The Rise of Artificial Intelligence

Topics in Economics, ESCP, 2025-2026

Introduction

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How do you see the future of AI ?

. . .

Blade Runner (1982) still

. . .

• 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…”?

. . .

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)

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Chad Jones

Christopher Tonetti

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

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• 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

More Readings

• Chad Jones and Christopher Tonetti: Nonrivalry and the Economics of Data, American Economic Review

• Avi GoldFarb: Prediction Machines: The Simple Economics of Artificial Intelligence 2018

• Gilles Saint Paul: Robots Vers la fin du travail ?

• Anton Korinek, Joseph E. Stiglitz: Artificial Intelligence and Its Implications for Income Distribution and Unemployment, chapter in Artificial Intelligence and Its Implications …, NBER

  • also on coursera