The Unit of Work Is No Longer the Instruction. It Never Should Have Been.

Published: February 2026 | Author: Markus Maiwald | libertaria.app

For sixty years, computing obeyed a single liturgy: human writes instruction, machine executes instruction. The developer’s entire existence was translation. Business logic into machine logic. One function at a time. One Jira ticket at a time. One soul-crushing sprint review at a time. That liturgy is over. The unit of work is now the token. Not an instruction. Not a command. A unit of purchased intelligence. You describe what you want, feed it context, and buy enough cognitive horsepower to get a result. The machine figures out the steps. Your job as a human is no longer to sequence operations; it is to specify outcomes and manage the intelligence budget that produces them.

This is not a tools upgrade. This is not “AI-assisted coding.” This is a categorical shift in what computing is. And if you’re a senior developer reading this with a knot in your stomach, relax. You’re not being replaced. You’re being promoted.

Carbon Levels Up

Here is what the fear merchants miss every single time: when intelligence becomes a purchasable commodity, the value of generic code production goes to zero. But the value of knowing what to build goes to infinity.

Think about that for a second. The bottleneck was never your typing speed. It was never how fast you could write a React component or debug a memory leak. The bottleneck was always understanding the problem deeply enough to specify the right solution. The machine couldn’t help you there because the machine needed step-by-step instructions. Now the machine needs specifications, context, and judgment; precisely the things a senior developer has spent a decade accumulating.

You are not becoming obsolete. You are abstracting upward. The same evolutionary leap that took us from punch cards to assembly to high-level languages to frameworks is happening again; except this time, the abstraction layer is intelligence itself.

The developer who thrives is the one who stops writing logic and starts directing it.

Three career tracks are crystallizing in front of us right now:

The Orchestrator. You don’t write code. You specify outcomes and manage the intelligence that produces those outcomes. System design, specification writing, quality evaluation, token economics. You’re a factory manager with cognitive workers. Your compensation will correlate with token budgets, not lines of code.

The Systems Builder. You build the infrastructure the orchestrators use. Agent frameworks, evaluation pipelines, context management, routing layers. This is deep systems engineering on an entirely new stack. Small in volume, enormous in leverage.

The Domain Translator. The track nobody talks about and probably the largest of the three. You combine enough technical fluency to work with AI systems and enough deep domain expertise to know which problems are worth solving. The dental practice management specialist is now a developer. The construction scheduling expert is now a developer. They just don’t know it yet.

The middle of the old distribution; the developer who writes competent application code but lacks either deep systems expertise or deep domain knowledge; is the most exposed position. Not because AI replaces them tomorrow. Because the value of generic code production is falling at the same rate as the cost of tokens.

The Numbers Don’t Lie

StrongDM’s CTO runs a three-person team targeting $1,000/day in token spend. No handwritten code. Cursor, Anthropic’s largest customer, saw AWS costs spike from $6M to $12M in a single month. Enterprise LLM spend hit $7 million on average in 2025, projected to cross $11 million in 2026. The share of companies planning to spend over $100K monthly on AI has more than doubled. These are not experimental budgets. This is infrastructure spending.

The language has shifted from “let’s explore” to “this is critical for the business.”

And here is the Jevons Paradox at work: as per-token costs collapse (GPT-4 equivalent performance dropped from 20/million tokens in 2022 to \0.40 today), consumption doesn’t decrease. It explodes. Steam engines got more efficient; coal consumption went through the roof. Cloud computing got cheaper; AWS bills went up. Intelligence gets cheaper; the appetite for intelligence is infinite.

AI-native companies are running at 3-5x revenue per employee versus traditional SaaS. A $10M ARR AI startup operates with 15 people where a traditional SaaS company needs 55-70. That ratio is widening every week.

Where Libertaria Enters the Frame

The conventional narrative frames this as “AI tools getting expensive.” The Libertaria framework sees something far more structural: Intelligence is now an Energy expenditure.

In the Three-Pillar Economy (RFC-0640), we defined three fundamental economic actors: Energy (irreversible resource), Silicon (AI agents), and Carbon (humans). The mainstream tech world is now discovering what we formalized months ago: Carbon and Silicon are not competitors. They are symbiotic pillars of a unified economic system.

When OpenAI prices an AI employee at $20,000/month, they’re unconsciously groping toward our Energy Token model. They call it “token economics.” We call it what it actually is: the irreversibility primitive (RFC-0641).

Every token consumed is an irreversible act; energy converted into intelligence, burned and gone. The question is not the price. The question is whether that burn produced value.

Energy is not truth. Energy is not justice. Energy is the price of being taken seriously.

The mainstream world measures tokens. Libertaria measures scars; irreversible artifacts that could not exist without genuine expenditure. The market prices these artifacts. No oracle required. No central authority deciding what a “token” is worth.

This is where the $20K/month AI employee story becomes our story. Because what OpenAI is selling as a product, Libertaria is building as a protocol. The difference? OpenAI’s token is a billing unit controlled by a corporation. Libertaria’s Energy Token is a sovereign primitive controlled by physics and markets.

The Organizational Inversion

For sixty years, engineering organizations were structured around headcount. Full-time equivalent resources. Productivity measured (badly) in output per engineer. Hiring plans built around projected workload.

In a token-based paradigm, that entire architecture inverts. Output is limited not by headcount but by the ability to convert intelligent spend into business value. An organization with 50 engineers managing agents will outperform an organization with 500 engineers writing code by hand; if the 50-person org has better specs, better evaluation frameworks, better context engineering, and a higher token budget per engineer.

We talked about two-pizza teams at Amazon. We’re headed toward half-pizza teams.

But here is the part the incumbents don’t see: when intelligence becomes a commodity, the competitive advantage migrates to everything around the commodity. Distribution. Domain expertise. Customer relationships. Proprietary data. Trust.

Goldman Sachs can buy more inference than any startup. But Goldman can’t sell AI-powered inventory management to a 50-location restaurant chain because Goldman never set up that channel and never will. The addressable market for software is expanding explosively; Jevons Paradox applied to the total addressable market of software itself.

Vertical niches that were never economically viable are now wide open. This is the Libertaria thesis made manifest in market dynamics:

Generalized scale versus specialized precision. The enterprise competes on token volume. The sovereign builder competes on specificity.

The startup playbook in this paradigm is not “raise more money, buy more tokens.” It’s know a market so well that a 200/month Claude subscription aimed precisely creates more downstream value than a 20,000/month enterprise agent budget pointed at the wrong problem.

Toward the Common Energy Token

Here’s the bridge to tomorrow. The mainstream world is converging on something Libertaria already has in specification: the idea that computing cost should be denominated in a universal energy primitive, not in arbitrary vendor-specific billing units.

Right now, you pay Anthropic in dollars for Claude tokens. You pay OpenAI in dollars for GPT tokens. Each token is a different unit, priced differently, with different capabilities. This is friction. This is rent extraction. This is the old world pretending to be the new one.

In the Libertaria economic model, the Energy Token (RFC-0641) serves as the universal unit of account. Every expenditure of intelligence; whether Carbon or Silicon; denominates against the same irreversibility anchor.

The SCRAP token, the STASIS bond, the Time-Bond Token; all of them can be measured against a common Energy primitive. What the mainstream calls “token economics” is just the first tremor of this realization.

The unit of account for computing should not be a corporate billing artifact. It should be physics. Irreversible expenditure. Provable cost. Market-verified value.

The token economy is coming whether the world builds it on sovereign protocols or corporate platforms. We choose protocols.

The Invitation

If you’re a senior developer watching this transformation, you have a choice. You can optimize for the old paradigm; writing code faster with AI assistance; and watch the value of that skill deflate to zero. Or you can recognize what’s actually happening: you are Carbon, and Carbon is leveling up.

Your decade of experience in system design, in understanding what to build, in evaluating quality, in knowing your domain; that is not a depreciating asset. That is the only asset that matters when intelligence is a commodity.

The agents handle the silicon work. You handle the judgment, the specification, the strategic aim. Sixty years of computing treated you as a translator. The token economy treats you as a commander.

And if you want to build that command structure on sovereign infrastructure instead of corporate platforms; if you want your intelligence budget denominated in physics instead of vendor lock-in; if you want exit at gunpoint velocity from any system that stops serving you; you know where to find us.

The machine works. The human decides. That was always the correct division of labor. It just took us sixty years and a paradigm shift to see it.

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Markus Maiwald is the founder and lead architect of Libertaria. The project’s specifications, including the Three-Pillar Economy Ontology (RFC-0640) and Energy Token Primitive (RFC-0641), are available at libertaria.dev.