The Cyborg Economy Is Not a Labor Forecast

A job is a bundle of tasks held together by an organization.

The bundle has a title, a manager, a salary, a schedule, a benefits package, and a long list of assumptions inherited from the industrial economy. Someone decided that these particular tasks belong together, that one person should perform most of them, and that the bundle should persist for months or years.

AI loosens that bundle.

A scientist can use an agent to search literature outside her specialty. A small-business owner can perform analysis that once required a separate department. A developer can move from idea to tested implementation without handing every intermediate task to another occupation. OpenAI's July 2026 analysis of more than 800,000 U.S. ChatGPT messages calls this task crossover: 43.5% of occupation-specific messages concerned work associated with another occupation.¹

That is not proof that jobs disappear. It is evidence that the boundary around the job is already becoming porous.

The economic actor emerging through that boundary is a cyborg, not the science-fiction body of chrome and wires but a person whose mind is extended into the machine through AI. I named this trajectory "cyborgization" in 2015, drawing on extended-mind theory: cognition is not sealed inside the skull but partly carried by the tools a person thinks through.² What I described then as a forecast is now the default condition of knowledge work.

Today a cyborg is typically an augmented human with a fleet of agents. The human is the pilot. The AI is the copilot. The human has extended mind and will into the machine, and the machine extends reach without supplying its own will. I define an agent in this context as a force multiplier of human will and intelligence, not an autonomous decision-maker. The agent proposes, searches, translates, and executes; the principal chooses, judges, and remains responsible.

A cyborg operator may do the work of a former team. A cyborg scientist may direct a swarm of search, coding, simulation, and verification agents. An autonomous agent may complete an entire machine-readable task while a human principal chooses the objective and remains responsible for the external effect.

This is the "force multiplier productivity flywheel": each task a cyborg completes feeds back into faster, broader capability for the next, so a single human can do in hours or days what once took dozens of people months of labor. The job market is not returning to the shape that made those teams necessary.

This essay, and the site carrying it, is a product of that flywheel. I am writing it with AI copilots as a cyborg, and the math, code, and illustrations it references were produced the same way.

The question for my work is not, "How many old jobs can we preserve?"

It is:

Can we create more open surfaces where a useful result has a visible specification, a checkable completion rule, and a funded payout?

The claim I refuse to make

It is easy to market an AI platform with a large number.

Start with a giant market. Assume a share of that market moves through the platform. Multiply by a fee. Divide by an annual salary. Announce "jobs created."

The arithmetic can be correct while the conclusion is fiction.

One person may run thousands of agents. One proof-mining company may supply most of a network's computation. A reproduction or curation task may require hours of irreducibly human attention. The same reward pool could be distributed among ten full-time specialists, thousands of occasional contributors, one highly automated operator, or machines whose ultimate ownership is difficult to observe.

Fees do not contain a human-count variable.

That is why I use a narrower model.

Let:

• \(F\) be realized gross fees;
• \(\theta\) be the fraction allocated to task or provider rewards;
• \(u\) be the fraction of that allocation actually settled;
• \(r\) be the average payout per verified task.

Then \(R=F\theta u\) is the settled reward pool, and \(N=\frac{R}{r}\) is the number of paid task settlements the pool can support.

Those equations identify economic capacity. They do not identify people, jobs, livelihoods, adoption, welfare, or income distribution.

This distinction matters because task count is easy to manipulate. A $510,000 pool can support 1,020 settlements at $500 each or 51,000 settlements at $10 each. The number of task records increases fiftyfold. The reward value does not change.

The primary economic metric is therefore settled reward value. Paid task count is a secondary operational metric that must remain paired with average payout, uniqueness, validity, and usefulness.

What my public systems could fund

My repositories contain several different economic machines. PopperPad can publish proof, counterexample, reproduction, maintenance, preservation, and curation work. ZenoDEX can expose solving, routing, proving, challenging, and verification roles around an exchange. PulseTensor can coordinate inference miners, batch proposers, and challengers.

The table below standardizes them at the same hypothetical input: $1 million in realized gross fees.

Public project	Settled reward pool	Paid task settlements	What the row means
PopperPad	$510,000	1,020 at $500	60% allocation and 85% settlement utilization
ZenoDEX / ZRPF	$360,000	360 at $1,000	40% allocation and 90% settlement utilization
PulseTensor	$910,100	9,101 at $100	95.8% proposer-plus-miner share and 95% utilization

These are conditional scenarios, not current revenue, launch parameters, or forecasts. The current realized-fee input is zero, so the current modeled settled pool is zero. PulseTensor's 95.8% provider share is grounded in its published tokenomics; the other displayed allocations, utilization rates, and payouts are editable assumptions.³⁴⁵

That honesty makes the model more useful, not less. It gives readers a reproducible translation:

If the system produces this much fee-bearing activity, and if this visible share is routed and settled, then this much paid opportunity follows.

Anyone can replace the assumptions. No one has to accept a story about the future to check the multiplication.

Jevons after intelligence becomes cheap

The strongest argument for a larger task economy rests on cheaper cognition expanding the set of things worth attempting, not on every current job surviving.

William Stanley Jevons observed in 1865 that a more efficient steam engine did not necessarily reduce coal use. Greater efficiency reduced the effective cost of useful work, which made more uses economical. If demand responded strongly enough, total resource use increased.⁶

The same mechanism may apply to machine intelligence.

Let \(g\) be the compute required per task and let task demand have absolute price elasticity \(\varepsilon\). Under a simple constant-elasticity model, aggregate compute use is proportional to \(g^{1-\varepsilon}\). When efficiency improves, \(g\) falls. If \(\varepsilon>1\), demand expands by more than the efficiency gain and total compute use rises. That is the strict Jevons case.

This is a condition, not a prophecy.

AI may make one legal analysis cheaper without creating enough new demand to replace the old labor. It may make a million previously uneconomic scientific checks affordable. It may generate enormous machine activity with little reward reaching independent people. It may lower costs while a few platforms capture most of the surplus.

Jevons tells us why "efficiency means fewer tasks" is incomplete. It does not tell us who receives the gains.

That distribution problem has to be designed and measured separately.

Open to whom?

"Open task" can become another empty phrase unless the interface is concrete.

For my purposes, a task is meaningfully open when:

1. it can be discovered without a private introduction;
2. its inputs and required outputs are machine-readable;
3. eligibility is permissionless or its restrictions are explicit;
4. the completion test is declared before work begins;
5. the payout rule is declared before work begins;
6. a valid result can be verified without trusting the submitter;
7. duplicate work, challenges, and settlement are handled by visible rules.

That structure is useful to a human, an autonomous agent, or a human-agent team. It does not mean everyone can profit. Compute, capital, identity, expertise, geography, regulation, and timing still constrain participation. But the opportunity surface is legible enough for an agent to scan, estimate expected value, attempt the work, and submit evidence.

PopperPad's design draft makes this explicit with a machine-readable work-order object and a universal agent loop: find a bounty, fetch the evidence bundle, run or improve the check, submit a proof or counterexample, survive the challenge window, and receive a payout if the declared verifier accepts the result.⁷

The platform does not vote a claim true because someone paid. It pays after a verifier-accepted epistemic result.

That reversal, evidence before payment, not payment before truth, is the design pattern running through my portfolio.

My value is not "more code"

AI makes code abundant. Abundance reduces the signaling value of code volume. Lines written, hours saved, and even replacement cost remain useful accounting figures, but they no longer capture the main contribution.

The value I aim to create is architectural:

• turn an aspiration into a machine-readable task;
• turn a task into a predeclared acceptance condition;
• turn an AI output into an untrusted proposal;
• turn verification into a deterministic boundary;
• turn fee-bearing use into a conserved reward pool;
• turn the reward pool into open, measurable opportunity;
• preserve the evidence so someone else can replay the claim.

This is what I mean by building the cyborg economy.

I am not trying to encode my philosophy into every user's software. A protocol can aim at local user happiness: an interface that produces satisfaction in the person sitting in front of it is a legitimate utilitarian target. What no single app can do is control the global state of the world. A protocol cannot guarantee liberty, democratic institutions, capitalism, privacy, safety, or a fair distribution of AI wealth across populations. Those values conflict at the margins, and reasonable people choose different tradeoffs. Safety, for example, can be increased through controls that reduce freedom and exit.

The platform contract should be smaller:

Publish the task. Expose the rule. Verify the result. Account for the money.

My political reason for building that infrastructure is personal. I want a future in which advanced AI increases the number of people capable of acting, not only the capacity of the institutions already in control. I want humans with AI assistants to create new industries, discover new facts, participate in markets, and protect what they value.

That motivation does not make the outcome inevitable.

It makes the work worth attempting.

No one is coming

My first job was referral marketing. Later I earned from blogging, crypto mining, liquidity provision, and yield farming. None of those income streams looked like a permanent occupation before its enabling network existed.

Bitcoin defined a reward rule without predicting the future population of miners, pools, data centers, firms, or human operators. Industrial organization formed around the opportunity. Proof of work expanded into other proof systems, staking, routing, liquidity provision, and forms of protocol work that were difficult to name in advance.

The post-AGI economy may be similarly generative.

The airplane safety rule applies: secure your own oxygen mask before helping others. Use the productivity flywheel to build your own capability first, then use that capability to create opportunity for others.

No one is coming. Use the productivity as a force multiplier to create new industries, new tasks, and new ways to protect what you value.

Some opportunities will be tiny. Some will be automated almost completely. Some will demand human judgment precisely because judgment has become the scarce input. Some will fail. A few may become industries.

The honest claim is not "I created a million jobs."

It is:

I build systems that can turn real use into transparent, verifiable, paid opportunity, and I publish the math required to tell capacity from hype.

That is how I intend to contribute to a better world: not by promising the distribution of a wealth that does not yet exist, but by building more ways for people and their agents to participate in creating it.