Wired for This: Neurodivergent Minds and the Workforce That Wasn’t Ready Chapter 7. Built to Be Outgrown. Why the Answer Isn’t One More Institution

I want to change register for this final chapter.

The first six chapters were built almost entirely on evidence. Peer reviewed studies, government data, national statistics, each figure traced back to its primary source. That was deliberate, because the argument needed to stand on something firmer than opinion. But this chapter is different, and I want to be clear about that from the outset. What follows is not a finding. It is a proposal. It is me, thinking out loud about what all, this evidence might actually point toward, and putting an idea on the table that I hope others will pick up, pull apart, and improve, rather than simply agree or disagree with. The evidence I draw on is real and cited. The idea I build from it is mine, and it is offered as a starting point, not a finished answer.

So here is the question I keep arriving at given everything in this series, what would help? And here is the answer I have come to, which is not what I expected when I started.

The answer is not one more institution.

Every number in this series was already old by the time you read it.

The Lancet underdiagnosis data describes a system as it was during the study window, not as it is today. The employment figures we relied on shifted between 2020 and 2024 even as we were writing. The waiting lists grew faster than any report could track them. This is not a criticism of the researchers, whose work is excellent and essential. It is a description of how knowledge moves through a system, slowly, in discrete publications, describing a world that has already changed by the time the ink is dry.

Now think about how policy responds to that knowledge. A government notices a problem. It commissions a review. The review takes months or years, it reports, a response is drafted, consulted on, and eventually funded. By the time money follows evidence, the evidence is describing a landscape that no longer exists, and the funding is calibrated to a reality that has already moved on.

This is governing by rear view mirror. It is not unique to neurodivergence. It is how most large systems make decisions. But it hits neurodivergent people especially hard, because theirs is a field changing on every front at once. The diagnostic criteria are shifting, our scientific understanding is expanding. The economy’s demand for the exact cognitive strengths in question is rising. When everything is moving, the lag between what is true and what gets acted upon becomes the whole problem.

So here is my idea, offered in the spirit of a first draft that others can build on.

Not one more fixed institution to “solve” neurodivergence. Instead, a coordinating body whose entire job is to keep the moving parts connected and current. Think of it as an observatory. A standing, independent centre that continuously gathers outcome data from every sector that touches neurodivergent lives, education, diagnostics, employment, health, and science, aggregates it, and publishes it on a regular cycle for everyone to see.

Each sector feeds its data in continuously rather than episodically. The observatory turns that flow into a clear, public, regularly updated picture. What is working, what is not, where outcomes are improving, where they are deteriorating, and how all of it is shifting as globalisation, technology, and understanding change the ground beneath us. Government could then make funding and policy decisions on current information, adjusting forward in something closer to real time, rather than responding to a snapshot taken a year earlier.

This is not, I should say plainly, a new concept. It is only new to this field. Continuous data infrastructures already exist in other domains. The proposal is simply to build one for neurodivergence, cross sector, and to build it well.

The deeper principle behind it is one I keep returning to, borrowed from the designer Bruce Mau. Commissioned once to produce a twenty-year plan for the city of Mecca, which could not cope with the growing numbers it had to accommodate, Mau refused the brief. A twenty-year plan, he reasoned, would have to be built around the car, and, in his words, “the car it’s about over.” he proposed a thousand-year plan instead, “because we can’t possibly know what’s going to happen,” concluding that the only sensible response was to “design an open system. A kind of open platform for innovation, which is the most important thing we could do.” Notably, Mau’s Mecca plan was never fully implemented by the authorities, yet it still prompted others to begin building on the idea, and he came to describe success not as having implemented it himself but as having been the catalyst. That is exactly the spirit in which I offer this.

An observatory is that open platform, applied to this problem. It does not try to predict what neurodivergence will mean in thirty years, or what the economy will need, or what the science will show. It builds the capacity to keep seeing clearly as all of those things change.

The moment you propose a central data body that steers national funding, you should worry about exactly one thing above all others. What stops it being captured, manipulated, or bent to whoever holds power? A single authoritative source of “the facts” is a dangerous thing if the wrong hands are on it. If this idea has a fatal flaw, that is where it lives, and any honest version of the proposal must confront it directly rather than wish it away.

Here is what reassures me somewhat, this problem is not new, and it has already been seriously, if imperfectly, addressed elsewhere. The clearest model is the governance of official statistics. In the UK, the UK Statistics Authority was created by an Act of Parliament in 2007 and operates at arm’s length from government, as a non-ministerial body accountable directly to Parliament rather than to any minister. That legal independence is the single most important safeguard. The people who produce the data cannot be ordered by a politician to change it, delay it, or bury it, because they do not answer to that politician. Their statutory duty is to serve the public good, not the government of the day.

That independence is backed by a separate regulator, the Office for Statistics Regulation, which sets a binding Code of Practice and has real power to enforce it. It can strip statistics of their official accreditation if they fall short, and it can publicly challenge ministers who misuse figures. There are concrete anti interference mechanisms too, such as tightly controlled pre release access, so figures cannot be quietly spun or suppressed before the public sees them.

But I want to be clear about the limits, because pretending this is a solved problem would undermine the whole point. Even this system is imperfect and perpetually contested,as recently as its own 2024 governance review, the Authority was still recommending its protections be strengthened, and public trust remains fragile. Years of surveys have found many people doubting that official figures are genuinely free of political interference. The lesson is not that independence is impossible. It is that trust in a body like this is maintained continuously, through vigilance and enforcement, rather than guaranteed once at the point of creation. Any observatory worth building would have to be designed with that permanent vigilance baked in from the start.

They do, and carefully.

Artificial intelligence is part of what makes an observatory feasible. It can process continuous, multi sector data at a scale no human team could manage, spotting anomalies, and flagging inconsistencies that might indicate either error or interference, however AI is double edged. A model’s outputs depend entirely on who built it, what it was trained on, and what it was told to optimise for. An AI system can introduce bias and manipulation as easily as it can detect them, so AI can be a tool of the observatory, but it cannot be the thing that makes the observatory trustworthy. Technology enables it. Only independent human governance legitimises it. That distinction must be held firmly, or the whole thing tips over.

Cybersecurity is not an optional feature here but a precondition. A central data asset that drives national funding decisions is, by definition, a high value target. Integrity protection, audit trails, and rigorous access control are the price of entry, not enhancements to add later. If the data can be tampered with, nothing else about the proposal matters.

There is one more danger, and it is the one I find most important, because it is the subtlest.

In bringing all neurodivergent profiles together into one coordinated system, there is a real risk of creating something static. A fixed model, a rigid category, a single settled definition of what neurodivergence is and how it should be measured. The trouble with a fixed model is that it cannot be outgrown. It calcifies at exactly the moment the science is telling us the picture is still forming.

We have been here before. Our own diagnostic history proves the danger. Until the DSM-5 was published in 2013, the diagnostic manuals formally prohibited diagnosing autism and ADHD in the same person, and an autism diagnosis was used to rule ADHD out. A generation of people, many of them the AuDHD adults now recognising themselves in this series, were left with incomplete or inaccurate identifications because the model of the time could not accommodate what clinicians were seeing. That was not a failure of individual doctors. It was a fixed system unable to update as fast as the understanding around it was changing.

The deepest requirement for any observatory is this, it must be built on the assumption that today’s understanding is provisional. Its value is not that it enshrines the current model of neurodivergence. Its value is that it becomes the mechanism through which that model keeps getting revised, as the science advances and the profiles we recognise expand. It must be designed, deliberately and from the outset, to be outgrown.

That is the whole point. Not a monument. A living thing that expects to be revised and is structured to welcome its own revision rather than resist it.

Let me be clear about what this is and is not.

It is not a finished blueprint. It is a direction of travel, offered by one person who works in this field, is neurodivergent herself, and has spent this series trying to map the problem honestly. I am certain parts of this are wrong, or incomplete, or naive in ways I cannot yet see. The governance is hard. The funding is hard. The question of who decides what gets measured, and therefore what gets valued, is genuinely difficult, because measurement shapes money, and money shapes lives. And there is a real danger, which I hold with discomfort, of turning neurodivergent people into data points in a system meant to serve them rather than participants in shaping it.

I do not have all those answers. What I have is a conviction that the current approach, waiting years to learn what has already happened and funding solutions to a world that has already moved on, is not good enough, that something is more adaptive, more continuous, and more honest about its own capability is worth seriously imagining.

So this is an invitation. If you work in policy, in data, in diagnostics, in employment, in education, in cybersecurity, in governance, I would genuinely rather you expanded this idea than dismissed it. Tell me where it breaks. Tell me what already exists that I have missed. Tell me how the safeguards should work. The best ideas in this series were the ones that survived being challenged, and this one deserves the same treatment.

Because here is where all of it has been heading. Everything in these seven chapters, the diagnosis that came too late, the classroom built for a vanished world, the economy that shifted without telling anyone, the talent that was always there, the infrastructure that fails it, and the question of how we might keep learning fast enough to act in time, all of it begins upstream, in the one system every neurodivergent person passes through before any of the rest.

The school.

That is where the next series begins. Not with the system we inherited, built two centuries ago to be endured, but with the harder and more hopeful question of what an education system built to be outgrown might look like.

Iris Walshe is an AuDHD equestrian athlete, ADHD/AuDHD coach, and founder of NeuroWave Coaching. She has competed across showjumping, eventing, and dressage, and holds ADDCA, PAAC CACP, and ICF credentials. She works with neurodivergent athletes and high-performing adults at neurowavecoaching.com.

A note on this chapter: Unlike the preceding six, this chapter is a reasoned proposal rather than an evidence review. The factual anchors below are real and cited. The central proposal, a living evidence observatory for neurodivergence, is the author’s own, offered for discussion and development rather than as established fact.

UK Statistics Authority. Established by the Statistics and Registration Service Act 2007. Operates at arm’s length from government, accountable to Parliament. Office for Statistics Regulation, Code of Practice for Statistics. (uksa.statisticsauthority.gov.uk and osr.statisticsauthority.gov.uk)

Lievesley, D. (2024). Independent Review of the UK Statistics Authority. Referenced for the finding that statutory safeguards were recommended for strengthening, and that public trust in the independence of official statistics remains contested.

Mau, B. Massive Change / Mecca 1,000 year plan. Quotations from Bruce Mau’s recorded talks (M.AD Insighters Series and SXSW 2018) on designing “an open platform for innovation.” Referenced as design philosophy, not empirical evidence.

Ramtekkar, U.P. (2017). DSM-5 Changes in ADHD and ASD: Implications for Comorbid Sleep Issues. Children, 4(8), 62. Confirms that prior to DSM-5 (2013), diagnostic criteria prohibited the simultaneous diagnosis of ADHD and ASD. Corroborated by CNS Spectrums (2016) and multiple peer reviewed sources.