II. Why 2026 won’t look like 1873

The railroad crash of 1873 corrected capital excess without changing the product.

The AI crash will correct capital excess by forcing the product itself to change.

Why the 1873 railroad analogy is attractive — and incomplete

The surface analogy is strong:

massive capital inflows
speculative overbuild
infrastructure ahead of demand
financial collapse followed by consolidation

Railroads after 1873:

fewer companies
better pricing discipline
less waste
same railroads

The product did not change

steel rails before the crash
steel rails after the crash
same physics
same user profiles
same value proposition

Capital corrected.
The product survived intact.

Why AI is fundamentally different

AI is not passive infrastructure. AI is:

interactive
cognitive
trust-sensitive
cost-sensitive per interaction

Railroads did not behave differently depending on who used them. AI does. This single fact breaks the analogy.

The mistake investors are making

Treating AI like railroads:

more capital fixes problems
scale produces efficiency
adoption smooths economics
the product stabilizes over time

This assumption is false.

AI 1.0 becomes more expensive as usage increases.
AI 1.0 becomes less trustworthy at scale.
AI 1.0 becomes harder to govern the more people touch it.
Capital amplifies the failure modes.

Why the AI crash will be a product correction, not just a financial one

When the AI bubble bursts:

excess data centers will be written down (same as railroads)
speculative capital will disappear (same as railroads)

But unlike railroads:

the product cannot remain unchanged
AI 1.0 does not survive consolidation

The crash forces a redesign because:

cost curves don’t close
revenue models don’t scale
trust erodes with use
memory is missing
governance is external, not user-based

These are product defects, not market timing issues.

Why classical economics fail

Classical models assume:

rational users
uniform behavior
aligned incentives
stable equilibrium

AI does not operate in that world. AI is a strategic system:

users adapt
abuse emerges
incentives diverge
equilibrium fractures

This is not a classical economics problem. It is a game-theory one.

Adam Smith vs. John Nash

According to Adam Smith:

markets self-correct
rational actors dominate
scale produces efficiency

According to John Nash:

actors behave strategically
incentives conflict
multiple equilibria exist
systems do not converge naturally

AI 1.0 is built on Adam Smith, which is why AI doesn’t make money yet.

AI reality behaves according to John Nash, who did not refute Adam Smith, but claimed he was “incomplete.”

The three classes of AI users – a challenge railroads never faced

AI serves three fundamentally different audiences:

Consumers who merely want answers to questions, who use AI like search
Knowledge workers who merely want to automate tasks, to save time and money
Creatives who want a highly intelligent collaborator, possessing the entire corpus of human knowledge, that will challenge them, enlighten them and is never offended when it is contradicted

A single uniform product cannot serve all three, without wasting resource on people who don’t want or need it.

Railroads never faced this problem.
AI cannot avoid it.

What AI 2.0 must be, and AI 1.0 is not

AI 2.0 is not:

a more powerful model
more features
larger context windows
better demos

AI 2.0 include:

These are not features.
They are product requirements.

Why the crash is necessary

The current system will not self-correct.

Capital discipline alone is insufficient.

The crash forces:

abandonment of broken assumptions
redesign of the product itself
separation of users
real economics

The winners will not be the biggest. They will be the ones that adopted a user-centric architecture.

The bottom line

1873 corrected railroads financially, without changing the product
2026 will force AI to change the product.

The only open question is who gets there first.

– Published Sunday, January 11, 2026