From Horses to Baseball Players

In the 2017 UBI Dialog, the “humans as horses” analogy made the case that human labor could become economically irrelevant — not because demand disappears, but because a cheaper and better mechanism for generating the same output arrives. The horse didn’t stop being useful because people stopped wanting transportation. The horse stopped being useful because the internal combustion engine did the same work for less.

The horse analogy is structurally sound, but it has a problem: it’s bleak. Horses got put out to pasture. They live on charity. They’re companions now, not contributors. If that’s the future of human labor, the conversation ends at “well, that’s depressing.”

There’s a more nuanced version that preserves the economic insight while accounting for the fact that humans — unlike horses — have agency, creativity, and the capacity to find meaning in work independent of its economic necessity.

Baseball.

How Baseball Works Economically

Baseball is something humans enjoy doing and watching. It has a real economy around it — stadiums, broadcast rights, merchandise, fantasy leagues. Some humans get paid extraordinary amounts of money to play it because they’re exceptionally good at it in a way that draws massive audiences.

But the economic structure is extremely stratified:

• A tiny number of players earn tens of millions per year
• A slightly larger group earns professional-level income in the minor leagues and international circuits
• A vastly larger number play college, amateur, and recreational baseball for little or no pay
• Millions more play purely for enjoyment — weekend leagues, pickup games, coaching kids

The falloff from “top earner” to “unpaid participant” is steep and dramatic. And yet: baseball thrives. People play it at every level. It generates joy, community, physical fitness, and meaning for participants who will never earn a dollar from it.

Engineering in 10 Years

Now consider engineering — or any currently high-value skilled profession — through this lens.

The trajectory of AI and automation suggests that within roughly a decade (see The Exponential Blind Spot for why “roughly a decade” should be taken seriously):

• Routine engineering tasks — the kind that currently employ large numbers of competent professionals — will be fully automated. Not “assisted by AI” but done by AI with no engineer in the loop.
• Novel and creative engineering — pushing boundaries, solving genuinely new problems, making judgment calls in ambiguous situations — will still involve humans, but far fewer of them.
• The very best engineers — the ones who can direct AI systems, define problems worth solving, and exercise taste and judgment at the frontier — will be extraordinarily valuable.

The distribution starts looking like baseball:

• A small number of engineers are indispensable and highly compensated
• A larger group contributes meaningfully but at much lower compensation than today
• Many people with engineering skills practice them in contexts that aren’t their primary income source — open source, personal projects, community contributions
• The work still happens, still matters, still generates value. But the employment structure around it is unrecognizable compared to today.

Why This Is Different from the Horse

The horse analogy suggests pure displacement — the horse becomes economically irrelevant and lives on charity. The baseball analogy suggests something more complex:

1. The activity persists — people still do engineering (play baseball) because it’s inherently satisfying, not just because they’re paid
2. Extreme stratification replaces broad middle-class employment — the bell curve of income flattens into a power law
3. The definition of “professional” changes — most practitioners aren’t paid, but they’re still doing real work
4. New forms of value emerge — community, meaning, creative expression, mentorship — that aren’t captured by traditional economic metrics

This is arguably a better outcome than the horse scenario. But it still represents a fundamental break with the current model, where a broad middle class of skilled professionals earns comfortable livings from their expertise. That middle is what gets hollowed out.

The Transition Problem

The baseball analogy describes a likely end state. The hard part is the transition. Right now, millions of people have organized their lives — mortgages, education investments, career plans, identities — around the assumption that skilled professional work will continue to pay middle-class-and-above incomes for decades.

The exponential blind spot means most of them don’t see the curve. They’re planning for a linear future in an exponential world. When the transition hits — and it hits different professions at different times — the Desire/Demand ratio (see Desire/Demand Distinction) for a large professional class suddenly widens dramatically. Not because their desires grew, but because their capacity to generate economic value shrank.

This is where structural responses (income floors, education restructuring, new forms of economic participation) become not ideological preferences but practical necessities. The baseball model works fine for baseball because nobody needs to be a professional baseball player to survive. It only works for engineering — or any skilled profession — if there’s a floor beneath the transition.

An Engineer Building His Own Replacement

There’s a version of this already playing out. Consider an engineer whose primary value to their employer is highly specialized domain knowledge — the kind that takes decades to accumulate and is held by very few people. That engineer is currently using AI tools to capture, structure, and make transferable the very knowledge that makes them valuable. Building a knowledge base that, once complete, means the business no longer needs to pay for that expertise in a human body.

This is not a tragedy. From the engineer’s perspective, it’s the most natural thing in the world: engineering is the practice of replacing human effort with better tools. An engineer who refuses to automate their own role has misunderstood what engineering is.

But it does illustrate the baseball dynamic in miniature: the skill to build the automation is extraordinarily valuable. The skill that gets automated drops toward zero. The question is what happens next — and whether the system is structured to make “what happens next” a transition or a cliff.

Questions for the Reader

• What does your profession look like through the baseball lens? Which parts are “major league” and which are “weekend pickup game”?
• If your professional skill became something people did for love rather than money — like most baseball — would you still do it?
• What would need to exist (income floors, new institutions, cultural shifts) for the baseball model to be acceptable rather than terrifying?