You Just Hired a Million Bad Employees
For the first time in history, humans are cheaper than software
America | Tech | Opinion | Culture | Charts
AI was supposed to replace human labor.
It did the opposite.
For the first time in history, humans are cheaper than software.
And AI is creating more jobs than it eliminates.
Technology has always solved one problem by creating another.
In the 1830s, the advent of the railroad drove one of the largest infrastructure buildout the world had ever seen. American track mileage 120Xed in a decade.
Then the system broke.
On October 5, 1841, two trains fatally collided on the Western Railroad in Massachusetts due to a simple coordination failure.
As the complexity of the railways grew, individual conductors were no longer enough to keep train travel safe. The railroad companies thus began a decades-long effort: hiring managers for each geography, defining roles in writing, and establishing clear hierarchies with reporting lines. Modern management was born. With it, rail became among the world’s first billion-dollar industries, at its peak representing roughly 60% of the stock market.
AI is breaking the system again.
We just gave every employee, even the worst ones, effectively unlimited headcount and budget.
Managing AI is harder than managing people, because AI scales dysfunction instantly. Fortunately, we can learn from the past:
Agent workforces and human workforces fail in the same way.
Understanding the 7 major parallels between the two will unlock the next trillion dollars of AI value creation:
The 7 Parallels of Agent and Human Workforces
1. Tokenmaxxing is throwing bodies at the problem.
The tokenmaxxing hype cycle ran its full course in under a month.
But the amount of tokens spent was never the real problem.
People are spending so much on tokens because they don’t know how to use them.
Maybe 1 in 100 employees knows how to give AI context. It’s a rare breed of person that can articulate a process clearly, that has the patience to empathize with a polluted context window, or even understands what that means.
Give an agent harness to the other 99 people and they will produce “loops”.
2. Loops are meetings about meetings.
In Claude Code/Cowork, Copilot, Karpathy’s Autoresearch, or any harness, loops are a bandaid for the fact that almost nobody can successfully prompt.
Loops are a brute force attempt to compensate for human inadequacy. Agents call themselves to fix themselves only because a human never articulated the task cleanly. Brute force becomes the system’s only path to progress. This all stems from human failure to successfully understand the task in the first place.
You are spending tokens on spending tokens.
3. Wasted tokens are the new headcount bloat.
Most companies today are mismanaged.
The vast majority of workers don’t meaningfully impact the business at all. They are cogs in the machine, stamping approvals at each layer and hiring more cogs to fuel a machine that exists to exist.
They’re looping.
Often it’s more efficient to cut the loop. Elon cut 80% of X’s staff and the company performed better. Private equity operating partners make a living arbitraging this simple fact.
Just like 80% of employees do nothing, 80% of tokens today do nothing.
People create more people. Tokens create more tokens. Looping is the new empire building.
4. 100X tokens are the new 10X engineers.
The promise of software was that we’d build it once, run it forever at low cost, and never need to supervise it. AI broke that promise. As soon as software could do anything, it couldn’t do any one thing predictably.
Tokens behave like a workforce, and as soon as you see tokens as employees, the promises of AI start to break down:
“Tokens are more accurate than humans” but only when prompted correctly.
“Tokens are faster than humans” yet speed means nothing across 100 retries.
“Tokens don’t play politics” but they build empires of token spend.
“Tokens don’t quit” but they die between new model releases and new sessions.
“Tokens can be trusted” yet they fail confidently with perfect formatting.
The one place AI really does beat humans is scalability. Scaling humans burns enormous energy across recruiting, onboarding, and attrition. Scaling tokens is instantaneous. Which is exactly why mismanaging them is so expensive, and why you must find and scale the 100X token.
The 10X engineer built the last era of companies. The 100X token will build the next.
In the same way a handful of employees make others 10X as productive, for any given job some amount of token context can cut AI effort down by orders of magnitude. There exist tokens that will give you 100X as much leverage.
Humans are cheaper than tokens on average, but good tokens are cheaper at scale.
Management converts one into the other.
5. Context hoarding is the latest job security tactic.
There’s a massive political problem with AI inside the firm, and it will only get worse.
Employees don’t want to teach AI systems their secret sauce.
They’re starting to pick up on the fact that these systems aren’t only there to “help them” or “increase productivity.”
Look at Meta, where stock-owning employees, who are wildly incentivized to get AI right, are outraged that the company is using employee context as training data. That’s at a tech company… a conflict that’s a microcosm of what is about to happen across every industry.
Tribal knowledge has been job security for centuries. Medieval guilds kept their methods secret. AI is the first technology that asks workers to hand all of it over at once.
Nobody trains their replacement for free.
The people who hold the 100X tokens have the least incentive to surrender them. Emotionally, structurally, and politically, firms are wired to reject the most important technology for their future.
6. Evals are the new OKRs.
The best way to manage a token workforce is the same as the best way to manage humans: by defining what good looks like.
The one breakout AI use case that escaped politics is coding. It expanded the pie and made engineers better.
The mechanism is evals. 99% of AI revenue today is coding because coding has built-in evals. Code runs or it doesn’t.
Broader, cross domain AI use cases will only come online when someone builds the requisite evals. Specific evals matter more than teaching your employees to prompt or giving them a chat harness. With them, AI will eat the parts of the economy code could never touch.
The real work of managing is turning fuzzy human processes into code, expressing the qualitative as quantitative.
A firm’s eval suite will become its most valuable resource.
Just like OKRs are key to leveraging a human workforce to optimal output, evals will be key to leveraging an infinitely scalable token workforce. Evals are the path to running 100X tokens.
Furthermore, no two firms will have the same eval set. Evals will be key to competitive advantage. An organization running generic evals or generic agents has no edge.
7. The next trillion-dollar opportunity is the transformation company.
Enterprises have been buying foundation model commits, the application layer, and internal builds for years now. All of it conceals a brutal truth about the economics:
Nobody has AI working reliably yet.
Silicon Valley is so convinced of this failure that its latest obsession is betting against the business of today. “Neofirms” or “AI Native Services” startups are being funded to capture the $21 trillion of services spend across the knowledge economy on the theory that incumbents, mired in their own politics and processes, will never manage the transition themselves.
Neofirms may well provide competitive pressure that catalyzes “tradfirm” AI adoption. But the greatest AI assets still sit inside the incumbents: differentiated processes that already work, scalable through distribution channels that already exist.
In fact, the next biggest businesses won’t be eating existing services spend. They will sell a net-new type of service to existing players:
“AI transformation companies” will be 10X larger than any neofirm.
Transformation sounds like a one-off project. But there’s a Jevons paradox at work: every use case an organization adopts can surface ten more. The more AI-enabled a firm becomes, the more transformation it consumes, while the frontier of what’s possible advances daily. Ongoing AI transformation efforts may become the only way to compete.
Consider Palantir, on paper the most Claude-disruptible company in software: a half-trillion-dollar business hand-building bespoke applications for the enterprise. By the logic that has made SaaS nearly uninvestable, $PLTR should be a zero before $NOW.
It’s not, because Palantir was never selling software. It was selling transformation.
But transformation itself has evolved since Palantir’s days of old. In an AI first world, it’s more than ontologies, custom software, and the rare bespoke prompt. The real work is in the evals, in token minimization, in understanding a business so deeply that you can program it.
Encoding each firm’s nuances into agents may become the largest economic task of the decade ahead.
It’s time to manage.
Every phase of the AI boom has had its guiding cliché.
We were told to sell pickaxes during a gold rush, and we built infrastructure. We were told to sell “Service-as-a-Software,” and we built neofirms. We have enough infrastructure. We have enough services. Now the work is making the trains run on time.
It’s time to survey the enterprise: to find the 100X tokens, record the loops that work, and direct intelligence that is being massively wasted.
Humans just became cheaper than software.
Someone still has to tell them both what to do.
Thanks to Sam Wolfe, David Oks, Will Manidis, and Alex Danco for their thinking here.
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The big question is whether AI will ever be cheaper than humans only for it to follow the typical Disrupter blue print that raises prices after the competition (humans) is irrelevant.
Agreed on the parallel, mismanagement is mismanagement whether it's people or tokens, and evals-as-OKRs is a sharp way to put it.
One thing worth adding to the context hoarding point: it's not just tribal knowledge people are protecting. It's the last visible proof of what they know. Once that gets encoded into an eval, the person doesn't just lose leverage, they lose the thing that made their value legible to anyone else. That's a harder ask than "share your secret sauce."