A SAFER Solution For The AI Abundance vs. Jobpocalypse Argument
We're all betting on a future of AI-driven abundance, but without any insurance for the alternative. Here's a policy proposal to fix this, and that both sides can support.
Which side are you on? One says AI is a natural “economic evolution” that will drive productivity, cheaper services, new firms, new jobs, and a broad expansion of prosperity. The other says AI is creating an “economic revolution” that will drive mass displacement, collapsing career ladders, and an economy where humans are slowly pushed out of the production function.
The key difference: Is this an economic evolution or revolution?
And AI tax proposals are being proposed that aim to deal with the revolution risks, yet they all currently focus on limited geography and miss one real risk - that the consumption endpoint is already leaking.
In reality, we do not know which side will be right. And the net result is that we’re betting on the abundance future without ever negotiating terms for if it goes wrong. There is a better way for both sides to manage this risk - the name for that type of bet is “insurance”. And there is a policy where everyone can win.
If the AI abundance story is right, then the policy I’m about to describe should barely activate. It should cost the industry little or nothing beyond reporting that they’re already working on.
But if the other scenario starts to play out, then the protections should activate automatically.
That is the bargain that I’m proposing. A policy, where if we do end up with an economic revolution (not just evolution) then AI funds it in a sustainable way.
A “Sustainable AI-Funded Economic Response” (SAFER) Policy
If you really believe the AI abundance story, then this should be a deal that you’d happily commit to. And if you really believe the jobpocalypse story then you should be equally happy with this type of deal. Either way, this should provide some certainty and let both sides move forward together in a productive way. This might even help with the growing unpopularity that AI is showing in the public polls.
So, how would this work? Let’s look at both sides in detail.
The Risk Is Not Just Unemployment
The mistake in most of this debate is that it keeps collapsing the problem into job counts. Will AI destroy jobs? Will it create jobs? Are postings up? Are layoffs up? Are people retraining? Are new categories appearing?
Those questions matter, but they are not the whole shape of the risk. The deeper problem is distribution during the transition window.
AI may deliver large productivity gains and still weaken the link between work and income. It may grow the economy while shifting the gains away from wages and towards compute infrastructure, capital owners, cloud platforms, chip suppliers, model providers, and the firms able to reorganise around these systems first.
If that is the case, then this will not be a normal recession. It would not be a cyclical downturn where demand falls, governments borrow, workers get support, and the economy returns to trend. It would be closer to what Anthropic’s June 2026 Economic Policy Framework calls:
“structural decoupling of productive contribution from income”.
That is a very significant sentence.
It means the economy could keep producing more while the old income distribution mechanism stops working properly. Output rises. Market caps rise. Capex rises. Data centres fill. Agents transact. Firms become more productive. But the human wage channel no longer receives enough of the flow. That would be a modern rentier economy, the AI equivalent of a petrostate at global scale.
And once that happened, the second-order effects would really start to matter.
A fired or not-hired symbolic-worker does not just cancel a SaaS subscription. They cut back on coffee, childcare, renovations, tutoring, dental work, restaurants, rent, mortgages, household services, travel, and local spending. They also pay less personal tax. That weakens the public sector just as the need for transition support rises. The barista, childcare worker, plumber, landlord, builder, teacher, council, and state budget all sit downstream of the symbolic-worker wage base.
This is why the standard answer of “people can move into services” is so incomplete. Many of those services are funded by the disposable income of the very workers being displaced, wage-compressed, or never hired into the ladder in the first place.
This economic cascade would also be geographically uneven. The places that benefited most from the symbolic-work boom are likely to be hit hardest if that wage base weakens. San Francisco, Seattle, New York, London, Sydney, Melbourne, Dublin, Toronto, Bangalore. The centres that built the modern knowledge economy may also become the first places where the second-order impacts show up.
But that is only one geography of the AI transition.
There is another.
Two Geographies, Two Claims
The displacement geography and the resource-host geography are not the same. It is where the symbolic-worker wage base is concentrated. These are the cities and regions where software workers, analysts, designers, consultants, marketers, lawyers, finance workers, product managers, researchers, and junior knowledge workers have carried a large part of local demand.
But the resource-host geography is different. These are the places carrying the physical AI buildout: the data centres, grid upgrades, transmission lines, water demand, land use, backup generation, cooling infrastructure, and local political bargains. Northern Virginia is not San Francisco. Memphis is not New York. Rural Sweden is not London. Parts of Ireland, Texas, North Dakota, Arizona, Nevada, the Gulf, and other data-centre regions may carry the physical load even when the highest-value firms, shareholders, and users are somewhere else.
Both geographies have claims, but they are different claims.
If the negative case plays out then the displacement geography needs income support, fiscal stabilisation, career-ladder repair, and protection for the local service layer that sits downstream of symbolic-worker wages.
While no matter the outcome, the resource-host geography needs grid compensation, water protections, land-use protections, infrastructure payments, and some share of the value produced on its doorstep.
Any serious AI transition policy has to see both. If it only compensates symbolic workers in the case of a revolution, it looks like knowledge-class self-protection. If it only compensates data-centre towns, it misses the economic cascade. Any political coalition has to include both the people whose wage base may be hollowed out and the communities whose physical resources are being used to build the new machine.
The Wrong Way To Solve This
There is a growing policy conversation around AI taxation. Some of it focuses on token taxes. Some of it focuses on public ownership or sovereign wealth funds. Some of it looks like a digital-services tax. Some of it looks like a Pigouvian automation tax.
A sovereign wealth fund that only takes stakes in the labs does not fully solve the problem. A public stake in AI labs may sound clean, because it goes straight at the capital ownership problem. But the frontier labs may not be profitable during the transition window. They may be economically central while still burning cash on training runs, inference subsidies, talent, chips, safety, enterprise sales, and infrastructure. In a hypercompetitive investment phase, the labs may be the customers of the rentiers rather than the rentiers themselves. That is a bad match for near-term transition insurance. And the displacement risk may be near-term, while the equity upside may be long-dated, uncertain, geographically concentrated, and captured elsewhere in the stack.
The tax instinct may seem right. If AI shifts income from labour to capital, then the tax base has to move too. But most of these proposals also have a geography problem.
If a tax is national, capital can move. If it is tied to corporate domicile, the company can restructure. If it is tied to profits, transfer pricing and IP games return. If it is tied to tokens, the accounting can be gamed or bundled away. If it is tied to the point of consumption, it assumes the human consumer remains the centre of value.
And the critical point of the economic revolution view is that the last assumption is already weakening.
The emerging agent economy changes the picture. Google has already announced the Agent Payments Protocol, built to let agents initiate and transact payments across platforms. Coinbase and AWS are now working to let publishers and API providers accept agents as customers through x402. Visa is building agentic commerce infrastructure too, with Intelligent Commerce Connect aimed at helping agents pay and merchants accept agent-led transactions.
This is critical because it shows how the consumption endpoint is already starting to leak.
If AI infrastructure migrates from serving billions of humans with declining spending power to serving trillions of agents transacting at machine speed, then human consumption is no longer the obvious place to capture value. It may still matter, but it is not the root.
The real root is resource command.
AI systems need chips, data centres, land, cooling, grid capacity, capital, and above all energy. They can route around tax jurisdictions, billing categories, accounting definitions, corporate structures, and human-facing transactions. They cannot route around physics.
Measure The Unavoidable Input
The cleanest input-side anchor is energy.
Not because energy is the whole story. It isn’t. Training, inference, chips, data, models, water, land, and grid capacity all matter. But energy is the simplest unavoidable physical substrate. There is no AI without power.
The point is not that energy is bad. The point is that AI-dedicated energy consumption is a measurable proxy for large-scale AI resource command.
This is also not some impossible measurement problem. Large AI infrastructure firms already know their power commitments, data-centre loads, compute purchasing, cloud expenditure, accelerator utilisation, and energy contracts. OpenAI’s compute spending is already a board-level operating category, with Reuters reporting that it expected to spend around $50 billion on computing power in 2026 and roughly $600 billion through 2030.
These are not unknowable externalities. They are invoices.
The broader system-level pressure is also visible. The IEA projects global data-centre electricity consumption roughly doubling to around 945 TWh by 2030, growing at around 15 percent per year from 2024 to 2030. That is more than four times faster than electricity consumption growth from all other sectors combined.
So the input-side proposal is simple.
Large-scale AI infrastructure operators should report audited AI-dedicated energy consumption. Above a meaningful threshold, that energy use becomes the base for the SAFER Dividend.
That should include grid-supplied power and self-generated power. If a lab builds an off-grid data centre, colocates with renewables, buys nuclear output, runs gas turbines, or eventually does something more exotic, that should not make the activity disappear from the base. Off-grid compute may reduce grid burden and should receive credit where it adds genuinely new clean capacity, but it is still AI resource command.
The same logic applies to unregulated jurisdictions. The location of the compute should not determine liability. Market access should. If a model, agent network, cloud service, or AI-generated product sells into participating jurisdictions, it should comply with the reporting and contribution rules of those jurisdictions. That is not radical. It is the same basic logic behind border adjustments and destination-based tax reform. You do not get to serve the market while pretending the production chain is nowhere.
Over time, the formula should also account for efficiency gains. If models become dramatically more efficient, a pure dollars-per-MWh levy could shrink even while AI’s economic impact grows. That would miss the point. The base should therefore use audited energy consumption as the physical anchor, but also consider a provider’s share of overall large-scale AI energy use or frontier AI resource command. The system should reward efficiency without allowing dominant systems to disappear from the contribution base simply because the hardware improved.
But importantly, the rate should not activate simply because energy is used. That would be a blunt tax on AI. Instead, the rate should be tied to whether the economic risk actually appears.
The more AI preserves broad labour income, the lower the rate stays. The more AI decouples productive capacity from human income, the more the rate rises.
That creates the right incentive. It does not punish AI for being productive. It taxes AI resource command only when productivity stops flowing through humans.
It also encourages efficiency. A token tax encourages token-accounting games. An energy-linked levy encourages better chips, better cooling, smaller models, smarter routing, off-peak compute, additional clean generation, and grid-aware scheduling. If the industry can do the same work with less energy, it pays less. That is good.
Humans Are The Key
The output side should not be consumption. It should be population.
That is the most important shift.
If the risk is structural decoupling, then the redistribution claim should not depend on where the remaining human consumption happens to occur. It should not depend on whether a human bought an AI service, whether an enterprise used an agent, or whether a transaction moved through a human-facing market at all.
The fundamental claim is human.
If AI weakens the old link between human work and income (the real claim behind the “economic revolution” case), then humans become the relevant distribution unit.
But this does not mean every dollar should instantly be distributed equally across the world. The political economy would not support that, and the transition starts from an existing structure of GDP, markets, tax bases, and infrastructure burdens. A workable version would start with some blend of GDP and population, then it could perhaps shift over time towards population.
Early on, richer market jurisdictions would receive more because they are where the existing market and tax base sit. Over time, as AI value moves further away from human consumption and towards machine production, the population weight could rise. Eventually the system could look less like a consumer rebate and more like a human dividend.
But even “dividend” may be too narrow if we only mean money.
In the transition window, cash matters because people still need to pay rent, buy food, service debt, keep households intact, and support the local businesses around them. But if AI moves us towards a modern rentier economy, where productive capacity is increasingly held in compute, energy, models, agents, and capital, then redistribution will need to include more than fiat transfers.
It will need to include public services. It will need to include local fiscal support. It may need to include public compute, sovereign model access, energy access, housing support, education, healthcare, childcare, and ownership claims in the productive stack itself. All distributed via the existing local governments of course.
The principle is not fiat. The principle is human claim.
If wages no longer provide humans with a sufficient claim on production, then policy has to create that claim somewhere else.
That also means national sovereign wealth funds are not enough. They are useful, but they are geographically limited. A US public stake in US AI firms may help Americans if those firms eventually pay off. It does not solve displacement in Australia, India, the Philippines, Europe, Africa, Latin America, or anywhere else exposed to the same technology. It may even deepen the rentier divide between countries that own AI assets and countries that merely import their effects.
A serious version needs market-access rules, international pooling, or at least a path from GDP-weighted allocation towards population-weighted human claims.
And as we’ve covered, host communities also need their own share. The towns and regions that carry the data centres carry real costs. Grid congestion. Water use. Land use. Infrastructure strain. Political bargaining around local tax concessions. Often they receive a handful of permanent (or even temporary) jobs while the real surplus flows elsewhere. So part of the revenue should be routed by data-centre load, peak demand, grid stress, water stress, and local resource burden.
This gives the policy three output channels.
One channel compensates humans because the labour-income link may be weakening.
One channel stabilises displacement regions because symbolic wage bases may be hollowed out.
One channel compensates host communities because the physical infrastructure lands somewhere.
The Trigger: Distribution Risk Index
The missing piece is the trigger.
We need a way to avoid prediction warfare. The industry says AI will create abundance. Critics say it may break the labour-income channel. Rather than asking politicians to pick a side in advance, build the trigger and pre-commit to the claims process before the claim arrives.
Call it the SAFER DRI (Distribution Risk Index).
The SAFER DRI should measure whether AI-linked compute and capital are scaling into broad labour-income growth or away from it. It should not be based only on unemployment, because unemployment is a lagging and incomplete signal. Jobs can remain while wages compress. Headcount can look stable while entry-level hiring collapses. Average wages can rise because junior workers vanish and senior workers remain. GDP can grow while the labour share falls.
It also has to catch displacement that does not look like unemployment at first.
A white-collar worker can be displaced into contracting. A graduate can fail to enter the ladder at all. A software worker can become underemployed in gig work. A laid-off analyst can become technically self-employed while earning less, working fewer hours, and carrying more volatility. Someone can leave the labour force because the entry path has disappeared. None of that necessarily shows up immediately in headline wages or labour share.
The trigger must detect downgrading, not just unemployment.
So the dashboard should track entry-level hiring, cohort-specific employment, hours worked, income volatility, underemployment, labour-force exits, payroll-to-contractor shifts, occupational downgrading, and graduate-to-career mismatch. Some lag is unavoidable, but that is exactly why the reporting regime has to exist before the levy fully activates, and why the trigger should include leading indicators rather than waiting for official unemployment to move.
But the SAFER DRI should not be a magic equation.
A simple version of this idea could be written as if the SAFER DRI were a product of four stress indices: compute-labour divergence times labour-share stress times wage and career-ladder stress times tax-capture stress. That is useful as a mental model, but it’s too fragile as policy design. If one term falls near zero, the whole index disappears. If one noisy term spikes, the index can overreact. And the multiplication implies a precision that does not yet exist.
A better design is a two-gate dashboard.
Gate 1: The Resource Command Gate (Is AI Scaling?)
The first gate asks whether large-scale AI resource command is rising fast enough to matter. This would track AI-dedicated energy use, frontier compute, data-centre load, accelerator deployment, AI capex, and concentration of AI infrastructure.Gate 2: The Income Absorption Gate (Are Humans Decoupling?)
The second gate asks whether broad labour-income absorption is weakening at the same time. This would track labour share, exposed-sector wages, entry-level hiring, underemployment, labour-force participation, AI-attributed layoffs, payroll employment, tax receipts, downgrading, and geographic concentration of labour-market stress.
The SAFER Dividend only activates
when both gates are open for a sustained period
That avoids taxing AI merely because compute is growing. It also avoids pretending that a normal labour-market downturn is automatically an AI event. The question is systemic, not forensic. We do not need to prove that a specific model caused a specific firing. We need to know whether large-scale AI resource command is rising while the human income channel is weakening. Is the economic revolution showing?
The exact construction is obviously a serious statistical design problem by real experts in this field. It could be a weighted index, a traffic-light dashboard, a threshold-count rule, or even some combination of all three. But what really matters here is that activation should require multiple independent signals, sustained over time, rather than one noisy number.
Who Watches The Trigger?
This is the most captureable and political part of the whole proposal.
If everything routes through the SAFER DRI, then whoever defines this becomes the claims adjustor for this global insurance contract. That cannot be left to the industry being assessed. It also cannot be left to a minister looking for a convenient number.
Every measurement choice will be contested. What counts as frontier AI compute? What counts as an exposed sector? How do we treat contractors? How do we separate AI-driven stress from interest rates, overhiring, offshoring, remote work, or normal restructuring? Which labour-share measure matters? Which geography matters? How long must the trigger stay elevated? How are revisions handled?
The party with the most resources to litigate those choices will usually be the party being taxed.
So DRI governance is not a detail. It is the core of this insurance contract.
The index would need to be governed like critical economic infrastructure. It should be run by an independent statutory body, with central-bank or statistical-agency style independence. Its components, weights, thresholds, data sources, and revision rules should be pre-registered before activation. Changes should require a high bar and multi-jurisdictional agreement. Industry must be able to challenge data errors, but not be able to relitigate the social meaning of every threshold for years.
The trigger must also be redundant by design. No single indicator should control the outcome. The system must look for a pattern across compute scale-up, labour share, wages, entry-level pathways, employment quality, downgrading, tax capture, and geography.
This is not “let the data decide” in some naive sense.
Data do not decide anything by themselves. Institutions decide which data matter.
The real claim is narrower and stronger than that:
Build a transparent, independent, pre-committed claims process
before the claim arrives (if we can)
This Does Not Capture Every AI Rent
The SAFER Dividend would not capture every part of the AI surplus.
In particular, it would not directly capture NVIDIA or similar upstream chokepoints. It would capture the large-scale use of AI infrastructure by labs, clouds, data centres, inference providers, and major deployers. It would not automatically capture the extraordinary rents earned by chip suppliers, semiconductor foundries, lithography firms, high-bandwidth memory suppliers, networking vendors, or other firms that sit upstream of energy consumption.
That is not a gap, this a real design feature.
It matters because some of the most concentrated gains in the AI transition may appear before any model is trained or deployed. The chip supplier, the foundry, the cloud vendor, the power developer, and the landowner may all capture value from the buildout itself. But these are issues for corporate anti-monopoly policies that deal with rent capture.
SAFER should be cleanly isolated from this. The answer is not to make the energy mechanism do everything. That would make it too complex and easier to attack. The answer is to admit that the SAFER Dividend is the automatic stabiliser, not the whole redistribution system.
This distinction is important.
Better SAFER Than Sorry
This is better than an automation tax because it does not require proving that a specific model caused a specific firing. Better than a token tax because it does not depend on visible human-facing usage. Better than a consumption tax because it does not assume human consumption remains the centre of value. Better than a simple energy tax because it only materially activates when the labour-income risk appears. And better than waiting because the rules are negotiated before the fiscal and labour-market damage arrives.
The AI industry should be able to accept this.
If AI generates broad prosperity, the trigger stays low. If workers capture the gains, the trigger stays low. If tax receipts rise with productivity, the trigger stays low. If career ladders remain open, the trigger stays low. If the abundance story is true, the policy is mostly an accounting exercise that is already under way.
But if AI produces the scenario even Anthropic and OpenAI now say is possible, if there is a structural decoupling of productive contribution from income, then society should not be negotiating from a weakened position after the fact.
The SAFER deal should be made now.
The input is energy, because AI cannot avoid it.
The output is human claim, because humans are the constituency that matters if work and income are torn apart.
The trigger is the SAFER DRI, because the policy should activate on evidence rather than fear.
The governance matters, because the trigger is only as neutral as the institution that controls it.
And the upstream rent layer matters, because the energy dividend is not designed to catch every NVIDIA-shaped chokepoint in the stack.
That is the deal.
If the AI abundance side is right, it costs the industry almost nothing.
If they are wrong, we will all be very glad the insurance was already in place.
Do Something!
If you have found yourself on either side of the AI abundance vs jobpocalypse argument recently then share this proposal with the people you argued with. Let’s see if this can provide some productive common ground.
If your local representative is proposing an AI tax, token tax or sovereign wealth fund, etc. then share this proposal with them and highlight why those proposals may be limited.
If you know a policy maker, an economist or someone inside a frontier AI lab then share this proposal with them and start a real discussion.
Personally, I really hope the AI abundance side is right and this is just another economic evolution. But I don’t want to just sit here hoping. I know that insurance policies are only useful if you negotiate them before the event, and that SAFER could help people sleep better at night. SAFER could free us all to focus on realising an abundant future instead of wasting energy arguing about what we “believe” the future will hold.
So don’t just sit there. Like it, share it or argue with it. But do something!
To make this important topic more accessible I’ve created a skill that you can use with your favourite AI in 3 easy steps. Here’s how:
Download the SKILL.md file of your choice
upload it to your AI
then just say “Run this skill”
Note: It’s important to download this SKILL.md file and upload it directly to your AI chatbot rather than linking to it . We also recommend that you use one of the leading models from the 3 main frontier labs (e.g. Claude, Gemini or ChatGPT). If you use a lower level model then your results may vary. And of course if you find any issues, or feel like you’ve found a real flaw in my arguments please let me know — I value constructive feedback.