Peter McCrory

speaker

628 appearances 3 recordings 3 series first heard Jan 2026 last heard 19 Jun

Peter McCrory’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 3 in all, peaking in Jun 2026 with 1.

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Azeem Azhar's Exponential View · Anthropic’s Head of Economics on AI adoption data, Claude Code, the burden of knowledge & the next generation of experts · 21 Jan 2026 podcast

But if it is constrained to a subset of tasks that are crucially involved in the innovation process, that will limit the extent to which you could have takeoff and the extent to which productivity growth might rise above average.

2%, 3%.

Anything higher would be like historically unimaginable in many ways.

And I think we have to think about these things with some level of humility.

The long 20th century from 1870 to the end of the early 2000s was a

You know, a period of immense technological transformation, immense automation, the decline in the share of workers in agriculture from above 80% to around 3% today.

And if you look over the long sweep of history in the U.S., at least over that time, it's about 2% points real GDP growth each year.

So I think the future is very uncertain.

And again, that's like the big motivation that I have in doing this work is like trying to help us, others, policymakers, researchers to see a bit further into the future, to see a bit more clearly.

Sort of the mismeasured consumer surplus.

I mean, that's also been a lesson of recent information technologies, the advent of Google, the ability to get information at your fingertips didn't show up in GDP.

It's sort of for most of us, it's free in some sense.

And

Yeah, I mean, I think this is a really interesting point that the greatest value might ultimately be unmeasured, at least from the standpoint of GDP.

I'm sympathetic to the critique of GDP as a measure of sort of technological progress and prosperity, but it is also the case that over time and across countries, so many other

choose your favorite measure of human prosperity, it tends to correlate very strongly with GDP.

So it might not be the case that it's capturing everything, but it might be pointing us in the right direction.

I'm a bit familiar with the empirical-based large-scale inference techniques that can allow you to sift out some signals when you do large-scale testing in this way.

But I totally agree with the point in general that

We may be overwhelmed, and this is not just in the scientific domain, we may be overwhelmed, an immensity of information that is very challenging to process, and you might be able to use large language models to help you process that information as well.

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