The Discontinuity Thesis: Essence, Statement, Reinforced Formulation, and Comprehensive Defense Blogged

As the thesis has evolved this section has sort of been made redundant so posting in the blog

Essence and Context

The discontinuity thesis can be summed up as an NP in P moment for human cognitive work. Jobs that once required costly exploratory search by skilled people can now be completed in near linear time and cost by a frontier model with a light human verifier. Three empirical patterns turn this technical insight into an economic break.

• NP in P effect Scalable cognition that approaches zero marginal cost collapses the wage floor that used to protect most skills.

• Zuckerberg moment One widely viewed product launch can flip global expectations inside a single quarter, moving capital from cautious pilots to blanket deployment.

• Surfer model Workers must paddle to stay on each technology wave, but if the wave steepens faster than they can learn, every surfer is left behind.

Together these patterns mean that once the crossover arrives, the window for adjustment closes quickly and the transition is a discontinuity, not a smooth S curve.

Thesis Statement

An economy wide collapse in wage driven demand follows if and only if two conditions occur together.

(P1) Unit Cost Dominance For a wide range of economically valuable cognitive tasks, an artificial intelligence system together with a single competent human verifier produces output at lower total cost, equal or higher quality, and faster turnaround than a standalone human worker.

(P2) Insufficient Re inflation No alternative channel, whether new human job classes, massive transfers, or comparable interventions, restores broad purchasing power fast enough.

When P1 and P2 are simultaneously true, falling labour demand meets stagnant or falling labour income and triggers the wage demand collapse.

0 Executive Summary

The thesis forecasts a rapid collapse in wage driven demand once the above two conditions occur.

Any critique must therefore either show that P1 will not scale or present a credible, timely mechanism that ensures P2 does not hold. Arguments aimed elsewhere leave the syllogism intact.

1 Precise Restatement of Premise One

P1 Unit Cost Dominance

Exists a set of tasks T in a large and economically material class C such that cost of AI performing T with one verifier is lower than the cost of a human performing T, while quality is at least equal. The verifier may be a human domain expert, an automated audit pipeline, or a hybrid. The verifier only samples or spot checks, so verifier cost rises sub linearly with throughput as AI accuracy improves.

2 Why Premise One Is Highly Robust

Exponential cost declines in inference. Over the last decade, the cost per thousand LLM tokens has fallen more than one hundred times, with further ten fold reductions every two years thanks to hardware, algorithmic, and scale gains.

Task breadth and generality. Frontier models already exceed median professional performance on more than eighty percent of benchmarked knowledge work tasks. Fine tuning and tool integration extend coverage faster than specialised human retraining.

Verifier efficiency. Spot verification shifts reviewer effort from order n to order log n. Empirical audits in code generation pipelines show less than five minutes review per three hundred lines of AI generated code while maintaining defect rates below human baselines.

Zero marginal cost replication. Once trained, an AI worker duplicates for electricity and depreciation only, often a few cents per hour, versus median labour cost in the OECD of around twenty five euro per hour.

Global market clearance. Firms arbitrage instantly; any task where AI plus verifier undercuts humans migrates, shrinking remaining domains and eroding the average human wage bill.

Historical precedent. Every prior automation wave shows labour hours contracted in directly substituted tasks. Wages stabilised only because new distinct tasks emerged. With broadly capable AI the adjacent possible for new human exclusive categories contracts sharply.

Collectively these forces make P1 almost inevitable once frontier AI reaches rough human parity on a critical mass of tasks. The threshold is not perfection, only good enough, cheap, and verifiable.

3 What a Valid Rebuttal Must Show

To overturn P1 at scale, a critic must demonstrate at least one of:

Persistent performance gaps. Durable tasks that matter macro economically, resist AI substitution despite ongoing improvement, and cannot be modularised into verifiable chunks.

Verifier bottleneck. Evidence that verification cost scales linearly or worse with AI output, nullifying the unit cost edge.

Hardware or energy stall. A binding physical or economic ceiling that halts cost declines before AI reaches wage elastic domains.

Regulatory firewall. Enforceable rules across jurisdictions that prohibit deployment even when economically superior and that survive arbitrage pressure.

Absent such evidence, P1 stands.

4 Interaction with Premise Two

Even if P1 holds, wage demand collapse is avoided only if large, timely transfers or genuinely new human exclusive job classes appear fast enough. Universal transfer schemes must scale to trillions annually before displacement bites, likely within two to five years after the P1 threshold. Novel job classes must absorb at least ten percent of displaced labour within a similar horizon and command wages set by human scarcity, not by an AI anchored reservation wage. Meeting either condition refutes P2.

5 Quick Reference Checklist for Debaters

The fast scan version:

• “AI will not be that cheap.” Premise 1. Provide comparative cost curves and scaling laws that contradict current trajectories.

• “Humans will move to creative roles.” Premise 2. Supply quantified demand projections and wage premium evidence that outpace the displacement rate.

• “Regulation will slow deployment.” Premise 1 and 2. Show binding global compliance together with enforcement strong enough to withstand cost arbitrage.

6 Implications If Premise 1 Holds and Premise 2 Fails

Median and modal wages trend toward the AI marginal cost plus verifier overhead.

Capital share of income rises steeply while labour share compresses.

Downstream demand contraction feeds back into further wage pressure, creating a deflationary spiral absent intervention.

7 Ethical Policy Coda

This economic argument does not diminish human worth. Any credible response must focus on distribution, specifically how to preserve dignity, autonomy, and genuine choice for people whose market wage collapses. That means designing transfer or shared ownership schemes that scale at the same pace as the technology, embedding democratic oversight into deployment, and guarding against exploitative uses of ubiquitous cognition. Ethical safeguards are essential so that a sharp economic discontinuity does not become a civic fracture.

8 Conclusion

Premise 1 is almost bullet proof because it combines demonstrably falling AI marginal costs, verifier schemes whose cost dilutes with scale, and economic arbitrage that relentlessly selects the cheapest adequate performer. Disproving the Discontinuity Thesis therefore requires either technical evidence that AI cannot clear the cost quality bar or policy economic proof that society will counteract the resulting demand shock quickly and comprehensively. Until such evidence appears, the syllogism remains intact.

9 Quick Dismissal of Common Weak Rebuttals

• Humans will always outperform in creative tasks. LLMs already co author bestselling novels, compose chart topping songs, and design brand logos. Creativity is pattern generation; once AI plus verifier reaches cost quality parity, firms adopt it.

• Empathy and the human touch cannot be automated. Voice cloned, sentiment tuned agents now exceed human customer satisfaction scores at less than ten percent of labour cost. Preference rarely outranks price and availability at scale.

• Upskilling will save workers. Upskilling shifts labour into domains that automate next, compressing the wage premium. Skill inflation without exclusivity delays but does not prevent substitution.

• Energy or GPU scarcity will cap deployment. Even with ten times higher inference cost, AI beats median wages by more than one hundred times on many tasks; the economic gap is too wide for hardware constraints to reverse the sign.

• Open source models are weaker, so adoption slows. Smaller models already automate junior analyst work. Competition requires only cheaper than human, not top of the line, performance.

• Regulation will prohibit substitution. Effective bans must be global and enforceable; history suggests this is unlikely. Partial bans simply move firms to permissive jurisdictions.

• History says new jobs always appear. Earlier automation left unexplored cognitive territory. General AI collapses that frontier. Without areas where humans enjoy comparative advantage, the historical pattern breaks.

• Consumers will pay premiums for human made goods. Artisan niches remain but cannot absorb millions of displaced workers. Luxury premiums do not restore broad purchasing power.