Google DeepMind’s Co-Scientist, formalised in a Nature paper in May 2026, compresses the early stage of scientific discovery, generating and refining a research hypothesis, from weeks to days. It works as a multi-agent system built on Gemini: separate AI agents continuously generate hypotheses, critique each other’s reasoning, and refine what survives, in a loop that used to depend on a human researcher’s slower cycle of reading, thinking and drafting. It isn’t a demo. Now rolling out experimentally to individual researchers through Gemini for Science, it has already produced hypotheses that survived contact with a wet lab: a novel drug-repurposing candidate for acute myeloid leukaemia, and a new epigenetic target for liver fibrosis, both independently confirmed rather than merely plausible-sounding. This is a real capability, working, now.

In the same literature this capability is meant to accelerate, something else is happening. Fabricated references in biomedical papers have risen twelvefold in two years. Maxim Topaz, an associate professor at Columbia University’s School of Nursing and Data Science Institute, led an audit of 2.5 million papers — published as a letter in The Lancet in May 2026 — that found roughly one in every 277 PubMed-indexed papers now contains some fabricated references, up from one in 2,828 three years earlier. Review articles, the papers other researchers lean on most heavily to orient themselves in a field, showed a fabrication rate 57% higher than other paper types. It’s already sitting in the permanent scientific record, not a future risk to be managed.

One documented case, precisely

Most of that twelvefold rise is inference from pattern, not confession. But at least one instance is on the record in detail. A 2026 retraction from the Journal of Medical Ethics was explicitly attributed to an author who had used generative AI to identify and understand referenced sources, without independently verifying them before submission. The citations looked right. Nobody checked whether they were.

That’s the mechanism in miniature: a tool that can write a fluent, well-formatted reference list faster than any human, attached to a verification step that still assumes a human wrote it carefully. Multiply that gap across an entire literature moving at AI speed, and the twelvefold rise stops being surprising. The higher fabrication rate in review articles matters more than the raw number suggests, because a review article is exactly the paper other researchers reach for first when they’re trying to orient themselves in an unfamiliar field — it’s the summary everyone trusts precisely because checking every citation in it individually is what the review was supposed to save them from doing.

The system nobody rebuilt

Peer review and citation-checking were designed for a slower, lower-volume era of publishing — one submission at a time, one careful human reader at a time. AI can now generate a polished hypothesis, a literature review, and a citation list faster than that process was ever built to check — and the researchers behind the audit say plainly that the checking side hasn’t caught up to the volume the generating side now produces. There is still no accepted, mandatory standard for flagging an AI-assisted finding as provisional before it enters the permanent scientific record.

My Opinion

I don’t read this as a publishing-etiquette problem, the kind a stricter house style or a sterner editor’s note could fix. Verification is infrastructure, in exactly the same sense that the wet lab confirming Co-Scientist’s drug-repurposing candidate is infrastructure. Nobody would fund a hypothesis-generation system this fast and then leave the checking to whoever happens to notice. Yet that’s precisely what’s happening on the citation side, because the money and the attention are on the generation half of the pipeline, not the verification half. Until that changes, the speed is the risk, not the safeguard.

That gap has consequences beyond the literature itself. A funding committee weighing a grant application, a systematic review synthesising a field, or a clinical trial recruiter screening candidates has no reliable way to know whether the citation or hypothesis in front of them survived independent checking, like Co-Scientist’s confirmed drug-repurposing candidate, or only survived generation, like the reference list behind the Journal of Medical Ethics retraction.

Even Google is candid about this. The company acknowledges Co-Scientist’s own limitations in literature review and factual accuracy — meaning the most rigorously validated AI-assisted discovery tool currently available still depends on external checking that the surrounding system isn’t built to provide at the speed the tool now works at. The researchers behind the audit have recommended that citation-checking move earlier, built into the submission workflow before peer review begins rather than caught, if at all, after publication — a change that depends on publishers adopting it, which none has yet done at scale.

On the page, a hypothesis nobody has independently checked looks identical to one that’s been rigorously verified. Right now, nothing in the publishing system forces that distinction to be visible before the finding starts shaping what gets funded and which patients get recruited into which trials. How many findings currently doing that work have never actually been checked — and would anyone know?

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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.

That reflex isn’t a personal failing. It’s the product working as designed. The interfaces, the notifications and the metrics an app chases are built to hold attention, and holding attention has real costs — disrupted rhythms, eroded focus, a sense of self quietly reshaped by whatever keeps someone scrolling. None of that is inevitable. It’s the result of design choices, made deliberately or left unexamined.

The card holds four practice areas across two columns: reclaiming attention through intentional habits and a healthier relationship with social media, then reconnecting through real-world presence and by pushing for design that answers to the people using it, not just the engagement metrics it’s measured against. Written for anyone whose phone currently has more influence over their day than they’d like — which is most people.

It comes from The Next Evolution, where the fuller argument for shifting from engagement to restoration lives. The card stands alone — you don’t need the book to use it.

Print it, or pin it somewhere you’ll actually see it — the kitchen, the desk, wherever the phone usually wins.

So here’s the honest question: the next time your hand reaches for the phone without you deciding to, whose interest is it actually serving?

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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.

Ask people at almost any company whether they use AI for work, and the honest answer is usually yes — whatever the official policy says. MIT researchers who studied enterprise AI adoption this year found that employees at more than 90% of the companies they surveyed were already using personal ChatGPT or Claude accounts for work, regularly, some of them every day — while only 40% of those companies had bought so much as one official AI subscription. The organisation didn’t approve any of it. It just didn’t get in the way fast enough to stop it.

That gap between what a business has sanctioned and what its people have already gone and done for themselves, is the real story behind a number that’s become almost a cliché this year: roughly 95% of generative AI pilots fail to deliver any measurable return, against $30 to 40 billion in combined enterprise spending. The same research found something more specific underneath that headline figure. Pilots built on generic, off-the-shelf tools reach implementation 83% of the time, because they ask almost nothing of the organisation around them. Custom pilots — the ones meant to touch a real process — mostly never get past the pilot stage at all.

What the pilots are actually funding

Look at where the $30–40 billion has gone and a pattern appears. Most of it has gone into things that generate enthusiasm rather than change: a chatbot here, a summarisation tool there, a demo for the board that photographs well in a slide deck. Each one is cheap to start and, when it doesn’t work, cheap to quietly abandon.

The harder work doesn’t get funded at the same rate: auditing whether the underlying data can actually be trusted, naming the political reason a broken process has never been fixed, admitting a failure that predates the technology by years. None of that demos well, none of it fits inside a single budget cycle, and none of it feels like progress in the moment — even though it’s the only work that would tell you whether a tool could ever have helped in the first place. So it keeps not happening, and the pilots keep multiplying instead.

The mirror, not the tool

AI doesn’t reveal what’s wrong with a business. It runs straight into it. A process that was already unclear stays unclear, just faster. Data that was already messy gets amplified rather than cleaned up, because nothing about deploying a model answers the question nobody asked about where that data came from or whether it was ever trustworthy in the first place.

The technology isn’t lying about the organisation it’s dropped into. It’s showing that organisation exactly what was already there — at a speed that makes the thing much harder to keep ignoring.

The alternative isn’t complicated

None of the harder work requires new technology. Deciding who owns a piece of data, and whether it can actually be trusted, is a decision an organisation could have made before any pilot started. Deciding who’s accountable when a model gets something wrong is a governance question, not an engineering one. Giving staff an approved tool that’s as fast and as unrestricted as the one they’ve already found for themselves is a procurement decision, not a research problem.

What makes this work hard isn’t complexity. It’s that it requires someone to admit, out loud, that the process was already broken before the model arrived — and admitting that is exactly what a shiny new pilot lets everyone avoid doing.

Who is left holding the gap

The employees quietly running work through their own AI accounts aren’t doing it to make a point. They’re doing it because the sanctioned tool doesn’t do the job and the deadline hasn’t moved. That leaves them carrying two things nobody chose to give them: the actual risk of putting company information through a personal, unmanaged account, and the actual judgement calls — what’s fine to paste in, what isn’t — that no governance process has ever talked them through.

None of that is a technology failure. It’s a business that outsourced a hard decision to whoever was closest to the deadline, then called the resulting mess an AI adoption problem.

A better model won’t fix what a business has never been willing to look at directly. It will just show that back, more clearly, and faster than before — and leave someone else to carry what it finds.

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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.

That’s the shape most personal breaches actually take. Not a hacker who singled you out, but an old habit catching up with you months or years later — a password reused once too often, a profile left public, a text answered half a second too fast. The shadow economy behind this doesn’t need to outsmart anyone. It just needs you to stay predictable a little longer than you should.

The card holds four practice areas, read as one sequence rather than split by audience: locking down the accounts and devices that are the front door to your digital life, recognising the manipulation aimed at your judgement rather than your hardware, curating the footprint that makes you easy to research, and building the habits — backups, statement checks, a rehearsed response — that turn a breach into an inconvenience instead of a catastrophe. Written for anyone with an inbox and an account worth protecting, which is everyone.

It comes from The Shadow System, where the fuller economics and anatomy of these attacks live. The card stands alone — you don’t need the book to use it.

Print it, or send it to whoever in your life still uses the same password everywhere.

So here’s the honest question: of the four fronts on this card, which one have you been quietly putting off?

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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.

Jordan had been using the platform for two years by the time the standard was announced. Not easily — with a screen reader that could parse about half of what the interface contained, and a set of workarounds so habitual they had ceased to feel like workarounds. Every week: export the dashboard data to a spreadsheet to read it, update the spreadsheet offline, re-enter the changes manually. Three extra steps for every task a sighted colleague completed in one. The platform was not designed to exclude Jordan. It was not designed for Jordan at all.

The standard arrived as a regulatory document with an eighteen-month compliance window. Jordan did not know about it immediately. There was no reason to. Standards are announced to the organisations that have to meet them, not to the people who will benefit from them.

In Sam’s team, the announcement landed differently.

The response the organisation had prepared

Sam was not against accessibility. Nobody in the team was against accessibility. The resistance was to the timing, the resource, and the assumption that eighteen months was enough to rewrite the parts of the product the standard required. The roadmap was committed. Three major features were already in progress. The compliance work was not budgeted. The concern was not that the standard was wrong but that it had arrived at the wrong moment.

That concern was genuine in its own terms. Within nineteen months, the team would need to audit the full product against the standard’s requirements, identify every failure point, prioritise the remediation work, retest with assistive technology, and document the compliance case. For a product of any complexity, that is not a small undertaking. The estimate that came back from the initial audit was uncomfortable.

What was also in that audit, if anyone looked carefully enough, was a list of things the team had wanted to do but had never been able to prioritise. The keyboard navigation issues that the accessibility standard required to be fixed had been in the backlog for eight months. The plain language requirement had been discussed at two design reviews and deferred both times. The colour contrast failures had been flagged by one of the designers the year before, logged, and then — as things are logged — left there.

The standard had not introduced new problems. It had made existing ones compulsory to address.

There is a pattern here that runs across the history of technology regulation. When the Disability Discrimination Act was introduced, the adjustments it required were widely resisted as disproportionate by the industries subject to them. The Web Content Accessibility Guidelines were described as technically unrealistic for large, complex products. The EU Accessibility Act was described in similar terms by the industries preparing for its June 2025 deadline. Each time, the argument is that the standard is too far ahead of where organisations are. Each time, the organisations that meet the standard find they were closer than they thought — and further from where they should have been.

From compliance to floor

The reframing that changed the team’s view was not philosophical. It came from the user testing.

The standard required testing with the people it was designed to protect. The team arranged the sessions. For most of them, they were the first time anyone had watched someone use the product with a screen reader.

What they saw was uncomfortable. They watched someone work through the same tasks the team had been designing for two years. The time it took, and the places where the screen reader fell silent because there was no text equivalent for a visual element. The workaround that had developed for the dashboard export — not because it was a natural way to work, but because it was the only way to work. The product had been declared a success on every internal metric. From outside the design assumptions that had produced it, it was a product with a secondary interface for a significant portion of its potential users, and that secondary interface was made of spreadsheets and manual re-entry.

The user testing did not produce guilt. It produced information. The team had not known. Now they knew. The standard had been the mechanism that produced the knowledge, and the knowledge changed what the work meant.

The reframe - from compliance burden to design floor - followed from that session. The standard was not telling the team how good the product could be. It was telling them how bad it was no longer permitted to be. One instruction sets a ceiling. The other raises a floor. The team had been treating the standard as the former. It was the latter. Once they understood that, the work changed from meeting a requirement to building from a new foundation.

What the floor revealed

The improvements came in waves. Screen reader compatibility - the most technically complex of the required changes - produced the first unexpected consequence. Making every interface element accessible to a screen reader required auditing the entire navigational structure of the product. The audit found redundancies, dead ends, and paths through the product that nobody had designed and that made no sense to any user. Fixing the screen reader compliance required fixing the navigation. The navigation that emerged was better for everyone.

The plain language requirement produced the second wave. Error messages rewritten for cognitive accessibility were clearer to all users. Support call volume for error-related queries fell within months of the changes going live. The calls had been telling the team something about the product that the product’s own metrics had not surfaced.

Colour contrast was the third. The standard required ratios that the team’s existing palette did not meet. The redesign of the colour system produced a product that performed better in direct sunlight, on low-quality displays, and - as subsequent user testing confirmed - for users over fifty whose colour sensitivity had shifted with age. The team had been designing for a calibrated monitor in a dim office. The contrast requirement forced a wider assumption about where and how people use things.

This is the curb-cut effect: standards designed for a specific group produce improvements that serve a wider population than the standard required. The lowered kerb that allowed wheelchair access also helped people with pushchairs, delivery workers with trolleys, and cyclists. The plain language, the contrast ratio, the keyboard navigation did the same thing. Jordan could use the product. So could everyone else, more easily than before. The floor that had been raised for Jordan turned out to be below where the product should have been all along.

The team had not set out to make a worse product than they were capable of. They had made the product their design process was equipped to produce, for the users their design process had in mind. The standard forced a wider design process. The wider design process produced a better product.

That sequence is not unique to accessibility. External requirements have forced expanded scope and produced better outcomes in electrical safety, food labelling, and data protection. The resistance is always first. The improvement follows.

What a standard is really testing

The question a well-designed standard asks of a technology product is not only whether it is accessible to people with disabilities. That is the specific requirement. The deeper question is whether the product assists the people who use it — all of them, not only the ones whose needs were visible in the original design brief.

A product that cannot be used by someone with a screen reader was, by definition, only helping the users whose circumstances it had accounted for. Meeting the standard did not add an accessibility layer to a complete product. It completed the product for the first time.

The second question is whether the product adds something real to the capability of the people who use it, or only enables interaction. Plain language that reduces cognitive load does not just make the product reachable - it makes it more useful. The user who previously needed three support calls to understand an error message needed none after the change. That is not accessibility as a separate category. That is a better product, full stop.

The third question - the one that runs underneath the history of the Disability Discrimination Act, the Web Content Accessibility Guidelines, and the EU Accessibility Act - is adaptability. A product designed for one profile of user, however carefully designed, is not adaptive. A product that has been required by a standard to respond to a wider range of human circumstance has, in meeting that requirement, become demonstrably more so. The standard did not produce adaptability. It required the work that produced it. That is what a well-designed standard does: it requires the work the organisation would not otherwise do, for the people the organisation had not otherwise considered.

My Opinion

Technology follows the path of least resistance. So does design. We build for the user we imagine, in the circumstances we assume - and that user does not exist. Real people are older, less sighted, less confident, using screens in conditions we did not test for. A standard forces the reckoning with that gap. It should not have to.

Four questions for the next standard

1. Think of a regulatory or technical standard that applies to technology you build or use. Has meeting that standard improved the product for a wider population than the standard required? If you do not know - would it be worth finding out?

2. If your organisation is currently resisting a new accessibility or ethical design standard, what is the specific argument against it? Is that argument based on evidence of what the standard would cost and produce - or on an assumption made before the work was done?

3. If you are involved in setting standards - in government, in industry bodies, in procurement specifications - are the people the standard is designed to protect part of the process by which it is written? And if not, how confident are you that the standard actually requires what those people need?

4. The argument against technology standards is consistently the argument of the organisation that has not yet had to meet them. What would it take for your organisation to be the one that argues for higher standards rather than against them - and what would that mean for the people your technology is supposed to serve?

Jordan can use the platform now. Not with workarounds. Just - use it. The standard required that as a minimum. The team went further because, once they understood what the floor was, they found the ceiling was higher than they had thought. The standard did not limit what was possible. It clarified what was no longer acceptable.

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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.

That’s what the future of work looks like most days: judgement swapped for optimisation, one scheduling system and one reskilling programme at a time. The tools are changing. So are the terms — what counts as valuable, who gets to intervene, what a fair transition actually means — and most of that is being decided by default, not by anyone in particular.

The card holds four practice areas, split by where the change actually happens: what people and institutions build in themselves, like lifelong learning and a redefined sense of what work is for, and what has to change in the wider economic system, like real pilots of universal basic income and worker ownership, and automation held to a standard of dignity rather than pure output. Written for anyone with a hand on the transition, from the policymaker weighing a UBI trial to the manager who signs off on the next scheduling system.

It comes from The Next Evolution, where the fuller argument lives. The card stands alone — you don’t need the book to use it.

Print it, or send it to whoever’s about to build the next system that quietly decides these terms for someone else.

So here’s the question I’d leave with Jordan’s employer: if nobody chose the terms this rota runs on, whose job is it now to choose better ones?

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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.

For most people with idiopathic pulmonary fibrosis, the question is not whether their lungs will fail but how quickly. There is no cure. The available treatments slow the damage; they do not stop it. Median survival after diagnosis is three to five years.

In June, at the BIO International Convention in San Diego, Insilico Medicine announced a collaboration worth up to $2.5 billion in milestone payments with SK Biopharmaceuticals for neuroimmune disorders — a year after publishing Phase IIa trial results for its lead IPF drug in Nature Medicine. The molecule at the centre of this is Rentosertib, a TNIK inhibitor designed to slow the fibrotic damage that kills IPF patients. It is the first drug candidate where both the target and the compound were identified entirely by AI. The platform synthesised and tested 79 candidate molecules; Rentosertib was the 55th. From target identification to clinical candidate: 18 months.

The question is what to do with that.

What 18 months actually means

Traditional drug discovery takes years before a compound reaches a trial. Identifying the right biological target is itself a years-long process — hypothesis, experiment, elimination. Then designing a compound that binds to it, screening variants, testing in the laboratory, a Phase I trial to establish safety. By the time a drug enters efficacy testing, four to six years have typically passed.

Insilico collapsed that phase. The AI generated and assessed virtual molecules computationally, identified the best candidates, and selected one for synthesis without the iterative cycle that makes traditional chemistry slow. The biology remained; the chemistry accelerated.

Rentosertib’s Phase IIa trial enrolled 71 patients across three dose arms, randomised, double-blind, placebo-controlled. Patients on the highest dose showed a mean improvement in lung function of 98.4 millilitres over the study period; the placebo group declined by 20.3 millilitres. For a disease measured in how fast function falls, that gap matters. The results are not definitive; no Phase IIa trial is. But they were published in Nature Medicine in June 2025, and they were positive.

Discovery has changed. The question is whether the rest of the process noticed.

The infrastructure was not built for this

After Phase IIa comes Phase III. Larger trials, typically thousands of patients, with full efficacy and safety data required for regulatory submission. Insilico opened Rentosertib’s own Phase III trial in July, enrolling 320 patients across 47 sites in China. The median time from Phase IIa completion to approval still runs several years, even with expedited review designations. Fast Track status accelerates the conversation with regulators, not the data requirement.

The regulatory requirement is appropriate: evidence from 71 patients is not enough to approve a drug for general use. Phase IIa is promise; Phase III is proof.

But the clinical trial system was not designed around a world in which a promising molecule can be found in 18 months. Recruiting patients, running trials across multiple sites, generating data, submitting for review: the queue was sized for a pipeline that moved at human speed. AI has shortened the front of that process without changing anything that follows it.

For someone diagnosed with IPF today, the arrival of AI drug discovery does not change their timeline. Rentosertib may eventually be approved, or it may not; Phase III trials fail more often than they succeed. The drug was discovered faster, but the path from discovery to patient has not changed. The bottleneck moved from the laboratory to the clinic.

What gets built next

The collaboration announced in June is, among other things, an industry bet that AI will produce candidates at a scale and pace the traditional pipeline never achieved. If that bet is correct — and the discovery-side evidence is accumulating — then the clinical trial system will face pressure it has not faced before: not a shortage of candidates but a surplus of them, each requiring the same years of Phase III data.

Some adaptation is already under way. Adaptive trial designs, accelerated patient matching, synthetic control arms built on existing patient data, earlier integration of real-world evidence from approved therapies — these approaches exist and are being explored. None of them is available to the patient who needs a drug now, and none changes the gap between AI-accelerated discovery and human-speed delivery.

The FDA and the EMA published joint guiding principles for AI in drug development in January, and the MHRA has spent the same period expanding its regulatory sandbox for AI tools. None of it touches the actual constraint. The guidance governs how AI models get validated for use inside a regulatory submission, not how many Phase III trials the system can run at once, or how fast a surplus of candidates gets through it.

My opinion

I think this is the pattern, not the exception. We keep running into the same asymmetry: what a technology like AI can now do at pace and at scale, set against governance and legal procedures that were never built to move at that speed. Some of that caution is earned — Phase III exists because it should be hard to approve a drug on the strength of 71 patients. But most of the controls now standing in the way were built for a more analogue world, and they have not moved into this one. Until they do, every acceleration in discovery just relocates the wait.

For IPF specifically, the constraint is concrete. Median survival after diagnosis is three to five years. The drug that might help was discovered in 18 months. The distance between those two facts is not a scientific problem. It is a system problem — and the system in question is not the one that found the molecule.

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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 card’s own framing names what’s actually happening. Your attention is being exploited, not because you’re careless, but because the systems behind scams, deepfakes and disinformation are built to outrun your judgement rather than persuade it. Noticing that in the moment is most of the defence.

The card holds ten prompts across two columns. Five habits to try this week — verifying any urgent request through a separate channel you trust, treating urgency itself as a warning sign, learning what a deepfake actually looks like, reporting what you catch instead of scrolling past it, pausing before you share anything built to make you feel first and think second. And five fields of research if you want to know why the manipulation works, from the psychology behind social engineering to the study of what makes some people harder to fool than others.

It comes from The Cognitive Crucible, where the fuller argument about attention as contested ground plays out. The card stands on its own — you don’t need the book to use it.

Print it, or keep it somewhere you’ll see it before you next reach for your phone.

And here’s the question I’d leave you with: the next time a message tells you to act immediately, whose interest does that urgency actually serve?

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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.

Machines don’t have conscience. But they operate in moral domains all the same — every choice they automate reflects a value, whether someone declared it or the system simply defaulted to one. The gap penalty was never a decision. It was a value, arrived at by accident, and nobody in the building would call it theirs.

The card holds six practice areas across two groups: what you design and encode before a system ever runs — naming the values it must serve, building ethical data practices, engineering for real transparency — and what keeps it honest once it’s operating: safety testing, clear accountability, and a standing habit of ethical review. Written for whoever builds these systems, though anyone who’s been quietly filtered out by one will recognise exactly what’s at stake.

It comes from The Next Evolution, where the fuller argument lives. The card stands on its own — you don’t need the book to use it.

Print it, or hand it to whoever’s about to ship the next model that will quietly decide who gets a chance.

So here’s what I’d ask the team behind that hiring model: if nobody chose the gap penalty on purpose, whose value is it now?

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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 email arrived at 3.17 in the morning.

Sam hadn’t slept. They’d been sitting at their kitchen table for four hours watching the progress bar on their screen crawl — file after file locked, folder after folder gone dark. The attack had started somewhere in the accounts system, spread quietly through the network, and by midnight the company’s billing platform, its customer database, and eighteen months of project files were encrypted behind a key Sam didn’t have.

Then the email came. It was polite. It opened with an acknowledgement that this was a difficult situation. It explained, clearly and without technical jargon, what had happened and what the options were. It included a link to a payment portal with step-by-step guidance. It offered a live chat facility for anyone who needed help navigating the process. At the bottom, there was a note that a support representative was available around the clock and would typically respond within the hour.

Sam had never received customer service this good from anyone.

The support desk that worked

This is not an unusual story. The criminal organisations behind ransomware attacks have built support infrastructures that would be impressive in any legitimate technology business — documentation in multiple languages, and in some documented cases a rating mechanism through which affiliates — the criminal operators who carry out attacks on behalf of the platform — could evaluate the service they received.

They have done this because it works. A victim who cannot navigate the payment process is a victim who might not pay. Friction costs money. They removed it.

The DarkSide group, responsible for the 2021 attack on Colonial Pipeline in the United States, operated with the organisational structure of a funded technology company. They developed proprietary tools, leased their capabilities to vetted affiliates who carried out attacks on their behalf, and maintained a support function specifically to guide victims through the payment and decryption process. The attack locked down the billing and operational systems of the largest fuel pipeline on the US East Coast, triggered fuel shortages and panic-buying across multiple states, and prompted a federal emergency declaration. Colonial Pipeline’s CEO, Joseph Blount, confirmed to the Senate Homeland Security Committee that the company had paid the ransom — 75 bitcoin, approximately $4.4 million at the then-exchange rate. The ransom note had demanded approximately five million dollars.¹ The support desk was open the entire time.

The question this raises is not primarily about the criminals. Their logic is coherent. They are running a revenue model and they have optimised it. The question is about what their model reveals about everything else.

What the legitimate system was built to do

A person dealing with a digital crime — whether ransomware, fraud, or identity theft — typically encounters a response system that was designed for a different era and a different scale of problem. The bank’s fraud line connects them with an adviser working from a script that was written before this kind of crime existed in its current form. The police non-emergency line takes a report and assigns a reference number. The relevant government body sends an acknowledgement within five working days.

Each of these institutions is staffed by people trying to help within a structure that was not built to help at speed, at scale, or at the point of maximum need.

The criminal support desk is better not because criminals are more talented. It is better because it was designed entirely around what the paying customer needs at the moment they need it. That is a design choice. The legitimate systems were designed around something else — compliance, liability, institutional process, historical precedent — and the human being in distress is not at the centre of any of it.

“The ransomware gang had better customer service than the bank that was supposed to help you recover from them.”

When a fraud line or a government body responds to a digital crime victim, the response is designed to help that person complete a process — file a report, receive a reference number, trigger a workflow. That is not the same as helping them do what they actually need to do. The paper trail grows. The problem does not. And the response does not adapt: it does not know where this specific person is right now — technically, emotionally — and it was never built to find out.

The criminal support desk was built around exactly that question. Not because of any ethical commitment, but because its commercial success depends on it. That is the uncomfortable mirror it holds up. The legitimate system, by contrast, was designed to be defensible. That is an institutional virtue. It is not the same as a human one.

The Colonial Pipeline attack disrupted fuel markets across multiple states for six days, driving fuel prices above three dollars a gallon for the first time since 2014 and prompting panic-buying across the south-east of the United States.² The polite emails were in service of extortion.

What the comparison reveals

The argument is that something in the legitimate system has been designed wrongly, and that the criminal model makes the wrongness visible by contrast. When a fraud victim gets better guidance from the people who defrauded them than from the institution that holds their money, that is not an anomaly. It is a diagnostic. The system that was built to protect and assist that person has, somewhere in its design, prioritised something other than them.

That matters because the design is not inevitable. It is a series of choices, made by people, in institutions, at particular moments in time.

My Opinion

Cyber crime is not the work of individuals in dark rooms. It is a structured business model, operated by organised criminals with the discipline and infrastructure of any revenue-generating enterprise. The support desk is not absurd — it makes complete commercial sense. Once you accept that framing, the question for every organisation changes. Not “could this happen to us?” but “what is our blast radius, and what do we have in place to recover?” This is not going to become simpler or rarer. Crime now operates at the speed of code. Our response still runs at the pace of policy and procedure. That is an asymmetric challenge, and naming it is the first step to treating it seriously.

This is the territory that The Shadow System covers in its opening section — the professionalisation of the criminal economy, and what that professionalisation reveals about the failures of the legitimate one.

Your experience is part of this

These patterns are not abstract. The design failures described here accumulate in individual moments — a fraud call that went nowhere, a report that produced only a reference number, a process that kept moving while the person at the centre of it stood still. If you’ve encountered any part of this, your account is relevant evidence.

• Does this story relate to you — have you ever found the process of reporting a digital crime or fraud more difficult than the crime itself deserved?

• If you have your own experience of how institutions responded when something went wrong digitally, share it. These patterns only become visible when enough people name them.

• If you could redesign the response a fraud or ransomware victim receives from legitimate institutions, what would be the first thing you would change?

• And the harder question: is this a technology failure, a culture failure, or an incentive failure — and does the answer change what we should do about it?

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References

¹ Joseph Blount, CEO, Colonial Pipeline — Senate Homeland Security and Governmental Affairs Committee testimony, 8 June 2021. Blount confirmed the payment was 75 bitcoin (approximately $4.4 million at the time of payment); the original ransom demand was approximately $5 million. The FBI subsequently recovered approximately $2.3 million by seizing 63.7 Bitcoin (the value had fallen since the date of payment). Sources: CNBC; Nextgov/FCW; Department of Justice press release, 7 June 2021.

² Colonial Pipeline shut down 7 May 2021, restarted 12 May 2021. Fuel price and disruption data confirmed across multiple sources; note that a University of Kansas study (December 2021) found the longer-term gas price impact was more localised than early reporting suggested. Sources: CISA retrospective; University of Kansas study.

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.

That’s not a lapse in willpower. The card’s own opening line puts it plainly: your phone has become an extension of your attention, memory and sense of self, mostly without your noticing. You don’t fix that with a digital detox weekend. You fix it by making the influence visible enough to choose.

The card holds ten prompts across two columns. Five small, concrete shifts to try this week — reviewing what your apps can actually access, building friction in front of the ones designed to pull you back in, using the well-being tools already sitting on the phone unused. And five research areas if you want to know why the pull feels engineered rather than accidental, from the design of the notification itself to the study of compulsive use.

It comes from The Cognitive Crucible, where the fuller argument lives. The card stands on its own — you don’t need the book to use it. It’s also the first card from that book to join the Practice Library: same standalone shape as the cards from The Next Evolution, this time drawn from the book about attention and cognition rather than systems.

Print it, or pin it somewhere your phone already lives — the charger, the bedside table, the place it lands first each morning.

And here’s the question I’d leave you with: the next time your hand finds your pocket before your mind’s made a decision, whose choice was that — yours, or the design’s?

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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.

Google pays Reddit roughly $60 million a year for access to its forums. OpenAI pays Reddit around $70 million for the same thing, and separately signed a five-year, $250 million deal with News Corp. None of the people who wrote the posts, comments and articles now changing hands for that money were party to any of these deals. They wrote for free, on platforms that were free to join, and the platforms sold what they wrote.

That should be the fact that stops you, before any argument gets built on top of it. Live-access licensing deals like these have grown from two in 2023 to a projected thirty-four in 2026 — a market that didn’t exist three years ago, already moving hundreds of millions of dollars a year, for something the AI industry used to scrape for nothing.

Why the market suddenly needs “real” data

The reason is a 2024 finding, published in Nature, that AI models degrade when they’re trained on their own output — not goodwill toward journalism or forum culture. Ilia Shumailov, a computer scientist then at Oxford, and his co-authors showed that models fed a diet of AI-generated text suffer what they called model collapse: an early drift away from the true distribution of language, followed by the permanent loss of rare, low-frequency information — the unusual phrasing, the minority view, the edge case that only shows up once in a million real sentences. Train a model on text that was itself written by a model, repeatedly, and each generation grows narrower and worse.

The supply of text that hasn’t been through that loop is shrinking fast. By April 2025, an estimated 74.2% of new webpages already contained AI-generated text. Epoch AI has projected that the internet’s stock of fresh, verifiably human-written material could be exhausted sometime between 2026 and 2032. Put those two facts together and the licensing boom stops looking like opportunism and starts looking like the market pricing in a resource it only just discovered was finite.

What buying real data gets you

For a lab that can afford it, a licensing deal buys something specific: a continuously refreshed pipeline of text a real, currently living human being wrote, with a paper trail behind it. That’s a genuine practical advantage: slower degradation, a defensible answer to “what did you train this on,” and a supply of language that keeps including the rare, awkward, human sentence that a model trained on its own past output would quietly lose.

Regulation has started reaching toward this problem, but not from the direction that matters here. The EU AI Act’s labelling mandate, which takes effect in August 2026, will require AI-generated images, video and audio to carry machine-readable labels, with fines of up to €15 million for non-compliance. Text is explicitly exempted — the European Commission’s own reasoning is that reliable detection of AI-written text doesn’t exist yet. So the one piece of law built to tag AI output has nothing to say about the market this article is describing: hundreds of millions of dollars a year, changing hands over exactly the kind of text the mandate can’t touch.

What it leaves out, and for whom

The money flows to the platform, not the person. Reddit and News Corp get paid because they own the pipe the content flows through; the individual who posted the comment or wrote the article does not see a share of either deal, and has no legal or technical route to ask for one. Nothing built so far reaches past the platform to the creator.

Smaller and open-source developers are shut out from a different direction. A verified, licensed data pipeline costs tens or hundreds of millions of dollars to assemble. A developer who can’t pay that price is left training on the same open web that’s now more than 74% synthetic, with no way to check what’s clean and what isn’t. The gap this opens is concrete: a well-resourced lab’s model gets steadily better data, a smaller developer’s model gets steadily worse data, and neither the developer nor their users are told which side of that split they’re on.

Government hasn’t closed the gap either. The UK’s March 2026 Report on Copyright and Artificial Intelligence chose to keep the existing rules in place, favouring voluntary licensing and transparency over reform, even though 88% of the more than 10,000 people who responded through the consultation’s Citizen Space platform backed the option that would have required licences for AI training — the strongest protection on the table. The market has already answered the question of what this content is worth — hundreds of millions of dollars a year and rising. Nobody in government has yet answered the question of who, if anyone, is owed a share of it.

My Opinion

I write a lot both here and in my published books, so I read this as someone whose words could end up in the same pipeline. The question of paying for what someone else created isn’t new — publishers have negotiated royalties for a century. What’s new is the scale: this isn’t licensing a book or a photograph, it’s the wholesale absorption of everything a person has ever written, folded into a model with no accounting for whose sentences did the work. Some form of monetary recognition has to follow the creator, not stop at the platform that happened to host them.

The AI industry has just told everyone, in real money, exactly what their unpaid words are worth. It has not told anyone how, or whether, they will ever see any of it.

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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.

That’s the trap efficiency and equity keep setting for each other, as if a service can only get faster by getting narrower. It doesn’t have to. The fix isn’t slowing everything back down — it’s building the service with the people who struggle with it, not just measuring how fast it moves for the people who don’t.

The card holds six practice areas across two groups: what individuals and citizens can do to document, feed back and demand better from the services they use, and what institutional leaders and service providers can build so the whole journey — not just the transaction — works for the range of people it actually serves. Written for anyone who designs a public service, and for anyone who has been the person a fast service wasn’t built for.

It comes from The Next Evolution, where the fuller argument for co-creating services lives. The card stands on its own — you don’t need the book to use it.

Print it, or send it to whoever is measuring a service’s success by average completion time alone.

The question I’d leave you with: when your organisation cuts a service’s average time in half, do you know who stopped using it — and why?

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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.

Jordan is sitting with a member of staff who will use the new system every day. Not in a workshop. Not in a project meeting. In the room where the work actually happens, watching how it is done, listening to what is said about it.

The person Jordan is talking to says something that stops the conversation mid-note. Not a complaint — a small, specific observation. A workaround that everyone on the team uses, that nobody has mentioned in any survey or requirements session because it has become invisible through familiarity, and that had the visit not happened would have been absent from every document the technology decision was based on.

Jordan writes it down. It will change what is built.

What the standard process does not capture

The technology decision process here is not badly designed. The business case is thorough. The vendor shortlist is credible. The project workshops have run, and the requirements document reflects what was said in them. The governance process is in place.

What the formal process cannot capture is what is now being heard. The requirements document reflects what the previous system did — the starting point, not the need. The project workshops produced consensus around the visible problems, the ones that are easy to articulate in a meeting room to a project manager with a template. The satisfaction survey measured experience against the current system’s performance, not against what the work actually requires.

Each layer of mediation between the decision-maker and the person doing the work reduces the fidelity of what arrives. The user research output has been simplified for an executive audience. The support tickets have been categorised and aggregated. The workshop outputs have been translated into requirements language. By the time the information reaches the decision-maker, the texture has been processed out of it — the hesitation before an answer, the third thing said after the first two, the small specific observation that reveals the real constraint.

The visit was to hear what the processing had lost.

Going before the direction was fixed

The conditions that distinguish this kind of listening from the kind that confirms rather than informs are timing, directness, and repetition — and each runs against what the project calendar prefers.

The first condition is timing. Going before the strategy is fixed — before the vendor shortlist is final, before the requirements are locked, before the business case has closed around a particular direction — means the listening can still change things. Listening that happens after the direction is set validates rather than shapes. The project team hears what it hoped to hear, because the questions have been shaped by what is already being built, and the person being listened to can sense the direction and answers accordingly. The visit happened when it could still matter, which meant going when the schedule pressure made going feel like a distraction from the real work.

The second condition is directness. Going in person — not through a project manager or a user research agency — means sitting with the people, watching the work, hearing the words in the order and with the weight the speaker intended. There is a real role for structured user research in technology decisions. But there is something it cannot carry: the unprocessed signal that reaches a decision-maker only when the decision-maker is present to receive it.

The third condition is repetition. The first visit produced one set of things heard. The second produced different things, because the trust that had not existed in the first conversation had started to build. People answer differently when they know the person asking is genuinely interested rather than completing a process — and that knowledge does not form in a single visit.

In the third conversation, someone said something they had not said in the first two — not because they had withheld it, but because it had taken that long for the context to feel safe enough for honesty to reach what actually mattered. Single visits produce first answers. Repeated visits produce the ones underneath.

What was not in any document

The visit that mattered most was the third one.

The spreadsheet had come up in the first visit — the workaround every person on the team maintained alongside the current system, because the system could not hold notes in the right place at the right stage in the process. Three years in, nobody had mentioned it in any requirements session because it had become invisible through familiarity. The requirement was added on the way back.

The second visit changed a vendor criterion. Someone was working with three applications open simultaneously, constantly switching, returning to the system from a backgrounded state. The shortlisted vendors had been evaluated against their interfaces during structured demos — clean, navigable, well-presented.

The question of how each vendor’s product behaved when interrupted and resumed had never been asked. It was asked after that visit. Two vendors answered adequately. One did not. The shortlist changed.

What the third visit produced was different. Someone described the hardest part of their day — a step that happened immediately before they opened the system. Technically outside the system’s scope, but one the implementation could have absorbed if the handoff had been designed differently. That single observation reshaped the implementation in a way that addressed the friction at its source, rather than treating the system boundary as a fixed line. The first two visits had not produced it.

None of the changes were expensive. Each required someone to have been in the room. The requirements document that did not include the notes capability was not careless — it simply had no mechanism to surface what the direct conversation produced.

Together, the changes constituted a technology decision made on what was actually true, not on what the formal process had captured. Not complete information — that is never available. But closer to what was actually true than any document had been.

Three questions a leader cannot answer from data alone

Before a significant technology decision, there are questions that no amount of analysis will answer reliably without direct listening.

The first is whether the technology will genuinely help the people who most need it to work. Not the average user in the user story — the person whose day is hardest, whose work is most complex, whose need the requirements document has approximated but not named. That answer is in the room where the work happens. Direct listening — going there — is the only way to hear it. The leader who does not go will answer it from assumption, and the assumption will be derived from data simplified precisely where the difficulty is most real.

The second is whether the technology will genuinely add to what the people it serves can do — or substitute one set of constraints for another. The genuine gain in capability shows up in the specifics of what people find difficult and what they could do if that difficulty were removed. Those specifics are not in aggregated data. They emerged in the second visit, from a person describing a capability they currently lacked that the new system could provide if designed to carry it. That capability had not appeared in any document because no document had asked for it directly.

The third is what the system needs to adapt to — the actual range of people and circumstances it will encounter in use. The answer is not in the modal user’s profile. It is in the outlier’s experience, in the third conversation with the person who finally said the thing they had been circling. Going back is the only way to hear it. A leader who makes the decision without returning will design for the average and discover the outliers after the system is live, when adaptation costs considerably more.

My Opinion

I have sat in enough rooms to know that the gap between the requirements document and what is actually needed is structural, not accidental. At one university, a single conversation around a table surfaced needs nobody had articulated — and capabilities some groups had no idea others could use. I have run procurement exercises where, over four days, real teams put real problems in front of vendors and evaluated how they responded. The vendors that won were not the ones with the best product. They were the ones that listened before they pitched.

Questions worth taking into your next decision

The four questions below are not rhetorical. They are worth answering — for yourself, before the next decision closes.

1. Think of the last significant technology decision you were involved in making. When did you last speak directly — not through a survey, not through a user research report, but in person or by phone — to someone who would be affected by that decision? What did you hear, and what did it change?

2. If you lead technology decisions, what is the mechanism in your process that brings direct, unmediated human experience to you before the strategy is fixed? If that mechanism does not exist — what is stopping you from creating it?

3. Think of a technology decision you made that did not land as intended. At what point in the process would direct listening have changed the direction? Was that opportunity available and missed, or was the process not designed to create it?

4. Is direct listening a luxury that busy technology leaders cannot afford — or is it the one thing that makes everything else more efficient by ensuring the direction is right before the resources are committed?

A McKinsey and University of Oxford study of large IT projects found that budget overruns averaged 45 per cent — but shortfalls in delivered value averaged 56 per cent. Those are not the same problem. The value loss lands quietly, across years of implementation, workaround, and eventual replacement.

Jordan’s listening did not guarantee the right direction. It made the wrong direction less likely — at a cost in time that is negligible against the cost of not going at all.

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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 walks the lower fields in winter before anything else. Not because it is efficient. Because it is the only way to know what the overnight rain has done — whether the water is moving through or pooling, whether the drainage that has been slowly improving for three years has held, whether the flood-prone corner that floods briefly and recovers quickly if left ungrazed for six weeks is beginning to saturate. This knowledge cannot be looked up. It was built over twelve years of walking this ground in all weathers, at all hours, through all seasons.

The soil sensor is in the barn. It has been there for fourteen months.

The phone would show no signal here even if they took it out. It has shown no signal on this part of the land for as long as they can remember. That is not a technical problem waiting to be solved. It is a characteristic of these forty acres, in this valley, in rural Wales. The land is what it is. They are doing what they have always done: feeling the soil, watching the drainage, reading what this particular ground is trying to say.

What the grant assumed

The precision agriculture package arrived two years ago with a government agri-environment grant attached and a marketing brochure that described “nature-inspired data systems designed to work with natural variation.” They spent three weeks setting it up: the soil moisture sensors pushed into the ground at the intervals the manual specified, the GPS-guided monitoring app configured on the tablet, the yield-mapping software calibrated against the field boundaries.

The sensors required persistent 4G signal to transmit data to the cloud platform. Signal on the lower fields drops to zero.

The app’s recommendations were calibrated for large-scale arable monocultures: uniform rows, single-variety plots, conventional seasonal rotation. This is a twelve-year permaculture polyculture — a food forest, a market garden, mixed livestock — designed around the specific drainage, soil, and microclimate of this ground. The app had no logic for any of it. The yield-mapping tool produced output that mapped onto nothing being grown there or recognisable on this land.

The problem was not the setup, or the interface. It was that the technology had been designed for a farm this land was not, and no amount of patient configuration was going to change that. Within eighteen months, the sensors were back in the barn and the grant had been spent.

The principle the industry never borrowed

Permaculture has a founding principle, stated plainly since Bill Mollison and David Holmgren developed the practice in 1978: observe and interact. You do not design until you know this place. You walk it across seasons. You watch what it does in rain and drought and frost. The design emerges from sustained attention to specific conditions — not from a template applied to all conditions.

The precision agriculture industry describes its products as nature-inspired. The language is in the brochures, the grant applications, the conference presentations. Adaptive systems. Intelligent sensors. Biomimetic design. What the industry has borrowed is the vocabulary. The founding principle — observe first, design second, never assume the average applies here — was left behind.

The reason is structural. Technology development is funded by scalability. A solution that works for eighty percent of farms is investable. A solution built from sustained observation of this specific land, and a different solution built from sustained observation of the farm next door, is not.

The observation step — the one that would reveal what this land actually needs — is designed out before a line of code is written. Scalable and specific are not the same thing.

In nature, there are no average organisms. Only specific ones, each the product of sustained adaptation to particular conditions. The precision agriculture platform was built on the logic that farms are variations on a theme. This land is not a variation. It is a place.

What twenty-five years of forecasts missed

In March 2026, a peer-reviewed study published in Agribusiness (Wiley) tracked twenty-six precision agriculture technologies across twenty-five years of adoption forecasting data. The researchers — Malone and colleagues — worked through the CropLife–Purdue Precision Agriculture Dealership Survey, the longest-running continuous assessment of precision agriculture technology in the United States. Their finding: input dealers overestimated adoption on nearly every technology, in nearly every year of the study. The overestimation gap widened in the early 2020s, as adoption on several technologies flattened or declined while dealers continued to project steady growth.

The industry’s explanation has been consistent: this is an adoption problem. Farmers are slow to change. They lack the skills, the confidence, the willingness to invest. Twenty-five years of the same explanation, in the face of twenty-five years of the same forecasting error, is not an explanation. It is a refusal to look at the other possibility.

The NFU’s 2025 connectivity survey puts a number on part of the gap. Only 22% of UK farmers have reliable mobile signal across their whole farm. Twenty-one percent of UK farms have broadband speeds below 10 Mbps — compared with a national average of less than one percent. The technology assumes infrastructure that the majority of small mixed farms in rural Britain do not have. These lower fields are not an outlier.

They are the norm.

The grant funding attached to the precision agriculture package does not ask whether the farm has 4G. It asks whether the farmer can demonstrate a commitment to sustainable intensification. They could. The application process had no field for “my lower fields have no signal,” any more than the monitoring app had a field for “food forest polyculture.” The system that allocated the grant and the system that failed to work were built on the same assumption: that the farm in front of them was a version of the average farm, and that any deviation from the average was the farmer’s problem to solve.

The kingfisher and the train

In the early 1990s, a Japanese engineer named Eiji Nakatsu was tasked with eliminating the sonic boom produced by what would become the Series 500 Shinkansen when it exited tunnels. The existing nose design was aerodynamically efficient in open air and catastrophically loud underground, where the pressure wave had nowhere to go. The engineer was a birdwatcher before they were a lead engineer, and the problem reminded them of something they had spent years watching: kingfishers moving from air into water without producing a pressure wave. The kingfisher’s beak is a precise solution to that exact problem — the transition between two media of different densities, at speed, without disturbing the surrounding medium.

The engineer did not spend a two-week research sprint watching kingfishers. They had been watching them for years, as a matter of personal attention rather than institutional programme. The insight was available because observation had been treated as real work, sustained over time, without a deliverable attached to it. The redesigned nose reduced the sonic boom, made the train 10% faster, and reduced electricity consumption by 15%.

The technology that arrived at this farm had borrowed nature’s language. It had not borrowed the engineer’s method. It had not spent seasons watching what these lower fields do after overnight rain. It had not asked what twelve years of permaculture observation had produced.

That accumulated knowledge — the only knowledge that could optimise this specific land — was invisible to the system’s design. It was not data. It was noise.

What observation asks of any system

The common thread between permaculture’s founding principle and the engineer’s decades of watching is not the sector or the technology. It is the decision to treat observation as real work, completed before design begins. The questions that follow are worth bringing to any system you deploy, inherit, or commission — because the answers will tell you whether the observation step was there, or whether it was designed out.

1. When a technology you use, or your organisation deploys, was designed — how much time was spent observing the specific conditions it would operate in, rather than the average conditions it was modelled on? Would you know if the answer was none?

2. What knowledge do you carry — about your work, your land, your patients, your tenants, your community — that exists only in your head and has no field in the system you’ve been given? What happens to a decision when that knowledge is absent?

3. If the technology you’re currently using was built for someone else — a different scale, different context, different connectivity — would the evidence be visible in how it performs, or only in what it cannot do?

4. Permaculture’s first principle has been available since 1978. What would technology design look like if it had the same founding requirement: that you do not design until you have observed?

Sam still walks the lower fields in winter. The soil sensor is in the barn. The land is doing what it has always done: varying, specific, unaverageable. The technology that was supposed to augment that knowledge had no knowledge of this place. It never did. It was built for somewhere else.

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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 ISS and what comes next

In a factory in Long Beach, California, a team of engineers is building a space station. Not designing one. Not modelling one. Building one — machining aluminium panels, rolling and welding the pressure vessel that will contain breathable air, testing solar arrays and life-support systems. The station is called Haven-1. The company building it is called Vast. And if the schedule holds, a SpaceX Falcon 9 rocket will carry it to low Earth orbit sometime in early 2027.

Haven-1 is small. At 45 cubic metres of habitable volume — roughly the interior of a small tour bus — it is far from the football-field sprawl of the International Space Station, which took over a decade to assemble and cost upwards of $150 billion to build. It will host four astronauts at a time for missions of up to thirty days. Its operational life is three years. It does not try to be everything. It tries to prove that a private company can build and operate a human habitat in orbit — safely, on a realistic schedule, and without the resources of a national space agency.

That proof matters because the era it is trying to inaugurate has a deadline. NASA plans to deorbit the International Space Station around 2030. After twenty-five years as humanity’s permanent outpost in orbit — the most ambitious collaborative engineering project in history, shared by fifteen nations — the ISS will be guided out of the sky and into the Pacific Ocean. What replaces it, if anything does, will not be built by governments. It will be built by companies.

Whether that is exciting or alarming depends on what you think space is for.

The International Space Station was conceived as a symbol as much as a laboratory. Built in the years after the Cold War, assembling modules launched by American Space Shuttles and Russian Soyuz rockets, it expressed something about what cooperation between nations could achieve. The science it enabled was real and significant — advancing our understanding of human physiology in microgravity, producing pharmaceutical research that could not have been conducted on Earth, hosting thousands of experiments across biology, materials science, Earth observation and physics. But it was also, always, enormously expensive.

NASA spends roughly $3 billion per year to operate the ISS. That sum, compounded over the station’s lifetime, has represented a significant fraction of the agency’s total budget. The calculation NASA is now making is whether the science and presence in orbit justify that cost when private companies are willing to build and run orbital infrastructure and sell NASA access to it. The model shifts from landlord to tenant. Instead of owning and operating the station, NASA becomes a customer, buying crew time and research capacity from whoever builds the next generation of platforms. The $150 billion question is whether that works.

The ISS cost over $150 billion to build and costs $3 billion per year to run. NASA’s bet is that private companies can do this cheaper — and that competition will do what government monopoly could not.

Haven-1 is not the only contender. Axiom Space, which has already been awarded a NASA development contract, is launching a Payload Power Thermal Module to the ISS in early 2027; roughly nine months later that module will undock and rendezvous with Axiom’s habitat module to form an independent two-module station by early 2028. Starlab — a joint venture between Voyager Technologies and Airbus, with Northrop Grumman providing autonomous docking capability, and a design that will launch in a single flight aboard SpaceX’s Starship — is targeting 2028. Blue Origin’s Orbital Reef, billed as a “mixed-use business park in orbit,” is in development. Between 2026 and 2031, NASA plans to provide $1–1.5 billion in funded Space Act Agreements to support commercial station development — a programme it has recently revised, shifting from firm fixed-price contracts after officials concluded that a fully commercial business case does not yet exist in low Earth orbit.

The gap between the ISS’s retirement and the arrival of its successors is a genuine concern. NASA’s planning assumes overlap between commercial station operations starting in 2028 and ISS retirement in 2030. If commercial timelines slip — as they have already for Haven-1, which moved from a May 2026 target to Q1 2027 — that overlap narrows. Axiom Space chief executive Michael Suffredini has said publicly that the market may not be large enough for more than one commercial station. The company has faced funding difficulties. The question of whether the economic case for private space stations is as solid as the engineering ambition is one that has not yet been answered by the market.

What space is actually for

The commercial case for space stations rests on a set of claims about what can be done in orbit that cannot be done on Earth — and what that is worth. The most substantive of these claims concerns microgravity manufacturing.

Gravity shapes almost everything about how materials form and how biological processes operate. Protein crystals grown on Earth are distorted by gravity-driven convection; the same proteins grown in microgravity form larger, more perfect structures that reveal their molecular architecture more clearly. Drug development that depends on understanding a protein’s shape can benefit from having better data. Major pharmaceutical companies and JAXA have conducted crystallisation experiments on the ISS. One JAXA study of proteins associated with Duchenne muscular dystrophy led directly to a drug candidate — TAS-205 — now in Phase 3 clinical trials. California startup Varda launched a manufacturing satellite in June 2023, producing crystals of ritonavir — an HIV medication — in orbit and returning the capsule to Earth in February 2024.

Optical fibres grown in microgravity achieve purity levels impossible on Earth. Alloys and semiconductors manufactured without sedimentation can have more homogeneous microstructures. What changes with commercial stations is not the science. It is the access — more capacity, lower cost, broader range of customers, and the possibility of operating manufacturing processes at scale rather than as research demonstrations.

Space tourism is the other commercial pillar. Axiom Space has already sold private astronaut missions to the ISS, at prices reported to start around $55 million per person. Haven-1 will accommodate private visitors. Blue Origin’s Orbital Reef explicitly frames its design around commercial hospitality. The market here is narrower — it requires the kind of wealth that makes $55 million an acceptable discretionary expenditure — but it is real, and it serves the less glamorous purpose of underwriting operational costs that the research market alone may not cover.

The science done on the ISS has already produced pharmaceutical advances, materials breakthroughs and discoveries in human physiology that could not have been achieved on Earth. The question is whether the commercial model can sustain access to that environment after the ISS is gone.

The honest uncertainty is about the size and reliability of the non-government market. NASA’s contribution — buying crew time, hosting government science, funding development — is the anchor. If that anchor holds, the economics of commercial stations are plausible. If NASA’s budget is cut, or its priorities shift, or the agency decides it can achieve its goals without a permanent crewed outpost in low Earth orbit, the business cases of every company in this race become significantly harder.

Who inherits the sky

The ISS was many things, but one of them was political. The same hardware that carried American astronauts carried Russian cosmonauts, European scientists, Japanese engineers, Canadian robotics technologists. The station’s assembly required fifteen nations to agree on standards, interfaces, safety protocols and operational procedures. It survived the post-Cold War period, survived Shuttle disasters, survived the tensions that followed Russia’s 2014 invasion of Ukraine — though the latter ultimately prompted the partners to plan for Russian withdrawal and ISS retirement. Whatever its limitations as a scientific platform, it demonstrated that sustained human cooperation in space was possible.

The commercial stations being built now are American. They are funded by American venture capital and American government contracts. They will be operated under American regulatory frameworks and serve primarily American and allied institutional customers. China, which has built its own Tiangong station — permanently crewed since 2022 — is not a customer. The transition from government-operated to commercially-operated orbital infrastructure in the Western world is also, in a geopolitical sense, a transition from international to national. That is not necessarily wrong. But it is a change that deserves to be named.

The science also changes. The ISS was an instrument of national prestige and international diplomacy, which meant science happened on it even when the immediate commercial return was unclear. Commercial stations will prioritise the experiments customers pay for. That will include pharmaceutical companies crystallising proteins, universities running long-duration biology experiments, technology companies testing hardware in the radiation and vacuum of orbit. It will also mean that certain categories of basic science, whose value is not easily priced and whose benefits arrive decades later, will struggle to find a berth.

What the commercial model has to prove

The case for commercial stations is clear enough on paper. A research environment that currently costs $3 billion per year to run could be made more accessible, more competitively priced, and faster to iterate — if the market works as advertised. The pharmaceutical researcher, the materials scientist, the physician studying bone density in long-duration spaceflight, the engineer testing life-support systems for deep-space missions: all of them benefit if orbital access becomes cheaper and easier rather than harder and more expensive. The platform itself does not matter. What matters is whether the transition sustains the access.

The honest question is whether private operation adds something or merely substitutes one expensive monopoly for another. The ISS cost $3 billion per year under government management. The benchmark for commercial stations is not zero. It is whether, over time, the cost per hour of orbital research comes down, whether the range of customers expands beyond governments and billionaires, and whether the scientific output justifies the investment.

The harder question — the one the market cannot easily answer — is what happens to research that does not have an immediate commercial return. A drug candidate for a neglected tropical disease. A long-term study of bone loss that informs treatment for earthbound osteoporosis patients. A materials science experiment whose applications are twenty years away. These are the kinds of research that the ISS accommodated because governments decided they mattered. Whether commercial operators will find business models that serve them too is not yet clear.

My Opinion

The commercial argument is not wrong on its own terms. Making orbital access cheaper and more competitive is good for science. What I’m not convinced by is the assumption that the market will fund the research that takes twenty years to return a result — the kind that doesn’t fit a quarterly report or a drug application. Space has always held something that cannot easily be priced: the pull of the unknown, the technologies that come back to Earth from solving problems nobody had imagined, the slow accumulation of knowledge about what the species can survive and where it might eventually go. The ISS existed because governments decided that the unknown was worth funding. I’d like the decade ahead to find a way to carry that conviction into whatever comes next — not because it’s commercially compelling, but because it’s how we stay curious about a universe that doesn’t owe us answers.

What the decade ahead has to answer

A commercial model optimises for what can be priced. Several of the most consequential questions about the next era in orbit cannot be priced — not yet, possibly not ever.

1. The ISS was built by fifteen nations working together across geopolitical fault lines. The commercial stations being built now are almost entirely American. What does the transition from international to nationally anchored orbital infrastructure mean for science, for diplomacy, and for who gets to go to space?

2. Haven-1 slipped from May 2026 to early 2027. Axiom is facing funding pressures. The gap between ISS retirement and commercial successors may be tighter than planned. What happens to continuous human presence in low Earth orbit if the commercial stations arrive late?

3. The ISS funded science that had no immediate commercial return because governments decided that science mattered. Commercial stations will fund the experiments that customers pay for. Is there a type of research you think is important that might not survive that transition?

4. A private astronaut mission to the ISS costs around $55 million per person. Haven-1 will be no different in its early missions. At what point — if ever — does the price drop far enough that the question of who gets to go to space becomes meaningfully different?

The last astronaut to walk on the Moon did so in 1972. For more than fifty years, human beings have not left low Earth orbit. The ISS has been the closest thing to a frontier that most of us will ever witness — a small, fragile, extraordinarily expensive outpost at the edge of the world’s reach.

What replaces it will be built not by nations but by companies, and financed not by ideology but by markets. Whether that produces something better, something worse, or simply something different from what the ISS represented will become clear in the decade ahead.

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Sources & references

Haven-1 and Vast

• Haven-1 — Wikipedia

• Vast Delays Haven-1 Launch to 2027 — Payload Space

• Vast Advances Haven-1 Into Integration Phase — Vast Space

Axiom Space

• Axiom Station — Wikipedia

• Vast and Axiom awarded new private missions to ISS — NASASpaceFlight

• Axiom Space Secures $350M in Financing — Axiom Space

• Axiom Space puts a brave face on claims of dire finances — The Register

• Axiom Space raises $350M backed by Qatar fund — CNBC

Starlab

• Starlab Space Station — Wikipedia

• Private Starlab space station moves into full-scale development — Space.com

• Voyager Space Announces Teaming Agreement with Northrop Grumman for Starlab — PR Newswire

NASA Commercial LEO Destinations programme

• Commercial Space Stations — NASA

• NASA releases details on revised next phase of commercial space station development — SpaceNews

• Payload Field Guide: Commercial LEO Destination — Payload Space

Varda Space / ritonavir

• HIV drugs manufactured in space return to Earth — Healio

• Return of the Ritonavir — Varda Space Industries

• Rocket Lab successfully returns spacecraft capsule to Earth — Rocket Lab

JAXA protein crystallisation / Duchenne muscular dystrophy

• Creating new and better drugs with protein crystal growth experiments on the ISS — NASA

• Creating New and Better Drugs with Protein Crystal Growth Experiments — SpaceNews

ISS context and commercial transition

• Commercial Efforts Flail to Replace Aging International Space Station — Engineering

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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.

That isn’t a broken process. It’s infrastructure working exactly as built — just built without anyone answerable for what it decides. The card’s own argument starts there: the most powerful systems are usually the invisible ones, shaping outcomes nobody gets to question because nobody can see them operating in the first place.

The card holds five practice areas across two groups: what you design and verify into a system before it ever makes a decision — transparency, user control, outside audit — and what you build around it afterwards, so accountability doesn’t depend on goodwill. Written for whoever builds, deploys or governs systems like this, though anyone who has waited for an explanation and received a form letter instead will recognise exactly what it’s asking for.

It comes from The Next Evolution, where the fuller argument lives. The card stands on its own — you don’t need the book to use it.

Print it, or send it to whoever is about to ship the next system that will decide something about someone who’ll never see how.

And here’s the question I’d leave you with: next time a decision arrives with no explanation attached, do you actually know who — or what — made it, and whether you have any real way to ask?

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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.

Alex is in the middle of a routine task when it happens. Not an epiphany — a small, specific thing. The step that used to require four form fields now requires one. Alex completes the task and then pauses, trying to remember when the change appeared, because it was not announced and it was not the subject of a release note and Alex only notices it now because the friction that used to be there is gone.

Somewhere in the background of that pause is a support call from eighteen months ago. Alex had reported exactly this — the unnecessary duplication in the form, the way the same information was asked for in three different places. It had taken fifteen minutes longer than it should have. Alex had said so. The call had ended with an apology and a reference number.

And now the form was different.

What usually happens to feedback

The standard experience of providing feedback to a technology product is an exercise in patience with diminishing returns. The survey that arrives after every interaction, rated one to five, with a final optional text box that almost no one fills in because nothing has ever clearly happened as a result of it. The support ticket that generates an automated acknowledgement, a reference number, and silence. The complaint that is logged, managed, and resolved — which means it is closed, not that the thing that caused it has been changed.

None of these are careless. They are systems doing what they were designed to do. The survey tracks satisfaction, the ticket resolves the case, the complaints process manages liability. None of them were designed to change the product. That distinction matters more than it sounds, because a system designed for one purpose will not accidentally perform another, however good its data.

The difference in Alex’s case was not that the organisation cared more. It was that the system had been designed for a different purpose: to move information from the person experiencing the product to the people building it, and to do something with what arrived.

The three conditions that make a loop

A genuine feedback loop breaks the moment any one of three conditions is missing.

• Feedback that works has to be easy to give — not a survey sent after the fact, not a formal process initiated at a separate URL, but a mechanism present at the moment the friction is felt. Alex did not fill in a survey. Alex called to report something broken, while it was broken, with the specific detail of what had gone wrong. The signal was clean because it was immediate and unsolicited — the kind of feedback that surfaces the actual experience rather than a retrospective reconstruction of it.

• Getting that signal to the people who can act on it is harder than it sounds. In most organisations, the path is long and obstructed: feedback arrives at customer service, which resolves individual cases and closes tickets, while patterns that would be visible to the product team — if they received the same data — never reach them. Alex’s organisation had a short path: a shared channel between support and engineering, a standing item in the sprint review for user-reported friction, a team that treated incoming complaints as product development input rather than cases to be managed. The information arrived with its context intact and at the people who could do something about it.

• Visibility is what closes the loop. Not announced, not marketed — just present. The next time Alex used the service, the thing that had been difficult was different. Without that, the person who provided the feedback has no reason to believe the loop exists and no reason to provide the next signal honestly. A feedback system that receives, processes, and acts — but never demonstrates that it has done so — produces exactly the same user behaviour over time as one that does none of those things. Trust requires evidence, not intention.

All three conditions were present in Alex’s case. Remove any one and the loop would have broken.

What two years of listening produces

Over two years, the service Alex uses has changed in small, specific ways that trace directly back to what people who used it said.

The file upload failure message used to say “Error: upload rejected.” Alex had reported this after twenty minutes trying to diagnose which of five files was causing the problem. The message now says which file failed, why it failed, and what the accepted formats and size limits are. The change took a morning to implement. The information needed to make it had been sitting in the support queue for eight months.

The address entry flow used to require four separate fields in an order that did not match the way most people read their own address. The most common support call in one quarter was people unsure which field their flat number belonged in. The form now uses postcode lookup. Completion time for that step fell by two-thirds.

A third change is smaller and easy to miss. The notification sent when a request was submitted used to say “Your request has been received.” Alex had written, in a feedback form, asking whether anyone actually looked at these requests — because the notification contained no information about what would happen next, or when, or who to contact in the meantime. The notification now includes an expected response time and a named contact for queries. It took one line of copy and the answer to a question that no one on the product team had thought to ask, because no one on the product team had ever waited on the other side of the process they had built.

The cumulative effect is a service that feels attended to. Not perfect — attended to. That quality is not cosmetic. It is the specific evidence that someone received what was said and acted on it, and it is the foundation of whatever trust Alex now places in the service.

What the design is actually for

1. Think of a digital service you use regularly. Can you point to a specific change that service has made in the past year that you believe was a response to real user experience? If you cannot — what does that tell you about whether the feedback loop exists?

2. If you build or manage a technology product, how does feedback from real users reach the people who can act on it? Is the path direct and short, or does it pass through layers that filter, aggregate, and delay?

3. If you have ever provided feedback to a technology product — a support call, a complaint, a survey response — were you ever aware of a change that resulted from it? How did that experience affect your relationship with the product and the organisation?

4. Continuous improvement is standard practice in manufacturing and in software development. Why is continuous listening — treating user feedback as an ongoing product development input rather than a post-launch compliance exercise — still the exception in most technology organisations?

Most feedback systems are designed to manage the organisation’s relationship with feedback rather than to let feedback drive the product. The organisations that reverse that design — that make the path from user experience to product change as short and unobstructed as possible — build technology that improves in the direction that matters. Alex noticed. That noticing is rarer than it should be.

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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.

Alex puts the phone face-down on the table while eating dinner. Not because there’s an argument brewing, not because anyone has asked. Just a small, deliberate act. The meal lasts forty minutes. At some point — it turned out to be fourteen minutes in — the phone gets picked up. There was no notification. Nothing rang. The impulse arrived with no identifiable origin, like a hand that reached before the mind could object.

It doesn’t feel like a problem. Just a habit. Something to work on. A matter of willpower.

That framing is exactly what the people who designed that impulse were counting on.

The story we were given

We have been taught to understand compulsive phone use as a failure of self-control. The language we reach for is personal: distracted, addicted, weak, unable to focus. Self-help books tell us to build better habits. App stores sell us tools to limit our screen time. The diagnosis is individual, and so the cure is individual.

This framing is not neutral. It is convenient — and it is convenient for a specific set of people.

The companies whose platforms you cannot stop checking did not produce compelling products by accident. They built something more precise: a compulsion. And they built it using the same mechanisms that keep a gambler at a slot machine at three in the morning, long past the point of enjoyment.

What the engineers understood

Variable reward schedules are the most reliably powerful mechanism in behavioural psychology. The principle is simple: when the timing and size of a reward is unpredictable, the brain cannot habituate to it. It cannot stop paying attention. It keeps seeking.

A vending machine that always delivers the same snack is satisfying but not compulsive. A slot machine that sometimes pays out, in unpredictable amounts, at unpredictable intervals, is something else entirely. The unpredictability is not a design flaw. It is the entire point.

Social platforms were redesigned around this principle deliberately. The pull-to-refresh gesture works like a slot machine lever — and what it delivers operates on the same principle: sometimes a funny post, sometimes a message from someone you love, sometimes nothing worth reading. The brain cannot tell in advance. So it keeps pulling.

The Hook Model — one of the most cited models in social platform design — describes the loop explicitly: trigger, action, variable reward, investment. It was not developed to help you get things done. It was developed to make sure you come back.

What the misdiagnosis costs

When we believe the problem is personal, we look for personal solutions. We download another app. We set a screen time limit. We move our phone to another room at night. Some of these things help, at the margins. None of them changes the underlying architecture.

The system benefits from our misdiagnosis. They want the conversation to remain about you.

The human cost is not abstract. Attention fragmented over years becomes harder to sustain. The capacity for deep work, for genuine presence in a conversation — these are not personality traits immune to outside pressure. They are capacities that can be eroded. And they are being eroded, deliberately, at scale.

Children who grow up inside these systems will have spent their formative years in an environment specifically engineered to prevent them from consolidating attention. The long-term consequences of that are only beginning to be understood.

Against the user

A system that manufactures compulsion rather than enabling choice is not assisting the person using it. The platform’s interest and the user’s interest diverged the moment engagement became the goal rather than the outcome of delivering something worth using.

These platforms amplify a biological vulnerability — the brain’s inability to habituate to variable rewards — and direct it toward commercial ends. That is not augmentation in any useful sense. It is a diminishment dressed as a feature.

The algorithm adapts, yes — but it adapts to exploit you more efficiently. It learns what keeps you engaged, not what serves you. These are different objectives. In most people’s lives, they are opposing ones.

A system that does not assist, does not genuinely add something, and does not adapt to serve is not a neutral tool that some people misuse. It is a system designed, tested, and refined to work against its users’ interests while appearing to serve them. That is where the responsibility lies.

The questions underneath the habit

• When you reach for your phone without knowing why, what exactly are you looking for?

• Has there been a time when you tried to reduce your phone use and found it harder than you expected? What story did you tell yourself about why?

• If you discovered that a product you used every day had been deliberately engineered to make it difficult for you to stop using it — what would you want to be different, and who would be responsible for making that change?

• Is the design of the technology in your life something you have ever actively chosen, or is it something that happened to you?

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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 case documented in the medical literature described a patient with pancreatic cancer who missed out on a potentially life-prolonging treatment because their scan result was delayed by eighteen months. The scan had been taken. The images existed. No one had looked at them in time.

That case was not unusual enough to be a scandal. It was unusual enough to make the journal. The background against which it sits — a radiology system under sustained and worsening pressure, in which delays are the norm rather than the exception — has become so familiar that it no longer registers as news. It is simply the condition patients navigate when something goes wrong inside their heads and they need to know what it is.

In September 2025, more than 386,000 people in England were waiting longer than six weeks for a diagnostic test. The total number waiting for any diagnostic test exceeded 1.7 million. The Royal College of Radiologists has documented a 30% shortfall of clinical radiologists in England — roughly 2,000 doctors short of what is needed to meet current demand — with the gap projected to reach 39% by 2029. Forty-six percent of NHS acute trusts are not meeting even the interim waiting time target.

A system that can read a brain MRI scan in seconds has arrived. Not a prototype. Not a laboratory demonstration. A system trained on more than 220,000 real clinical scans, tested across more than 30,000 studies in a running health system over a full year, published in February 2026 in Nature Biomedical Engineering. The question is not whether the technology works. It is why, given that it works, the gap between scan and diagnosis remains so wide for so many patients.

What the AI can actually do

The system is called Prima. It was developed by a team at the University of Michigan led by neurosurgeon Todd Hollon, and it works differently from most AI diagnostic tools that have preceded it.

Earlier AI models in radiology were built for specific tasks: detecting a particular kind of lesion, flagging a certain cancer type, screening for a defined abnormality. They were useful for the thing they were designed for and nothing else. Prima was trained on everything — every MRI study taken at the University of Michigan Health system since radiology records were digitised, covering more than 220,000 studies and 5.6 million imaging sequences. It was trained not just on the images themselves but on the patients’ clinical histories and the reasons each scan had been ordered by a physician. It works, as its developers describe it, the way a radiologist works: integrating what the image shows with what the patient’s medical history tells you.

Across more than 50 different radiological diagnoses — stroke, brain haemorrhage, tumours, multiple sclerosis, aneurysms and more — Prima outperformed other state-of-the-art AI models, achieving a mean diagnostic accuracy of 92% across that range. It can identify which cases require urgent attention and immediately alert the appropriate specialist: a stroke neurologist if the scan suggests a stroke, a neurosurgeon if it suggests a bleed. Feedback is available as soon as imaging is complete. The result that would otherwise take days or weeks to arrive takes seconds.

Prima was trained on every MRI taken at a major health system since records were digitised. It works the way a radiologist works — integrating image data with clinical history. The result takes seconds.

UCL and King’s College London published parallel work in December 2025. Their model, trained on 60,000 brain MRI scans and the radiology reports written about them, accurately distinguished normal from abnormal scans approximately 19 times out of 20. A randomised multicentre trial across UK hospitals started in 2026 to assess how the system performs in live clinical workflows. These are not the same research programme, but they point in the same direction: AI that can read a brain scan fast and flag what matters is operationally real, not theoretical.

For acute neurological conditions, stroke above all, the stakes of speed are measurable in the specific. In stroke care, the standard principle is time is brain: for every hour of untreated ischaemic stroke, roughly 120 million neurons are lost. The gap between scan and treatment decision is a gap in which irreversible damage accumulates. A system that compresses that gap from hours to seconds is not a marginal improvement in workflow efficiency. It is a different category of clinical event.

Why the gap persists

If the technology exists and the problem is severe, the natural question is why the gap between them remains so large. The answer is not simple and it is not flattering to any single part of the system.

Regulatory approval is one obstacle. AI diagnostic tools that make clinical decisions about individual patients are classified as medical devices in the UK and the EU. The approval process is rigorous — necessarily so, given the consequences of getting it wrong — but it is not designed for the pace at which AI research is producing results. The models that receive approval tend to be narrow-task systems, because narrow tasks are easier to validate; a foundation model like Prima, which makes diagnoses across 50 conditions, presents a different and more complex challenge. The UCL team’s next step — a randomised multicentre trial in UK hospitals — is the appropriate pathway, and it is one that takes years.

Integration is a second obstacle. An AI model that produces a result in seconds is only useful if that result reaches the clinician who needs it, in a format they can act on, connected to the patient record system they are already using. NHS radiology departments typically operate on legacy IT infrastructure. The connections between imaging systems, electronic patient records, and clinical communication tools are incomplete, inconsistent, and often not interoperable. The radiologist shortage means that even when AI flags a case as urgent, there may not be a specialist immediately available to review the flag and make the treatment decision.

Liability sits beneath both. When an AI system incorrectly categorises a scan — flags something urgent that is not, or misses something that is — the question of who is responsible is unresolved in most legal and professional frameworks. The EU AI Act, whose main provisions came into application in August 2026, classifies medical AI diagnostic tools as high-risk and requires documentation of training data, bias checks, and human oversight policies — though the provisions specifically covering CE-marked medical devices are not due to apply until 2027. Radiologists operating under professional accountability for their diagnoses have every reason to be cautious about delegating to a system whose error profile is not yet fully understood.

The technology compresses the gap from hours to seconds. The regulatory pathway, the IT infrastructure, and the liability framework compress it from years to months. That asymmetry is the problem.

The Royal College of Radiologists has been clear that AI represents one of the most significant changes in medical care since the inception of the NHS — and equally clear that far from replacing radiologists, AI creates new roles and responsibilities for them. The challenge is not the technology’s relationship with the profession. It is the system’s capacity to absorb a disruptive tool without the disruption falling, as it usually does, on the patients.

Who bears the cost of the gap

Between October 2024 and September 2025, an average of more than 500,000 people were waiting each month for a CT or MRI scan in England. That figure increased by nearly 19,000 per month compared to the previous year. More than 74,000 of those waiting longer than six weeks were waiting for a CT or MRI scan necessary to diagnose or rule out cancer and other time-critical conditions.

The people who bear the cost of the gap are not evenly distributed. Delays in imaging disproportionately affect patients in regions with the fewest radiologists, which are not the regions with the least need. The East of England had the highest proportion of patients waiting too long for a diagnostic test in September 2025. Rural and lower-income areas consistently face longer waits than urban centres with large teaching hospitals. The patients most likely to experience an eighteen-month wait for a scan result are not the patients with the most access to private imaging, second opinions, or the capacity to advocate loudly for themselves in a system under pressure.

The documented case of the pancreatic cancer patient is one example of a pattern that oncologists and radiologists describe consistently. One consultant oncologist, quoted in a Royal College of Radiologists report, put it plainly: delays in scans and treatment were resulting in missed or late cancer diagnoses, with some patients’ conditions deteriorating to a stage where treatment was no longer possible. These are not patients who would be well-served by a technology that exists in a Michigan laboratory and a multicentre trial and not yet in their district general hospital.

My Opinion

Wait times are widely reported. What is less discussed is that even reaching the top of the queue offers no guarantee — patients are routinely displaced at the last moment when capacity shifts. Technology addresses demand; it does not address what happens after the scan. A faster result still requires a specialist to act on it, a treatment pathway to follow, and a system prepared to move quickly.

I have seen, first hand, what happens when effective health technology runs into departmental self-preservation. In much of the NHS, budgets are allocated on the basis of patient volumes. A department that uses AI to reduce the number of patients requiring manual review is a department that may face a reduced budget in the next cycle. The incentive runs directly against adoption. The most promising diagnostic AI I have encountered in NHS settings has repeatedly stalled not because it failed clinically but because it succeeded — and success threatened the funding model. Until the NHS addresses how it allocates resource to departments that adopt technology effectively, the gap between what is technically possible and what patients actually receive will remain wide.

Between the result and the patient

For a system 2,000 radiologists short of its stated need, an AI that effectively multiplies radiologist capacity is not a productivity tool — it is a patient safety intervention. The question is whether it is being deployed as one. There is a version of AI adoption in radiology that improves throughput statistics without improving what happens to the patient at the end of the queue: a system that accelerates the reporting of straightforward scans while leaving the complex and urgent ones with the same human bottleneck has improved the metric without improving the care. The test is whether the AI is being deployed where the clinical need is highest — in stroke pathways, in urgent cancer triage, in rural hospitals with no overnight radiologist — not where the implementation is easiest.

The infrastructure for deploying it that way does not yet exist in a consistent form across the NHS. What is needed is not just a model that works in a research setting, but a system that responds to the specific circumstances of each patient — their geography, their condition’s urgency, the resources available at their local trust. The Community Diagnostic Centres being built across England are a step toward that. The NHS 10-Year Workforce Plan consultation, in which the Royal College of Radiologists called for AI to be central to workforce strategy, is a step toward that. But the distance between the step and the patient who waited eighteen months for a scan that was already taken remains, for now, very wide.

The decisions that haven’t been made

The technology, the shortage, and the incentive problem have been outlined, what follows are not rhetorical questions. Each one has an answer — and each answer requires someone in government, in NHS leadership, or in clinical governance to decide that the gap matters enough to close it.

• If you or someone close to you has been on a waiting list for an MRI or CT scan, how long did it take? Was the delay ever explained? And did you know that 500,000 people are in that position every month in England?

• The technology to read a brain MRI in seconds exists. The shortage of radiologists to read them at all is severe and worsening. If you were designing a health system’s response to that combination, where would you start — with the AI, with the workforce, or with the infrastructure that connects them?

• The documented case of the patient who waited eighteen months for a scan result is one example. How many similar cases are undocumented — unreported because the patient did not know the delay had happened, or because no one connected the delayed report to the deteriorating outcome?

• AI that triages brain scans is being developed primarily in large academic health systems in wealthy countries. The global radiologist shortage is most severe in low and middle-income countries. What would a deployment pathway that addressed that imbalance look like — and who would need to build it?

A brain MRI takes around thirty minutes to acquire. Prima reads the result in seconds. The average NHS patient waits weeks. The technology does not close that gap automatically. Deploying it at the scale and in the places where it would matter most requires decisions about regulation, infrastructure, liability and investment that have not yet been made.

Those decisions are not technical. They are political. And they will be made — or not made — while patients wait.

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Sources and references

• Prima AI system: Hollon et al., “Learning neuroimaging models from health system-scale data”, Nature Biomedical Engineering, February 2026.
  https://www.nature.com/articles/s41551-025-01608-0

• Michigan Medicine press release: “An AI model that can read and diagnose a brain MRI in seconds.” https://www.michiganmedicine.org/health-lab/ai-model-can-read-and-diagnose-brain-mri-seconds

• UCL and King’s College London brain MRI model: UCL News: “AI model helps speed up diagnosis of brain disorders”, December 2025. https://www.ucl.ac.uk/news/2025/dec/ai-model-helps-speed-diagnosis-brain-disorders

• King’s College London: “AI brain scan model identifies stroke, brain tumours and aneurysms.” https://www.kcl.ac.uk/news/ai-brain-scan-model-identifies-stroke-brain-tumours-and-aneurysms

• NHS diagnostic waiting times (September 2025): Royal College of Radiologists: “Nearly half of NHS trusts missing test waiting time target as backlogs grow.”
  https://www.rcr.ac.uk/news-policy/latest-updates/nearly-half-of-nhs-trusts-missing-test-waiting-time-target-as-backlogs-grow/

• NHS Digital: “Diagnostic waiting times and activity for September 2025.”
  https://digital.nhs.uk/data-and-information/publications/statistical/nhse-diagnostics-waiting-times-and-activity-data/for-september-2025

• Radiologist workforce shortfall: Royal College of Radiologists: “2024 workforce census reports lay bare the challenges facing radiology and clinical oncology.”
  https://www.rcr.ac.uk/news-policy/latest-updates/2024-workforce-census-reports-lay-bare-the-challenges-facing-radiology-and-clinical-oncology/

• Stroke neuron loss: Saver JL, “Time Is Brain — Quantified”, Stroke, 2006.
  https://www.ahajournals.org/doi/10.1161/01.str.0000196957.55928.ab

• EU AI Act — medical device provisions: Kennedys Law: “The EU AI Act implementation timeline: understanding the next deadline for compliance”, 2026.
  https://www.kennedyslaw.com/en/thought-leadership/article/2026/the-eu-ai-act-implementation-timeline-understanding-the-next-deadline-for-compliance/

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.