So I read the Citrini piece. You probably did too — it was everywhere last week. “The 2028 Global Intelligence Crisis.” AI automates white-collar jobs, those people stop buying things, demand collapses, companies automate harder, repeat until civilisation ends I guess. Markets apparently moved on it.
And look, the core loop is sound. If you fire the people who buy your stuff, eventually nobody’s buying your stuff. They even have a good name for it — “Ghost GDP” — where productivity looks great on paper but the actual flow of money between humans has dried up. The mortgage analysis is genuinely nasty: most household wealth is property, most property is financed against salary assumptions, and those salary assumptions are about to stop being true.
I don’t want to dismiss any of that. It’s a real mechanism.
But I keep getting stuck on two things they never address.
The system was already breaking
First: Citrini treats the current economic system as a stable baseline that AI disrupts. But it isn’t stable. It hasn’t been for a while.
We’ve got a climate crisis driven by incentives that reward pollution and punish prevention. A housing crisis driven by treating shelter as a financial instrument. A healthcare system that subsidises disease and then charges us to treat it. Infrastructure that’s been underinvested for 30 years. A food system so misaligned it’s cheaper to make people sick than to feed them well. A recycling system where — and I love this one — an AI audit of a UK facility found 93% of what was being thrown away as “residue” was actually recoverable material. We’re literally burning value because the accounting can’t see it.
None of this needed AI to go wrong. The incentives have been broken for decades. AI didn’t cause the polycrisis — it’s arriving into one.
Which means the “before” state Citrini is trying to protect doesn’t actually exist. There is no stable status quo to defend. The system needed redesigning whether AI showed up or not.
AI is either the straw that breaks the camel’s back or the repair tool. Possibly both, simultaneously. The question isn’t “should we change the system?” — we don’t have a choice. The question is what we change it to, and whether we do it well or badly.
What’s getting cheaper (and why that’s not enough)
Okay, so what’s actually changing?
Khan Academy’s AI tutor costs $4 a month. A human tutor is $25-80 an hour. Not a worse tutor — the studies say outcomes are as good or better. Four dollars. In refugee camps in Mali and Chad, AI is now screening for TB. Not “fewer radiologists than we’d like” — zero radiologists. The AI is it.
AI therapy: $75 a year, clinically validated. A human therapist in the UK: north of £1,500 for twelve sessions.
“Yeah but you can’t eat a chatbot.” True. But DeepMind ran one model and discovered 2.2 million new crystal structures. 800 years of materials science. 528 new lithium-ion conductors — which is how batteries get cheap, which is how energy gets cheap, which is how quite a lot of physical stuff follows. AI demand forecasting is cutting food waste in half in industrial kitchens. A hundred thousand Tesla home batteries are being coordinated as one 535-megawatt power plant.
So the cost of essential services and increasingly physical goods is collapsing. Citrini’s death spiral assumes wages go down and costs stay the same. But if costs are falling too, the floor isn’t the same floor.
Here’s the thing though — and I want to be honest about this because the essay I was tempted to write would have stopped at “stuff gets cheaper, problem solved.” It’s not solved. Even if healthcare and education cost a tenth of what they do now, people still need income. The mortgage broker who gets laid off in 2027 has a mortgage denominated in today’s money. “Everything’s cheaper” doesn’t help if your income is zero.
The cost collapse buys time and lowers the stakes. It’s necessary but not sufficient. You also need a transition.
What the transition actually looks like
A friend made a point I keep coming back to: “Nobody has ever written a detailed scenario showing the exact steps to a positive future. You’d think someone would have a plan for how this goes well. Nobody does.”
I think that’s wrong, actually. But the pieces haven’t been assembled in one place. Let me try.
Step one: freelance constellations. We’ve already got a massive job crunch — not mass unemployment yet, but accelerating displacement of white-collar work. The obvious pattern: people don’t just sit around waiting for new jobs to be invented. They freelance. The difference now is that AI-mediated coordination means freelancers can co-op and act as a larger entity — a flock of highly qualified consultants that a business can engage like a company, even though it’s a flexible constellation of individuals. Businesses-as-usual still get their headcount problem solved. Displaced workers still earn. Nobody had to wait for policy. This is already starting to happen.
Step two: incumbents slow the bleeding. Four-day work weeks. Job sharing. Reduced hours instead of full layoffs. The UK four-day week pilot showed 92% of companies continued after the trial — 65% fewer sick days, 57% less turnover, revenue flat. This isn’t charity, it’s good business, and it buys time. If you can spread the same work across more people with fewer hours each, you reduce the pace of displacement without stopping it. That matters because the cost deflation from step one needs time to feed through.
Step three: the work that needs doing. Here’s the bit that I think most people miss. There is an enormous amount of work that desperately needs doing — environmental restoration, community care, infrastructure repair, eldercare, education support — that we’ve never figured out how to pay for. Not because it’s not valuable, but because the accounting system doesn’t price it.
As work weeks shrink from five days to four to three, those freed days become available for this kind of work. Call it Earth Corps or community service or regenerative labour or whatever. The point is: there’s a huge mismatch between “there’s nothing for humans to do” (wrong) and “there’s no way to pay humans to do the things that need doing” (a solvable accounting problem). As AI makes the expensive stuff cheap, the fiscal space opens up to fund the stuff that was always too expensive to organise. And before the “but people won’t work if you just give them money” crowd shows up: the Stockton SEED experiment gave 125 people $500 a month for two years. Full-time employment went up — from 28% to 40%, versus 5% in the control group. It inspired over 100 similar pilots across the US. Turns out when people have a floor to stand on, they don’t lie down. They get to work.
This isn’t a utopian fantasy. It’s three practical steps, each of which is already happening in some form, that together add up to a transition: freelance coordination absorbs the initial shock, reduced hours spread the impact, and redirected time goes to work that was always needed but never funded.
The accounting has to change (and someone’s already modelled it)
“Great, but who pays for all this?”
Fair. This is where the Ex’tax Project comes in, and the reason I keep bringing it up is that it’s the most concrete, serious answer I’ve found.
Cambridge Econometrics, working with Deloitte, EY, KPMG, and PwC — all four Big Four — modelled a specific fiscal shift across all 27 EU member states. Right now, about 51% of EU tax revenue comes from labour. About 6% comes from pollution and resource use. So we’ve accidentally built a system where it’s expensive to employ people and cheap to trash the planet. (If you did this on purpose you’d be a Bond villain.)
Their proposal: shift it. Tax pollution and resource use more, tax labour less. Budget-neutral — no new money needed, just move where the tax falls.
The results: GDP 1.6% higher. Employment 3.0% higher — six million more jobs. CO2 emissions down 7.1%. EU fossil fuel imports drop by €56 billion. All within five years.
Six million jobs. GDP up. Emissions down. Budget-neutral. Backed by all four Big Four. And basically nobody I talk to has heard of it.
Maybe this isn’t the final answer. But it proves that when people throw their hands up and say there’s no credible path forward, they’re wrong. There’s a plan on the table with numbers that add up. The barrier isn’t knowledge. It’s coordination.
The coordination problem (which is actually the whole problem)
Getting governments, companies, unions, and regulators to agree on something this big has always been a nightmare. Tax is connected to welfare. Welfare is connected to property law. Property is connected to trade. Pull one thread, whole sweater.
But — and I keep coming back to this — what if coordination just got cheap?
DeepMind had an AI design new ways to distribute wealth. Put it to a vote against standard left-wing and right-wing proposals. The AI’s version won. It found a compromise nobody else had seen. CO2 AI gave a company precise emissions data across 25,000 products in two months — stuff that was invisible is now measurable. Quadratic funding — basically a clever way to make a thousand small donations count more than one big one — has moved $67 million to over 5,000 public projects.
Small examples, obviously. But five years ago none of this existed. The cost of getting large groups of people to agree on complicated things is collapsing. So why hasn’t anyone assembled the detailed positive scenario? Not because it’s impossible. Because it requires coordinating an insane number of moving parts — more than any previous generation could handle.
We might be the first that can.
One last thing
You can’t run a new operating system if everyone’s staring at the old scoreboard. If GDP is your only metric, cheaper services look like shrinking output. That’s collapse by definition — even if lives are getting better.
Over 50 cities are running Doughnut Economics dashboards. Amsterdam has measurably cut CO2 with one. New Zealand’s treasury is legally required to report against wellbeing indicators. Scotland’s tracking 81 different measures of how the country’s actually doing.
If your scoreboard includes health, education, environment, and connection, then a world where AI makes essential services nearly free doesn’t look like collapse. It looks like the goal.
Citrini is right that the current system can’t absorb what’s coming. But the current system was already failing. The polycrisis didn’t start with AI and it won’t end by protecting the status quo.
We designed this economy. We chose how to measure value, where to put the tax, what to count. None of it is a law of physics. We made it up, it was useful, and it’s becoming less useful. The question was never “will AI break the economy?” It was always “the economy is already breaking — can we coordinate the update in time?”
There are modelled proposals. There are practical transition steps. There are new coordination tools. There’s even a new scoreboard.
It shouldn’t be easier to imagine the end of civilisation than the end of an accounting system.