The Rise of Artificial Intelligence

Topics in Economics, ESCP, 2025-2026

Pablo Winant

2026-01-12

Introduction

How do you see the future of AI ?

Science Fiction has explored many issues associated with AI.
- what happens to individuals, markets, firms, government?
- who wins, who loses?
Very often economic future is bleak…
Why is that so?

What is AI?

A simple definition (enough for this course)

AI is a set of methods that learns from data to produce useful outputs (predictions, classifications, text/images, actions).

Key idea: it improves with experience/training, not just explicit rules
We do not need AI to “think like a human” for it to matter economically

Why it is hard to say what AI will never do

Two recurring facts:

The frontier moves: tasks once seen as “uniquely human” become routine (translation, chess, image recognition, writing)
Our labels move too: once a task is solved reliably, we stop calling it “AI”
- (Tesler’s theorem / “AI is whatever hasn’t been done yet”)

So claims like “AI will never do X” usually age badly.

What we can be more confident about

Even if we cannot bound capabilities, we can analyze economic mechanisms:

which tasks become cheaper (prediction, drafting, search, monitoring)
which inputs become valuable (data, compute, human attention)
who captures rents (owners of complements and bottlenecks)

The Classical View

“This Time it’s Different” or “Same old, same old…”?

David Ricardo Adam Smith

Do you remember the neoclassical production function?

The (neo)classical production function

What are its main properties?

production takes several factors as inputs
- capital
- labour
- … (natural resources, land, …)
each factor has a market price
marginal returns w.r.t. each factor are decreasing
factors are paid according to their marginal productivity
the technology is the particular process through which inputs are combined

AI and the (neo)classical production function

Production function with AI shift (illustration)

the precise description depends on the problem under consideration
what could you change to take into account the effect of AI?
- data, technological change ?

Three hypotheses about the economic nature of AI

A technological change
A new kind of factor: Data
Yet another kind of factor: Robots
Something else Completely

AI is a change in the cost structure

AI as a shift in cost structure (illustration)

Ajy Agrawal, Joshua Gans and Avi Goldfarb: Prediction Machines: The Simple Economics of Artificial Intelligence 2018

Prediction Machines

many production tasks can be formulated as prediction problems
examples:
- regression, classification: predict Y as a function of X
- student -> pass or fail ?
- should I invest in A or B ?
- even chatbot: what is the appropriate continuation for an ongoing conversation?

Will I lose my job ?

AI is a decrease in the cost of predictions
The demand for all prediction-intensive tasks will rise (law of demand)
The salary of workers with prediction-intensive tasks will rise (market price)
Value of other tasks will fall (general equilibrium effect)
More precisely:
- demand for tasks that are substitute to predictions will be low
- demand for tasks that are complement to predictions will be high

A simple tasks-model framing (why employment effects are ambiguous)

Jobs are bundles of tasks; AI rarely replaces entire occupations at once
If AI substitutes for tasks you do, your marginal product may fall
If AI complements tasks you do (e.g., better targeting, better diagnostics), your marginal product may rise
Aggregate employment depends on substitution, new tasks/products, demand expansion, and reallocation frictions
Distributional effects (who owns the complements/data/capital) are usually first-order

👉 More on it next week.

AI is Data

Data added to the production function (illustration)

Chad Jones and Christopher Tonetti (Stanford) Nonrivalry and the Economics of Data (Sep 2020, American Economic Review)

Data is a factor not a technology

Data is a factor, not a technology
- Can you explain it ?
The difference between an idea and a factor? Examples:
- idea: use machine learning to build self driving cars
- factor: each car-maker gathering his own data to train cars
Data (even anonymous) improves quality of existing products

What kind of good is data ?

Remember the classification of goods?
- nonrival: can be used with leftovers
- excludable: use can be limited to paying customers
- data is a: club good
Nonrivality implies
increasing returns to scale
- 🤔: check why
marginal value of new data increases more than proportionally

AI: adds data to the production function (consequences)

increasing returns to scale implies natural monopoly
- ->GAFAMs
- increasing suboptimal monopoly rents (already a problem before existence of AI…)
- should you regulate a monopoly?
  - it depends what is the barrier to entry: data-gathering or data-processing (cloud)
other relevant questions
- where are the markets? (empirically seems to be “undertraded”)
- who owns the data ? Consumer, producer.

How do you regulate a Data-monopoly ?

solutions:
- split the monopolies (if deadweight loss is too big)
- outlaw data gathering (big productivity loss)
- force data-sharing: make it a public good
- let the consumer be free to decide whether to rent their data (remove externalities)

AI: competition between humans and robots

Robots substituting for labour in production (illustration)

Economic singularity

In the very long run, could technology be bad?
Recall the neoclassical world
- market economy
- technological progress reduces production cost
- always good for consumers. Increase (real) total income.
- becomes an inequality problem
But
- whether technology reduces salaries depends on whether growth is labour augmenting or capital augmenting
- if AI is a close enough substitute for labour, salaries of “humans” as a whole are at risk
- there is an economic singularity when the wage of humans falls below the subsistence level

Anton Korinek Joe Stiglitz Korinek Gilles Saint Paul

Two sets of authors reach very similar conclusions
- Anton Korinek and Joseph Stiglitz (left): more complete/technical
- Gilles Saint Paul (right): more political economy

Some very long run scenarios

Analysis taken from Gilles Saint Paul
Main hypothesis: all humans can be replaced by more productive robots
Comparative advantage logic:
- humans specialize in work where their comparative disadvantage is lowest (services, art, crafting…)

Scenario 1: society redistributes income from robots

Four political subscenarios:

Welfare state

robot-owners are taxed, income is redistributed
for instance as universal income
some productivity losses
what about international competitiveness?

Rentiers society

robot owners invest the rent over many generations
capital concentration increases

Neo-Fordism

firms pay huge salaries for essentially useless jobs (powerpoint presentations, 😉 …)
useful to sustain demand

New roman empire

robot owners: patricians (top 2%)
rest of population: plebeians
- survive thanks to clientelism
robots: slaves

Scenario 2: wars, starvation, epidemic

human income (marginal productivity) falls below subsistance levels
malthusian effect: population growth decreases
not unheard of (Leontief): consider population of draft horses

Scenario 3: the Matrix

human wage decrease
subsistence level decreases dramatically too

Something Else Completely?

Right now AI is a technology (or a factor)
What if it becomes another intelligent agent?
- has its own goals
- its own preferences
- with superhuman thinking abilities…
Response in the literature (if curious):
- Anton Korinek: if market economy survives
  - malthusian and non-malthusian scenarios
- At that stage humans might be something different completely
  - transhumanism

Conclusion

Research on AI is very speculative: especially about the long run
But concepts from classical economics still help
Very important assumption: “if market economy survives”
For next time:
- make sure you understand all concepts in bold