00:01 Good afternoon everybody.
00:02 Uh,
00:03 thank you also for those who are joining us online.
00:06 Um,
00:07 welcome to the WDR 2026 and,
00:10 um,
00:11 AI Digital Development Research
00:13 and Group initiative,
00:15 uh,
00:15 seminar.
00:16 Um,
00:18 if you're like me,
00:19 any discussions we've had on
00:21 whatever we're discussing about the future,
00:23 you will have someone telling you,
00:24 but you know,
00:24 with AI everything is going to change.
00:27 And so,
00:28 and if you dig a little bit into,
00:30 into what that means,
00:31 it presupposes that
00:33 it's going to be widely adopted by everybody and therefore changes,
00:37 or it's possible and not possible,
00:38 but I think,
00:39 um,
00:40 the best person to talk to us about this
00:41 today is Ivy Goldfar from the University of Toronto.
00:45 Who's going to compare those,
00:46 uh,
00:46 general purpose technology,
00:47 GPTs,
00:49 which by the way has nothing to do with the GPT and GPT.
00:52 Uh,
00:52 I checked,
00:52 I checked this morning,
00:54 uh,
00:54 in,
00:55 uh,
00:55 those differences and similarities in,
00:57 uh,
00:57 the adoption of general purpose signage which will help us a little bit
01:01 go beyond that general statement that we hear quite,
01:04 uh,
01:04 often.
01:05 For those who haven't also read it,
01:06 I think Avi has contributed a lot into this,
01:08 uh,
01:09 in very early on,
01:10 on,
01:10 on Latin literature.
01:12 And I would strongly urge you also to read another contribution which is predictive
01:17 Machines,
01:17 you know,
01:18 correct,
01:18 where,
01:19 uh,
01:19 I think there are a lot of insights,
01:20 um,
01:21 that are coming up,
01:22 um,
01:23 that I strongly urge you if you have some interest into
01:26 looking into what AI is really doing
01:28 and potentially contributing.
01:30 So without further ado,
01:31 I will
01:31 give the floor to Avi,
01:33 uh,
01:33 for 45 minutes-ish,
01:35 and then we'll open
01:37 for,
01:37 for questions,
01:38 uh,
01:38 from the audience.
01:40 Um,
01:40 and I'm,
01:41 I'm happy to take.
01:42 Questions about a particular slide as we go.
01:46 Um,
01:47 I think that'll just be,
01:48 and then like more general questions about AI or or whatever else for you.
01:52 Um,
01:53 thanks so much for
01:54 the opportunity to be here.
01:56 It's nice to see some familiar faces,
01:57 lots of new ones,
01:59 um,
02:00 and
02:01 With this,
02:02 the paper I'm talking about uh came about from an NPR conference
02:06 a couple of years ago
02:08 on productivity,
02:09 or technology,
02:10 productivity and economic growth,
02:11 um,
02:12 and it was run by uh
02:15 John Haltiwer and others
02:17 to try to
02:19 understand the role of technology
02:21 being impacted.
02:23 And
02:25 What
02:27 Jay Joshua and I had this sort of
02:30 Uh,
02:30 nagging
02:32 challenge as we were thinking about the opportunities for AI,
02:35 which is
02:37 We talk about AI is general purpose technology,
02:40 and almost all the economic literature on general purpose technologies
02:43 treats them as the same.
02:45 So whether you're talking about electricity or
02:48 um or AI or the internet or computers or the steam engine,
02:52 for good reason,
02:53 you know,
02:53 we we try to find patterns in economics,
02:55 and so
02:56 we find patterns that's the same underlying ideas of,
03:00 OK,
03:00 well,
03:00 we can take ideas from electricity and apply them
03:02 to how we might anticipate AI will play out.
03:06 And
03:07 That's useful to a point,
03:09 but the other thing you want to think about when you're implementing policy,
03:12 whether
03:13 at the national level or within a company,
03:15 is
03:16 what's different about this technology
03:18 relative to what preceded it.
03:19 And so,
03:20 uh,
03:21 this paper was,
03:23 um,
03:23 it came out in this this volume,
03:26 uh,
03:26 really recently.
03:27 And it was an attempt to reconcile the
03:30 general purpose technology literature
03:32 with the digital economics literature with what we've
03:35 done on AI and a few other things
03:37 to try to say what's similar and what's different.
03:40 And what we were really doing is
03:43 Combining
03:44 the old GBT work
03:46 and
03:48 along with a repurposing of a paper that
03:51 Ben Greenstein,
03:51 Chris Foreman,
03:52 and I wrote
03:53 and published in 2005,
03:55 and a repurposing of a paper that Santa and Pia Obermayer
03:59 wrote that was published a couple of years ago.
04:03 So first,
04:04 general purpose technologies,
04:05 um.
04:07 I'm here
04:09 actually read the
04:11 paper,
04:13 maybe a couple of you,
04:16 uh,
04:16 it's a pretty brutal read,
04:17 to be honest,
04:18 uh,
04:19 cause it's like
04:20 they're microeconomists and it's a macro paper,
04:22 um,
04:23 and it reads like a couple of microeconomists writing a macro paper,
04:26 uh,
04:27 but there's this
04:28 like,
04:28 but that combination of micro and macro is exactly what
04:32 Uh,
04:32 is the essence of the idea in the paper,
04:34 which is
04:36 they argue there's a handful of technologies
04:39 that lead to an outsized impact on economic growth,
04:42 not because the technology itself is useful,
04:45 but because the technology leads to follow-on technologies.
04:49 So the roots of this,
04:50 notice like paper was published in 1995,
04:53 it came out
04:54 um.
04:56 First draft was 1990,
04:57 1991.
04:59 Just as we started thinking about endogenous growth.
05:02 And so,
05:03 and it,
05:03 you gotta think of this paper as very
05:05 much rooted in the endogenous growth literature.
05:07 So,
05:07 the puzzle it was trying to solve is
05:10 Solo
05:10 from 1957 said,
05:13 in the long run we don't get economic growth.
05:15 Uh,
05:15 because eventually sort of things fade unless we get exogenous changes
05:20 in total factor productivity.
05:23 And um
05:24 they said,
05:25 OK,
05:25 well,
05:25 let's,
05:27 it's weird that we get these exogenous changes in total factor productivity,
05:30 but some technologies seem to lead to
05:32 a small increase
05:34 in some like steam engine and electricity seem to lead to this big increase,
05:38 and
05:39 the
05:40 The essence of the general purpose technology
05:43 uh model
05:45 is
05:46 that.
05:47 Um,
05:48 that first.
05:50 That
05:50 most technologies just lead to a single increase in total factor productivity,
05:54 then you're done.
05:56 But some technologies lead to
05:58 maybe even a smaller
06:00 increase in total activity,
06:02 but that leads to follow-on innovation.
06:04 So there's a sort of a
06:05 complementary downstream innovation
06:07 that in turn leads to complementary upstream innovation,
06:10 which in turn leads to complementary downstream innovation,
06:11 and we get this feedback loop in innovation
06:14 that leads that generates over the course of 30 to 50 years
06:17 an outsized impact on to effective productivity.
06:21 Eventually we still end up in the solo trap.
06:22 So eventually,
06:24 for a given general purpose technology,
06:26 we still end up back at zero growth,
06:28 but we
06:29 endogenously get
06:31 some technologies lead to a bigger impact
06:33 on total factor productivity than others.
06:36 That's the essence of the GPT literature
06:38 or the,
06:38 the original model.
06:39 OK,
06:39 so it's it,
06:40 and there's this handful of technologies,
06:42 so
06:43 you define them as characterized by potential for pervasive use
06:45 in a wide variety of sectors in their technological dynamism.
06:49 Um,
06:50 Pres some use in a wide variety of sectors.
06:52 We
06:53 got lots of technologies like that.
06:56 Little things are used across the economy
06:58 that peak impact total productivity once
07:01 in the short term,
07:02 and then we're kind of
07:03 then the impact is done.
07:05 But it's a technological dynamism that's particularly important here,
07:07 which is that
07:08 it's not just that they're used in a wide variety and variety of sectors,
07:12 but those sectors then innovate
07:14 and make the technology
07:16 uh tailored and targeted to their own uses.
07:19 So,
07:19 um,
07:20 there is a separate literature.
07:23 In strategy,
07:24 led by David Ties,
07:25 uh,
07:26 that focuses on what they call enabling technologies.
07:29 So,
07:29 as,
07:30 as an aside here,
07:31 a general purpose technology
07:34 is a technology feedback loop.
07:36 That
07:37 essentially affects the entire economy.
07:41 He comes along and says,
07:43 there's actually
07:44 um a number of technologies that have this feedback loop phenomenon.
07:49 But
07:49 don't really affect the entire economy,
07:51 only affects sort of a small number of sectors.
07:53 So he gives the example of LDA
07:55 as an example,
07:56 like
07:56 LiDAR in of itself isn't that useful,
07:59 but LiDAR led to follow on innovations
08:02 uh on what do you do with a good sensor,
08:05 uh,
08:05 that
08:06 led to a feedback loop in productivity,
08:07 but only in a couple of seconds.
08:09 So it's enabling technology,
08:11 the general purpose technologies are a subset,
08:13 the most important subset of the enabling technologies.
08:17 Uh,
08:18 A really frustrating aspect of the GPT literature.
08:22 Is the same phenomenon
08:25 is called different things,
08:26 even in papers with the same authors.
08:30 So,
08:31 in this paper,
08:32 Tim Bresnahan calls it innovational complementarities.
08:35 In a set of papers with Shane Greenstein,
08:37 he calls them co-invention costs.
08:39 Um,
08:40 in a set of papers
08:42 that,
08:42 oh,
08:43 in
08:45 Bresnahan's papers with Brynjolfsson,
08:47 they call them innovational complementarities.
08:49 In Bernholtz's paper with other people,
08:51 he calls them organizational commentaries.
08:54 Uh,
08:55 Moki at roughly the same time was talking about micro inventions.
08:59 These are all
09:01 essentially the same phenomenon,
09:02 which is one big change
09:05 leads to these follow-on innovations,
09:07 and it's the follow-on innovations that
09:08 generate the outsized productivity impact.
09:14 Um,
09:17 So,
09:19 Uh,
09:19 I should have,
09:20 um,
09:21 you look at some of,
09:22 um,
09:25 One thing that's interesting about this paper is
09:28 it started pretty slow in terms of citations.
09:30 No one really paid attention to it.
09:32 Then,
09:32 uh,
09:34 suddenly around
09:35 the time when AI
09:37 became obviously useful,
09:39 we saw a real
09:40 sharp increase.
09:42 Um,
09:42 and I'm sorry,
09:43 I shouldn't,
09:43 I should have updated this.
09:44 It's 2021,
09:45 but,
09:46 um.
09:47 So
09:48 there's a sense that the GPTs are now
09:51 interesting again.
09:51 Why are GPs is now interesting again because
09:54 um
09:55 we have AI which we think is likely to be a GPT.
09:58 So,
09:59 and
10:00 Part of the challenge here,
10:02 so we talked about this feedback loop,
10:04 is we're not gonna know if AI is actually a GPT for another 20 to 30 years.
10:09 So,
10:10 um,
10:12 it's a
10:13 Unfortunately,
10:14 the way they defined it.
10:16 I you can only know if something's a GDP
10:19 after it's diffused and after you observe this feedback loop play up.
10:23 We,
10:24 it seems like it's an important technology.
10:26 It seems like it's hitting a lot of different parts of the economy.
10:30 Um,
10:30 there's been
10:31 some follow on innovation,
10:33 as in,
10:34 uh,
10:35 you know,
10:36 beyond chips and models,
10:37 we see the application of G AI tools or,
10:41 uh,
10:42 predictive maintenance or other things using AI in industry.
10:46 But whether that's then feeding back into further AI innovation,
10:48 which is feeding back into further
10:50 um
10:51 downstream innovation is like really an open question.
10:55 Planteitis and I.
10:56 Um,
10:57 I believe the paper,
10:59 uh,
10:59 came out in research policy in 2023 trying to assess
11:02 is AI a general purpose technology.
11:04 Um,
11:06 I think that
11:07 this was,
11:07 this was back before we were always calling it AI,
11:09 so we,
11:10 the papers titled
11:11 Could Machine Learning be a General Purpose Technology.
11:14 And
11:15 um
11:17 in reading through how do you define general purpose technologies,
11:21 this is sort of how we realized we can't know,
11:24 and in fact,
11:25 um,
11:26 so two subtleties there.
11:27 It's possible there have been hundreds of potential general purpose technologies
11:32 that never happened.
11:33 GPTs are an equilibrium
11:36 because they can only happen with the feedback loop,
11:38 and so.
11:40 And under the definition,
11:41 it's possible that there were,
11:42 there were things
11:43 that could have turned out to be general purpose technologies
11:46 that never really got going because the downstream innovation never happened,
11:49 which never led to the upstream innovation.
11:52 Um
11:54 And so,
11:55 uh,
11:56 the other thing we learned in trying to
11:58 define whether AI is a general purpose technology
12:01 is,
12:02 um,
12:03 beyond
12:04 is that,
12:05 um.
12:08 How do you,
12:09 how do you figure it out?
12:09 So we looked at about 30 different technologies.
12:13 That we're hyped
12:15 The original version of it is,
12:18 we looked at every technology that was on the cover of Science and Nature.
12:22 Um,
12:23 and of those technologies like AI
12:26 fracking,
12:28 um,
12:28 cloud computing.
12:30 Blockchain,
12:31 a handful of others.
12:32 AI sort of
12:34 was the one that was widest in the economy and most useful for innovation.
12:37 So,
12:37 uh,
12:38 and our reviewers came back and said we don't like your Science and Nature title.
12:41 Uh,
12:41 why don't you use something more like
12:43 industry related work
12:45 based on hype.
12:46 So we went to the Gartner hype cycle
12:48 and found the same phenomenon.
12:51 But the essence of all that work is that,
12:55 um.
12:57 At least of the technologies we have now,
12:59 this is the one that's most likely to be a general purpose technology,
13:02 and the fact that you guys are here
13:04 makes me think you guys are already believers
13:06 that it's likely to be general purpose technology.
13:07 Yeah.
13:08 Could you maybe define a little more what you mean by AI
13:11 because I kind of think I'm sort of surprised that you were
13:14 saying that we don't know yet,
13:16 because,
13:16 for example,
13:16 when I think of computer vision,
13:18 which I guess was a part of AI.
13:21 It's used in
13:23 Airplane
13:24 verification of wind turbulence,
13:27 it's used for,
13:28 you know,
13:28 logging into your computer.
13:29 It's used for
13:31 security.
13:32 So on some level,
13:33 AI is already everywhere in that sense.
13:36 It's just we
13:37 all now think LLMs are the only AI that exists.
13:40 No,
13:40 I'm definitely not thinking that,
13:41 um.
13:43 Uh,
13:43 so,
13:44 in our,
13:45 OK,
13:45 first definition,
13:46 so in our machine is machine learning jump work technology paper,
13:50 uh,
13:50 the definition,
13:52 uh,
13:53 essentially a bunch of
13:55 related technologies around data and data science,
13:58 uh,
13:59 for the purpose of predictive analytics.
14:00 So keywords and job titles might have been AI or machine learning,
14:04 it might have been natural language processing,
14:06 um,
14:06 in
14:07 an earlier manifestation,
14:08 it might have been called big data.
14:10 Uh,
14:10 but it's a series of data-driven
14:12 sort of,
14:12 uh,
14:13 insights,
14:14 technologies.
14:15 Um,
14:16 that study,
14:17 our data ended in 2019,
14:19 so it's before modern LLMs,
14:21 um,
14:22 but LLMs would be part of it,
14:24 uh,
14:24 agentic models would presumably to be part of it.
14:27 And in general,
14:27 when you,
14:29 um,
14:29 so
14:30 a bunch of different ways to think about definitions,
14:32 definition,
14:34 way to do it,
14:34 number one is just to say,
14:36 OK,
14:36 let's just,
14:36 if someone's calling it AI,
14:38 let's call it AI.
14:39 So use that.
14:40 Definition number 2 is to find a couple of sort
14:42 of research papers or patents that we think are focal,
14:46 and then look at anybody.
14:47 So if you're citing,
14:48 uh,
14:49 hinting on deep learning or you're citing,
14:51 um,
14:52 uh,
14:52 attention is all you need,
14:54 then we can think about that as an AI paper,
14:56 and then look at sort of.
14:58 The patents for papers or skills that are related to those innovations.
15:03 And then,
15:04 uh,
15:04 category three is we can just say like we don't see it
15:08 and do some version of it it doesn't really matter how you play it out.
15:11 Um.
15:13 To your point,
15:13 don't we know already?
15:14 Uh,
15:15 we know
15:16 that it is.
15:18 To my knowledge,
15:19 we don't see the productivity impact yet.
15:22 OK.
15:23 Uh,
15:23 there's some suggestive evidence here and some suggestive evidence not there,
15:26 and,
15:27 uh,
15:27 but to my knowledge,
15:28 we don't see the productivity impact,
15:29 so it seems to be widespread.
15:30 I think that's correct.
15:32 Uh,
15:33 we see some follow-on innovation,
15:36 but the evidence of the productivity impacts is at the micro level.
15:39 So we can say
15:41 in a call center,
15:42 if you adopt AI just like renew and Leah Raymond.
15:45 We see
15:46 increased productivity.
15:47 Ordinary Microsoft co-pilot studies where they run experiments and
15:50 who gets co-pilot and who doesn't say leads to,
15:53 uh,
15:54 depending on the study,
15:55 20 to 90% increase in your productivity and coding.
16:00 Uh,
16:01 but then
16:02 you do things like
16:03 at the companywide level that productivity and Tomma has this,
16:06 you know,
16:06 his work on Denmark.
16:08 Shows
16:10 maybe,
16:10 but it's small.
16:12 So we're not there yet.
16:15 OK.
16:17 Um
16:22 So the
16:23 Um,
16:27 The key empirical theme
16:29 of the AI literature
16:31 is
16:32 that we see
16:34 AI adoption in places
16:36 where it's going to be easier to
16:40 um adapt your organization
16:42 to take advantage of the technology.
16:45 So,
16:46 um,
16:47 Bres Hannah Greenstein is
16:50 the we see adoption in places um.
16:54 This is the adoption of IBM computing,
16:57 uh,
16:57 in places that were,
16:59 uh,
17:02 Relatively good at like
17:04 the head still workers who were going to
17:05 be good at integrating it into the workflows,
17:08 um,
17:08 and I bring upson Wu is kind of a similar thing a few years later showing,
17:12 you know,
17:13 uh,
17:14 you're gonna adopt better IT
17:16 when you,
17:17 um,
17:17 when you have those skills.
17:21 So the core common adoption patterns
17:23 is
17:24 the impact
17:26 of
17:27 both adoption and productivity impact depend on
17:31 co-invention.
17:32 Um,
17:34 the
17:35 measured productivity gains take time.
17:38 So we're saying Ber Nelson hit,
17:40 say it's something like 3 to 6 years,
17:43 we're looking
17:44 uh in in medicine in the jars that dry off at all,
17:47 that's something like 6 years.
17:49 So
17:49 Bernie Nelson Rocket Cyber and talk about the productivity,
17:52 where at least even measured wise,
17:53 if anything,
17:54 productivity goes down or it goes back up.
17:57 Um,
17:59 And
18:00 the like,
18:03 Another way to think about this literature is
18:06 firms that have the ability and conditions to innovate,
18:09 do better,
18:11 um,
18:11 and those typically are defined by
18:15 either
18:15 you have skilled workers in your company,
18:18 or you are located in a place where you have access to skilled workers.
18:23 Um,
18:24 and there's versions of that where access to skilled workers
18:27 can be narrowly defined as
18:30 Uh,
18:30 you know,
18:30 in the context of IT,
18:32 you have IT consultants who live in your city,
18:35 who
18:36 work in your industry.
18:38 Um,
18:38 and then another takeaway that showed up in Brit President
18:42 Nielsen hit and also sort of really popped in our,
18:44 in our work on EHRs
18:45 is many,
18:46 many adopters failed.
18:48 The doctors don't always succeed.
18:51 Now
18:53 But the GPT literature doesn't emphasize,
18:55 but the management literature does,
18:57 is that each general purpose technology
18:59 differs from the previous.
19:02 And so if you look at the management literature,
19:04 the IS,
19:05 information systems literature,
19:06 or,
19:07 you know,
19:07 or marketing or strategy,
19:08 they tend to emphasize,
19:10 oh,
19:10 here we have
19:12 artificial intelligence.
19:13 It's fundamentally different because it's,
19:15 you know,
19:15 in our work it's prediction technology
19:17 or
19:18 Spi and Marian emphasized that the internet
19:21 was different because it reduced communication costs
19:24 and transportation costs and information
19:27 and search costs,
19:27 and that's going to sort of lead to to various consequences.
19:31 So.
19:32 The
19:34 another way to think about each technology is it did something different.
19:36 That's why we care about it,
19:37 right?
19:38 Like
19:38 if
19:39 AI did the exact same thing as the internet,
19:41 then we wouldn't
19:43 care about AI in purpose technology would be incremental by definition.
19:45 And so you can think about steam engines and electricity,
19:48 um,
19:49 are,
19:49 uh,
19:50 reduced power.
19:51 Steam engines are just,
19:53 uh,
19:53 initially,
19:54 uh,
19:55 reduced brute force power.
19:56 Uh,
19:57 electricity is also distributed power.
19:58 It allows you to put your machines wherever you want them,
20:01 um.
20:02 Interchangeable parts allow you to produce scale.
20:05 The internet,
20:06 um,
20:08 was fundamentally about electronic communication,
20:10 and railroads and automobiles for transportation technology.
20:14 And so then you can look at the adoption patterns,
20:16 and you're gonna see
20:17 that steam engines and electricity were adopted
20:20 um by companies
20:22 that were in industries where the benefit of power,
20:25 like a
20:25 uh machine power was,
20:27 was huge,
20:28 like
20:28 to be able to do more than what humans could do and horses could do.
20:32 Um,
20:33 interchangeable parts were adopted by
20:35 firms that wanted to produce scale.
20:38 Um,
20:38 and a lot of our work is on a lot of my work,
20:41 but also a lot of the
20:43 empirical evidence,
20:43 cause it's recent,
20:44 is on the internet,
20:45 and you can see that
20:47 many of the early adopters of the internet
20:50 were firms that would benefit from electronic communication
20:53 in two different ways.
20:54 One,
20:55 they were,
20:56 uh,
20:57 outside of major cities.
20:58 And so they've adopt like the early 90s
21:01 adopt mid 90s adopters of the internet were often
21:04 uh
21:04 not
21:05 uh
21:07 doctors have email and having a website
21:09 for people outside the major centers who would
21:11 then have that website to be able to attract
21:13 uh users from elsewhere.
21:15 Um.
21:17 And the railroads and automobiles are about transportation,
21:19 OK.
21:21 Um,
21:23 So
21:24 what
21:26 Uh
21:28 Just.
21:30 So,
21:31 um,
21:34 We can then think of each GPT
21:37 as a drop in the cost of some fundamental input.
21:39 So,
21:40 um,
21:42 uh,
21:43 Tim Brynahan
21:44 and Bill Nordhaus have separate papers arguing that all computers do
21:48 is act.
21:49 Computers do arithmetic.
21:50 And it turns out when arithmetic gets cheap enough,
21:54 we find new applications for arithmetic.
21:56 So you should think about what happened between 1950 and
22:00 something like 2000
22:02 as
22:03 the cost of arithmetic got cheaper,
22:05 and that
22:06 the early applications were these good old
22:08 fashioned arithmetic problems like in accounting,
22:10 uh,
22:10 and a national defense,
22:12 and then over time,
22:14 uh,
22:14 cheap arithmetic allowed us to do all sorts of other things
22:17 like
22:18 mail and music and pictures.
22:19 You didn't.
22:20 Like Kodak,
22:21 for example,
22:22 was solved images,
22:23 self pictures
22:24 with chemistry.
22:25 They're a chemical engineering company,
22:27 but then Machine arithmetic came along.
22:29 Machine arithmetic got cheap enough
22:30 that we realized we could solve,
22:32 uh,
22:33 that problem,
22:33 which is imaging
22:35 with uh
22:37 math
22:37 with adding up numbers
22:39 and not with chemical engineering anymore.
22:41 OK,
22:42 um.
22:44 So,
22:46 largely in and you can think about
22:48 like the,
22:49 the early ideas on this framing
22:51 were first uh
22:52 those Brestan and Nordhouse papers on
22:55 um
22:56 on computers and also Spi and variant,
23:00 which I talked about already,
23:02 um
23:03 their
23:03 sort of management oriented book information rules
23:06 is rooted in this late 90s literature
23:09 that
23:10 Um,
23:11 talk about the idea of the internet is producing search first,
23:16 the
23:16 theory that it would lead to sort of,
23:18 um,
23:19 reduced price dispersion
23:21 and maybe more competitive markets,
23:24 but also that might lead to issues around information asymmetry.
23:27 It was like the,
23:27 the book Information Rules,
23:28 the title
23:29 was,
23:30 uh,
23:31 the rules of the Information Age,
23:32 but it was also to say that,
23:33 you know,
23:34 the information economics,
23:35 which is I'm very
23:36 very are both.
23:37 How they got their careers.
23:39 In information economics was now at the center of how we understood the economy.
23:44 Um,
23:45 and then,
23:45 so taking that.
23:47 Um,
23:48 two things happen.
23:48 So Captain Tucker and I have a Journal of Economic literature
23:52 where
23:52 we argue that
23:54 if you look at the digital
23:55 economics literature
23:56 from 1995 to 2018,
23:59 you can categorize almost all of it as a drop in the cost
24:02 of different types of
24:04 ways to manipulate information,
24:06 search,
24:06 communication,
24:06 transportation,
24:08 uh,
24:08 verification,
24:09 tracking,
24:09 etc.
24:11 um,
24:11 and then.
24:13 Um,
24:13 inspired by this,
24:14 Joshua and I in our AI work,
24:17 talk about AI is dropping the cost of prediction.
24:20 And
24:22 to
24:23 uh
24:24 what we mean by that is under the hood,
24:25 it's computational statistics.
24:28 Which means that it uses data.
24:30 That hasn't gone away with GII or agents.
24:33 It uses data
24:34 in order to fill in missing information.
24:37 And that we do have a little bit of
24:39 a breakdown on our intuition about what predictions are,
24:42 just like
24:42 intuitively,
24:43 we don't think about
24:44 um
24:45 the fact that we use digital cameras as arithmetic in the background.
24:51 OK,
24:52 so,
24:53 um,
24:53 our goal here is to bring together these two literatures,
24:56 um,
24:57 the GPT literature that emphasizes commonalities and the
25:00 management-focused literature that emphasizes reducing economic frictions.
25:04 So,
25:06 Um,
25:09 The
25:10 sort of reframe two papers around this idea.
25:12 So,
25:13 uh,
25:13 Chris Foreman,
25:14 Shane Greenstein,
25:14 and I,
25:15 um,
25:16 this was
25:17 my,
25:17 my first publication,
25:19 uh,
25:19 or it's pretty close to it,
25:21 um,
25:22 in the Journal of Urban Economics where,
25:24 um,
25:25 the punchline,
25:26 we looked at
25:27 adoption of the internet
25:29 circa 2000 by US companies
25:32 and
25:34 Uh,
25:35 the
25:37 Core takeaway number one is big cities were more likely to adopt.
25:42 Why were big cities more likely to adopt?
25:44 Um,
25:44 that was
25:47 That was consistent with what we understood about co-invention.
25:51 And um,
25:52 so co-invention benefits in cities because you have access to local expertise,
25:56 and so they were the ones most likely to adopt.
25:58 And in particular,
26:00 what they were more likely to adopt were
26:02 internet technologies that allowed you
26:04 to coordinate better within the establishment
26:06 and like enterprise resource planning.
26:08 You could
26:09 um
26:10 run your company more efficiently.
26:11 It wasn't about communication outside the company.
26:17 In contrast,
26:20 Um,
26:21 email
26:22 and websites
26:23 were disproportionately adopted in rural areas.
26:27 And so because the example of communication
26:29 technology and who's gonna benefit from communication,
26:31 it's people in relatively rural areas.
26:33 And so
26:34 what
26:35 um
26:36 what this paper showed,
26:38 which we didn't realize at the time,
26:39 this is the point of this revisionist writing,
26:42 um,
26:43 is
26:44 both the,
26:45 the co-invention result,
26:46 and this is what's consistent about all GPTs that for
26:49 advanced uses you need access to the expertise in cities,
26:53 as well as cheap communication benefits for others.
26:58 So,
27:00 Um,
27:05 So related to this idea,
27:06 so Chris Foreman has another paper with Anne Braun,
27:08 um,
27:09 that
27:10 argues that
27:12 I just realized the cameras.
27:15 In the back of my head.
27:15 That's OK.
27:16 OK,
27:16 um.
27:19 Foreman and Gran,
27:20 uh,
27:21 showed that internet adoption was faster,
27:24 um,
27:25 for insurers that were vertically integrated
27:27 because like
27:28 you're think about you're an insurance agent,
27:30 you're independent
27:31 from
27:32 your
27:33 from the insurance company.
27:35 But uh if you're vertically integrated,
27:37 then the benefit of smoother communication is sort of more obvious,
27:40 you don't have to deal with monitoring.
27:41 And so they showed um
27:43 that the benefits are clear.
27:45 And then like in another paper,
27:47 Foreman showed that like in internet distribution
27:49 channel Foreman and a bunch of co-authors,
27:51 so the internet distribution channel requires like
27:55 A change in product offerings,
27:56 um,
27:58 to,
27:59 uh,
28:00 be appealing to a more diverse set of customers.
28:02 What I mean by that,
28:03 this is related to Joe Walfogo's ideas of like preference minorities.
28:07 So,
28:07 do I even have that
28:08 as an example?
28:09 No.
28:10 OK,
28:10 so.
28:12 Joel,
28:12 in a series of papers with various co-authors
28:15 has showed that um
28:19 When the internet came along,
28:21 um,
28:21 it enabled people who were,
28:23 who had tastes unlike their neighbors
28:26 to benefit from technology.
28:29 So,
28:29 um,
28:30 an early example of this is in Wolfvogel,
28:33 he showed that,
28:35 um,
28:36 Uh,
28:37 that,
28:38 uh,
28:39 Trying to remember the details of this,
28:41 uh,
28:42 looked at
28:42 what kind of news people consume in different neighborhoods of Chicago.
28:46 And he found that people who were living in African American neighborhoods,
28:50 but who are not African American,
28:52 with the rise of the internet,
28:54 started to read different news than their neighbors.
28:57 And similarly,
28:58 people who were African American,
29:00 but lived in non-African American neighborhoods,
29:02 when with the rise of the internet,
29:04 they again started to read different news than their neighbors.
29:07 And so that was a sense that if their
29:09 behavior,
29:09 if their preferences uh
29:12 on news might be different from their surroundings.
29:15 Then you could get a different,
29:17 um,
29:18 then you could see you had the opportunity to consume
29:21 something that might have been closer to your preferences.
29:23 And in other work,
29:24 he showed that,
29:25 uh,
29:25 in terms of urban and rural and music preferences,
29:29 and there's been some suggestive evidence in terms of like
29:32 international
29:33 news consumption.
29:34 So,
29:35 uh,
29:35 for those of you who,
29:36 you know,
29:37 may not be,
29:38 may not have grown up in Washington DC.
29:40 I imagine some of the news you consume isn't your local paper.
29:45 Um and that is possible because of the internet,
29:48 and it was very difficult before that,
29:50 with
29:50 the exception of maybe the New York Times in the late 90s.
29:53 OK.
29:55 Um
29:57 Foreman foreman and
29:58 uh
29:59 then Zbrock showed that my internet connections
30:01 sort of affect collaboration
30:03 and um allow
30:06 within a company.
30:07 This is sort of,
30:08 this was Bitnet in the early days,
30:09 so this is all patents at IBM.
30:12 That was the company that dominated the computer industry in the 80s.
30:16 The patents at IBM,
30:17 uh,
30:17 we do see.
30:19 That inventors who aren't at
30:21 the main locations of the company start doing
30:23 better with the diffusion of the internet,
30:25 uh,
30:26 and so each of these says that changed communication patterns,
30:29 and that meant,
30:30 uh,
30:31 in turn,
30:32 uh,
30:32 that
30:33 firms need to change some processes.
30:37 So the takeaway from the like
30:40 the internet literature is we do see
30:42 both
30:43 this
30:44 what's similar,
30:45 we see evidence of co-invention.
30:47 Uh,
30:48 but we also see that this technology was
30:49 different because it was fundamentally about communication.
30:52 OK.
30:53 Um,
30:55 So,
30:55 as much as I just said,
30:57 we don't know if AI is a GPT,
30:59 uh,
30:59 for now we're gonna assume it's a GPT,
31:01 uh,
31:02 and,
31:04 uh.
31:05 We're gonna think about it as prediction technology,
31:08 um,
31:09 in terms of
31:10 predicting whether a website,
31:11 uh,
31:12 you know,
31:12 what's Google Search doing?
31:13 Google search like early AI,
31:15 it's predicting,
31:16 um,
31:18 the,
31:18 you know,
31:18 in order,
31:19 the 10 things you're most likely to want,
31:22 uh,
31:22 in response to a query.
31:24 Uh,
31:25 your newsfeed is similar product recommendations,
31:27 thanks to predicting fraud and healthcare,
31:29 you're predicting diagnosis.
31:30 Uh,
31:31 but
31:32 Gen AI tools are also filling in missing information.
31:34 So if you,
31:35 I don't have my image,
31:36 so if you,
31:37 uh,
31:37 if you ask
31:38 like,
31:40 Uh,
31:40 one of the image generation tools for an image of an
31:42 astronaut on a horse in the style of Andy Warhol.
31:45 You'll get
31:46 actually something that astonishing looks like
31:48 an Andy Warhol of an astronaut on a horse,
31:50 OK.
31:51 Um,
31:52 but it's unlike Google,
31:54 it's not because it's a search engine.
31:56 What's happening in that model
31:58 is it's been trained with images of astronauts,
32:01 images of people on horses,
32:02 and images of the style of Minnie Warhol,
32:05 and it's predicting the set of pixels that you want in response
32:09 to that query,
32:10 right?
32:10 So it's still taking data
32:12 that feeds the model,
32:13 uh,
32:14 to fill in missing information
32:15 that might be,
32:16 you know,
32:17 a little bit outside the the original
32:20 data.
32:21 Um,
32:23 and
32:23 at least in this paper we're gonna be focused on
32:26 data from pre-2022,
32:28 right?
32:28 November 30th,
32:29 2022 was chat GPT.
32:31 Our data in this paper is before that,
32:32 so we're gonna be focused on
32:34 really
32:35 prediction technology for the purpose of improving decision making.
32:38 That's,
32:38 that's the data we're gonna have.
32:41 Um,
32:42 it,
32:43 and it's not
32:45 necessarily a story about automation.
32:47 So AI sometimes leads to automation,
32:50 but,
32:51 uh,
32:51 it doesn't always.
32:52 Sometimes you can have a human in the loop,
32:54 and in fact,
32:54 if you look at this is census data on where adoption happens,
32:57 it's sort of a,
32:59 it's not sort of,
33:00 it is a
33:01 chapter in the same volume,
33:03 um,
33:05 where
33:07 Yes,
33:07 uh,
33:08 you see,
33:09 sometimes AI is automating existing processes,
33:12 but often it's creating new processes or improving quality.
33:15 The,
33:15 the number one use case that,
33:17 you know,
33:17 this is in manufacturing,
33:19 uh,
33:19 circa 2019.
33:22 In the US it's to improve quality,
33:24 not to automate our processes.
33:26 And what we're seeing with G AI is something pretty similar,
33:29 which is there's evidence that some of it is
33:33 Um,
33:34 is automation,
33:35 but a lot of it is,
33:37 um,
33:38 doing things that are totally new.
33:40 So,
33:40 um,
33:41 what
33:43 The last thing I wanna do here is,
33:44 is reframe this,
33:45 this other paper.
33:45 So Sendel,
33:46 Melanathan,
33:47 and Z Obermeyer
33:49 have this,
33:50 uh,
33:51 really fantastic paper,
33:52 um,
33:53 about
33:54 a machine learning,
33:55 using a machine learning tool
33:58 to improve medical diagnosis,
34:00 OK?
34:01 So,
34:02 um,
34:04 There's uh
34:09 I'm trying to remember the medical term.
34:11 Is it on the slide.
34:13 OK,
34:13 well,
34:14 they just use heart attack.
34:14 It's not actually quite right,
34:16 but uh
34:17 so
34:17 it's uh so.
34:20 They have an AI,
34:21 so
34:22 people walk into the emergency department
34:24 and they might have chest pain,
34:26 OK?
34:26 Or some other symptoms that are consistent with,
34:29 uh,
34:30 with real risk to your heart.
34:32 And when that happens,
34:34 the
34:35 typical process is if the physician thinks you're at risk for a heart attack,
34:40 they'll send you to get an ECG,
34:42 uh,
34:43 which is a relatively,
34:46 um,
34:48 Low
34:49 Non-interventionist test,
34:51 and if there's still,
34:52 they think it's a very high risk that you're having a heart attack,
34:55 then you go
34:55 to what's called catheterization.
34:58 Which is they
34:59 they stick,
35:00 uh,
35:02 essentially a wire into your
35:04 bloodstream
35:05 and check and actually check if there's a problem.
35:08 What's nice about catheterization from the point of view of the physician,
35:12 is if there is a problem while they're there,
35:14 they can fix it
35:15 and put a stent.
35:16 So it's like a perfect diagnostic test,
35:18 and then you can put a stent in if it's
35:20 stent.
35:21 So.
35:23 Um,
35:23 in this paper,
35:24 they show
35:25 like the primary purpose of this paper is to show
35:28 that if you use an AI algorithm,
35:30 it's better than
35:31 physicians.
35:33 And it's particularly
35:35 good
35:36 for
35:37 uh
35:38 relatively vulnerable populations.
35:41 One emphasis being women versus men,
35:42 so men
35:44 I'm not,
35:45 I have no medical expertise,
35:46 so I just wanna be a little careful.
35:47 As described in the paper,
35:49 men,
35:50 um,
35:52 you know,
35:52 uh,
35:53 I can
35:53 check this with ZI at some point.
35:55 I think you guys have some,
35:56 uh,
35:56 so,
35:57 uh,
35:57 as described in the paper.
36:00 Men present with a heart attack,
36:01 often with chest pain,
36:02 and so the physician sort of jumps to,
36:05 oh,
36:05 this problem with your chest.
36:07 Women often present without chest pain,
36:09 with other symptoms,
36:10 and so physicians often miss that
36:12 and send women home without getting tested,
36:14 and that's a problem.
36:16 And so they show that their model
36:19 is better at predicting
36:21 um
36:23 Who's at risk of a heart attack,
36:24 or who's likely having a serious heart problem,
36:27 and then
36:28 um sends them to,
36:30 you know,
36:31 and then it's a behavioral economics paper.
36:33 But in this paper,
36:35 Uh,
36:36 there's a bunch of appendices.
36:38 And they have this online appendix 3.
36:41 An online appendix 3
36:43 is in many ways a throwaway.
36:46 But we're gonna do
36:48 for my last
36:49 5 minutes or so is take Online Appendix 3 super super seriously,
36:52 OK.
36:53 Um,
36:54 and
36:56 So here's the story.
36:57 Patient arrives in the emergency department,
36:59 the medical staff assesses the likelihood of various ailments,
37:02 um,
37:02 and,
37:03 um,
37:04 you know,
37:05 if you miss a heart attack,
37:06 it's
37:07 really bad,
37:08 but if you send people for testing that's who don't need testing,
37:11 that's costly
37:12 and pretty unpleasant too,
37:14 so you don't,
37:14 you don't wanna do it.
37:16 Um
37:17 They developed their AI and then published its,
37:20 uh,
37:20 here's all the things that are wrong with physicians.
37:24 Now,
37:25 Um,
37:28 You can think there's also,
37:29 so physicians are making decisions.
37:31 Physicians are deciding to test and diagnose each patient,
37:35 and,
37:35 but
37:36 remember in healthcare,
37:37 we have physician decisions,
37:38 and then there's another core decision maker.
37:40 So you like read a,
37:41 uh,
37:41 you know,
37:42 health economics textbook,
37:43 they talk about their physicians as the core decision maker,
37:45 and there's administrators.
37:47 And administrators decide
37:49 what resources to give to the physician.
37:51 And then physicians are,
37:53 uh,
37:54 by law
37:55 and by obligation,
37:58 uh,
37:58 given the resources at hand to do the best thing they can for the patient.
38:02 So Essentially,
38:03 uh,
38:04 if there were infinite resources,
38:05 the physician's obligation is to try to
38:06 use those infinite resources for the patient.
38:09 Um,
38:09 now what Lan and Obermeyer point out is
38:14 Physicians might want to begin with stress testing
38:15 to get a sense of whether it's worth
38:17 um
38:19 doing the catheterization.
38:20 But in principle,
38:21 if you're very sure,
38:22 if you're very,
38:23 not very sure,
38:23 if you're pretty sure
38:25 someone is having a heart attack.
38:27 Why
38:29 wait for that stress test?
38:30 You'll just want to send them straight to catheterization,
38:31 right?
38:32 Like,
38:32 you don't wanna
38:33 waste,
38:33 you don't wanna risk the time.
38:35 You don't need to waste the resources,
38:37 so send them straight to
38:38 catheterization.
38:40 Currently,
38:41 in terms of people presenting in the emergency department,
38:43 or at least the ones they study,
38:44 there's a 15% likelihood of
38:46 of
38:46 um
38:48 needing a stent.
38:49 And a stress test is efficient for both doctors
38:52 and
38:53 um and administrators.
38:55 But if you take the numbers in their papers super seriously,
38:58 as AI gets better,
39:01 the administrator and the doctor end up having different incentives.
39:04 So right now we're here.
39:07 Which is the
39:08 doctor's best thing to do is a stress test.
39:11 You can sort of trust my arithmetic,
39:13 or more precisely,
39:14 trust my arithmetic,
39:16 recognize that these numbers are taking sort of the the numbers out of a paper way way
39:21 more seriously than we should,
39:23 OK,
39:23 but it's a
39:24 like a an example.
39:25 Um,
39:26 and the administrator also wants to do the stress test first.
39:30 Um,
39:31 now,
39:33 If
39:34 The likelihood that the person is having a heart attack is really,
39:36 really low.
39:38 Then they again agree,
39:39 send the person home.
39:42 Uh,
39:42 if the likelihood of having a heart attack is really,
39:44 really high.
39:45 They agree,
39:46 why do the stress test and the straight to catheterization?
39:50 Where it gets interesting.
39:53 Is here
39:55 Which is,
39:56 there's some probability.
39:58 Where
39:59 uh someone present at the
40:01 at the emergency department
40:02 where
40:03 the doctor wants to stress test them.
40:06 The administrator
40:07 doesn't wanna offer the stress test because that's not worth the cost.
40:10 Don't we wanna offer the doctor the option.
40:13 And so if you have an AI tool
40:15 that's doing much better than our current one,
40:19 then your ER department.
40:22 Uh,
40:22 your emergency department
40:23 will,
40:24 will be incentivized to have a different set of tools.
40:28 Yeah.
40:30 So I have to ask the question.
40:31 I haven't seen the paper,
40:32 but.
40:33 Isn't the decision of the doctors really biased to go to liability questions
40:38 that the administrator may not have?
40:40 Um
40:43 I think the administrators have those too,
40:45 so like the hospital administrators are very much at risk for being sued,
40:48 um,
40:50 but,
40:50 um.
40:52 But absolutely,
40:53 if you're thinking about
40:54 these numbers,
40:55 you
40:56 Um,
40:59 Let's just assume that they take into account liability risk.
41:02 So whether it's internal or external,
41:04 like why they do all this testing,
41:05 you can think about it as related to liability risk.
41:11 OK.
41:12 So,
41:13 This is a co-invention point.
41:14 So just trying to,
41:16 those of you who are wondering where this is going,
41:19 um,
41:20 so as model the solutions are reported,
41:21 administration should not offer the stress test.
41:23 The doctor is gonna choose catheterization,
41:24 OK,
41:26 um.
41:27 As practice,
41:28 that's gonna be really hard.
41:30 And so this is,
41:32 so why is it gonna be hard?
41:33 Well,
41:33 the doctor's gonna say,
41:33 well,
41:34 wait,
41:34 I've always had the stress test.
41:35 Why are you taking that away from me?
41:37 I want the option to have the stress test.
41:39 And so some regulatory body is gonna be called in.
41:41 The American Medical Association is gonna say,
41:43 now,
41:43 these hospitals are messing with the way I want to practice,
41:46 and then
41:46 patients' rights are gonna be discussed.
41:47 Patients can say,
41:48 well,
41:48 I want the option of having that,
41:50 um,
41:50 and then they'll be maybe this is another version of the liability story.
41:54 And so
41:55 when decision makers are no longer aligned.
41:58 Then the equilibrium of the game as we might have modeled it
42:02 in the textbook,
42:04 sort of goes out the window.
42:06 Because there ends up being this whole aspect of non-market strategy
42:09 on the lobbying side.
42:11 And so,
42:12 in general,
42:13 like,
42:14 Successful adoption of this technology,
42:16 like,
42:17 another way to think about it is.
42:20 The AI
42:23 from the administrative point of view.
42:25 Offering the physicians this tool that allows them to diagnose better.
42:29 Might only be worth it.
42:32 If
42:32 they're allowed to sometimes skip stress testing.
42:36 But the physicians are gonna like stress testing,
42:38 and so that might not be
42:40 like.
42:41 That might be a very difficult
42:43 uh outcome to achieve.
42:45 And so,
42:46 what can happen is because of these misaligned incentives,
42:50 we can end up never adopting in the first place.
42:52 Or at least if we adopt,
42:53 it never really impacts productivity,
42:55 cause for this tool to impact productivity,
42:57 you're gonna have to sort of
42:58 um
43:00 sweep through
43:01 um.
43:04 If you're just doing what you always did,
43:05 but a little bit better,
43:05 it's not gonna have a meaningful impact on productivity.
43:09 Um,
43:11 OK,
43:12 so,
43:13 The
43:16 goal here was to say,
43:17 OK,
43:17 what is a GPT?
43:18 GPTs are about co-invention costs,
43:21 and or
43:22 GPTs have these feedback loops.
43:24 Feedback loops depend on co-invention,
43:26 and so we're gonna see GPTs adopted
43:28 in places where co-invention costs are relatively low
43:32 or feasible.
43:34 And when you see that invention,
43:36 you're gonna see a productivity boost.
43:37 Uh,
43:37 but if you look at the literature on any particular
43:40 technology or even the economics literature on any particular technology,
43:43 it's phenomenon-based,
43:44 and it
43:45 doesn't think about the cost side.
43:46 It thinks about,
43:47 what's,
43:48 why would a firm adopt AI or why would a firm adopt the internet or something else?
43:51 It's because of the benefits.
43:53 And,
43:53 uh,
43:54 our point here is very simple,
43:55 which is to say when we're thinking about the next
43:58 GPT,
43:59 which is
43:59 likely to be AI.
44:01 Uh,
44:02 we need to sort of understand those co-invention costs and try to,
44:05 uh,
44:06 recognize it's gonna be adopted in places where co-invention costs are low,
44:08 but also to think through where the benefits of adopting are highest.
44:12 Um,
44:13 thank you.
44:13 We got lots of time for questions.
44:19 Thank you very much.
44:20 So,
44:20 um,
44:20 we open the floor for questions.
44:22 Uh,
44:23 for people who are joining,
44:24 uh,
44:24 remotely,
44:25 I think there are 36 of you.
44:27 Please raise your hands and I will,
44:28 uh,
44:29 give the floor to you,
44:30 um,
44:30 in the order of,
44:31 uh,
44:31 of,
44:32 um,
44:32 that you've raised your hands.
44:34 Otherwise,
44:35 we'll start with questions,
44:36 uh,
44:36 from the room.
44:46 So then I,
44:47 I'll take my uh uh chaprivish for the first question is,
44:51 I think from,
44:51 from a,
44:52 a policymaker's perspective,
44:54 you've shown a difference between incentive of different actors.
44:58 So how do we define the scope of,
45:00 uh,
45:00 government intervention in that space?
45:03 Yeah,
45:03 so.
45:04 Um,
45:05 we can think of a.
45:06 Uh,
45:08 two ways.
45:08 One,
45:08 you can think about it,
45:09 government can help resolve market failure,
45:12 right?
45:12 So as in,
45:13 uh,
45:13 this is just there's
45:16 We take this,
45:17 this result
45:18 more seriously than we should,
45:20 um,
45:21 then,
45:22 uh,
45:22 this suggests potential for market failure because incentives are misaligned
45:25 and it's not clear that bargaining will solve it,
45:27 the legal system,
45:28 etc.
45:29 Um,
45:29 so government can step in and say,
45:31 you know what,
45:31 this technology is good.
45:33 And so we're going to
45:36 uh assert that best practice in medicine is to skip
45:39 the stress test and go straight to catheterization.
45:42 Um,
45:43 another version of it is to model government as a third actor,
45:47 and then think about the government incentives,
45:49 and then
45:50 does
45:51 our government incentives aligned with the physician
45:53 with the hospital,
45:55 and when they're not aligned,
45:56 then,
45:57 um,
45:58 to think through and model.
46:04 Yeah.
46:06 Well,
46:07 on your last slide with the conclusion,
46:08 I would,
46:09 I would add one rider here given that we're in a health sector
46:13 context and that's that
46:15 um
46:17 Uh,
46:17 I think,
46:19 Adoption
46:20 will also be influenced by
46:22 the level of regulatory approvals that,
46:25 that are needed,
46:26 right?
46:26 So in you,
46:27 and it's the reason
46:28 in healthcare,
46:30 these technologies,
46:30 we're still,
46:32 the United States generates something like 95% of faxes in the world
46:37 because all medical decisions,
46:38 uh,
46:39 an order that's sent to the most advanced MRI machine maybe in the world.
46:43 Needs to be sent via fax,
46:45 right?
46:45 It,
46:45 it is the reality of healthcare.
46:47 And so that's just one example of where healthcare has,
46:51 has always been this industry where
46:53 the potential is so immense,
46:55 but the actual application is low because of this,
46:59 this
46:59 tendency to avoid risk and the regulatory dimension.
47:03 And
47:03 I'm interested in,
47:04 in
47:05 your thoughts on
47:07 GPTs because none of the previous GPTs have had the same.
47:11 I guess regulatory
47:13 impediments,
47:13 right?
47:14 I mentioned something to steer your thoughts on that.
47:20 So we,
47:21 uh,
47:22 an NPR volume came out that we added a couple of years ago on AI and healthcare,
47:26 right,
47:26 and the conference,
47:27 we're super excited about the conference.
47:29 We brought in like
47:30 a bunch of leading economists in healthcare.
47:31 We didranoff and did Cutler and,
47:34 uh,
47:35 Sanel Zia,
47:36 Stern and a bunch of others.
47:37 And then for each of their papers,
47:39 we had two discussions,
47:41 one for medicine and one for your science,
47:43 OK.
47:44 And so it's like a,
47:45 we're very excited about like this,
47:47 what could be a very intellectually stimulating
47:49 event,
47:49 and it was,
47:51 but it was also extraordinarily depressing,
47:53 uh,
47:53 for exactly the reasons you described,
47:55 which is
47:56 what the papers ended up being about were
48:00 They all opened with,
48:01 here's where the world could go.
48:03 Here's what like
48:04 healthcare is an industry where productivity has been slow,
48:08 productivity growth has been slow,
48:09 almost cost disease means we have more and
48:11 more and more of our economy and healthcare.
48:13 That's miserable.
48:14 Um,
48:15 and so why don't we improve healthcare productivity?
48:17 AI has the potential to do that through diagnosis and a bunch of other things.
48:21 And then,
48:22 um,
48:22 we had Sanal Zia or one of them and Obermeyer come along and say,
48:26 uh,
48:26 yeah,
48:26 but there's no data,
48:28 um,
48:28 and it's in fact because it's healthcare,
48:30 there's all sorts of laws against shared data,
48:33 uh,
48:33 because it's personal data.
48:34 There's good reasons for those privacy laws,
48:36 but,
48:36 uh,
48:36 Catherine Tucker and Molly Miller have shown out
48:38 that shown out that those privacy laws,
48:39 as wonderful as they are,
48:40 they also kill people,
48:41 and we have to think about those traits,
48:43 um.
48:44 Then,
48:45 um,
48:46 Ariel Stern,
48:48 uh,
48:48 came along and said,
48:49 well,
48:50 there's also the regulatory.
48:52 And
48:53 uh there's good reasons to be cautious,
48:55 but in general,
48:56 when we think about cautious,
48:56 it means cautious about change.
48:58 And so,
49:00 where,
49:01 what we've seen in
49:03 AI and healthcare is this slow regulatory process of 2
49:06 to 5 to 10 years before these are approved.
49:08 OK.
49:09 Um.
49:10 The
49:12 the latest stuff from her on this is much more optimistic
49:16 because that conference was I think 2022 or 2023,
49:19 when
49:20 yeah,
49:20 when things still hadn't taken off yet
49:22 and um now.
49:26 Now there's adoption.
49:27 The FDA sort of figured out how to do AI
49:30 on medical devices.
49:31 So there's been real change,
49:32 especially in radiology.
49:34 Uh,
49:34 and then Craig Garthwaite and Dave Dranoff came along and said,
49:37 yeah,
49:37 but then there's physicians.
49:39 So like,
49:39 even if you solve the,
49:41 the regulatory problem and you solve the data problem,
49:43 uh,
49:43 physicians run the healthcare system,
49:45 and so everything has to be consistent with physician incentives.
49:49 And so that's a real barrier.
49:50 Um,
49:51 and so,
49:51 and then there was,
49:52 you know,
49:53 another,
49:54 you know,
49:54 challenge after challenge after challenge,
49:55 and
49:56 Uh,
49:57 and
49:57 trying to think through how does this
49:59 all get,
49:59 so I agree,
50:00 and here's the optimism,
50:01 OK,
50:02 uh,
50:02 which is
50:04 there's
50:05 So 1,
50:06 we are now suddenly seeing
50:08 devices approved,
50:09 at least in the United States.
50:10 Um,
50:12 2,
50:14 There's
50:14 a handful of things that
50:16 many doctors do see as transformative.
50:18 Most obviously the AI scribe.
50:20 So this is,
50:21 uh,
50:21 I don't know if you guys have now been to a doctor
50:23 where the doctor no longer spends the time looking at a screen,
50:26 but actually looks at you.
50:27 That's a meaningful change
50:29 that the doctors like.
50:31 Patients generally like it,
50:32 and it seems to work.
50:33 And that's like a,
50:34 a real AI application that's,
50:36 that's,
50:36 that's better.
50:37 It's,
50:37 it's within existing workflows,
50:38 but it's better.
50:39 Um.
50:41 Then there's
50:42 a set of opportunities
50:44 that are going to be really hard,
50:46 that could be transformative.
50:47 And I've written a bunch on this,
50:48 and I think,
50:49 I think
50:50 literally you guys are the
50:52 most important audience for it,
50:53 so I'm gonna
50:53 indulge myself and talk about it,
50:55 uh,
50:56 which is,
50:57 um,
50:59 Keeps talking about the doctor
51:01 as having the monopoly on diagnosis.
51:02 In most healthcare systems around the world,
51:05 diagnosis has to happen from a doctor.
51:07 Maybe your pharmacist is still allowed to diagnose like 10 things,
51:10 but not really.
51:11 Um,
51:12 if we have diagnosis machines,
51:15 Which are as good as the median doctor,
51:17 which we do.
51:18 They're not as good as the best doctor.
51:20 And if you talk to doctors,
51:21 they're all the best doctors.
51:23 But they are as good as the median doctor in many cases.
51:26 And,
51:27 uh,
51:27 that could enable pharmacists and nurses in the United States or Canada or the OECD
51:33 to superpower themselves.
51:35 Uh,
51:35 but
51:36 perhaps more importantly,
51:37 in terms of impact and feasibility,
51:40 there are many people in communities around the world.
51:43 That are the local medical expert.
51:46 And to the extent that these people do their best.
51:50 Giving them access to a machine.
51:53 A diagnosis machine that could be on a device that's as small as this,
51:57 um,
51:58 or,
51:58 you know,
51:58 and certainly cheaper than that,
51:59 uh,
52:00 would be incredibly powerful,
52:02 uh,
52:02 to allow sort of
52:04 medi
52:05 medical diagnosis to happen well at scale.
52:09 Now,
52:10 there's a system challenge to that,
52:11 which is,
52:12 OK,
52:13 so
52:13 there's a regulatory point there,
52:15 which is that,
52:15 you know,
52:16 are those local metal experts allowed,
52:17 and how do you train them and all that?
52:19 Sure.
52:20 Uh,
52:21 that might be easier to overcome than the other problem.
52:24 The other problem is now once you're diagnosing disease at scale all over the world,
52:29 you need to find treatments.
52:30 And right now our treatments aren't designed for those communities,
52:34 and so there needs to be
52:35 complementary innovation,
52:37 right?
52:38 So think about the co-invention.
52:40 So
52:40 we think,
52:41 oh,
52:41 co-invention is just the AI diagnosis tool.
52:43 Now that's almost like step one.
52:45 Now we need co-invention downstream on the
52:48 uh medical provider,
52:49 but we also need co-invention by
52:51 the pharmaceutical companies
52:53 and the medical device companies
52:55 to figure out how do we treat the things that we can now diagnose at scale,
52:58 uh,
52:59 for,
52:59 uh,
53:00 to adapt to the needs of those communities.
53:02 It's sort of a big open question.
53:04 That was a long answer,
53:05 but
53:07 I have a question about that.
53:07 Yeah.
53:08 Thank you for,
53:09 for very insightful presentation.
53:10 Uh,
53:11 I have a question.
53:12 So government regulation was mentioned.
53:14 Is there any research into the best practices of how
53:18 regulation at the government level is done currently,
53:21 uh,
53:21 outside the health industry?
53:25 So,
53:25 um.
53:27 We know,
53:28 I
53:28 think the best
53:29 we can say about research is there are trade-offs,
53:31 right?
53:32 And
53:32 best practice actually depends on the values of the country.
53:35 Um,
53:36 and so,
53:37 for example,
53:37 we have lots of evidence that there was a meaningful trade-off
53:40 between privacy and art.
53:42 Because AI and data,
53:44 uh,
53:45 are useful
53:46 for
53:46 companies,
53:47 for healthcare,
53:47 for education across the board.
53:49 And so if you have laws that restrict data flows,
53:52 that restricts your ability to use data that reduces productivity.
53:56 And so,
53:57 the,
53:58 to the extent of the best practice,
53:59 it's to say,
54:00 going with eyes wide open,
54:01 saying,
54:01 look,
54:01 if we're going to adopt
54:03 uh
54:05 GDPR like practices,
54:07 We need to recognize that
54:08 that will protect our citizens' privacy
54:11 better than perhaps the practices we have today,
54:14 but will also come to costs.
54:16 Um,
54:17 and in general,
54:18 I would say,
54:21 Um,
54:22 OK.
54:23 So your,
54:23 your Goldilocks,
54:25 your like your ideal version of this
54:27 is
54:28 a regulatory environment that facilitates,
54:30 that fosters trust in AI.
54:32 That trust is warranted because the AI is trustworthy,
54:35 um,
54:36 and also doesn't restrict,
54:38 uh,
54:39 limit what AI can do.
54:41 Um,
54:42 so it's a regulatory environment that fosters
54:43 trust that then increases innovation and adoption.
54:47 Um
54:49 What
54:51 Empirically,
54:51 it's happened in practice
54:53 in
54:54 Certainly,
54:55 the
54:56 best research is about Europe versus the US
54:59 uh is that the regulatory environment slows down technology adoption.
55:04 And that is a trade-off that people should be making with Abio.
55:09 Yeah,
55:13 many questions.
55:14 OK,
55:14 two questions.
55:15 Yeah,
55:15 first one,
55:16 you mentioned that your story stopped
55:19 before check PT.
55:20 No,
55:20 that this revolution getting9.
55:22 With
55:23 this story change,
55:24 I'm assuming you're already probably work on the next version.
55:26 Oh yeah,
55:26 yeah,
55:28 and then my second question is,
55:29 uh,
55:30 I think something interesting about the uh on the diffusion of,
55:33 of,
55:33 uh,
55:34 for business,
55:34 for example,
55:35 is that it depends
55:36 on the diffusion on the previous GPT,
55:38 right?
55:38 So,
55:39 so basically
55:40 it depends on the internet,
55:41 on electricity,
55:43 and if you think that,
55:43 for example,
55:44 developing case like the case of Africa.
55:46 The challenge of the diffusion of the previous GPTs tend to
55:50 base the big barrier to to the adoption after this one.
55:53 Is this a common pattern that you should expect moving forward,
55:56 or are there other examples of GPTs in which you didn't see this cumulative process,
56:01 um.
56:03 So I think
56:04 each technology generation is dependent on some previous technologies,
56:07 and not all of them,
56:08 OK.
56:09 And so
56:10 the,
56:11 what do I mean by that?
56:12 Um,
56:14 The diffusion of
56:17 Um
56:18 AI does not depend on like the internal combustion engine.
56:22 It could have,
56:23 like,
56:23 maybe that's where the power would have come from,
56:24 but it,
56:25 it just doesn't.
56:26 Um,
56:27 and
56:28 so
56:29 it
56:30 seems to depend on the internet,
56:31 at least so far,
56:32 um,
56:33 but maybe not as much bandwidth
56:35 as
56:36 Google search.
56:37 So there's like Dan Bjorkre has a fantastic paper.
56:39 I'm not sure if you,
56:40 you've seen it,
56:41 uh,
56:41 where he looks at,
56:43 um,
56:45 AI adoption and usage by teachers
56:47 um in Sierra Leone,
56:48 I think,
56:49 yeah,
56:50 um,
56:51 and
56:52 the takeaway of that paper is
56:55 Uh,
56:55 so what,
56:55 what does he mean by AI?
56:57 Essentially in your WhatsApp,
56:58 you can have like AI responses to questions,
57:02 and
57:03 that's low bandwidth.
57:04 But if you do a Google search,
57:05 it's high bandwidth.
57:07 The AI results are,
57:09 he argues,
57:10 definitely worse
57:12 than what teachers can find if they use Google.
57:15 OK.
57:16 But it's so much lower bandwidth
57:19 that the teachers use it more,
57:21 and because they use it more,
57:22 it ends up having a more positive.
57:24 Access to it has a
57:26 hugely positive impact on the classrooms,
57:28 at least to see measures.
57:30 So like.
57:31 It's,
57:31 yes,
57:32 it's dependent,
57:32 but there's subtleties to it,
57:34 and in that case,
57:35 it's dependent on internet,
57:36 but actually it's dependent on internet with less
57:38 uh
57:40 it's less dependent on the internet in a way
57:42 it's not dependent on broadband the same way that
57:45 that no.
57:49 My question is a continuation of this question,
57:51 because
57:52 how do you find,
57:53 I mean,
57:53 how do you see the adoption of co-innovation in low-income countries
57:57 and mainly about the quality of data at entry because they don't have,
58:02 they don't have any data.
58:03 And then I would like to,
58:04 to hear more about the development of those low-income countries through IA.
58:09 So,
58:10 uh,
58:13 OK,
58:13 so
58:14 first pass is honestly,
58:15 we,
58:16 we do not have as much data about low income countries as we do about high income,
58:19 um.
58:21 The
58:22 two most impactful papers so far that I,
58:24 uh,
58:25 are one is the standard paper which is,
58:27 uh,
58:27 which is about adaptation to the set,
58:29 the context,
58:30 low income,
58:31 which I think is super important.
58:33 Um,
58:34 another is by Makon and Dave Holtz,
58:35 and others
58:36 that looks at,
58:37 they gave access to essentially
58:40 entrepreneurial advice chatbot,
58:42 entrepreneurial advice chatbot
58:43 to entrepreneurs in Kenya.
58:46 OK.
58:46 And there,
58:47 the punchline was,
58:49 uh,
58:50 relatively
58:52 skilled
58:52 entrepreneurs,
58:54 uh,
58:55 used,
58:56 use the chatbot,
58:56 used it effectively and improve their businesses,
58:59 and relatively unskilled it.
59:01 And
59:02 What makes that result so interesting is
59:05 the same kinds of studies have been done
59:07 in the developed world with the opposite results.
59:10 And so when we do those studies in the developed world,
59:12 we get,
59:13 in general,
59:14 when we use AI,
59:15 it seems to be helping the less relative to the more,
59:17 at least within a,
59:18 a current,
59:19 a given context.
59:20 And so,
59:22 what,
59:22 um,
59:23 one interpretation of that,
59:24 my interpretation of that,
59:25 I should say,
59:26 is is boundary condition,
59:29 which is that
59:31 Yes,
59:32 AI helps relatively low-skilled people.
59:36 Within limits.
59:37 They still need to be able to read at least current generations of AI,
59:39 this is a generative AI
59:40 still need to be able to read well.
59:42 And think through what the goal of the business is.
59:45 And so you do need something like what we would think here is a high school education
59:50 in order to take some advantage of what the tool
59:52 can do and get some advice in your business.
59:54 Um,
59:54 and in developing countries,
59:56 that's middle skill,
59:57 not low skill,
59:58 or maybe even high skill.
59:59 And so there's these,
1:00:01 like,
1:00:01 as we think about co-invention,
1:00:03 and that's not even co-invention,
1:00:04 that's within your group,
1:00:05 as you think about co-invention,
1:00:07 like
1:00:07 the
1:00:08 The essence of doing it well
1:00:11 is gonna be adaptation to events,
1:00:13 and I can tell.
1:00:16 Right now,
1:00:17 I can tell a bunch of stories of,
1:00:19 OK,
1:00:19 you know,
1:00:19 here's,
1:00:20 here's an example of it works in a high income,
1:00:22 it's not gonna work low income cause
1:00:23 it's not adapted to the appropriate environment,
1:00:26 uh,
1:00:26 but we don't,
1:00:27 I don't have great examples of this,
1:00:30 that adaptation happening on the ground,
1:00:31 but it,
1:00:32 like,
1:00:32 it's not to say it won't,
1:00:34 it's more to say,
1:00:35 well,
1:00:36 one,
1:00:36 it's not my restraint,
1:00:37 it's like,
1:00:37 I don't have as many examples,
1:00:39 uh,
1:00:39 but two,
1:00:40 just co-invention takes time.
1:00:42 And we,
1:00:44 we need to think through like what are those.
1:00:47 What are the versions of that,
1:00:49 uh,
1:00:50 school AI,
1:00:51 um,
1:00:52 applied across colleges?
1:00:56 Yes,
1:00:57 um,
1:00:57 two questions.
1:00:58 One,
1:00:58 in terms of what I mentioned,
1:00:59 you mentioned there are two factors,
1:01:00 right?
1:01:01 One is the lower cost of adoption adoption.
1:01:03 The other is very few benefit.
1:01:05 So from the lens of lending,
1:01:08 what do you focus on?
1:01:09 You focus on the former the matter
1:01:11 and how do you think of it from a sectoral perspective,
1:01:14 because say for health it's easier,
1:01:17 you have to.
1:01:19 Example
1:01:20 of maybe cost of adoption
1:01:21 is a better uh lending for investment or a same tools for health but for education.
1:01:27 So,
1:01:28 um,
1:01:29 the good news about lending is it's a pure friction problem,
1:01:31 OK?
1:01:32 It's gonna be the oldest prediction problem,
1:01:33 right,
1:01:34 uh,
1:01:35 in business,
1:01:35 right?
1:01:36 You know,
1:01:36 is someone gonna pay back a loan
1:01:37 and so
1:01:38 there's
1:01:40 like this.
1:01:41 Gen AI tools might help you organize data,
1:01:43 but ultimately this is like old fashioned machine learning,
1:01:46 uh,
1:01:47 and machine learning can,
1:01:48 can deal pretty well with sparse data.
1:01:50 Um,
1:01:52 and so there's real opportunities there.
1:01:53 I think
1:01:54 some
1:01:55 organizations are taking advantage of that and like
1:01:57 non-traditional data to help with lending.
1:02:00 Now,
1:02:01 um,
1:02:01 that's thinking through just
1:02:03 this technology is prediction technology,
1:02:05 predictions cheaper,
1:02:06 lending is a prediction problem,
1:02:07 we should see more accurate lending.
1:02:10 That doesn't mean more lending or less lending,
1:02:12 just means more accurate lending,
1:02:13 um.
1:02:15 It
1:02:15 could be more.
1:02:17 Um,
1:02:18 now,
1:02:19 in terms of
1:02:20 Co-invention.
1:02:21 Well,
1:02:21 now I'm gonna think through if we have a better prediction tool,
1:02:24 um,
1:02:25 and
1:02:26 we have a better sense
1:02:28 of whether,
1:02:30 so let's go to the extreme.
1:02:32 Most lending
1:02:33 in the developed world happens to people who have a credit score and history,
1:02:37 and we can sort of follow them in all sorts of details.
1:02:40 Let's say AI tools now allow us to get a decent prediction
1:02:44 for people who
1:02:46 are unbanked
1:02:47 or really just not part of the um
1:02:53 the regulated economy.
1:02:55 So
1:02:56 well that might
1:02:58 enable new types of lending tools that don't exist yet
1:03:01 and maybe I think it already isn't but it's some the new types of lending tools
1:03:04 and you can think about that as colorful.
1:03:06 And then if those people can get,
1:03:08 uh,
1:03:08 have access to
1:03:10 the resources,
1:03:11 um,
1:03:12 and can borrow money,
1:03:13 does that then change,
1:03:15 uh,
1:03:15 their economic outcomes in some way?
1:03:17 And if it does,
1:03:18 then we then need to think through different property rights,
1:03:20 like you can sort of think through all the follow-ups.
1:03:24 Um,
1:03:24 so I think there's lots of
1:03:26 In many ways it might be an easier problem,
1:03:28 not a hard problem.
1:03:28 That's an easy problem,
1:03:29 but it's easier than,
1:03:30 than,
1:03:30 for example,
1:03:31 healthcare where
1:03:32 the
1:03:33 people get so antsy about data.
1:03:35 So I can
1:03:36 expand on that question a little bit.
1:03:37 So besides,
1:03:39 um,
1:03:39 treating,
1:03:40 uh,
1:03:40 AI as a
1:03:42 prediction,
1:03:43 GBT as a prediction technology,
1:03:45 what are the
1:03:46 execution aspects,
1:03:47 the automation?
1:03:48 How would that impact?
1:03:50 Oh,
1:03:51 so
1:03:53 it's,
1:03:54 it's useful to think about it as prediction technology,
1:03:57 uh,
1:03:58 still,
1:03:58 even as an agent,
1:04:00 uh,
1:04:00 not
1:04:01 to find intuition about
1:04:02 where the applications are,
1:04:04 but as discipline to remember our data.
1:04:07 And discipline
1:04:08 to,
1:04:08 for now,
1:04:09 remember that we still have human judgment in every decision.
1:04:12 So,
1:04:13 what do I mean by that?
1:04:14 Um,
1:04:16 There's lots of stories about using AI for human resources,
1:04:21 OK.
1:04:21 And some of them sound quite dystopian,
1:04:22 like
1:04:23 the
1:04:25 That's
1:04:26 Who
1:04:28 Your company is
1:04:31 Who,
1:04:33 uh,
1:04:33 directly get hired based on algorithm
1:04:35 and
1:04:36 time to leave on time and what you want to like in restaurants and,
1:04:38 uh,
1:04:39 if you're not doing well,
1:04:40 you get a text that says don't come back tomorrow
1:04:43 automated.
1:04:44 And the narrative is that's terrible because AI is making decisions.
1:04:49 I have no opinion about whether or not it's terrible,
1:04:51 but let's be clear,
1:04:52 it's not an AI making a decision.
1:04:53 AI is taking in data
1:04:55 and making predictions.
1:04:57 Someone at headquarters decided,
1:04:59 OK,
1:05:00 this is the threshold for somebody to be a good employee,
1:05:02 and this is the threshold for someone for us to send them an audio text.
1:05:06 So
1:05:06 what AI does in that case is it changes who gets to make decisions.
1:05:11 So
1:05:11 as
1:05:12 Recognizing AI's prediction is important because it reminds us that the values,
1:05:16 the decisions that happen
1:05:18 are all a result of human choices.
1:05:20 And this is absolutely true of Gen AI.
1:05:23 If you're using chat GPT,
1:05:25 chat GPT was trained to be helpful,
1:05:27 honest,
1:05:27 and harmless.
1:05:29 Uh,
1:05:29 those are values,
1:05:31 and there's weights on helpful,
1:05:32 honest,
1:05:32 and harmless.
1:05:34 And
1:05:34 those weights
1:05:36 also embed values,
1:05:37 right?
1:05:38 If you,
1:05:38 uh,
1:05:40 If you,
1:05:41 I don't know if you've ever like uploaded a picture of yourself or someone
1:05:43 you love and chat to BT and asked it to describe that picture,
1:05:47 OK,
1:05:47 uh,
1:05:48 it will make you feel good,
1:05:50 OK.
1:05:51 And,
1:05:53 and that,
1:05:53 that may be,
1:05:55 in many cases,
1:05:56 that's honest,
1:05:58 but it can't always be honest,
1:06:01 right?
1:06:01 And so that's the algorithm,
1:06:04 the leadership essentially of OpenAI decided on things like that,
1:06:08 harmlessness matters more than honesty.
1:06:10 And there are meanings.
1:06:11 So when you sort of think you're delegating things
1:06:14 to the model,
1:06:16 what you're actually doing is dedicating it,
1:06:18 delegating it
1:06:19 to the humans who work in those companies
1:06:22 and their minds as opposed to yours.
1:06:26 Just,
1:06:27 uh,
1:06:28 yeah,
1:06:28 just uh,
1:06:28 yeah,
1:06:30 interesting,
1:06:30 very interesting discussion here.
1:06:32 So,
1:06:32 uh,
1:06:33 obviously the
1:06:35 statistical prediction is the baseball and it,
1:06:38 it will remain,
1:06:39 yeah.
1:06:40 But looking at the current development
1:06:43 just like,
1:06:44 you know,
1:06:44 for example,
1:06:45 uh,
1:06:46 in Albania
1:06:48 they chose AI as a minister for corruption control,
1:06:52 right,
1:06:52 but they appointed
1:06:53 AI too as one of the chairman of one of the political parties,
1:06:57 right,
1:06:58 and,
1:06:58 uh,
1:06:59 we know that,
1:07:00 uh,
1:07:00 we talk a lot about the AGI.
1:07:02 We have seen the developments as well,
1:07:05 really seen those things.
1:07:07 So looking at all those things,
1:07:09 uh,
1:07:09 when we talk about the vertical GPTs at the moment,
1:07:13 isn't it already a time to talk about kind of,
1:07:16 uh,
1:07:17 governance level,
1:07:18 you know,
1:07:19 governance of the whole
1:07:20 human and robotics
1:07:22 together?
1:07:23 Isn't it already a time to discuss those things,
1:07:26 so.
1:07:28 Um,
1:07:33 That's
1:07:34 Right
1:07:34 now,
1:07:35 AI is a tool,
1:07:36 OK.
1:07:36 It is a tool that helps us make better decisions and helps us do things,
1:07:40 but it is very much a tool
1:07:42 embedded with human values,
1:07:43 and we can identify the humans who embedded other values into it,
1:07:47 um.
1:07:48 He said,
1:07:49 is it time to start talking about that?
1:07:50 Probably.
1:07:51 I'm not the right person to talk about it,
1:07:53 um,
1:07:54 but
1:07:55 we like,
1:07:56 we have an MDR Economics Transformative AI conference in September
1:08:00 led by Urban Olsen and Jay Agarwal and Anton Kornak,
1:08:03 and they are really thinking about what does the world look like with AEI.
1:08:07 Look,
1:08:07 I have a paper in that conference,
1:08:08 but that's not.
1:08:09 Um,
1:08:10 I tend to think through,
1:08:11 like,
1:08:12 right now we have a really useful tool.
1:08:14 I don't even know what the definition of AGI is,
1:08:17 and so I'm gonna leave that aside and say
1:08:20 we're in a world where we can't identify the humans who are responsible
1:08:23 for decisions,
1:08:24 and let's focus on that.
1:08:25 So when a country has an AI minister,
1:08:28 what they actually have is a set of people who designed the AI.
1:08:32 Uh,
1:08:33 who decided to get their current values going forward,
1:08:37 and
1:08:38 if they managed to,
1:08:38 they've administered they've done it pretty successfully,
1:08:40 they scaled
1:08:41 themselves.
1:08:43 Is,
1:08:43 isn't it also true that
1:08:45 There's this gap
1:08:47 between
1:08:48 the G AI world we're in today
1:08:50 and AGI and there's this assumption that it's gonna be something linear,
1:08:54 right?
1:08:54 Whereas
1:08:55 I think the next generation of AI,
1:08:57 we're already seeing
1:08:58 signals of that,
1:08:59 it's gonna be a different type of AI technology,
1:09:02 right?
1:09:03 And so,
1:09:04 uh,
1:09:04 just as Gen AI is a different type of learning technology than,
1:09:08 than what.
1:09:09 Machine learning was before that.
1:09:10 And so
1:09:11 I think
1:09:11 it's the unknown of that that we,
1:09:13 there's a lot you can't really answer,
1:09:15 right?
1:09:15 But
1:09:16 there's this focus from Gen AI.
1:09:18 If we just do Gen AI better,
1:09:20 we'll get to AGI.
1:09:21 There's a lot of people now starting to say
1:09:23 that that's not necessarily the case,
1:09:25 right?
1:09:25 Uh,
1:09:26 it is not necessarily the case.
1:09:27 I would say
1:09:29 reasonable people,
1:09:30 very smart people.
1:09:32 Disagree
1:09:33 on
1:09:34 how imminent ATI is.
1:09:36 People who
1:09:37 I think of as true experts,
1:09:39 uh,
1:09:39 assert that it's within 2 years.
1:09:41 People who I think of as true experts assert that
1:09:44 maybe it's 40 years or 100 years or never.
1:09:47 And as an economist,
1:09:49 I have no idea.
1:09:51 I don't really understand the various definitions,
1:09:53 honestly.
1:09:54 I think they're
1:09:57 If we define AI AGI the way we did in 1990,
1:10:00 then we had it in 1996 as soon as Deep Blue beat Gary,
1:10:03 OK,
1:10:05 um,
1:10:05 and so it's just hard to really think
1:10:08 through
1:10:09 what these,
1:10:10 what these things mean and how they're gonna impact your life.
1:10:12 Our is also a forthcoming volume that might be of interest to you guys,
1:10:15 which is the NDR Trans Economics Transformative AI
1:10:18 where our task was.
1:10:21 They defined a transformative AI as AI that can do almost all human work.
1:10:26 And then what is a world like?
1:10:28 What does the world look like with machines that can do almost all human work?
1:10:31 I got a bunch of us to speculate on what that might look like.
1:10:33 It's like,
1:10:34 if that's where you wanna go,
1:10:35 that volume and the chapters are all available online,
1:10:37 is,
1:10:38 it's a good place to start.
1:10:39 Uh,
1:10:39 you know,
1:10:39 I just,
1:10:39 I think that
1:10:41 just as the power of prediction is what drives thinking,
1:10:44 I think in the,
1:10:44 in an AGI sense,
1:10:46 it's the power of reliable prediction.
1:10:49 That's gonna drive whether it's AGI or not,
1:10:51 and I think it's this reliability thing
1:10:53 that's,
1:10:54 that's the piece that makes it's a doctor,
1:10:57 not,
1:10:57 not.
1:10:58 It's an open question whether
1:11:00 prediction is the essence of intelligence,
1:11:02 um,
1:11:02 and it's like it's an open philosophical question.
1:11:05 Jeff Hawkins asserts that it's true,
1:11:08 and there's lots of smart people who assert it's not true.
1:11:12 Maybe we'll figure it out someday.
1:11:14 So on this philosophical note,
1:11:15 I'd like to close it.
1:11:17 Thank you very much and uh,
1:11:18 you know,
1:11:18 our colleagues have been uh very,
1:11:20 very patiently more than the,
1:11:21 uh,
1:11:21 the usual hour
1:11:22 and thank you for,
1:11:23 for very insightful discussion.
1:11:25 Thank you.
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