AI Doomsday Theatre: The Reality

The AI transformation and worker transition inside one institution

The first two essays in this series argued that the AI-2027 framing was theatre — first by critiquing the scenario directly, then by mapping the actual probability structure with the Markov tool. This essay is the third move. It describes what the operational mid-risk looks like inside one institution where it is happening right now.

Imagine a financial services company. It employs more than five thousand and fewer than twenty thousand people. It runs a suite of consumer brands that compete on logo colour and marketing voice. They do not compete on the underlying service or price. They exist to support the illusion of competition where none exists, and to maximise customer exposure — like having more shelf space and multiple locations in a supermarket. Repeated exposure increases sales.

Two years ago the executives got board approval for a $150 million AI transformation. The benefits case promised substantial productivity and revenue gains with modest workforce reduction. Two years in, the benefits are not materialising. The productivity and revenue gains were grossly exaggerated. The response was to change the reporting so the gap is harder to see at the board level and increase the planned job shedding to half the workforce.

In parallel, the company switched to fixed-term contracts instead of full-time positions for all new hires. These contracts combine the worst features of contracting and standard employment, without pay scales to compensate. Contract end dates function as preset firing dates without redundancy or notice pay. The structure makes it possible to reduce headcount without redundancy costs. Legacy employees, hired before this change, cannot be shed so cheaply. They will lose their jobs through AI restructure redundancy at minimum legal cost.

The communications around this run at three layers.

The external communications layer says nothing. The market is not being told.

The internal communications layer says jobs as usual. Town halls, intranet posts, leadership messaging — all calibrated to growth, productivity, augmentation, opportunity.

The AI manager plans layer is the actual plan. Headcount targets, benefits cases, transformation roadmaps. This layer has planned the reduction. It is not visible to the second layer. The retraining program belongs here too. Examined, it consists of pointing workers toward trade qualifications they would have to fund themselves. Desk-bound knowledge workers in their fifties are offered brochures about electrical apprenticeships. The brochure exists so the company can say it is being a good corporate citizen. It will not pay for the retraining if it is not legally required to.

Each layer is defensible in isolation. External silence is normal in the lead-up to share-price-sensitive announcements. Internal jobs-as-usual messaging is standard to avoid industrial action. Operational planning for headcount reduction is common in large corporates. The executives never have to defend the architecture as a whole. They defend each layer separately, with three reasonable answers to three different questions. The uninformed workers bear the cost. They organise late or not at all, take legal advice late or not at all, and accept whatever exit terms are offered when the gates have already closed behind them. This is the company in our hypothetical. Now let me describe its industry.

The Operating Model

The brand multiplicity is the entry point. Multiple consumer-facing brands at similar price points, with coordinated economics behind separate marketing surfaces. This looks like competition from outside, but it is really a confusopoly that holds prices above what a transparent market would deliver, because consumer search costs are structurally inflated. AI cost reductions will not create pressure to pass savings to customers. The deeper pattern shows across the products the financial services industry sells — credit cards, wealth management, lending, insurance, payment processing. It is apparent in the gap between marketing communications and the legally binding ones. The industry treats its customers as morons.

Pick up any Product Disclosure Statement. Sixty plus pages, dense prose calibrated to satisfy disclosure requirements rather than convey meaning, operative clauses buried, delivered after the consumer is psychologically committed. The PDS minimises the seller's risk and obscures the customer's understanding. Everyone knows the customer probably did not read it and could not have understood it if they had.

Credit cards. The behavioural design is to get customers to max out their cards and make the minimum payment. Interest rates are very high. A low-value reward points layer is plastered over the top to distract customers from the real cost of credit card use. Marketed as freedom, structurally set up as addiction by design.

Wealth management. The standard fee structure is a percentage of the customer's funds under management charged regardless of performance. The customer pays the same fee in losing years as winning years. The manager has no skin in the game. The structural argument for this fee model has been weak for thirty years. It persists because customer information asymmetry and switching costs are sufficient to maintain it.

Lending. The criticism is not that banks lend cautiously. It is that the regulatory architecture produces compliance theatre rather than risk management. Customers with cash flow and security cannot get loans because the LVR rules say no. Customers without either can get loans if the serviceability calculation passes the buffer tests. The bank optimises for the rules, not for actual risk, because optimising for the rules generates revenue without generating regulator friction.

Insurance. Most insurance purchases are essentially an error. Everyone will suffer their share of losses over their lifetime. Either they deal with these events or they get insurance. Not having insurance means the cost to make whole is the cost of losses. Having insurance means the cost to make whole is the cost of losses plus the overhead, marketing and profit of the insurance company. Over a lifetime, the premium and excess must cost more. The only insurance anyone should buy is for an event they cannot make themselves whole from. Most people could not afford to rebuild their house if it burnt down. They could probably get a cheap second-hand car if their car was written off. They could pay for a dent or a broken windscreen and replace a failed toaster out of warranty. So insuring your house makes sense. Paying for a longer warranty on a toaster does not. But even for catastrophic loss, insurance firms cap their maximum liability. The defensible case for insurance — the only case that justifies paying overhead and profit on top of expected losses — is precisely the case the industry refuses to underwrite at the level the customer would actually need. The industry's own surveys then complain of "chronic under-insurance" as if it were a customer education problem rather than a product design choice. The vocabulary itself is captured: the industry will not let you say "I am not insuring this." It forces you to say "I am self-insuring." The absence of insurance is reframed as a deficient version of insurance.

Payment processing. The definitive example is PayPal, which I wrote about in another article. PayPal's terms of service reserve the right to suspend accounts and hold funds for six months without explanation.

Five product categories, five regulatory regimes. Insurance under APRA and ASIC. Wealth management under FOFA-modified frameworks with explicit best-interests duty. Credit cards under the National Consumer Credit Protection Act. Lending under prudential and conduct regimes calibrated for stability. PayPal under whatever applies to non-bank payments. A similar extraction pattern in each. The failure is not in the rules. The failure is in the relationship between the regulators and the firms they regulate, and in the architecture that relationship has produced over decades.

The Mechanism

The financial services market does not function properly because it is not a free market.

The regulatory architecture caps upside per transaction and eliminates institutional downside at the same time. People sometimes describe this as a trade-off. It is not. The cap on per-transaction upside is irrelevant because firms simply gear up until the return on the gearing produces their target return on equity. The case study firm targets 10–15% ROE. With downside eliminated by the regulatory architecture, gearing carries no genuine risk, so leverage scales freely until the returns are whatever the firm wants them to be. The downside elimination is genuinely valuable — it means the firm can lever up without the constraint that genuine risk would impose.

The equity at risk is not the firm's. It is the customers'. Financial services firms invest depositor, policyholder and unitholder money. Their shareholder equity is the thin slice on top, protected from loss by regulatory guarantee. The system runs as a sure thing for the firms, with the equity at risk being the customers' all the way down.

This is why the friction the regulators produce does not constrain extraction. The protection is worth more than the friction costs. The industry never lobbies to remove the protective architecture in exchange for genuine market freedom. The friction is the production of legitimacy that makes the protection politically sustainable. Without visible regulatory activity, the public would have no reason to believe the industry is constrained, and political support for the architecture — implicit deposit guarantees, APRA oversight, the whole structure — would evaporate. The firms need the friction. They manage it to a level that is visible enough to sustain political legitimacy and low enough not to constrain extraction. The regulator is not keeping the industry honest. It is keeping the industry's political protection intact by performing the appearance of oversight.

The regulators may also be asleep at the wheel. ASIC and APRA have mandates that explicitly include competitive markets, consumer protection, fair conduct, and contestability, and the Royal Commission documented their failure to act on the powers they already had. But the deeper issue is the frame itself. The regulators have permitted, and continue to operate, a regulatory architecture that makes financial services a sure thing for the firms and extractive for the customers, all in the name of stability. Stability means the firms do not fail. It does not mean the customers are treated fairly. The mandate has been captured by the incumbent firms at the level of what the mandate means, not just how it is enforced.

This is what makes the regulators Fuckwits in the Paragentism sense. The book's definition: a person who acts in ways that erode agency, their own and/or others'. No stupidity is required. No malice is necessary. Often, quite the opposite. The regulators have eroded customer agency through architectural choices made in good faith for stability reasons. They have eroded their own agency by accepting frames in which the questions they would need to ask to do their jobs are not the questions on the agenda.

The corporates have the capture playbook down pat. They know which regulators respond to which inputs, how to shape draft legislation during consultation, how to populate advisory committees, fund the think tanks that produce the analytical frames the regulators adopt, time disclosures, settle enforcement actions at amounts that look severe and are immaterial, and place regulator-friendly executives onto their boards. It works.

What Was Predicted

The counter-case I made against AI-2027 argued the more probable near-term harm path was massive job losses concentrated in white-collar work, with the speculative tail risk being used to obscure the operational mid-risk. The case I have just described is operational specificity for that prediction. Not plausibility — operational specificity. I am not telling you it could happen. I am telling you what it looks like inside one institution where it is happening.

What this case adds is the manner claim. The counter-case wasn't just "there will be job losses." That is predicted by everyone with a pulse. The sharper claim was that companies would execute large workforce reductions under cover of AI narratives, and workers would be harmed not just by job loss but by the conditions under which the job loss was managed. Without the misleading, workers would be losing jobs to AI like workers have lost jobs to technology cycles before. With the misleading — the three-layer communications architecture, the false benefits case, the brochure retraining, the fixed-term contract structure designed to bypass redundancy obligations — the harm is larger, more prolonged, and less visible to the people experiencing it. They cannot organise against what they do not know is coming.

The Fifth Wall

The optimistic story about AI in financial services says the technology will reduce costs and competitive pressure will pass the savings to customers. The market failure analysis tells you the transmission mechanism is broken. The regulatory architecture tells you the protection of incumbents is structural. AI applied within this architecture industrialises what the architecture already produces. The institutions gain new capabilities. The regulators do not. The capability gap widens.

The standard response to this prognosis, in the AI safety discourse, is to call for AI regulation.

This is the fifth wall.

The fourth wall is the one between actor and audience — the convention that lets the audience watch the play without becoming part of it. The fifth wall is the one the audience cannot see. The boundary that defines what counts as theatre at all. The four layers of moral theatre I have described are the play. The fifth wall is the meta-frame that hides from the audience that they are watching one.

AI regulation is the next surface of the same enclosure. It is being constructed by the regulatory class the financial services industry has been training the capture playbook on for decades. The corporates entering the AI regulatory conversation include the financial services firms that perfected the playbook. They are bringing all of it. The institutional designs being proposed are recognisably similar to the designs the playbook already defeats.

I predict AI regulation will be moral theatre — same regulators, same corporates, same playbook. Just like the GDPR regulations were. The visible activity will be there. The fines will be issued. The conduct codes drafted. The parliamentary inquiries held. The disclosure statements will run sixty to one hundred and twenty pages. The customers and workers and citizens will be informed in the legal sense and uninformed in the actual sense. The extraction will continue in the form the regulation permits, which will be the form the firms negotiated during consultation.

This is not an argument against regulation in principle. Regulation works in domains where harms are concentrated and obvious and regulators have frames distinct from the regulated. Food safety. Aircraft maintenance. Pharmaceutical efficacy. AI extraction in financial services will work differently. Each customer's slightly worse loan, slightly worse insurance product, slightly worse credit card offer — none of it visible, none of it scandalous, none of it politically actionable. The architecture's perfection is that no one ever has the experience of being defrauded. They have the experience of paying a little more than expected, getting a little less than they thought, finding the customer service slightly more frustrating than last year. Multiplied across populations, the yield is enormous. From any individual customer's perspective, there is nothing to complain about that anyone outside their household would care about.

This is what AI regulation will fail to constrain. Not the dramatic harms. The boring ones. The fifth wall will produce visible activity in the dramatic categories, because that is what regulators do. The boring extraction will increase, because nobody is built to see it.

The Reveal

I asked you to imagine the company. The company is real. So is the architecture. So is the workforce reduction. The people who told me about it would lose what protection remains to them if I named the firm, so I will not. But everything in the case is operational, dated, and underway right now in offices across Australia.

I presented it as hypothetical because that is the one way to get you to read it without defending against it. The architecture I am describing depends on the people inside it not knowing they are inside it. You have just experienced, in compressed form, the gap between what you thought you were reading and what you were actually reading. That gap is similar to the gap that keeps the workers in the case in the dark. They will lose their jobs, go on welfare and stop paying taxes so their employer (who already cannot lose) can make even more money faster without covering their forced job transition costs.

What Cannot Be Absorbed

The firm is not stupid. It is rational. Externalising costs to its workers and to the state is correct behaviour given the incentives it faces. The firm that voluntarily internalises more transition cost than legally required is disadvantaged relative to competitors who do not. It cannot afford to be the good actor in a field where good acting is penalised by the market. The individual firm's rationality is not the problem.

The problem is the systemic effect of every comparable firm playing the same rational game simultaneously, with AI accelerating the rate of redundancy across the economy. Each firm rationally externalising produces an aggregate burden the state cannot absorb at the scale this is about to come at it. The fiscal cost of mass mid-career white-collar unemployment. The political cost of a generation of voters who feel discarded. The productive-economy cost of skilled workers languishing on social security instead of being repositioned. None of this appears on any individual firm's balance sheet. All of it lands on the system the firms collectively depend on for their continued operation.

I want to be clear about what I am not arguing. I am not pro-labour. I think unions are largely extractive. I am not arguing the workers are owed anything as a moral matter. The shedding itself is not the issue. AI displaces labour. The economy reorganises. The question I am asking is whether the reorganisation will work this time at the scale and pace AI is producing it, given the manner the firms have chosen — minimum legal notice, no funded training, externalisation of the transition cost onto the state.

The Paragentic principle is that scaled organisations in protected positions need constraint proportional to their power, because they will reliably otherwise produce Fuckwittery of a magnitude the system cannot absorb. The firms here are scaled, protected, and producing externalities at a rate the system cannot absorb. The principle says they should be constrained — not stopped, not punished, but constrained to internalise enough of the cost that their decisions reflect the actual systemic cost of those decisions. Four to twelve weeks notice and no training is too little. Five years salary is too much. Consider a year of salary plus real funded training as a starting idea. The workers get retraining and a year to find another job. The company gets a 13–14 month payback on a guaranteed reduction in labour costs for every worker who takes the deal. The only mechanism that could deliver this is regulation that is not captured.

Regulation that is not captured is unlikely to come into existence. In this domain, three constituencies move to capture any regulation the moment it is proposed. The firms try to lock in the lowest certain compliance cost they can negotiate. The government tries to convert the regulation into a tax rake — additional revenue justified by the new framework, severed from the harm the regulation was meant to address, redirected to consolidated revenue. The unions try to extract worker privileges that exceed what the underlying balance would support, building organisational power on top of the framework rather than addressing the systemic problem.

These three are the relevant constituencies for industrial regulation in financial services and labour markets. They are not the universal set. Different policy domains have different capture constituencies, and the constituency a proposal favours is not always an institutional player. It can be an electoral base. Trump's tariffs were publicly framed as restoring American manufacturing jobs. They will not re-shore manufacturing, because the timeline required exceeds a single presidential term. Re-shoring car manufacture or meat processing at scale is a decade-plus project.

The tariff was therefore never a re-shoring policy. It serves two favourites simultaneously. The voter base receives the symbolic performance of concern for manufacturing workers who lost jobs offshore. The federal treasury receives substantial tariff revenues for whatever the administration wants to fund. Neither favouring required jobs to occur. Both were served regardless.

The cost is borne by US consumers. Tariffs are technically levied on importers, but importers pass the cost through, because in the affected categories there is no domestic substitute available at competitive price. Meat tariff means all US consumers pay more for meat. Steel tariff means all US consumers pay more for cars and appliances and construction. Instead of creating jobs for a particular group of blue-collar workers, the tariffs just increase the prices those workers pay for the things they want to buy. The favoured voter base is paying the tariff alongside everyone else, while believing it is benefiting. The dishonesty is not just in the framing. It is in what the favoured constituency is told the proposal is doing for them, when the proposal is doing the opposite to them.

Every policy proposal that emerges in any of these spaces is corrupt by construction. Each favours one or more constituencies while staging the public conversation around others. Consider what an honest version of a worker-retraining proposal would say: we are levying a corporate tax on workers to pork barrel more funding for TAFE and universities, with government taking administrative overhead off the top, and the workers receive whatever residual survives the institutional layers above them. Consider what an honest version of the tariff proposal would say: we are increasing the prices the workers we claim to be helping have to pay for the things they want to buy, with the additional revenue going to the federal treasury for general expenditure, while no manufacturing jobs are created because the investment timeline for re-shoring exceeds the policy's operating period. No proposal is ever stated this way, because stating it this way would defeat its purpose. The dishonesty is structural. A proposal that named its actual beneficiaries could not attract the constituencies whose support it requires, and a proposal without sponsoring constituencies does not exist as a proposal. Honest proposals do not get sponsored. Sponsored proposals are not honest. This is the second-order capture that operates above the first-order regulatory capture — the architecture by which the proposal-generation process itself filters out anything that would actually address the underlying problem.

Every available institutional mechanism for delivering the constraint principle is captured by one or more constituencies whose interests are not aligned with addressing the underlying problem, and every proposal to build a new mechanism is itself a capture vehicle for whichever constituencies are sponsoring it. The Paragentic principle remains true. The operational means to apply it do not currently exist, and the proposal-generation process is structured to ensure they do not come into existence.

What is left is calling out the problem and naming how the cheaters will try to cheat. The pretence that there is an institutional fix available is itself part of the architecture being diagnosed — every new layer of regulation recruits hope and converts it into theatre. The regulations legitimate the channel extractions rather than constraining them.

The workers in those offices across Australia will lose their jobs over the next eighteen months. They will lose them while being told they will not. They will lose them under a transformation narrative whose productivity claims did not deliver. They will be offered retraining brochures the company will not pay for. They will fall into a labour market where every comparable firm is running the same playbook. And the discourse that was supposed to be about the dangers of AI will be about superintelligence, paperclips, alignment, and the other things the architecture has been engineered to point at.

The first four walls are already built. The fifth is being constructed now. The play continues.