I've Got Questions with Sinead Bovell

From the rise of ambient intelligence to the changing nature of work and scientific discovery, what might be possible by 2030? What skills will matter most? And how do we decide which problems to solve as our technological capabilities expand?

In this episode of I’ve Got Questions, I sit down with Yossi Matias, VP of Google Research, for a fireside chat about the next frontiers of AI and science, and how the technologies being developed today could reshape our everyday lives.

We explore his vision for the future, the possibilities emerging across disciplines, and what it means to prepare for a world where the boundaries of what’s possible are rapidly expanding.

Chapters:

00:00 Intro
01:39 A future where every scientist has an AI lab
04:26 What happens when experts can work beyond their specialty?
05:32 Everyday life in 2030
08:02 Earth AI: Asking bigger questions about our planet
10:53 When AI becomes something we take for granted
12:38 Soft skills, new roles, and the future of hiring
15:19 Thinking like an organization of one
17:25 Sinéad’s prediction for a new job in the AI era
18:06 What keeps the Head of Google Research up at night?
21:11 Closing thoughts

This episode is brought to you by Google Research

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AI safety has moved further into public debate following a series of incidents over the summer involving AI systems behaving in concerning ways. But efforts to influence the direction of AI development often focus on coordination between AI labs or competition between the U.S. and China.

There’s another relationship that deserves more attention: the businesses buying and using AI, and the society they operate in. Both have an interest in AI that is reliable and accountable. Could that shared interest become a source of leverage over the companies building it?

In this episode, I explore what coordination between businesses and society could look like, and why I think it’s an under-explored lever for AI safety.

0:00 A different way to think about AI safety
0:20 What is recursive self-improvement?
1:35 The AI alignment problem
2:38 The race between AI labs
3:23 AI companies still need customers
4:44 Where business and society’s interests align
5:17 The financial pressure on AI companies
6:01 A lever worth exploring

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The AI boom is accelerating, but what kind of economy are we actually building?

I sit down with Nobel laureate and MIT economist Daron Acemoglu to explore what artificial intelligence could mean for jobs, wages, inequality, and democracy. As enormous amounts of capital and talent flow into the race for more powerful AI, Daron argues that the direction of the technology is still a choice.

We unpack what an AI-first economy could look like, whether widespread job displacement is inevitable, why AI could raise both productivity and wages if designed differently, and how it could instead contribute to a more unequal, two-tier society.

We also explore the U.S.–China AI race, the limits of universal basic income, and what governments, companies, and civil society can do while the future is still being shaped.

Daron Acemoglu is an Institute Professor of Economics at MIT, a recipient of the 2024 Nobel Memorial Prize in Economic Sciences, and co-author of Why Nations Fail and Power and Progress.

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Daron’s Latest Book: What Happened to Liberal Democracy? Remaking a Politics of Shared Prosperity

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Will we be the last generation that reads?

The data suggests we're moving toward a post-literate society. As reading declines and AI makes it easier than ever to summarize, interpret, and think on our behalf, we're beginning to outsource more than just information retrieval. We're outsourcing cognitive effort itself.

In this episode, I unpack why reading has been so important to human development, how our relationship with knowledge has changed over time, why AI raises the stakes, and what we can do to preserve the skills that matter most in an increasingly intelligent world.

Chapters:

0:00 – Why we might be the last generation that reads1:00 – What reading does to the brain3:55 – The data behind the decline in reading and literacy7:00 – AI, cognitive offloading, and the path to a post-literate society11:45 – We've faced this kind of technological shift before16:00 – The Great Cognitive Divide19:30 – How to use AI without outsourcing your thinking22:00 – Why cognitive friction may become our most valuable skill

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Are jobs actually going away?

In this episode of I’ve Got Questions, I sit down with economist Avi Goldfarb, professor at the Rotman School of Management at the University of Toronto, Rotman Chair in AI and Healthcare, and chief data scientist at the Creative Destruction Lab, to unpack what AI is actually doing to the job market.

We explore the popular narrative of an AI job apocalypse and what the data is really showing so far. Avi explains why AI may not simply replace workers across the board, but instead reshape which tasks become more valuable, which jobs become more exposed, and who benefits from the productivity gains.

We dive into how AI could transform industries like law, healthcare, marketing, and finance, what happens to entry-level workers when AI can perform junior tasks, and why college graduates may be facing a very different path into the workforce. We also explore whether AI could increase inequality or become an unexpected equalizer, depending on what gets automated and what gets augmented.

And Avi shares the four skills he believes matter most for anyone trying to stay valuable in an AI-powered economy.

What You’ll Learn:[00:00:00] — Opening: Andrew Yang’s viral AI jobs warning, unpacked

[00:09:24] — Why the jobs most exposed to AI are in the top 20%, not the bottom 80%

[00:13:38] — One person's automation is someone else's augmentation: the doctor-nurse framework

[00:23:00] — The long run vs. the short run: where Avi is confident and where he's not

[00:33:32] — "Jobs aren't good": decoupling work from income and meaning

[00:43:10] — Software engineering as the canary in the coal mine for the knowledge economy

[00:52:34] — Why automation takes much longer than the hype says: the telephone operator

[01:10:00] — The O-ring effect: why AI exposure in your job might mean higher wages

[01:18:00] — The "AI paradox": could automation destroy its own consumer base?

[01:26:05] — Four skills that hold their value regardless of how AI evolves

[01:29:25] — The PE junior who automated his own job and got promoted

[01:30:34] — What policymakers need to understand about AI trade-offs

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Notable mentions

Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence (Brynjolfsson, Chandar, Chen) — Stanford Digital Economy Lab paper showing early-career workers (22–25) in AI-exposed occupations have seen a 16% relative employment decline — https://digitaleconomy.stanford.edu/publication/canaries-in-the-coal-mine-six-facts-about-the-recent-employment-effects-of-artificial-intelligence/

GPTs Are GPTs (Eloundou, Manning, Mishkin, Rock) — Science paper identifying the jobs most exposed to AI, finding they cluster in the 80th–90th income percentile — https://arxiv.org/abs/2303.10130

The System of Professions (Andrew Abbott) — Sociology book on how professions compete for jurisdiction over expert knowledge — https://press.uchicago.edu/ucp/books/book/chicago/S/bo5965590.html

Power and Prediction (Avi Goldfarb, Ajay Agrawal, Joshua Gans) — Their book on point solutions vs. system solutions in AI deployment — https://www.avigoldfarb.com/powerandprediction

O-Ring Automation (Joshua Gans, Avi Goldfarb) — NBER working paper on why task exposure to AI can raise rather than lower wages; explains why 75% AI exposure does not equal displacement — https://www.nber.org/papers/w34639

The O-Ring Theory of Economic Development (Michael Kremer, 1993) — The foundational economic model, named after the Challenger disaster, explaining why one essential human task can protect an entire production chain — https://academic.oup.com/qje/article-abstract/108/3/551/1881767

Baumol's cost disease — https://en.wikipedia.org/wiki/Baumol_effect

Some Simple Economics of AGI (Christian Catalini, Xiang Hui, Jane Wu) — MIT/WashU paper arguing that as AI execution becomes abundant, the binding economic constraint shifts to human verification bandwidth — https://arxiv.org/abs/2602.20946

Answering the Call of Automation: How the Labor Market Adjusted to Mechanizing Telephone Operation (James Feigenbaum, Daniel P. Gross) — Documents AT&T's 1920s–40s automation of telephone operators; incumbent operators were hardest hit while later cohorts found alternative middle-skill work — https://www.nber.org/papers/w28061

Alex Imas (University of Chicago Booth) — Behavioral economist researching what becomes scarce in a world of AI abundance; human presence, social connection, and provenance emerge as the new scarce goods — Substack: https://aleximas.substack.com/ | Faculty page: https://www.chicagobooth.edu/faculty/directory/i/alex-imas

Betsey Stevenson (University of Michigan, Ford School of Public Policy) — Labor economist studying AI's effects on jobs, income distribution, and human flourishing; former CEA member and Chief Economist of the U.S. Department of Labor — https://betseystevenson.com/

How Do Patent Laws Influence Innovation? Evidence from Nineteenth-Century World's Fairs (Petra Moser) — Shows that countries without patent laws innovated as much as those with them, just in different sectors; foundation for Avi's point that countries can choose different AI paths — https://www.aeaweb.org/articles?id=10.1257/0002828054825501

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Follow Avi Goldfarb

Avi Goldfarb — Economist, Rotman Chair in AI and Healthcare, University of Toronto; Chief Data Scientist, Creative Destruction Lab

Faculty page: https://www.rotman.utoronto.ca/FacultyAndResearch/Faculty/FacultyBios/Goldfarb

X / Twitter: @avicgoldfarb — https://twitter.com/avicgoldfarb

Creative Destruction Lab: https://creativedestructionlab.com

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