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# Lessons from Avi Goldfarb
- URL: https://www.antoinebuteau.com/lessons-from-avi-goldfarb/
- Published: 2026-06-30T17:08:07.000Z
- Updated: 2026-07-18T21:45:22.000Z
- Description: Avi Goldfarb is a University of Toronto economist who reframes artificial intelligence as a measurable decline in prediction costs, clarifying how cheaper prediction reshapes human judgment, organizational design, market structure, and the tradeoffs surrounding data privacy.
- Author: Antoine Buteau
- Tags: Profile, Finance & Economics Profiles

![Visual summary of operating lessons from Avi Goldfarb.](https://www.antoinebuteau.com/content/images/2026/06/lessons-from-avi-goldfarb-profile-infographic.webp)

## Lessons from Avi Goldfarb

Avi Goldfarb is an economist at the University of Toronto’s Rotman School of Management who studies how digital technology changes markets. In his co-authored books *Prediction Machines* and *Power and Prediction*, he reframes artificial intelligence from a vague concept into a measurable drop in the cost of prediction. This profile collects his arguments for how cheaper prediction changes the value of human judgment, forces companies to restructure, and affects data privacy.

### Part 1: The Economics of Artificial Intelligence

1. **On reframing AI:** "Thinking about AI as a drop in the cost of prediction is transformational because it shifts the conversation from science fiction to fundamental economics." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
2. **On economic principles:** "When a core input becomes cheap, we start using it for everything. Just as the internet made distribution cheap, AI makes prediction cheap." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
3. **On defining intelligence:** "From an economic standpoint, we shouldn't view AI as an artificial brain. We should view it as a statistical tool that fills in missing information." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
4. **On the history of technology:** "We tend to look at AI as unprecedented, but economically, it behaves like electricity or semiconductors—it requires complementary innovations to be useful." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
5. **On the limits of automation:** "Economics tells us that when prediction becomes cheap, the complements to prediction—like human judgment—become more valuable." — [*Source: \[Harvard Business Review*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
6. **On decoupling tasks:** "AI allows us to unbundle the process of making a decision, separating the prediction of an outcome from the judgment of its value." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
7. **On productivity growth:** "We often worry about AI destroying jobs, but the primary economic function of a general-purpose technology is driving long-term productivity growth." — [*Source: \[NBER Working Papers*](https://www.nber.org/?ref=antoinebuteau.com)*\]*
8. **On cost curves:** "The history of computing is a story of falling arithmetic costs. The history of AI will be a story of falling prediction costs." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
9. **On business strategy:** "You don't need a computer science degree to understand AI strategy; you just need to understand what happens to your business when prediction costs fall to zero." — [*Source: \[Rotman Management Magazine*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
10. **On identifying opportunities:** "Look for processes where the lack of accurate prediction is the primary bottleneck to efficiency. That is where the economic returns of AI will be highest." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*

### Part 2: AI as Cheap Prediction

1. **On the definition of prediction:** "Prediction isn't just about forecasting the future. It is about using information you have to generate information you don't have." — [*Source: \[Harvard Business Review*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
2. **On medical diagnosis:** "A medical diagnosis is fundamentally a prediction problem: taking data about symptoms and using it to fill in the missing information about the underlying cause." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
3. **On driving:** "Autonomous vehicles do not program every possible rule of the road. They predict what a good human driver would do in any given situation." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
4. **On translation:** "Machine translation is not about understanding language; it is about predicting which word in English most likely corresponds to a word in French based on past texts." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
5. **On the expanding scope of prediction:** "As prediction gets cheaper, we will start treating problems as prediction problems that we never previously thought of as such." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
6. **On data as an input:** "Data is the raw material of prediction. Without vast amounts of historical data, the cost of generating accurate predictions remains high." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
7. **On the limits of machines:** "Machines are terrible at knowing what to do with a prediction once it is made. They only output a probability." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
8. **On resolving uncertainty:** "The primary value of cheap prediction is that it reduces the uncertainty that prevents organizations from acting decisively." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
9. **On inventory management:** "Retailers used to rely on rules of thumb to stock shelves. Now, they use cheap prediction to stock precisely what consumers will buy, reducing massive overhead." — [*Source: \[Harvard Business Review*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
10. **On creative industries:** "Even creative work involves prediction—predicting which sequence of pixels will form a pleasing image or which sequence of words will form a coherent paragraph." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*

### Part 3: The Rising Value of Human Judgment

1. **On defining judgment:** "Judgment is the process of determining what the reward or penalty is for taking a specific action in response to a prediction." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
2. **On human comparative advantage:** "As machines take over the math of predicting outcomes, humans are left to do what machines cannot: decide which outcomes actually matter." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
3. **On ethical trade-offs:** "If an AI predicts a 10% chance of rain, it cannot decide if you should carry an umbrella. Only a human knows how much they dislike getting wet versus the annoyance of carrying the umbrella." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
4. **On managerial roles:** "The manager of the future is not someone who calculates probabilities, but someone who exercises judgment over complex, ambiguous organizational priorities." — [*Source: \[Harvard Business Review*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
5. **On the complementarity of AI:** "Prediction and judgment are complements. The cheaper prediction becomes, the more frequently we will require human judgment to act upon those predictions." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
6. **On automation anxiety:** "Jobs that are heavily weighted toward prediction are at risk. Jobs that are heavily weighted toward judgment will see their wages and demand increase." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
7. **On rare events:** "Humans possess the unique ability to exercise judgment in novel situations where historical data is sparse—something prediction machines fundamentally struggle with." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
8. **On engineering judgment:** "The hardest part of implementing AI is often not the algorithm, but explicitly programming the human judgment of payoffs into the system's objective function." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
9. **On objective functions:** "When you tell an AI to optimize a metric, it will do exactly that, ruthlessly. Human judgment is required to ensure the chosen metric actually aligns with the firm's true goals." — [*Source: \[Rotman Management Magazine*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
10. **On empathy:** "Judgment often requires empathy—understanding how a decision will affect employees or customers emotionally. Machines have no mechanism for this." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*

### Part 4: The Between Times and Adoption

1. **On the adoption gap:** "We are currently in the 'Between Times'—the period where the potential of AI is obvious, but the widespread economic impact has not yet materialized." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
2. **On historical parallels:** "When electricity was introduced in factories, productivity didn't jump immediately. It took thirty years for managers to realize they needed to redesign the factory floor." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
3. **On point solutions:** "Most organizations are stuck implementing AI as point solutions, dropping new technology into existing workflows to do the exact same work, just slightly faster." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
4. **On organizational friction:** "The barrier to seeing massive returns from AI is not the technology itself; it is the friction of internal resistance and legacy organizational structures." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
5. **On the necessity of patience:** "Transformational technologies always require complementary innovations. The Between Times last exactly as long as it takes for an industry to invent those complements." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
6. **On misplaced expectations:** "Companies often buy AI expecting an immediate ROI, failing to realize they are buying a capability that requires them to change how their business operates." — [*Source: \[Harvard Business Review*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
7. **On legacy systems:** "You cannot simply plug a highly accurate prediction machine into a bureaucratic process designed around human guesswork and expect a revolution." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
8. **On first-mover advantage:** "The winners in the Between Times are not necessarily the companies with the best algorithms, but those most willing to endure the pain of reorganizing around the algorithm." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
9. **On the length of the transition:** "We often overestimate the short-term impact of AI while severely underestimating the long-term, system-level transformation it will enforce." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*

### Part 5: System-Level Innovation

1. **On redefining the business:** "System-level innovation means asking not 'how can AI make our current process faster?' but 'if prediction were perfect, why would we do this process at all?'" — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
2. **On airport security:** "A point solution uses AI to read luggage X-rays faster. A system solution predicts who is a threat before they arrive, eliminating the security line entirely." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
3. **On shipping:** "If Amazon's recommendation AI becomes accurate enough, it will be cheaper for them to ship goods to your house before you order them and handle the occasional return." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
4. **On creating winners and losers:** "System-level changes are difficult because they alter the power dynamics inside a firm. Middle managers who used to make forecasts lose status to algorithms." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
5. **On architectural changes:** "To get the most out of AI, you have to change the architecture of the organization. You have to move decision rights to the people who can best exercise judgment." — [*Source: \[Harvard Business Review*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
6. **On the role of the CEO:** "Because system solutions require cross-departmental reorganization, they cannot be delegated to the IT department. They must be driven by the CEO." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
7. **On healthcare systems:** "AI won't just help doctors read scans; it will eventually redesign the hospital, shifting care from reactive treatment to proactive prevention based on prediction." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
8. **On risk tolerance:** "Pursuing system-level innovation requires a high tolerance for risk because you are dismantling workflows that currently generate revenue for workflows that are untested." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
9. **On the inevitability of change:** "Eventually, new entrants unburdened by legacy processes will build AI-native system solutions, forcing incumbents to adapt or die." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*

### Part 6: Decision-Making Under Uncertainty

1. **On the anatomy of a decision:** "Every decision can be broken down into data, prediction, judgment, action, and outcomes. AI only handles the prediction component." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
2. **On managing uncertainty:** "The primary economic cost of uncertainty is that it forces organizations to build expensive buffers—extra inventory, extra time, extra staff." — [*Source: \[Harvard Business Review*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
3. **On reducing buffers:** "When prediction improves, uncertainty drops. When uncertainty drops, companies can strip away the costly buffers they built to protect against the unknown." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
4. **On default actions:** "Organizations rely on rules and defaults when prediction is expensive. AI allows firms to replace rigid rules with dynamic, context-specific decisions." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
5. **On the limits of past data:** "A prediction machine assumes the future will look like the past. When structural breaks occur—like a pandemic—these machines fail and human intervention is required." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
6. **On the illusion of certainty:** "No matter how good AI gets, it produces probabilities, not certainties. Leaders must still learn how to act confidently on probabilistic information." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
7. **On asymmetric payoffs:** "AI is incredibly useful in situations where the cost of a false positive is drastically different from the cost of a false negative, provided human judgment sets those costs." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
8. **On real-time decision making:** "By lowering the cost of prediction, AI increases the frequency at which decisions can be made, shifting strategy from annual planning to real-time adjustment." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
9. **On the paradox of better data:** "Having better predictions often makes the remaining uncertainty more glaring, forcing executives to confront the limitations of their own judgment." — [*Source: \[Rotman Management Magazine*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*

### Part 7: The Privacy Paradox and Regulation

1. **On the privacy paradox:** "Consumers consistently say they value privacy highly in surveys, yet they willingly trade their personal data for tiny conveniences or small discounts." — [*Source: \[Journal of Political Economy*](https://www.journals.uchicago.edu/toc/jpe/current?ref=antoinebuteau.com)*\]*
2. **On the cost of privacy:** "Privacy regulation is not free. When you restrict data flow, you inevitably decrease the efficiency of digital advertising and online markets." — [*Source: \[NBER Working Papers*](https://www.nber.org/?ref=antoinebuteau.com)*\]*
3. **On advertising effectiveness:** "Empirical evidence shows that when strict privacy regulations prevent behavioral targeting, the effectiveness of digital ads drops precipitously, especially for smaller publishers." — [*Source: \[Management Science*](https://pubsonline.informs.org/journal/mnsc?ref=antoinebuteau.com)*\]*
4. **On entrenching incumbents:** "Complex privacy laws like GDPR often inadvertently benefit large tech monopolies, because only they have the resources to comply and the massive first-party data to survive without third-party tracking." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
5. **On the trade-off with innovation:** "Policymakers must realize that aggressively restricting data access to protect privacy directly slows the development of accurate prediction machines." — [*Source: \[Harvard Business Review*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
6. **On contextual sharing:** "People do not value privacy uniformly. They are highly protective of medical data but largely indifferent to sharing their geographic location with a weather app." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
7. **On consent fatigue:** "Forcing users to click 'I agree' on endless cookie banners does not necessarily protect privacy; it mostly increases transaction costs and induces consumer apathy." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
8. **On data as a barrier to entry:** "In a world heavily regulated for privacy, a firm's historical stockpile of user data becomes an insurmountable moat against new competitors." — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
9. **On the future of regulation:** "The challenge of the next decade is designing privacy laws that protect consumers from real harms without accidentally outlawing the data-driven systems that run the modern economy." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*

### Part 8: Market Competition and Digital Strategy

1. **On economies of scale:** "Prediction machines exhibit massive economies of scale. The algorithm gets better with more data, attracting more users, which generates more data." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
2. **On the value of data vs. algorithms:** "In many industries, the algorithms are open-source and commoditized. The true competitive advantage lies in proprietary access to training data." — [*Source: \[Harvard Business Review*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*
3. **On competitive dynamics:** "When prediction is cheap, the basis of competition shifts from who can forecast the market best to who can act on the forecast fastest." — [*Source: \[Power and Prediction*](https://hbr.org/2022/11/how-to-spot-an-ai-opportunity?ref=antoinebuteau.com)*\]*
4. **On substituting capital for labor:** "AI allows firms to substitute capital in the form of prediction machines for human labor in tasks that require forecasting and pattern recognition." — [*Source: \[The Mixtape with Scott*](https://scunning.substack.com/)*\]*
5. **On digital moats:** "A strong digital strategy does not just accumulate data; it identifies the specific data that will generate predictions valuable enough to lock in customers." — [*Source: \[Rotman Insights*](https://www.rotman.utoronto.ca/Connect/Rotman-MAG?ref=antoinebuteau.com)*\]*
6. **On the cold start problem:** "The hardest part of building an AI business is getting the initial data to train the model before you have a product good enough to attract users." — [*Source: \[Invest Like the Best*](https://joincolossus.com/episodes?ref=antoinebuteau.com)*\]*
7. **On liability and risk:** "As AI takes over more decisions, companies face new strategic risks regarding liability. If the machine makes a disastrous prediction, who is legally responsible?" — [*Source: \[Schwartz Reisman Institute*](https://srinstitute.utoronto.ca/?ref=antoinebuteau.com)*\]*
8. **On specialized AI:** "Broad, general AI makes headlines, but the most profitable corporate strategies involve highly specialized, vertical AI trained on niche industry data." — [*Source: \[Talks at Google*](https://www.youtube.com/watch?v=13%5FUfbF8VBo&ref=antoinebuteau.com)*\]*
9. **On the ultimate commodity:** "As prediction becomes universally cheap and widely available, the only true scarcity left in business will be human vision and judgment." — [*Source: \[Prediction Machines*](https://hbr.org/2018/04/how-ai-will-change-the-workplace?ref=antoinebuteau.com)*\]*