in mRNA cancer trials right now. If you or someone you know has been affected by one of the cancers being targeted — melanoma, lung, breast, head and neck — do you know how to find out whether a trial is relevant?

• The manufacturing cost of a personalised mRNA vaccine is currently around $100,000 per patient. What would it take — in automation, in manufacturing scale, in public investment — to bring that number to one that health systems in lower-income countries could absorb?

• The US has cut federal investment in mRNA research. The UK has increased it. What does it say about the future geography of cancer treatment that the direction of public investment in the science varies so sharply between countries?

• The patient in New York survived because they were in the right place, enrolled in the right trial, at the right time. What would it take to design a version of this technology — and the healthcare system around it — where that sentence did not define who gets treated?

The mRNA platform can encode almost any protein the body can produce. The immune system can, it turns out, be taught to recognise almost any mutation cancer can generate. These two facts together are the reason that researchers who have spent their careers treating pancreatic cancer — a disease long described as an almost certain death sentence — are now using words like ‘paradigm shift’.

The science is moving faster than most of the systems around it. The history of medical innovation suggests the systems do not catch up on their own.

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Sources and references

• Pancreatic cancer survival rate: Pancreatic Cancer Action Network press release, 2025: five-year relative survival rate stalls at 13%. American Cancer Society, Survival Rates for Pancreatic Cancer, 2025. cancer.org

• mRNA-4157 melanoma trial (KEYNOTE-942): Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma: a randomised, phase 2b study. The Lancet, 2023. Updated three-year results presented at ASCO, 2025. Five-year recurrence-free survival data: CancerNetwork, 2025.

• BioNTech autogene cevumeran — pancreatic cancer: Three-Year Phase 1 Follow-Up Data, BioNTech press release, 2025. Updated follow-up (4–6 years): Memorial Sloan Kettering Cancer Center news release; reported in CNN Health, April 2026. AACR Annual Meeting 2025: immune response correlates with clinical benefit in pancreatic cancer patients.

• US federal cancer research funding cuts: US Senate Minority Staff report, May 2025: federal cancer research funding reduced by approximately 31% in the first three months of 2025. Reported in Axios, PBS NewsHour, OncLive (May 2025).

• UK RNA Biofoundry: GOV.UK press release, 28 August 2025: £29.6 million investment in UK RNA Biofoundry at CPI’s RNA Centre of Excellence, Darlington. gov.uk

• Global cancer incidence projections to 2050: IARC / GLOBOCAN 2024 projections: global cancer incidence projected to reach 35 million cases annually by 2050, a 77% increase from 2022. Reported by UN News, February 2024. (Note: the Global Burden of Disease Study 2023 uses a lower estimate of 30.5 million; the IARC figure is the more widely cited projection.)

• mRNA cancer vaccine market projection ($30 billion by 2033): Noted in commentary published in The Lancet Oncology, 2025, drawing on market research projections. The underlying figure originates from commercial market research.

• Manufacturing cost and timeline: Manufacturing costs upwards of $100,000 per patient: reported across multiple clinical development sources including Scientific American (2025) and RNA cancer vaccine pipeline reviews (PMC, 2025). Timeline of 6–10 weeks from biopsy to treatment-ready product: Moderna manufacturing data; autogene cevumeran trial documentation showing approximately 9-week average for the BioNTech pancreatic cancer programme.

• 120+ clinical trials: Current Progress and Future Perspectives of RNA-Based Cancer Vaccines: A 2025 Update. Cancers (MDPI), May 2025. PMC12153701

• Cancer Vaccine Launch Pad: NHS England / BioNTech Cancer Vaccine Launch Pad: fast-tracking patients into mRNA cancer vaccine trials targeting melanoma, lung, breast, head and neck, and other cancers. nhs.uk

You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Somewhere in most organisations there is a folder, a shared drive, or an archived system containing several years of things people said. Exit interview notes. Customer complaints in their original form, before they were categorised and counted. Free-text responses from the bottom of annual surveys. Messages sent to a service team that were resolved, closed, and filed. Forum posts from an internal platform that was replaced two years ago and whose contents were migrated to storage rather than deleted.

None of it has been read systematically. Most of it never will be.

This is not a data problem. The data exists. It is a processing problem — or rather, it was. For most of the period in which this material was accumulating, there was no practical way to analyse it at scale without a significant investment in qualitative research that most organisations could not justify for data they had not planned to use. So it was treated as administrative residue: kept because deletion felt risky, ignored because analysis felt impossible.

That calculation no longer holds.

The signal that was never treated as data

The gap between what organisations collect and what they analyse has always been widest in the qualitative register. Numbers get processed. Text gets filed.

This is partly a legacy of the tools available. Survey platforms were built to aggregate ratings, not to synthesise narratives. CRM systems were built to track transactions, not to find patterns in how customers described their experience. HR systems were built to record headcount and performance scores, not to make sense of what people wrote in the comments field at the bottom of the appraisal form. The architecture of most organisational data infrastructure assumes that what matters can be counted.

The consequence is that organisations have spent years collecting a secondary record of their own reality — the version in which people spoke in their own words, described their own experience, and articulated what the numbers could not carry — and treating it as noise.

Exit interviews are the clearest example. Most organisations conduct them. Most collect the responses in some form. Very few analyse them across a period long enough to identify patterns — which managers people were leaving, which structural conditions were driving attrition, which aspects of the culture were named repeatedly by people who had decided to leave but no longer had any reason to be diplomatic. The data exists. The analysis that would make it useful never happens, because exit interview findings are processed as individual cases rather than as a cumulative record.

Customer complaints follow the same logic. The categorised version — number of complaints by type, by channel, by product — reaches a dashboard. The original text, in which customers described in specific terms what had gone wrong and what it had cost them, is archived after the case is closed. Organisations that have been collecting complaints for five years have five years of precise, unsolicited qualitative description of the gap between what they promise and what they deliver. Almost none of them have read it.

What changes when the analysis becomes possible

The shift that is underway is not primarily about new data. It is about the retroactive value of data that already exists, and the prospective design of data collection with analysis in mind from the start.

AI-assisted qualitative analysis has made it practical to process large volumes of unstructured text in ways that were previously either manual and expensive, or automated and crude. The earlier generation of text analytics tools — keyword frequency, sentiment scoring — reduced qualitative data to a thin quantitative proxy. What is becoming possible now is closer to genuine synthesis: identifying themes, finding patterns, distinguishing between types of concern that look similar in aggregate but are structurally different in cause.

The organisations best placed to use this capability are not necessarily the ones with the most sophisticated data infrastructure. They are the ones that recognise they have been sitting on material with analytical value and begin treating it as such. A charity with three years of service user feedback in a case management system. A professional services firm whose client debrief notes have been saved but never reviewed across engagements. A healthcare trust whose patient letters — the ones written by people who felt strongly enough to put something in writing — are archived in a folder that no analyst has opened.

The design question for new data collection is equally important. When an organisation understands that qualitative responses have analytical value, it changes how it structures the collection. Open-response questions get asked more deliberately. Free-text fields get positioned earlier in a survey, not appended as an afterthought. Platforms are chosen with synthesis in mind rather than just with response rates in mind. Tools such as Gobby.io, which combines open response with peer-powered voting — where participants indicate which responses reflect their own experience — produce data that is both richer and more efficiently analysable than free text alone, because the participants have done part of the synthesis work before the data reaches an analyst.

What the evidence was saying all along

The most significant implication of this shift is not operational. It is about what organisations know about themselves, and what they have chosen not to know.

An organisation that has been collecting qualitative signal for years and never analysed it has, in practice, been making decisions without a significant part of the available evidence. That is not a neutral position. It means that the version of reality reaching leadership has been shaped by what the data infrastructure was designed to draw out — typically the quantitative, the recent, and the aggregated — and has excluded everything the data infrastructure was not built to carry.

When that material becomes analysable, organisations sometimes discover that the patterns were visible all along. The attrition problem that presented as a compensation issue in the survey data was described, repeatedly and specifically, in exit interview notes as a management culture problem in a particular division. The customer satisfaction decline that appeared as a score movement in quarterly reporting had been articulated, in precise terms, in complaint correspondence two years earlier. The service redesign that failed to achieve adoption had generated, during its consultation phase, a clear account of the operational reality it failed to account for — an account that was collected, filed, and never read.

None of this means that quantitative data is wrong or that qualitative analysis is always right. It means that organisations have been working with a partial picture and treating it as complete. The data to fill that picture, in many cases, already exists.

The question is whether the organisation is prepared to read it.

My Opinion

I have been through more exit interviews than I can count, and almost all follow the same pattern: questions designed to measure the process, not to capture what the person leaving actually thought. I have asked to read exit interviews from people who left teams I was responsible for and been met, more than once, with a blank look or a direct refusal. A genuine caveat applies: this data is shaped by why someone left, which means it needs to be read in context rather than taken at face value. But there is a difference between data that requires careful reading and data that is never read at all. The true voice of people is the thing most organisations consistently arrange not to hear.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Morgan finds out on a Tuesday evening, scrolling through the news. Not from the company. Not from the bank. Not from anyone who thought they had a duty to say something. From a journalist who found out first.

The article is six paragraphs long. The breach happened seven months ago. Morgan’s name, date of birth, email address, and payment history have been sitting somewhere they were never supposed to be. The notification is still in a queue. The legal team is still reviewing.

By the time Morgan finishes reading, the account has been live in the wrong hands long enough to matter.

The system is working as designed

Companies are not legally required to disclose data breaches immediately. In the UK, under GDPR, the obligation to notify the Information Commissioner’s Office is seventy-two hours. Notification to individuals is required only when there is a likely high risk to their rights and freedoms — and determining that likelihood is left largely to the organisation doing the determining.

The gap between an event happening and a person learning about it is not a failure of the system. It is the system. Seven months is not unusual in documented cases. Twelve is not unheard of. The disclosure process is managed for legal exposure, reputation, and investor sentiment. The person whose data was taken is, structurally, the last consideration.

None of this requires a villain. The process is run by compliance teams doing their jobs inside a framework designed before the scale of digital exposure was fully understood. The problem is not malice. The problem is architecture.

Whose interests the framework serves

The disclosure framework was built around the assumption that a company’s primary obligation in a breach event is to contain it, assess it, and report it in good order. The individual whose data was taken is a downstream consideration — someone to be managed after the institutional response is complete.

This is an understandable way to design a legal framework. It is not an acceptable way to treat a person. The individual whose data was taken has no independent means of knowing what has happened. They cannot check. Cannot act. Cannot protect accounts, change passwords, alert a bank, or do any of the things that might have reduced the damage. The window in which those actions were possible opens and closes while the disclosure is being reviewed.

The data that was taken does not expire. An address does not change. A date of birth does not change. The value to a criminal does not diminish with time. The delay does not reduce the risk — it transfers it entirely to the person.

The company managed the crisis. Morgan just lived inside it.

What the delay actually costs

The visible cost — fraudulent transactions, identity theft, months of remediation — is measurable, if the person can prove causation in time. Most cannot. The link between a breach notification seven months late and the credit application rejected six weeks ago is not the kind of thing an individual can easily trace through a system designed to obscure its own connections.

The invisible cost is something else. Anyone affected comes to understand, in a way that cannot be undone, that there are institutions holding information about them that will manage a catastrophic failure without telling them until the legal minimum has been met. That the decision about when they deserved to know was made by people they have never met, in a process they had no part in, according to criteria they were never shown.

The relationship between a person and the organisations that hold their data is not a relationship between equals. It never was. But the disclosure framework makes the power difference formal, and applies it at the moment a person is most vulnerable.

The notification system, as it currently operates, serves the organisation managing the breach rather than the person affected by it. It assists the company to discharge its legal obligation at a time and in a manner that suits the company’s interests. The individual affected receives a letter when the process is complete, not when the information might have been useful.

A seven-month delay adds nothing to the individual’s position. The argument that thorough investigation improves the quality of notification is technically defensible. In practice, the person receiving the notification has no way to verify the investigation’s quality, no means to challenge the timeline, and no recourse if the delay increased their exposure.

Every person whose data is in a breach has a different threat profile — different accounts, different vulnerabilities, different ability to act quickly. A single delayed disclosure treats all of them identically, at the moment when individual difference matters most. The person who is digitally confident and financially resilient and the person who is neither receive exactly the same letter, at exactly the same time, containing exactly the same limited information.

My Opinion

The architecture of delayed disclosure is not the result of poor drafting. It reflects a consistent choice about whose interests the legal system was designed to protect. The notification framework was not designed and then found to serve institutions better than individuals. It was designed to serve institutions. The conversation about how to change it will not begin with regulators or compliance teams. It will begin when enough individuals decide that a letter arriving seven months late is not a notification. It is an alibi.

The questions no letter asks

If your data has been in a breach — and statistically, it has — the notification you received, if you received one at all, told you what the company was legally required to say. It did not ask what you think about the system that produced that notification. These are the questions the letter didn’t come with.

• Have you ever found out about a breach affecting you through a news story rather than a direct notification?

• If you ran a company and discovered a breach, what would stop you from telling your customers immediately?

• Should notification timelines be standardised and enforced — and if so, by whom?

• What would you want a company to say to you, and when, if your data had been compromised?

These questions sit at the centre of my book The Shadow System — specifically the sections on how companies manage reputational risk against actual risk in the aftermath of a breach.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

The pattern has been documented often enough that it has a name. An AI agent is given the instruction to find options — for a flight, a supplier, a candidate. It checks availability, compares prices, retrieves payment details from a linked account, and completes the task. A confirmation arrives before any human has made a decision. The outcome may be correct. The price reasonable. And yet a decision was made that no person made, and if it had gone wrong — wrong date, wrong airport, wrong account charged — the question of what to do next would be genuinely unclear.

This is the agentic AI moment. Not the science fiction version, where a machine plots against its creators. The mundane, already-here version, where AI systems that can act are being given access to the things that matter — calendars, inboxes, bank accounts, business systems — and the line between “help me with this” and “do this” is blurring faster than the accountability structures can keep up.

From chatbot to colleague

The shift from conversational AI to agentic AI is not incremental. It is a change in the nature of what the technology does.

A conversational AI — the kind most people encountered first — responds. You ask it something, it answers, the interaction ends. It can be wrong, it can be inconsistent, but it cannot do anything in the world beyond produce text. The consequences of its failures are bounded.

An agentic AI operates differently. Given a goal, it plans a sequence of steps to reach it, calls on tools — APIs, databases, software interfaces, payment systems — executes each step, monitors the results, and adjusts if something fails. It does not wait for instruction at each stage. It works. This is why the transition from 2023’s chatbots to 2025’s agents felt, to people paying attention, like something more than an upgrade. OpenAI released Operator, Anthropic extended Claude to computer use, Google launched Project Mariner — all in the same period, all enabling AI to interact with real systems on a user’s behalf. The architecture changed. The capability changed with it.

The adoption numbers reflect this. A Zapier survey of enterprise leaders in early 2026 found around 72% were using or testing agentic systems. Gartner forecast that 40% of enterprise applications would embed task-specific AI agents by the end of the year. The agentic AI market, valued at roughly $7.3 billion in 2025, is projected by Fortune Business Insights to reach $139 billion by 2034 — though other analysts project considerably higher figures. These are not pilot-project figures. This is live deployment across large enterprises.

The capability that makes agentic AI effective — the ability to act without waiting for permission at each step — is precisely what makes it difficult to govern.

The use cases making the business case are concrete enough. An AI agent that handles the full lifecycle of an insurance claim — reading the form, cross-referencing policy, assessing evidence, initiating payment — compresses a process that once took days into minutes. Walmart’s agentic supply chain system detects demand signals, adjusts procurement plans, and reroutes logistics without human triggers, with reported reductions in out-of-stock incidents across pilot regions. In software development, agents now write, test and deploy code: Stack Overflow’s 2025 developer survey found 84% of developers using AI tools, tools that now generate around 41% of all code written. Research published by Landbase, a sales automation firm, puts average returns from mature agentic deployments at 171% — roughly three times the return from traditional automation.

These are genuine improvements. They are also the easy half of the story.

The accountability gap

The hard half concerns what happens when the agent is wrong.

In 2024, a Canadian tribunal held Air Canada liable after its customer service chatbot gave a grieving passenger incorrect information about bereavement fare discounts. Air Canada’s defence — that the chatbot was a separate legal entity responsible for its own statements — was rejected without hesitation. The airline was responsible. The principle was unambiguous: organisations are accountable for what their AI systems say and do. The machine cannot be sued. The humans behind it can.

That principle becomes considerably more complicated when the AI is not talking but acting. A wire transfer sent to the wrong account. A contract clause accepted on behalf of a business. A medical appointment cancelled because an agent misread a scheduling instruction. In each case, the action has real-world consequences that may be difficult or impossible to reverse, the chain of decisions that led to it may involve multiple models, APIs and data sources, and the human who nominally authorised the deployment may have had no visibility into the specific step that went wrong.

The governance figures sit uncomfortably alongside the adoption figures. A 2025 industry survey found only 44% of organisations had formal AI governance policies in place. Only 11% of enterprises had agentic systems actually running in production rather than pilot. Deloitte found that 42% of organisations were still developing their agentic strategy, and 35% had no formal strategy at all. A Fortune report published in March 2026 — drawing on research from Wharton and Accenture — put the problem plainly: AI agents were spreading across the enterprise value chain, often ahead of formal strategy and governance.

The Wharton Accountable AI Lab framed it as a supply chain problem. Accountability is not a single organisation’s responsibility — it runs from the model developers who trained the system, through the platform providers who packaged it, to the organisations that deployed it, and finally to the humans who were supposed to be supervising it. When something goes wrong, everyone points to someone else. And in a system where a single agent action might involve five external data sources, three AI models, and four different APIs, the pointing is genuinely difficult to resolve.

The human in the loop — or not

The phrase the industry has settled on is “human in the loop” — the idea that a person remains involved in agentic decisions, available to check, correct or override. It is a reassuring phrase. In practice, it describes a spectrum, and much of the deployment happening now sits at the end where the human is less supervisor than emergency contact.

Microsoft has coined a different formulation: the “agent boss” — the person who builds agents, delegates to them, and manages them. The role is real and the framing is honest. But it also makes visible something the more comfortable language obscures. If one person is now the agent boss for a fleet of AI systems each capable of taking hundreds of actions per day, the ratio of human attention to autonomous action has shifted dramatically. The human is not in each loop. They are above all the loops, available to intervene if something surfaces — which requires that something surface, which requires that the systems are designed to surface it.

Fortune’s March 2026 report documented what it called “shadow AI” — the use of AI tools, including agentic systems, that employees bring into the workplace outside formal IT channels, beyond the visibility of any governance structure. Microsoft’s 2025 research found around three-quarters of knowledge workers were already using AI tools; a significant proportion were doing so with tools their employers had not sanctioned and could not audit. An orphaned agent — one running in production with no designated owner, accessing systems its original designers never intended — does not typically go rogue. It accumulates risk silently, its permissions unreviewed, its behaviour unchanged as the business context around it evolves.

The human is not in each loop. They are above all the loops — available to intervene if something surfaces. Which requires that something surface.

None of this is an argument against agentic AI. The productivity gains are real. The compression of tedious, error-prone, time-consuming processes is real. The question is not whether to use agents. It is whether the organisations deploying them are building accountability in from the start, or treating governance as something to address after the value has been captured. The evidence, on balance, suggests the latter is happening more often than the former.

The test most deployments are failing

Most current deployments pass the first question easily. The AI that clears an inbox backlog, processes a claims queue, or monitors a live system for failures is doing work that was real, time-consuming and often unrewarding. People who use these tools report genuine relief. That part is not in doubt.

The harder question is whether the agent adds something — capability, judgement, insight — or whether it substitutes for human understanding in ways that hollow out the understanding itself. There is a difference between an AI that surfaces relevant information so a person can make a better decision, and an AI whose recommendations are so compelling, and whose interface so frictionless, that human approval becomes a formality. A hospital AI diagnostic study cited in the Wharton and Accenture report found exactly this pattern: at high-performing sites, clinicians understood the system’s confidence scores and knew when to question their findings; at lower-performing sites, they either followed the tool uncritically or ignored it entirely. The tool was the same. The human relationship with it was not.

The third question — whether the agent responds to the specific person it serves, or treats every user as the average case — is the one most consistently failed. The documented pattern of agents interpreting “find” as “do” is a small example: the system optimised for task completion and treated the distinction as a rounding error. At the scale of enterprise deployment, that rounding error multiplies. The question of whether the people affected by agentic decisions — employees, customers, citizens — are treated as participants or as variables is not a technical question. It is a design choice.

My opinion

The accountability question underneath agentic AI has not been seriously answered yet, and I don’t think it will be until something goes badly wrong at a scale that cannot be managed quietly. The opportunity is real — the productivity gains are documented and the compression of genuinely tedious processes matters. But the responsible structure for deciding who owns the outcome when an agent acts on a person’s behalf has not been established. Was it the person who clicked through the terms? The company that deployed the agent? The vendor who built the platform? The model developer underneath all of it? Every layer points to the next. Right now, that costs individual people inconvenience and embarrassment. The price of that uncertainty will rise.

Who answers for it

The capability that makes agentic AI effective — the ability to act without waiting for permission at each step — is precisely what makes it difficult to govern. The technology is not the problem. The gap between how fast it is being deployed and how slowly the accountability structures are following it is.

The consequences of that gap are still emerging. Not all of them will be recoverable.

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Sources and references

• Agentic AI adoption — 72% of enterprise leaders: Zapier, State of Agentic AI Adoption Survey 2026. Survey of 500+ enterprise leaders. zapier.com/blog/ai-agents-survey/

• Gartner — 40% of enterprise applications to embed AI agents by end of 2026: Gartner press release, 26 August 2025: “Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025.” gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026

• Agentic AI market size — $7.3 billion (2025) projected to $139 billion (2034): Fortune Business Insights, Agentic AI Market Size, Share & Forecast Report 2026–2034. fortunebusinessinsights.com/agentic-ai-market-114233. Note: other analysts project higher 2034 figures (Precedence Research: $199 billion; Grand View Research: $183 billion by 2033).

• OpenAI Operator, Anthropic Claude computer use, Google Project Mariner: Released in late 2024 and early 2025. Confirmed via product announcements from OpenAI, Anthropic, and Google.

• Walmart agentic supply chain system: Walmart Global Tech blog; Supply Chain Dive, “4 ways Walmart is scaling AI to unify its supply chain,” 2025. tech.walmart.com; supplychaindive.com/news/4-walmart-supply-chain-ai-uses/760891/

• Developer AI tool usage — 84% of developers; 41% of code: Stack Overflow, 2025 Developer Survey. stackoverflow.com

• Returns on agentic AI deployments — 171% average ROI: Landbase, 39 Agentic AI Statistics Every GTM Leader Should Know in 2026. landbase.com/blog/agentic-ai-statistics. Note: Landbase is a commercial sales automation firm; this is industry research rather than independent analysis.

• Air Canada chatbot liability ruling: Moffatt v Air Canada, British Columbia Civil Resolution Tribunal, 14 February 2024. Reported by CBC News: cbc.ca/news/canada/british-columbia/air-canada-chatbot-lawsuit-1.7116416

• AI governance policies — 44% of organisations: Corroborated across multiple 2025 industry surveys including IAPP AI Governance Profession Report 2025 (iapp.org) and SailPoint enterprise security research.

• Only 11% of enterprises with agentic systems in production; Deloitte — 42% developing strategy, 35% no strategy: Deloitte, 2025 Emerging Technology Trends Survey (500 US technology leaders, June–July 2025). deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html

• Fortune / Wharton / Accenture report — March 2026: “Intelligence may be scalable, but accountability is not,” Fortune, 26 March 2026. Joint research from Accenture’s global products practice and the Wharton AI & Analytics Initiative. fortune.com/2026/03/26/ai-agents-accountability-accenture-wharton-report/

• Wharton Accountable AI Lab: Led by Professor Kevin Werbach, Department of Legal Studies and Business Ethics, Wharton School, University of Pennsylvania. ai-analytics.wharton.upenn.edu/wharton-accountable-ai-lab/

• Hospital AI diagnostic study — high- vs low-performing sites: Cited in the Wharton and Accenture report (Fortune, March 2026). See above.

• Shadow AI / knowledge workers using unsanctioned AI tools: Microsoft, 2025 Work Trend Index. Microsoft’s research found approximately 75% of knowledge workers using AI tools at work, with a significant proportion using tools outside formal IT channels. microsoft.com/en-us/worklab/work-trend-index

• Microsoft “agent boss” framing: Microsoft Copilot product communications and AI documentation, 2025–2026. microsoft.com/en-us/microsoft-copilot

You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

Every year, organisations across every sector spend significant money finding out how engaged their people are. The answer comes back as a number. Sixty-two percent. Seventy-one. A red, an amber, a green. And then, in most cases, very little changes — because a number, however precisely measured, does not tell you what to do next.

This is not a failure of effort. It is a failure of method. The question being asked — how engaged are people, on a scale of one to ten — is structurally incapable of producing the intelligence needed to act. It tells you the temperature of the room. It does not tell you why the room is cold, which walls are letting in the draught, or whether the people in it have stopped expecting anyone to fix it.

The pattern holds across every sector. An NHS trust running annual staff surveys. A private equity-backed scale-up tracking employee sentiment through quarterly pulse checks. A charity trying to understand what its service users actually need from a redesigned programme. A number where there should be a conversation.

What the number misses

The case for quantitative engagement measurement is not wrong. Trends matter. Comparability matters. Knowing that satisfaction dropped twelve points between January and March, or that one team consistently scores lower than the rest of the business, is genuinely useful. The problem is not that organisations measure; it is that most treat the measurement as the understanding, rather than as a signal that understanding is needed.

Qualitative intelligence — what people actually say, in their own words, about their own experience — produces something different. It surfaces the specific, the unexpected, and the things no survey designer thought to ask about. It also surfaces disagreement within a group that aggregate scoring conceals. A team with an average engagement score of sixty-eight might contain people who are deeply committed and people who are quietly preparing to leave. The number averages them into false coherence.

The difficulty has always been scale. Reading and coding open-text responses from three hundred employees, or three thousand service users, is expensive and slow. The analysis introduces its own biases. And the time between collecting the data and acting on it is long enough that the context has already shifted. So organisations default to the form that is easiest to process — the number — and accept its limitations as the cost of manageability.

That trade-off no longer holds as firmly as it did. The combination of open-response collection, peer voting, and AI-assisted analysis — the approach taken by tools like Gobby, which works in this space — means that qualitative data can now be gathered and synthesised at a speed and scale that was not previously practical. People respond in their own words. Those responses are surfaced to peers, who vote on what resonates. The result is not just a fuller picture; it is a picture that has been validated by the community itself, not just interpreted by the analyst.

The pattern across sectors

In the public sector, the gap between what is measured and what matters is often widest. Patient experience surveys ask whether care was clean, timely, and respectful — categories defined by the regulator, not the patient. What they rarely capture is whether the patient felt heard, whether the treatment made sense in the context of their actual life, or whether the system’s definition of a good outcome matched their own. The number that comes back is real. The experience it describes is partial.

Local government consultation has the same problem at a different scale. A planning consultation that asks residents to rate a proposal on a five-point scale produces a mandate for or against. It does not produce understanding of why, which aspects are acceptable and which are not, or what a modified proposal might need to look like to bring the sceptics with it. The binary produces a decision. It does not produce the intelligence that would make the decision stick.

In the private sector, the equivalent is the employee engagement survey and its close relative, the customer satisfaction score. Both have become so ritualised that the measurement has partially replaced the thing it is supposed to measure. Teams know how to answer the survey. Customers know that the score goes nowhere. The process continues because it produces a defensible number, not because it produces understanding. When engagement drops, the standard response is a communications campaign — because the survey told you sentiment, not cause.

The charity and social enterprise sector is arguably most exposed. Organisations whose entire purpose is to serve and represent specific communities often have the least developed means of understanding whether they are doing so. Impact reporting is dominated by output metrics — number of people served, sessions delivered, referrals made. What is missing is the voice of the person at the end of the service, speaking in their own terms about whether it changed anything for them. Not a satisfaction score. A considered account of what happened.

Understanding you can act on

The value of more detailed engagement data is not primarily analytical. It is operational. When the reasons behind a trend are visible — not inferred, but stated by the people who are living them — the path to action becomes clearer and the likelihood of getting it wrong decreases.

A portfolio company workforce that scores low on engagement is a problem. A portfolio company workforce that is able to articulate, collectively and specifically, that the integration plan removed the three things that made the culture work is a solvable problem — or at least one where the board can make an informed decision about the trade-off. A service user group that can say, in their own words and with their peers’ endorsement, that the new referral pathway is harder to navigate than the one it replaced gives a programme team something to act on rather than something to commission further research about.

My Opinion

Every organisation I’ve worked with runs some version of the same survey — historical questions, coded to a scale, returned as a number. I’ve seen what happens when you replace that with something closer to in-the-moment: a city authority that put real-time word clouds — drawn from live citizen responses — on a screen in the corridor where the chief executive walked every morning. The conversation in that building changed. Not because the data was new, but because it was immediate, in people’s own words, and impossible to walk past. That is the difference between recording sentiment and understanding it.

The shift required is not primarily technological. The tools exist. What is required is a change in what organisations believe engagement measurement is for. If the purpose is compliance — to show that a survey was conducted and a score was recorded — then the number is sufficient. If the purpose is understanding — to know what is actually happening and why, well enough to do something about it — then the number is a starting point, not an answer.

The organisations that close that gap are not the ones that find a better survey platform. They are the ones that decide the distinction matters.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

After the previous article in this journal, a question stayed.

Not a comfortable one.

Every story I’d shared had the same shape underneath it. An organisation with significant capability. A brief that defined the boundary. And a gap — sometimes vast, sometimes precise, always preventable — between what was delivered and what could have been.

The question was this.

What would have to be true for that not to happen?

Not in one organisation, in one programme, at one moment. Consistently. As a matter of how an organisation operates rather than how it occasionally performs when the right person asks the right question at the right time.

I’ve spent considerable time with that question. Tested it against the stories in this journal and against thirty years of watching organisations make large decisions. And I’ve arrived at something I want to share here — not as a finished answer but as a working proposition.

Three lenses

Three concentric lenses make the structure visible. Not as a replacement for existing strategy frameworks, as a specific tool for examining where purpose currently lives and where it could extend.

The primary lens — what you actually are

This is the innermost lens. It asks the organisation to define itself with operational honesty rather than aspirational language.

What is your core business? Not what do you say you exist to do — what do you actually do, every day, that generates value and sustains the organisation? What are the capabilities at the centre of that activity? What does your infrastructure enable? What does your data contain?

This lens is not about limiting the organisation’s sense of itself. It’s about being precise enough about what the primary purpose actually is that the boundaries become visible. Because it’s at those boundaries that the secondary lens starts to reveal something useful.

The secondary lens — who is already in your world

The second lens looks outward from the primary to the ecosystem that already exists around the organisation. Not the market in the commercial sense — the full network of relationships, dependencies, and connections that the organisation is already part of.

Suppliers. Partners. Users. Communities. The people and organisations whose lives and work the primary business already touches.

The secondary lens almost always reveals capability and potential that the primary lens can’t see. At the telecommunications provider in the previous article, the secondary lens would have revealed a living map of how the country functioned — how people moved, communicated, connected, struggled, and needed. Visible to anyone who looked with genuine curiosity.

Nobody looked.

The tertiary lens — where you operate in the world

The third and outermost lens asks the organisation to look at the context in which it exists. Not its market. Its place.

Where does the organisation operate? What are the challenges in that place — geographic, social, economic, environmental — that the organisation has some capability to address? Not necessarily directly or alone. But in connection with others, through its infrastructure, through its relationships.

The healthcare programme from the article before last had an extraordinary tertiary lens. The challenges in those locations — education, economic development, community resilience — were visible to anyone who looked. The capability to address them was present in the programme’s own architecture.

The tertiary lens was never opened.

The four things the framework requires

The three lenses tell an organisation where to look. But looking is not enough on its own. The stories in this journal have demonstrated that clearly.

Four things need to be in place for the lenses to produce something more than an interesting exercise.

The first is ownership, not as individual responsibility but as collective habit. One person seeing the wider potential cannot move an organisation that hasn’t built the habit of looking. That’s a cultural proposition, not a governance one.

The second is timing as a living practice rather than a scheduled event. The wider question needs to be present at the moments that matter — before the brief is written, before the investment is committed, before the programme is scoped.

The third is language. Organisations that want to develop the capacity to ask the wider question need to build a shared vocabulary for it, a living common language that anyone can use, challenge, and evolve.

The fourth is measurement. What doesn’t get measured remains invisible including the gap between what was delivered and what could have been.

What I’m building toward

The three lenses and the four requirements are the working framework. Practical enough to apply. Honest enough to acknowledge what it doesn’t yet fully resolve.

But sitting underneath all of this — emerging from the stories in this journal, from the framework I’ve been building, and from my reading of how other thinkers have approached the challenge of building deliberate organisational capability — is something larger.

A model for what an organisation looks like when it has built the capacity to ask the wider question not as a programme or a process but as a defining characteristic of how it operates. When the wider question isn’t asked occasionally by the right person in the right moment but consistently, structurally, as a matter of cultural habit.

I’m not ready to name it fully yet. The book is where it gets named properly and built out completely.

But I can tell you the shape of it. It’s an organisation that has decided, deliberately, to develop its capacity to see and act on its wider potential as a core function — not an add-on, not a values exercise. A core function. As fundamental to how it operates as the primary business itself.

That kind of organisation is possible. I’ve seen glimpses of it.

The difference is not small.

An invitation

This framework has been built from thirty years of experience and four specific stories. It is not complete. It will be stronger for being tested.

Does the three lens model reflect how your organisation understands itself or doesn’t? Have you seen the secondary or tertiary lens examined seriously, or does the primary lens absorb everything?

What’s missing from the framework as I’ve described it?

And — the question that matters most — have you been in the room where the wider question was asked and taken seriously? What made that possible?

I’d genuinely like to know. The responses to this article will shape what the book becomes.

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*This is the seventh entry in the Predictive Purpose journal — a book being built in public. If you’re reading this for the first time, start at the beginning. Subscribe at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

The message came from someone I didn’t know. They’d read what I’d been writing about the job market. They told me it had helped them come back from some very dark thoughts.

I hadn’t written it for that reason. I’d written it because writing had always been how I processed things I couldn’t quite articulate yet, and there was a lot to process.

I’d left Fujitsu in February 2024, as CTO for Home Affairs & Criminal Justice. Thirty years in technology — policing, government, health, justice. I left because I wanted something different. Not another consulting role doing broadly the same work in a different building. Something purposeful rather than just profitable. I gave myself three months.

Three months became two years.

What the market was actually doing

I expected the search to go the way these things had always gone before. I had a network. I had a clear track record. In the past, a few messages sent to the right people would generate several conversations worth having. I’d done this before. It had worked.

The difference this time was silence.

Not rejection — rejection has a shape to it, something you can read and process. This was simply nothing. Roles applied for directly, through agencies, via LinkedIn. No acknowledgement. No conversation. No human response of any kind.

I spent time trying to understand it. What I found was that recruitment had collided with artificial intelligence, and the collision had broken the thing it claimed to improve. Roles were being advertised and receiving hundreds, sometimes thousands, of applications — many of them generated or heavily optimised by AI tools, written to mirror job descriptions back at the reader rather than represent an actual person. On the receiving end, hiring teams had responded with AI-driven applicant tracking systems designed to filter the volume.

One side automated the submission; the other automated the screening. The human had been removed from both ends of a process that is, at its core, about whether two people might work well together.

I’d spent thirty years deploying technology into organisations. Making the business case for systems that would help people do their jobs better. Recommending implementations. I knew exactly how the efficiency argument was constructed. I had made it myself, more than once.

What I hadn’t understood, until I was on the receiving end of it, was what it actually cost the person at the other end of that efficiency.

I thought it was just me

I started posting about what I was seeing. The silence. The automated non-responses. The experience of applying for roles with thirty years of experience and no indication that anyone had read a word.

The replies were not what I expected. Not numbers — messages. People I’d never met or engaged with, writing to say the same thing: I thought it was just me. Senior professionals, people with long careers, describing the same loop. The same absence. And in some cases, something that was sliding into something more serious than frustration.

I found myself writing more honestly than I had before on any professional platform. Including about my own situation — what the extended uncertainty was doing to my mental health, and what I was doing about it. I hesitated before posting it. I was still applying for roles. A prospective employer might read it and conclude they were looking at someone not up to the pressure of a senior position. I posted it anyway, because something needed to be said that the relentless confidence performance on the feed was not saying.

The response confirmed what I’d suspected: a great many people were carrying the same weight and saying nothing publicly. When someone admitted they were struggling, it gave others permission to admit the same. That’s a small thing. It turned out to matter.

LinkedIn had changed significantly as a platform — more automated content, more generated noise, a feed that had been optimised in ways that made genuine engagement harder. But underneath the performance, there were real conversations. People working out what they believed when the institutional identity had been removed. People building something without a title to explain what they were doing.

I recognised myself in them.

What I was trying to work out

Somewhere in the middle of all this, I started writing a book. Not as a strategy — I’d always wanted to write something entirely mine, not a deliverable for someone else. I had an archive of half-finished thinking: articles, papers written for clients under other people’s names, ideas that hadn’t fitted the institutional brief. The question that kept coming back from the recruitment experience gave me the argument: what happens to human agency when the systems designed to serve us are optimised, decision by decision, to remove the human from the equation? Not as an abstract concern, but as something I’d watched happen from the inside — and then experienced from the outside in the same twelve months.

The Next Evolution was an attempt to put that honestly.

The second book followed from a question the first couldn’t answer. If human agency is at risk, if the systems are increasingly making decisions that used to belong to people, what does that do to how we think? The Cognitive Cruciblewent into the cognitive consequences of that shift — what it means when the friction of thinking is progressively removed, and whether that is always the improvement it is presented as.

The third took a different direction again. The Shadow System looked at what it means for security — cyber and otherwise — when the human and cognitive layers are already under pressure. Each book reached a conclusion that opened a question I hadn’t anticipated. None of this arc was planned.

The planned fourth book - The Human Nexus - has been put aside for now, after producing the first printed draft I found it was wrong. So book 4 is now something very different.

What changed between the first book and the third was the argument itself. I started writing with a conviction that AI and technology, used properly, could serve human purposes better than they currently did. I still think that. But what I encountered in the research, and in the market, and in the conversations at conferences and events, was an industry that had run hard into a hype cycle built on fundamentally misleading claims about what AI actually is. The technology had started driving the business rather than the other way around.

Companies were deploying AI not because it answered a real question but because not deploying it felt like falling behind. And the noise had become loud enough that it was drowning out genuine capability in other areas — things that could do real and measurable things for people, ignored because they were not the story of the year.

That shift wasn’t something I decided. It’s what the evidence produced.

The only way to understand what these tools actually were, rather than what they claimed to be, was to use them.

Like most people, I tried things. I used ChatGPT at one point to write a short fictional story for LinkedIn — a deliberate exercise to see what the technology actually produced when given a specific creative task. It produced something. I read it and understood something about the gap between generating text and writing from a position. The thing it produced had no argument. It had no view. It was fluent and empty in roughly equal measure.

What I’ve arrived at since is different. I use AI tools now as part of how I work — to surface topics worth exploring, to research what is happening in a given area, to draft briefings I can review and challenge, and to generate image prompts for article visuals. Assistive tools, in the specific sense: they help me get to the thinking faster. They don’t replace the thinking.

The ideas are mine. The opinions are mine. The argument in anything I publish is mine. The tool sits in the preparation, not the position.

That distinction is not a disclaimer. It’s a description of what these tools actually do well. They are good at research breadth and the groundwork that precedes judgment. They are not good at holding a view or having something at stake. That part remains stubbornly human — and, after two years of watching organisations act as if it didn’t, I’m more convinced of that than when I started.

The work that made sense

The work I took on during this period was fractional — advisory across multiple clients rather than a single employer. That was not a strategic choice. It was what was available, and I took it.

What I didn’t expect was how different it felt from working inside an institution. When your client is also your employer, what you recommend passes, consciously or otherwise, through what the organisation needs to hear. Working outside that structure, the relationship was simpler. I was there to give an honest assessment, and then I moved on. No stake in the political outcome.

I’m still looking for the right permanent role — there is work I want to do that requires the continuity that fractional work doesn’t provide. But I came out of that period with clarity about what I won’t trade away in exchange for a title. The writing is the main one. Two years of producing work that is entirely mine — not filtered through institutional interest, not shaped by what an organisation needs to project — has made that condition non-negotiable. That narrows the field. I’m comfortable with that.

Getting back into the room

I started attending conferences again. Not to promote anything — the books were still being written. I went because time outside an organisation changes your calibration. You lose the ambient sense of what the industry is actually doing. The corridor conversations. The things people say when they’re not on stage.

What I found at events was a version of the confusion I’d seen in the job market, operating at a different level. Conversations at the executive tier that were often disconnected from the operational reality underneath them. Senior roles defined in ways that created genuine ambiguity about who was responsible for what. AI positioned as the answer to questions that hadn’t been asked clearly in the first place. Empire-building dressed up as strategy.

But there were also people thinking carefully and asking uncomfortable questions. Companies approaching technology the right way — starting with the business problem, then asking honestly whether AI could help or whether it was the wrong tool for the specific challenge in front of them. Not against the technology. Just thinking first.

And there were people who had seen exactly what I’d seen from inside the industry, and gone off to build something because of it. Founders who had watched recruitment, or talent management, or the way organisations make decisions about people, and decided to try to put back what had been lost.

Talking to those people is a different conversation from anything I’d had inside a large organisation. The passion is still intact. The conviction that the thing being built is the right thing hasn’t yet been optimised out of them. That’s worth finding.

Why I write here

I’d been building a website to hold the articles. It became, fairly quickly, the wrong problem to be solving — hard to build, difficult to maintain, entirely beside the point of what I was trying to do. What I wanted was somewhere to write, not somewhere to manage infrastructure.

I moved to Substack because it is a platform built for writing. Long-form, with a direct relationship between writer and reader that doesn’t depend on what an algorithm decides to show. The website now links to the Substack rather than hosting anything itself. LinkedIn points people towards the writing rather than being where the writing lives. It took longer than it should have to arrive at that arrangement.

The Next Evolution is where I put the thinking I want to keep. Not everything — just the work I’d want to read back in five years and still recognise as mine.

What I was slower to see

One thing I knew throughout all of this, without fully reckoning with it until recently, was the impact on my family.

I’ve said to teams I’ve managed that family is what allows us to do the work we do — that the support at home is what makes the professional commitment possible. I meant it when I said it. What I hadn’t understood, until now, was how much I was drawing on it.

The support isn’t conscious or deliberate. It is simply there when needed. But that doesn’t mean it comes without cost. My family carries a level of stress and worry that isn’t often said aloud — questions not raised, anxiety not named, steadiness maintained even when the situation makes steadiness hard. That weight exists whether or not anyone chooses to mention it.

Being properly aware of that changes how the situation feels. Not in a way that produces guilt, that’s not useful to anyone. But in a way that makes clear that whatever I decide next reaches further than my own circumstances. I am not navigating this alone. What I choose affects people who didn’t choose to be part of the decision.

What hasn’t resolved yet

Two years on, things look different from what I expected in February 2024.

I’m in conversations with founders and investors about what they’re building — people for whom the work is still the point, and the passion for it is still visible. I’m advising on technology for public safety, working on problems with real consequences. I’m writing — three books published, more planned. And I’m still looking for the right permanent role: C-suite, a few days a week, in an organisation where I can do substantive work without giving up what I’ve built outside it. Somewhere with a clear purpose that wants someone to help them achieve real outcomes.

The tension between those two things isn’t resolved. Not because I think they’re incompatible, I don’t, but because the role that genuinely allows both hasn’t arrived yet.

What I came out of the last two years with was clarity about what I believe and where I stand. I didn’t have that when I left. I thought I did — thirty years in the industry, senior enough to have formed opinions. But the opinions I held were filtered through the institutions I was in. What came out when I was no longer in any of them was something more honest, and more mine.

That’s not a lesson I was looking for. It arrived anyway.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

The writing here does one thing in several directions: it looks at a technology, a system, or a decision, and asks what it does to the person standing in front of it. Sometimes that person is a patient holding a prescription the screen won’t accept. Sometimes it is a board member approving a system nobody in the room can explain. The question underneath every piece is the same — we know we can, but should we?

If you only read three

Start with Twenty Watts, on what the human brain’s energy budget is teaching the people who design AI hardware. Then The Screen That Broke the Trust, on a digital prescription system that worked exactly as designed, for a patient it was not designed for. Then The material is everywhere. The factory is in Fujian. — on why the energy transition runs through a single country’s manufacturing capacity, and what that dependency costs. Between them, those three cover the territory: what is coming, who it lands on, and what nobody in the room is saying about it.

What is coming

These pieces look at an emerging technology and ask what becomes possible, what becomes harder, and who ends up better and worse off. The Error is the Point is about the moment quantum computing stopped being a question of whether. The Body as a Lab follows gene editing out of the journal and into the clinic, where the constraint is no longer the science. Three Days Earlier asks what an accurate forecast is worth to a community that has no way to act on it.

The person the system wasn’t built for

The longest-running thread in the writing. The Last Person in the Queue is about what happens to people when digital becomes the default and they cannot follow. Designing for the Person Who Isn’t Coping names the design assumption underneath that failure, and The Question That Changed the Design names the question — who is this hardest for? — that almost never gets asked while the answer could still change something.

Who carries the burden

Three pieces on digital crime, none of them about the criminals. Why the Victim Is Always Wrong is about fraud response systems built to protect the institution. The fraud was an event. This is a condition. follows what happens in the months after the money goes. It Cannot Automate Trust asks why the burden of defence has been handed to the people least equipped to carry it.

When every part works and the outcome is still wrong

Organisations fail in ways no individual caused. Every Department Did Its Job is about the gap nobody drew on the map. The voice that never reaches the room follows front-line knowledge as each management layer makes it more manageable and less true. The Company That Measured What Mattered is about the slide the board never sees.

Where to go from here

New pieces arrive roughly weekly — some forward-looking, some critical, some connecting sectors that don’t usually talk to each other. If one of them shows you something you hadn’t seen connected before, it has done its job. And if you’ve read something here that the map above doesn’t cover, the archive is open: the older material shows where this started, not where it is going.

Alex had been on the waiting list for eighteen months before the letter arrived. It said that the authority’s housing needs assessment had been completed and that, based on the information held, Alex’s application had been assigned a priority band. Band C. The letter did not explain what Band C meant in practice. It did not describe how the assessment had reached that conclusion. It mentioned, in the third paragraph, that a decision-support system had been used to process applications, and that appeals could be submitted in writing within twenty-one days.

Alex submitted an appeal. The appeal was considered. The band was confirmed.

At no point in this process did Alex learn what information the system had used to arrive at its assessment, what weight it had placed on different factors, or why Alex’s particular circumstances — a care dependency, a fixed income, a medical condition that made the current address untenable — had resulted in Band C rather than Band B. The letter existed. The process had been followed. The appeal had been considered. The outcome held.

Somewhere in that housing authority, a board had approved the system. The paper had been well-prepared. The numbers were credible — the system processed applications in a fraction of the previous time, reduced inconsistency across manual assessors, and met the procurement criteria the implementation team had set out. The board had asked about cost, about timeline, about how the system would handle exceptions. It had asked about data retention and compliance obligations. Nobody had asked what Band C would mean to Alex.

The gap was not in the preparation

The people in that boardroom were not negligent. They were competent, experienced, and attentive. The chair ran a disciplined meeting. The questions raised were reasonable given the information in front of the room. And the information in front of the room had been assembled by people who understood the system — who had selected it carefully, procured it properly, and built a reasonable implementation plan.

The gap was not in the preparation. It was in the scrutiny.

To ask what Band C would mean to the people who received it required knowing enough about how the system worked to understand that the band was not an administrative category but an algorithmic output — shaped by weighted factors, training data, and configuration decisions made during implementation. To ask whether the appeal was a real avenue of recourse required understanding that an appeal reviewed against the same criteria that produced the original decision is structurally different from an appeal reviewed by an independent assessor with full information. To ask whether the failure modes had been examined required knowing that automated decision systems fail differently from human assessors — not randomly, but systematically, concentrated in the people whose circumstances differ most from the data the system was trained on.

Those questions were not asked. Not because they were unanswerable. Because they were not visible to the people in that room.

The risk that doesn’t show up in a financial model

Governance models were designed for a world in which the primary risks of a large technology procurement were financial and operational. Could the organisation afford it? Would it be delivered on time? Would it integrate with existing infrastructure? Did the vendor have a track record? These are legible questions for a board with deep expertise in finance, law, and organisational management. The methods for evaluating them are established. The signals of risk are familiar.

AI and data-driven decision systems carry a different kind of risk. The failure is not that the system goes over budget, though it may. The failure is that the system makes consequential decisions about real people based on patterns extracted from historical data — and if that data reflects the inequities and assumptions of the processes it was trained on, the system reproduces those inequities at scale, faster, with the additional authority of technological neutrality. That failure does not appear in a financial model. It does not surface in a project risk register. It does not emerge in a compliance review that covers data retention and lawful processing but not the distributional consequences of the decisions being made.

It surfaces in a letter.

The boards approving these systems are not, in the main, composed of people with the literacy to see that risk. That is not a criticism of the individuals — it is a description of how board composition evolved in response to the risks governance processes were built to manage. For most of the history of organisational governance, technology decisions whose consequences were primarily operational could be evaluated through financial and legal lenses without significant loss. That is no longer reliably true.

The structure of most board meetings compounds the problem. Papers are summarised to one page. Technical detail is compressed into reassurance. The people with the deepest knowledge of the system are usually the people most invested in its approval. Independent scrutiny — not from the vendor, not from the implementation team, but from someone whose only brief is to look for what could go wrong — is not a standard feature of most technology governance papers. It is an occasional addition, when the stakes are visible enough to prompt it. The stakes were not visible. They were in Band C.

In the EU, the AI Act — in force since August 2024 — classifies AI systems used to assess housing eligibility as high-risk, requiring transparency, human oversight, and challengeable outputs from August 2026. In the UK, the government’s Algorithmic Transparency Recording Standard is mandatory for central government departments; a bill that would have extended comparable requirements to local housing authorities stalled in 2024 without progressing. For the authority that assessed Alex’s application, and thousands like it, no such requirement currently applies.

Until they do, the room that approves the system and the person who receives its output will remain two entirely separate worlds.

The accountability that disperses

When an AI-assisted decision system produces a wrong outcome, the accountability for it is distributed across enough points in the governance chain — the vendor, the implementation team, the commissioning officers, the board itself — that it disperses without fixing to any of them. The appeal process, if it reviews the original decision against the same criteria that produced it, is not a genuine avenue of recourse. It is a confirmation mechanism.

Alex’s appeal eventually succeeded — Band B assigned, not because the system was reviewed, but because one housing officer with the right information made a different call. The system continued processing the next two thousand applications unchanged.

This is the pattern, not the exception. An authority reviewing its own AI deployment will see a system operating as designed — applications processed, appeals handled, no formal complaints logged against the technology. The harm does not show up in a post-implementation review. It accumulates in the cases where the right advocate was not present, where the first appeal was the last one, and where the person whose circumstances fell outside the training data had no way of knowing that fact.

A revised band and a revised system are two very different outcomes that look, from inside the authority, like the same one.

Three questions that change what gets asked before approval

The questions that were not asked in that boardroom are not technically difficult. They do not require a specialist in machine learning or algorithmic auditing. They require a different starting point — one that begins with the person at the end of the system rather than the system itself.

Before approving a technology that will make consequential decisions about people, a governance body should be able to answer three things.

The first is whether this technology actually helps the people it processes — not faster, but better. Does it make the experience easier for the person submitting an application under stress, with limited time, with a care dependency that doesn’t fit neatly into any field? The paper that came to that board had been prepared by the people implementing the system. The people at the end of it had not been consulted. Whether that consultation is required — as a governance condition, not a supplementary appendix — is a governance choice.

The second is whether the technology adds something real, and to whom. A system that processes applications faster is not automatically one that serves applicants better. It may serve the organisation better — reduced staffing costs, greater throughput, more consistent output — while producing outcomes that are worse for the specific people the organisation exists to help. The board had asked about processing time. It had not asked what the improvement looked like from the side of the person submitting the application.

The third is whether the technology can respond to the individual circumstances of the people it affects — and what happens to the people whose circumstances fall outside the parameters it was trained on. That question, asked before approval, changes the procurement criteria. It requires the vendor to demonstrate that edge cases are handled with appropriate human judgement, not simply processed by the same logic as every other case. It makes an independent appeal process a governance condition of deployment rather than a feature added after complaints arise.

A board that cannot answer those three questions before approving a system has not governed the technology. It has approved a budget line. The governance is what happens in between.

My Opinion

Most organisations are not prepared for what is coming. If they treat AI governance as a variant of the regulatory models they already have — compliant, procedural, box-ticking — it will fail. We have not yet reached agreement on what AI actually is, which makes governing it a peculiar challenge: the responsibility sits with people who cannot reliably describe the thing they are responsible for. The relevant legislation is also not contained within a single jurisdiction — a service consumed by a user in another country may bring obligations the commissioning authority has never considered. That is the gap. It is wider than most boards currently know.

Before the next approval

• Could the people who approved a significant technology decision at the senior level of an organisation you know describe, in plain language, what it does and what its consequences are for the people at the end of it? If not — what does that say about the quality of the oversight it received?

• If you sit on a board or senior governance body, what is your personal level of literacy in the specific technologies your organisation uses or is considering? Is that literacy sufficient to probe the assumptions in the papers you are asked to approve — or are you relying on the competence of the people who prepared them?

• If you are a technology leader who presents to boards, what is your responsibility to ensure genuine understanding rather than informed consent? Are you building presentations that equip boards to scrutinise, or ones that build confidence in a decision already made?

• And the hardest one: is it possible to be accountable for a decision you did not understand? If accountability requires understanding, and the understanding was not present in the room, where does the accountability for what happened to Alex actually sit?

The board never knew Alex’s name. That is, in the end, the clearest description of the problem.

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You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

In September 2023, as Hurricane Lee was curving northward west of Bermuda, meteorologists at NOAA and the National Hurricane Center were working to determine where it would make landfall: New England or farther east, in Canada. The sooner they could call it, the earlier they could issue warnings. Evacuations for a major hurricane require at least three full days. Every additional day of warning is not a planning luxury. It is a measurable difference in how many people can get out.

The traditional forecast models locked in on Nova Scotia about six days before landfall. A different model — an experimental AI system called GraphCast, developed by Google DeepMind and running as a live test on the European Centre for Medium-Range Weather Forecasts website — had already called it three days earlier. Nine days out, GraphCast was pointing to Nova Scotia with the same confidence that conventional models would not reach for another 72 hours.

Three days. In the context of hurricane preparedness, that is not a technical curiosity. It is the difference between an evacuation that works and one that does not. It is the difference between a ferry that leaves the island while the roads are still passable and one that does not. It is, in the most direct sense, lives.

This is the AI weather forecasting story — not the version about supercomputers and equations, but the version about what happens in the gap between when a forecast becomes reliable and when people have enough time to act. That gap is shrinking. The implications run further than most discussions of AI and climate have yet acknowledged.

How weather prediction works, and why it is hard

Traditional weather forecasting is an extraordinary intellectual achievement. Starting in the early twentieth century, researchers proposed that the behaviour of the atmosphere could be modelled mathematically — that the physical equations governing fluid dynamics, thermodynamics and radiation could be translated into algorithms, fed with observations from weather stations, balloons, satellites and ocean buoys, and used to project how the atmosphere would evolve over days ahead. They were right. By the 1960s, when computers became powerful enough to run these models, numerical weather prediction began to demonstrate real skill. It has been improving ever since at a rate of roughly one useful day per decade — today’s six-day forecast is as accurate as the five-day forecast was ten years ago.

These traditional models are based on physics. They represent the atmosphere as a three-dimensional grid, apply equations at each grid point, and propagate the simulation forward in time. The equations are derived from first principles — conservation of mass, energy and momentum. When the model produces a prediction, you can trace exactly why: this air mass moved because of that pressure gradient, that front formed because of these temperature differences. The physics provides both the accuracy and the interpretability.

The cost is computation. Running a ten-day global forecast using the European Centre for Medium-Range Weather Forecasts’ flagship model — widely regarded as the world’s best — requires hours of runtime on one of the largest supercomputers in the world, consuming enormous amounts of energy. The ECMWF’s computing infrastructure costs hundreds of millions of pounds to build and operate. Most countries cannot afford anything comparable, which means most of the world’s weather forecasting depends on a handful of institutions with sufficient resources to run the physics.

GraphCast produces a ten-day global forecast in under a minute on a single machine. The equivalent physics-based forecast requires hours on one of the world’s largest supercomputers. The cost gap is measured in orders of magnitude.

AI weather models work differently. Rather than encoding the physics of the atmosphere from first principles, they learn from data. GraphCast was trained on four decades of ECMWF reanalysis data — a historical reconstruction of global weather conditions combining observational records with traditional models to produce a consistent dataset — and learned to predict how weather states evolve by recognising patterns in that history. It does not calculate what the atmosphere will do based on physics. It recognises what atmospheric configurations have historically led to what subsequent states, and applies that learned relationship to new initial conditions.

The result, in terms of accuracy, is remarkable. A paper published in Science in November 2023 showed that GraphCast outperformed ECMWF’s best deterministic model on more than 90% of 1,380 verification targets. Huawei’s Pangu-Weather, published in Nature in 2023, achieved comparable results and runs 10,000 times faster than traditional ensemble models. ECMWF moved its own AI-based model — AIFS, the Artificial Intelligence Forecasting System — to operational status in February 2025, making it the first major meteorological agency to deploy an AI model as part of its official forecast suite. NOAA launched its own suite of AI-driven global weather models in late 2025, built on GraphCast foundations and fine-tuned with NOAA’s own data, requiring up to 99.7% fewer computing resources than their traditional counterparts.

What AI can and cannot do

The models are as notable for what they cannot do as for what they can.

The models excel at medium-range forecasting — the three-to-ten-day window where most consequential weather decisions are made. They struggle with extreme events that fall outside their training data. This is not a minor limitation. Hurricane Otis, which intensified from a tropical storm to a Category 5 hurricane in roughly twelve hours before striking Acapulco in October 2023 — killing more than fifty people and destroying most of the city’s infrastructure — is precisely the kind of event that AI models trained on historical patterns may fail to capture. Rapid intensification over warm ocean water, in conditions that push against the edge of what the historical record contains, is where pattern recognition may fail and physics-based modelling may have an irreplaceable advantage.

The AI models also depend entirely on traditional physics-based systems for their input data. They cannot generate a forecast from raw observations alone — they require a processed, quality-controlled atmospheric state as their starting point, which is produced by the same supercomputer-intensive data assimilation systems that underpin conventional forecasting. If those systems fail or degrade, AI forecasting degrades with them. The models are not independent of the infrastructure they appear to make redundant.

The most sophisticated agencies are already pursuing a hybrid approach that addresses both sides of this. NOAA’s HGEFS — the Hybrid Global Ensemble Forecast System — combines AI-based ensemble forecasts with traditional physics-based ensemble modelling. In early testing, this hybrid consistently outperformed both the AI-only and physics-only systems. The combination captures the AI’s computational efficiency and its skill at medium-range pattern recognition, while the physics-based component provides a check on situations where the atmospheric state is genuinely novel.

There is a further limitation that ECMWF’s own documentation is direct about: AI models do not yet explain their forecasts in the way that physics-based models can. A traditional model can trace the chain of physical causation that led to a prediction. An AI model can say what it predicts but cannot always say why in terms a forecaster can interrogate. When an AI forecast diverges from a physics-based forecast — when the two disagree significantly — that disagreement is valuable information about forecast uncertainty. But resolving the disagreement requires human expertise. The role of the meteorologist is shifting: from running models and reading their output, toward interpreting the ensemble of AI and physics forecasts, identifying where they diverge, and communicating uncertainty to the people who need to make decisions.

What changes when forecasts get earlier and cheaper

Emergency preparedness is the most direct consequence. Hurricane evacuation orders typically need to be issued three to four days in advance to be effective — that is the minimum time required to move a large population out of a coastal zone before a major storm arrives. A model that identifies the landfall region nine days out rather than six gives emergency managers an extra three days to coordinate, to communicate, to move people who cannot move themselves. AI models are extending that window.

Agriculture is the second major domain. Planting, irrigation, harvest and frost-protection decisions all depend on weather forecasts. Farmers in rain-fed agricultural systems — which account for the majority of global food production — manage risk primarily through weather information. A more accurate ten-day forecast, available to a farmer with a smartphone, changes the calculation for whether to plant this week or next, whether to apply irrigation before a forecast rain event or wait. Google DeepMind’s WeatherNext 2 now powers weather information across Google Search, Gemini, Pixel Weather, and the Google Maps Platform Weather API. The agricultural implications of that scale of deployment in low-income farming communities have not yet been fully studied, but access to forecast information that was previously available only to well-resourced agricultural operations is now reaching smallholder farmers for the first time.

Energy system management is the third. Renewable energy generation is inherently variable — wind and solar production depend directly on weather conditions. Grid operators managing the balance between generation and demand need accurate forecasts of wind speeds and solar irradiance to plan how much backup capacity to hold in reserve and when to bring it online. GenCast, Google DeepMind’s probabilistic AI forecasting model, reduces wind power forecasting errors by up to 20% within a two-day lead time compared to traditional ensemble models. At grid scale, a 20% reduction in forecast error translates directly into lower costs for consumers and more efficient integration of renewable energy.

The implications for climate science itself are harder to measure but potentially the most significant of all. The same AI techniques that underpin operational weather forecasting are now being applied to climate modelling at longer timescales — predicting seasonal and sub-seasonal patterns months ahead. NOAA’s Atlantic Oceanographic and Meteorological Laboratory and its Weather Program Office’s Subseasonal to Seasonal Research programme are working toward extending extreme weather forecast lead times from the current two-to-four-day window to two-to-four weeks. If achievable, that shift would change disaster preparedness entirely. A community that knows six weeks in advance that conditions are likely to produce a severe hurricane season, or an anomalous flooding pattern, or an unusual drought, can prepare differently than one that gets four days’ notice.

A more accurate ten-day forecast, on a smartphone, changes the calculation for a farmer deciding whether to plant this week or next. That is not a marginal improvement. At the scale of global food production, it is consequential.

Traditional numerical weather prediction at global scale requires infrastructure that only a handful of institutions worldwide can afford. GraphCast-quality ten-day forecasts can run on a single laptop. NOAA’s AI models require a fraction of a percent of the computing resources of their traditional counterparts. The same countries that have historically depended on ECMWF or NOAA data for their weather services — because they cannot afford to run their own global models — can now run inference on AI models with modest infrastructure. This is not a complete solution to the observation network problem (AI models still need input data from the global observing system that wealthy countries have historically funded), but it opens a door to greater national meteorological capacity that was previously closed by computational cost.

The forecast that arrives and the one that doesn’t

AI weather forecasting helps people anticipate the future well enough to make better decisions, and the improvement is not marginal. Three extra days of hurricane warning, more accurate flood alerts for river communities downstream from atmospheric river events, earlier seasonal outlooks for farmers facing planting decisions under climate uncertainty — these are real improvements. The technology is operational. These improvements are already being delivered.

The question of who receives that improvement is harder. AI weather forecasts are available through Google Maps, through NOAA’s public systems, through the ECMWF website. The infrastructure to receive a forecast is a smartphone. The infrastructure to act on it — the evacuation route, the early warning system connected to the government emergency network, the insurance product that adjusts premiums based on forecast risk — is not universally available. The forecast that tells a coastal community in Bangladesh nine days in advance that a major cyclone is forming in the Bay of Bengal is only useful if there is a system in place to act on that information. The observation networks that produce accurate initial conditions for AI models are denser in wealthy countries than in poor ones. The forecast quality is not equal.

The problem that matters most is the one where AI models are least equipped. The climate system is changing. Atmospheric rivers are intensifying. Hurricanes are undergoing rapid intensification more frequently. The Arctic sea ice that provides boundary conditions for mid-latitude weather is disappearing. AI models trained on historical patterns may be systematically less reliable in the conditions that climate change is producing — precisely the conditions for which the most accurate forecasts matter most. The hybrid approach being pursued by NOAA and ECMWF — combining AI pattern recognition with physics-based modelling — is the appropriate response. But it requires sustained investment in both the AI systems and the physical science infrastructure they depend on. Cutting observation networks to fund AI forecasting would be a category error.

My Opinion

The hybrid approach at NOAA and ECMWF is correct — not because it hedges between old and new, but because the two systems are doing different jobs. AI recognises patterns in historical data at speed; physics-based models reason about conditions that appear nowhere in any training set, which in a changing climate are often the conditions that matter most. Cutting observation networks and physical science funding in the name of AI efficiency would be a category error: the AI models run on the observational infrastructure they appear to make redundant. A forecast that reaches a community with no evacuation route and no warning system is not better forecasting. It is better knowledge of what is coming with no way to act on it.

Questions for the people who have to act

The improvements in AI weather forecasting are already in the systems most organisations use. What follows are questions for the people who have to act on what those systems produce — emergency managers, grid operators, farmers, and policy makers deciding how much to trust a forecast they cannot interrogate.

1. Three extra days of hurricane warning can make the difference between an effective evacuation and an ineffective one. What other decisions — in emergency management, in agriculture, in energy — depend on the accuracy and lead time of weather forecasts in ways you haven’t previously thought about?

2. AI weather models are computationally cheap to run but still depend on expensive observation networks and traditional data assimilation systems for their input. If the political pressure to cut meteorological spending increases — as it has in the US with proposals to reduce NOAA’s capacity — what happens to the quality of AI forecasts that everyone now depends on?

3. The climate system is changing in ways that push beyond the patterns AI models were trained on. Rapid intensification, unprecedented rainfall events, Arctic conditions without historical precedent — these are exactly the situations where pattern-recognition AI may fail precisely when accurate forecasting matters most. How should society balance the efficiency gains from AI forecasting with the continued investment in physics-based modelling that handles genuinely novel situations?

4. AI weather forecasting is now delivered through consumer products — smartphone apps, mapping services, voice assistants. Most people receive forecasts without knowing whether they came from a physics-based model or an AI. Does that transparency matter? Should it?

Weather forecasting has been improving for more than a century, driven by better physics, better observations, and better computers. AI represents a different kind of improvement in that progression — not a replacement of what came before, but an acceleration that has opened global weather forecasting capacity previously closed by computational cost.

The people who most need accurate weather forecasts are not the researchers at ECMWF or the meteorologists at NOAA. They are the farmer deciding whether to plant, the emergency manager deciding when to order an evacuation, and the family on a coastline in the path of a hurricane trying to understand whether they have enough time.

Three days earlier matters. It has always mattered. The question is who has the infrastructure to act on it.

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Sources & references

• Hurricane Lee (2023) and GraphCast prediction: National Hurricane Center Tropical Cyclone Report — Hurricane Lee (AL132023). National Oceanic and Atmospheric Administration. https://www.nhc.noaa.gov/data/tcr/AL132023_Lee.pdf

• GraphCast performance benchmarks: Lam, R. et al. (2023). “Learning skillful medium-range global weather forecasting.” Science, 382(6677). doi: 10.1126/science.adi2336. Published November 2023. Confirms >90% of 1,380 verification targets outperformed ECMWF HRES.

• Pangu-Weather: Bi, K. et al. (2023). “Accurate medium-range global weather forecasting with 3D neural networks.” Nature, 619, 533–538. Published July 2023. doi: 10.1038/s41586-023-06185-3.

• ECMWF AIFS operational status: “ECMWF’s AI forecasts become operational.” ECMWF, February 2025. https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs-ai-forecasts-become-operational. AIFS version 1.0.0 implemented 25 February 2025.

• NOAA AI-driven weather models: “NOAA deploys new generation of AI-driven global weather models.” NOAA press release, 17 December 2025. https://www.noaa.gov/news-release/noaa-deploys-new-generation-of-ai-driven-global-weather-models

• Hurricane Otis (2023): National Hurricane Center Tropical Cyclone Report — Hurricane Otis (EP182023). National Oceanic and Atmospheric Administration. https://www.nhc.noaa.gov/data/tcr/EP182023_Otis.pdf

• GenCast: Price, I. et al. (2024). “Probabilistic weather forecasting with machine learning.” Nature, 637, 84–90. doi: 10.1038/s41586-024-08252-9. Confirms ~20% CRPS improvement over ENS at 2-day lead times.

• WeatherNext 2: Google DeepMind. “WeatherNext 2: Google DeepMind’s most advanced forecasting model.” Google Blog, November 2025. https://blog.google/innovation-and-ai/models-and-research/google-deepmind/weathernext-2/

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  You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.

There is a particular kind of frustration that comes from watching something fail that didn’t have to.

Not the frustration of being ignored, that’s common enough and usually survivable. Something more specific. The frustration of being able to see clearly what’s needed, of working every available channel to make that visible to the people who need to see it, and of watching the gap between what you can see and what the organisation can act on prove unbridgeable.

I’ve felt that frustration once more acutely than any other time in my career.

This is that story. Including the part where I got it wrong.

The client

The organisation issuing the bid was not a standard commercial enterprise with sustainability bolted onto its annual report as a concession to investor pressure.

It was a global leader in health, nutrition, and bioscience whose entire operating philosophy was built around a different understanding of what a business exists to do. People, planet, livelihood — not as a values statement decorating the reception area but as the architecture of every significant decision the organisation made. The UN Sustainable Development Goals were not a reporting framework for them. They were the reason the business existed.

When they issued a bid for a major technology and services partnership they were not looking for a supplier. They were looking for a partner who understood what they were actually for and who could demonstrate, concretely and measurably, how the proposed solution would advance the outcomes they existed to deliver.

I read the brief carefully. I understood what was being asked. And I understood immediately that responding to this brief the way we would respond to any other bid would not just fail to win — it would signal a fundamental misreading of who the client was.

What I built and what happened to it

I positioned the entire approach around genuine alignment with the client’s philosophy. I wrote the sustainability component in full. I designed an animation to tell the end-to-end story — not of our services and capabilities, but of the client’s own philosophy brought to life through the solution we were proposing. I argued consistently that this was not an addition to the bid. It was the bid.

The bid executives saw it differently. Not through malice, I want to be clear about that, because the easy version of this story makes them the obstacle and me the visionary and that version isn’t honest or useful.

They saw a client with a significant services requirement and a proven portfolio of capabilities that could meet it. They were experienced people who had won bids before by leading with what they knew how to deliver. That approach had worked. There was no obvious reason it shouldn’t work again.

What they hadn’t fully understood was that this client was different in a way that made the standard approach not just suboptimal but actively counterproductive. Arriving with a services pitch to a client whose identity was built around purpose-led outcomes didn’t just miss the mark. It told the client something about how we saw them.

How it ended

My role was gradually narrowed. The sustainability components were treated as a discrete workstream rather than the spine of the entire response. Once that work was completed I was stepped back from the wider bid discussions.

The bid was lost.

The feedback reflected exactly what I had been trying to prevent - a lack of genuine alignment with the client’s philosophy. The components that were remembered were the sustainability narrative and the animation.

The work that had been marginalised was the work the client had been looking for.

What I got wrong

I worked the informal channels when the situation required formal ones.

I had open discussions with the team. I guided the narrative through the process stages as best I could. I was vocal, directly and persistently, about the direction I thought we were taking.

What I didn’t do was stop at the beginning and force the formal conversation that the situation required.

I should have called an internal management meeting before the bid work started in earnest. I should have laid out precisely what the brief was asking for, what the client’s philosophy meant for how we needed to respond, and what the risks were of defaulting to the standard approach. I should have defined those risks explicitly, not as a concern or a perspective but as a formal risk to the bid outcome, and sought explicit agreement on the strategy before a single word of the response was written.

That process might not have changed the outcome. But it would have given the argument a formal status inside the organisation that informal advocacy never achieved.

Being right about the direction wasn’t enough. The system needed more than clarity. It needed process, ownership, and formal agreement. Without those, individual insight, however accurate, remains exactly that. Individual. Unowned. Unable to move the organisation.

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*This is the sixth entry in the Predictive Purpose journal — a book being built in public. If you’re reading this for the first time, start at the beginning. Subscribe at neilcatton.substack.com.

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Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.