Sep 1, 2020
A conversation with Professor Andrew Odlyzko about the forces that have driven the paradigm changes we’ve seen across the research world in the past several decades. Andrew is a professor at the University of Minnesota and worked at Bell Labs before that. The conversation centers around his paper “The Decline of Unfettered Research” which was written in 1995 but feels even more timely today.
The decline of unfettered research is part of a complex web of causes - from incentives, to expectations, to specialization and demographic trends. The sobering consequence is that any single explanation is probably wrong and any single intervention probably won’t be able to shift the system.
The Decline of Unfettered Research
A Twitter thread of my thoughts before this podcast
(Automated, and thus mistake-filled) Transcript
[00:00:00] In this conversation. I talked to professor Andrew Odlyzko about the forces that have driven the paradigm changes we've seen across the research world. In the past several decades. Andrew is a professor at the university of Minnesota and worked at bell labs for that our conversation centers around in his paper, the decline of unfettered research, which was written in 1995, but feels even more timely today.
I've linked to it in the show notes and [00:01:00] also a Twitter thread that I wrote to get down my own thoughts. I highly recommend that you check out one of them either now or after listening to this conversation. I realized that it might be a little weird to be talking about a paper that you wrote 25 years ago, but it, it seemed when I read it, it sort of blew my mind because it seemed so like all of it just seemed so true today.
Um, and so I was, I was wondering, uh, like first do you, do you, do you sort of think that the, the core thesis of that paper still holds up? Like how would you amend it if you had to write it again today? Oh, absolutely. I'm convinced that the base thesis is correct. And as the last quarter century has provided much more evidence to support it.
And basically if I were writing it today, I would just simply draw on this experience all those 25 years. Yeah. Yeah. Cause, okay, cool. So, so like, um, I sort of wanted to [00:02:00] establish the baseline of like asking questions about it is still, is still super relevant. Um, So, uh, just, uh, for, for the, for the listeners, um, would you sort of go through how you think of what unfettered research meets?
Because, uh, I think many people have heard of, of sort of like, like basic or, or curiosity driven research, but I think that the distinction is actually really important. Mmm. Well, yes. So basically unfettered researchers, emotional curiosity, driven research, very closely related to maybe some shades of difference with the idea here is that you kind of find the best people.
You can most promising researchers and give them essentially practically complete freedom. Give them resources, making them complete freedom to pursue the most interesting problems that they see. Um, and that was something which, uh, kind of many people still think of this as being the main mode of operations.
And that's still thought [00:03:00] the best type of research in that case, but it's definitely been fading. Yeah. So, uh, would you, would you make the art? So what, like, what is the, is the most powerful argument that unfettered research is actually not the best kind of research. Well, so why is it not the best kind of research?
So again, this is not so much an issue of world's best in some global optimization sense. And so on my essay. It wasn't really addressed to the forces that were influencing conduct of science technology research. Um, and, uh, I'm not quite saying that it's kind of ideal that it was happening. I said, well, here are the reasons.
And given the society we live in and the institutions, the general framework here is what's happened and why it's happening. Yeah. [00:04:00] Now and a particular outfit. Yes, there was an argument coming out of my discussion was that, uh, this unfettered research was, uh, becoming a much smaller fraction of the total.
And this was actually quite justified. But yes, uh, even so to a large extent, research did dominate for a certain period of time. Um, that era was ending now. It was likely to be the con kind of consigned to a few small niches. So evolving on the, a small number of people, much more of the work was going to be kind of oriented towards particular projects.
Yeah, the, the, the thing that I really like about the term unfettered research that I feel like draws a distinction between it and curiosity European is that, uh, unfettered research, the idea of fettered versus unfettered, uh, feels like it refers to, um, Sort of like [00:05:00] external constraints on a researcher, whereas curiosity driven versus, uh, not curiosity driven is, uh, the motivation
uh, um, Where, where is like, curiosity? Do you have any, is like the internal, no motivation for a researcher. And I think it's, my whole framework is around incentives. So it's like, what are the incentives on researchers and, and, uh, fettered versus unfettered really sort of, uh, touches on that. Yes.
Um, personally, I don't draw a very sharp distinction between the two, I think has got into very fine gradations and so on. I'm not sure they kind of necessarily in most meaningful is our sons. Is that when we're talking, just driven around unfettered research, People are never kind of totally acting in isolation based on is our curiosity.
They always react to the opportunities. They react to what they hear from other people. And very often also they are striving for recognition. Yeah, [00:06:00] invitations to stock home to receive about price and so on. That's something many people in the proper disciplines of course keep in mind or so, so there are always some constraints coming from particular group in that case, I kind of, I know these terms as almost synonymous.
Yeah, that makes a lot of sense. And so sort of a, the upshot of the decline of unfair research for me was, uh, kind of mind blowing. And it makes so much sense when you put it this way, that research has become a commodity. And I'm not sure how much you've been paying attention to sort of what I would called, like the, the, um, stagnation literature, where there's been a lot of literature around the idea of, of scientistic stagnation.
And I realized that sort of at the core of that was this assumption about [00:07:00] research being a commodity. Like you look at these economic models and it's just like, okay, well we need more researchers to produce more research and it's this undifferentiated. Thing. Um, and so, so like in your mind, what are the implications of something specifically research becoming a commodity, right.
Let me maybe kick it back a little bit. I'm not sure commodities quite the right term. Uh, I think we can relate it to something that has been documented and discussed very extensively in various areas, such as sports. Sports or maybe music and so on named new that what happens is, well, it's becoming very music, becoming very competitive, uh, schools, cranking out people are selecting them for the ability to perform at a certain level, scolding them, and then letting them go on the stage and so on and compete.
And so what you find, for [00:08:00] example, you sport typically the gap between the. Top, whereas leads say the gold medal winner as a silver medal winner has been narrowing performance has been increasing in practically all areas of sports people, jump throws that are higher. They run faster. So on again, that seemed to be leveling off in many cases.
People studying human physiology, argue with some quantitative models that we're approaching the limits of what's possible to do with our human body, unless we go to some other planet and other environmental assaults. Uh, so you hire these people, but you still have the best ones in there. Um, you were saying bolt, you know, kind of, uh, sprint or repeatedly case is I got a good example.
And so you, you, you couldn't, it's not quite. Correct to say is that the hundred meter [00:09:00] sprinters are a commodity. There is definitely a differentiation there, and there is a reason to encourage them to compete and get better and train to do better and better. On the other hand, you come to a situation losing anyone knows the top around nurse makes less and less of a difference to the performance.
It should observe. And I think that something similar happening with the research, you said that she saw you. And so I think that presupposes something that I love your take on, which is that sort of, there are natural limits to human physiology. I think like that's a pretty clear, right? Like, um, but there's.
Not as clearly a limit to technological ability or the, the amount that we can know about how the universe works [00:10:00] like possibly. Um, and so, so this is, this is almost like, it feels almost philosophical, but so the, the analogy to sports, um, Would presuppose some, some natural limit on, uh, sort of like the amount of science and the amount of technology that we could do.
Um, and so, so do you think that that's, that's the case. Okay. Yes, there definitely is a difference in those kind of general research in science. We don't have these very obvious, very obvious reasonably well defined limits. On the other hand, what we're coming up against is the fact that these fields still are becoming more and more competitive, soft sciences are sort of growing.
Uh, it's also your current number of sub fields is growing. A volume of information that's available is growing while that also means that watch any single individual can master [00:11:00] smaller and smaller fraction of that total. So in some sense, you could say that human society is becoming much more knowledgeable.
The algorithm each individual we can say is becoming less, less knowledgeable, knows less and less about the world. And we depend much more on the information we got from others. Uh, there's this extensive concern right now about the postural world and all of these filter baubles and such tied to how being created.
And is that this almost inevitable because. How do you actually know anything? Um, sort of surveys show that maybe 10% of the people believe the earth is flat. And all those theories and all those pictures from space as being fake or creations of people with video editing tools and so on. And well, uh, most people can [00:12:00] live quite well with the mental model of that world.
Uh, as long as they are not in charge of plotting rocket trajectories or airplane trajectories, and so on, same thing, vaccinations I'll do you know the vaccination is good. I'm assuming you're not . I, I believe that vaccinations are. Pretty good. How would you, you prove to me that vaccinations work again, there's a whole long chain of reasoning and data and so on.
That has to be put together to really come to this conclusion that vaccinations work. Some is sometimes I ask my questions and my students. Whether they come through as the artists around now, you from Caltech, you may remember enough physics to be able to come up with a convincing argument. Most people can't.
That's all. It would have been thought. It's consistent with everything sounds fine. So is [00:13:00] the result. Is that we have people, large groups of people working very hard and as much as very competitive, uh, in many cases, and you look many projects require extensive collaborations. Uh, and this has been documented in a kind of quantitative terms in some of my presentation decks.
I had some, this slide. Where I showed the degree of collaboration amongst mathematicians. So similar, similar graphs could be drawn from other disciplines. Many of them moved towards more collaborative form, a head of mathematics, a lot less, but slower and so on in mathematics background, 1940 around I focused the exact numbers now, but there are 95% of the papers where it's in the bystander or, sorry.
By year, 2000, 60 years later. No, it was down to about under 50%. Wow. And by now a check, I haven't [00:14:00] gotten the latest numbers. I suspect it's probably well under 40%. And so what does it reflect? Uh, I suspect to a large extent, I think that's consistent with what other people found in other disciplines who started more carefully is design need to combine different types of expertise.
Great. Um, not knowing enough to be able to cut out the project. That's crazy. And so, so this, this paints for me, a really sobering picture of a world in which. Basically like as, as you need to collaborate on more things, there's more specialization. So you need more people to collaborate, uh, which just sort of by its very nature, nature increases, coordination costs.
And so it feels like it's almost like just more and more friction in the system. And so each new that just like has more friction involved, um, and. [00:15:00] So like, is it, this is like the inevitable trajectory, just for things to, uh, to stall out or like, is there an escape hatch from this, uh, this conundrum? Well, I say we simply have to deal with it.
No, I don't think so. So I don't see any kind of silver bullet. I don't see a big breakthrough people doubt AI, and yes, I'm not downplaying the usefulness of various AI tools, but I still think they are likely to be fairly limited in this kind of real creative sense.
Um, and so we'll simply have to deal with a fact cause that's things are getting messier. That requires more effort. Marshall was the low hanging fruit has been picked up. We'd have to work harder. And also there will be men,
highly [00:16:00] undesirable features. Uh, people going off on tangents, uh, kind of, kind of creating their own alternate realities, such like going astray. That was all of those kind of build up kind of elaborate alternate realities where certain kind of art attempts are assembled together into convincing pictures.
I think we'll be, we'll have to deal with that. Yeah. And, um, so, so. Another piece that you, uh, like sort of core to the thesis, is this increasing sense of competition? Um, w would it be too extreme too? Say that the, the game has sort of changed from I'm a sort of absolute game, uh, to a relative game in, in a lot of research where instead of trying to produce a.
The best thing, it's just trying to produce something [00:17:00] that's better than the other person. Uh, I'm not sure, uh, whether I would put those stamps out there to think of it. Uh, I mean, there was always this element of competition. You simply look at these bitter disputes, Newton versus Libin. It's about calculus.
For example, other cases. Sometimes they were resolved amicably, Darwin evolution and so on. But again, people often they're reacted to not to competition. So Darwin that, getting his book into print because he heard that well, that's just coming out with the work and so on. That's a really good point.
Things like that. Uh, so I think the competitive aspect was always there. It's actually very important to get people to accelerate themselves to, to, to, to, to do their best. So I think that is always been important. Yeah, probably much more important now than used to be the occasion before is the [00:18:00] need for collaboration.
A need to for collaboration, need to kind of assemble a group to work with groups towards some common goals. And especially that universities, you often see it now where the professor is less the. Yeah, investigator created more, almost like a thought leader or manager because ideas and by the customer assemble, you know, get the grants, bring graduate students and post docs who was an executor, a program.
And you know, the head of the lab who gets his or her name, you know, other publications, not necessarily just lead the sense that. Because they're all found that person really is the inspiration kind of on maybe overly original ideas they use there by the second is very different from what it used to be.
Say a hundred years ago. Yeah. Even a hundred [00:19:00] years ago, you saw some of it at the sun Edison. Well, it was a very good example, this larger lab, which was working under his guidance and trying out various things, all of the different materials for light bulb filament and such like it was clear that kind of Edison was driving it, but lots of people working on it and so on.
But I mean, Edison was very unusual for that period these days. That is how research operates. Yes. And the, the pieces that you allude to in your paper is that, um, there's sort of, there's, there's more competition and, uh, what I would call less Slack, um, in terms, I think of those as being, uh, sort of like to counter opposing systems or to capture opposing forces.
And if you. Have that, uh, like competition is what drives you to some [00:20:00] equilibrium. And then Slack is what lets you sort of like jump out of local equilibria. Um, And, and the thing that really drove this home for me was the example you give of, uh, the, the contrast between Xerox, having years and years to sort of do development around their patent and build up additional patents versus, um, the, the superconductor research where multiple groups, uh, came up with the same discovered the same thing, like within weeks of each other.
And, uh, I wonder if there's. That is that, um, sort of phenomena is actually playing into the stagnation piece in that, like, this is probably not true in of itself, but like, is it possible that the reason we don't have room temperature superconductors is actually because, uh, nobody. Could would profit from bill, like could actually build up a patent portfolio around them [00:21:00] to the point where they would, where it would be profitable for them.
And so like this, this competition is actually sort of like, uh, driving out, uh, paradigm shifts. Well,
It's hard to say, because here we're talking about the real kind of, uh, um, natural barriers kind of room temperature, semiconductors exist, easy abstract. Okay. We don't know for certain. Yeah, of case on the other hand, what you can observe is that there have been a few labs that were established over the last couple of decades, which tried to kind of come up with this moonshots and so on.
Well, I mean, Google has this X lab. I think something like that, that's been called it. Hasn't produced the very much, uh, Ellen was bill Gates collaborate on creating Microsoft. He had this kind of. So silver bullet, I mean the kind of lab in [00:22:00] Silicon Valley, uh, I forget his name right now. Again, not much has come out of it.
Uh, so I think it's simply very difficult to come up with breakthrough ideas. Uh, and I mean, you know, my main area that I can talk to you about shape mathematics itself. Uh, there have been a few kind of. Really incisive ideas, new breakthroughs, last few decades, I would say many few words and used to be like I used to be for other areas more closer to applications cryptography.
I used to work a lot. Uh, I would say March of what has been done over the last couple of decades have been pretty much incremental. There hasn't been all that much either way of significant breakthroughs. Um, if you look at something like Bitcoin has excited, uh, attention of many people, uh, in our work produced almost a dozen years ago.
On the other hand, all the basic [00:23:00] technologies unit has been known for at least 30 years. Yes the result. Uh, so I think it's more, more a case that it's really harder to achieve breakthroughs, the kind of the low hanging fruit or the big pick. Only a few of them are, are maybe hiking around and maybe occasionally somebody will find them, but not too often.
Yeah. I, I guess it's, I always, I find the, the low hanging fruit. Explanation sort of unsatisfying, I guess. And I'm always trying to, to at least like tease that apart and because, you know, it's, it's sort of like there are low hanging fruit until you find a different tree. And I feel like the, the history of the 20th century is one of just fi like repeatedly finding trees.
And so the question sort of becomes like, Less [00:24:00] like, have we picked the low hanging fruit and more like, why aren't we finding more trees? And so, um, the, the argument could be like that the trees themselves are, are, are fruit. And so that they're they're low-hanging but, um, I just, I just feel the need to like, keep poking at that.
Um, and like, uh, like another, another thing that I found really compelling in your paper was this idea of, uh, expectations shifting from discrete, uh, discoveries to continue to discoveries. Um, that, that was, that was pretty mind blowing. And I think it actually has to do with, uh, These the, the idea of these trees of like finding new trees.
Um, do you think that. Perhaps because of the expectations of continuous improvement, that we're less, uh, less willing to sort of like start like picking fruit from new trees. Do, did stretch the analogy to the limit. Well, my, so again, from looking at [00:25:00] everything, I'm kind of inclined into the view that are simply are not that many trees.
It is occasional. Things are harder. Um, you just look at many disciplines. What can you do? Even when people have brilliant new ideas? Um, they often don't go very far because they have to be incorporated with other things so on. So I've encountered various areas like networking, cryptography and things like that, where people come up with really great inventions.
But they are not implementable for one reason or another. So it's hard for me to see the breakthroughs. Uh, again, something might happen. Uh, look at something like fusion, lots of resources have been devoted to trying to come up with practical way of doing controlled fusion and get cheap energy out of it.
It hasn't happened. Of course it would say, well that's because, you know, maybe when [00:26:00] maybe, or maybe all of these governments brought all their resources into a blind Ellie. On the other hand, there has been a lot of clever people trying to think of other ways of doing things that will bypass that barrier.
And none of them have worked out for a while. There was a great excitement about cold fusion, but that kind of flamed out very quickly. So
some people talk to the culture seriously for a while. Um, now I think it's gone. Uh, is there some other way of doing it again? People have been, nobody has found that even though there definitely are incentives to do it. And these are people who have basic technical knowledge, scientific knowledge, to be able to address that question.
Yeah. I want to go back to something that you, you just said, um, and, uh, and almost sort of argue against you with what I see as [00:27:00] something that you wrote, uh, which is that. So, so you mentioned that people come up with these, uh, like brilliant technical solutions, but they're not implementable for one reason or another.
And. Based on, on my, my reading of your, your paper. Um, one could make an argument that the reason that they don't get implemented is, uh, not because of like some fundamental, uh, reason in the world, but because the people who would implement it, like the people who pour the resources into making it implementable and then implementing it, um, Instead prefer, like expect that, uh, the systems they're using right now and the paradigms that they're using will just get better at a continuous rate.
And so there's sort of no point in making the effort to switch. Um, would that be, would that be a fair reading of, of the paper? Oh, very much so well, not cellular paper, but in journal evidence. I mean, we see it very much in the [00:28:00] computer arena and so on. Uh, kind of we've had now kind of a domination of a few kinds of operating systems.
Um, we have, uh, uh, the browser. Taking over there on the internet. There's an interesting case. One of my areas, papers kind of downside non technical papers was about electronic publishing written about 1994 and a user. I kind of. The rotor balls are tools for access of information as a web. And I said, well, we have all these great tools, like gopher ways, and there are better ones coming along.
Like this thing called browser. It sounds like it will be better generations. Well, I mean, some, since I was the right browser was the better, but that was the end. The browser has just evolved to absorb or to incorporate all these other things that people wanted to do. And again, many people have commented.
How, if you were [00:29:00] designing a web from the ground up, you would make various decisions in different ways, but we're pretty much tied to it. And you can only do incremental change. Yeah. That's so, I mean, for me, at least, at least through another really uncomfortable conclusion that it's like, we're almost like suffering the effect of our own success.
Right. Like, because we've had such continuous improvement, um, we, uh, are sort of like unable, like the discreet improvements get crowded out. Um, yeah. And it's like, And, and so, and I feel like that's sort of a different effect from, uh, the, the increased friction from, from collaboration. Right. And so I guess, uh, do you have, it's not too much different.
It has to do with the greater complexity. So we kind of, uh, Each into our brains are not growing. Okay. It's not much, you know, the event [00:30:00] and we're not certainly acquiring that much knowledge. We would just acquire waste to incorporate or excess graters amounts of knowledge case. And so we're. Devoting more and more of our energy managing the complex, trying to figure out how these things interact.
It's much less, much less the point of trying to find some very simple principles F equals ma. Okay, brilliant
Newton in that case. Uh, well, uh, people try to come up with such simple concepts that would explain a lot of what God's in his own world and they're failing. That was because of all this complicated. Yeah, no, that's, that's actually, that's a really good point. And that increases the switching costs for, for new systems.
Um, if everything is super interconnected, you can't just sort of like [00:31:00] take off the, the module and stick on, on the other module. Although it might suggest that like in a, like a amazing world, uh, We would pay more attention to making things modular, um, right. Like, and, and that sort of like that is at least in some way, a way to abstract away complexities.
Right, but we do a bad job of that. Yep. We're doing a very, very poor job of it in the case and yes. Uh, it's one of these other kinds of customers. It's a trade off, uh, the issues. How much effort do you put into making this modular? And one of the problems is that people. I've always had this mental image of software, something that can be modified very quickly.
And so therefore there is really no need to, or about interoperability and so on. Well, we fix it when we, and then we generally, we don't, uh, a friend of my browsers [00:32:00] several decades ago. I had this great saying this was in a context of talk communication switches. And you said, what's the difference between hardware and software versus hardware?
You can change.
Brutal because the engineer is developing hardware sort of knows that these things are now going to be out there and people will have to live with them and then maybe have to repeat replaced and so on. So they pay much more. Attention to modularity. You have all these standards about this, which define what kind of connectors you have, what are the voltages and phase electricity and everything else, the attitude as well.
Okay. It's all kind of can be modified. So you end up with this mess of spaghetti code. Okay, you cannot change. And so you have all of these crucial [00:33:00] systems running around, powering our economy. That's all written in Kabul. I've heard. I see, I saw a news article recently that like different governments were desperately trying to hire COBOL engineers that were paying like massive amounts of money because they can't switch.
Um, Although, you're also seeing a sort of what I would call it, decreased modularity in hardware systems as well. You know, it's like the, the, the car engines that you can't service yourself for the batteries that you can't replace. Um, and obviously there are good things. There are good reasons for doing that.
Um, well of course it's good or maybe not so good. So again, a lot of it has to do with intentional design Lakin. So one manufacturer's making difficult for you to kind of do things. Is there a largely in order to control the user, the other guy gets into the economic incentives. He's also STEM. [00:34:00] Yeah. And, um, I I'd actually love to, to rewind.
And, um, what was the question text of you writing this, this paper in the first place again? Because it feels, it feels so contemporary that it's shocking that you wrote it, uh, like more than two decades ago. Yes. Well, that's a very interesting question and very easy to explain. So those written into context of working at bell labs and bell labs was one of these people because of jewels or whatever it is.
So it's a really wonderful place. Uh, I joined it right after graduation from high school graduate school. That'd be very impressive. No, no, no, that was it. I spent a summer there before that, but not out of getting my PhD. I joined it and work as there for extended, you know, for several decades. She, most of my career so far has been spent at bell labs [00:35:00] and ATNT labs afterwards.
And when I joined, uh, bell labs was still dominated by the of unfettered research. It wasn't completely unfettered. Actually. It was one of the big strengths of it. Namely, there was a certain thing to kind of pressure to do things and also being part of the bell system. We had contact with real world in some sense, closer than say academics did, but otherwise they're all still this ideal of almost unfettered research.
Give me lots of freedom. And that was changing. When I was there over the decade before I wrote the essay or declined research. And so basically I was looking around the whole scene, not just that bell labs, what a world, the science of technology and so on, and was trying to explain to my fellow, uh, kind of colleagues at bell labs.
[00:36:00] Why is it that we were experiencing this pressure, which many were very concerned about, you know, fearful, resentful or other kinds of things. So that's really, what's the context of it. It was really about these traditional. Uh, large industrial research labs, like bell labs. IBM kind of what's on the research lab and there's some algorithm.
Uh, and are your answer? I already alluded that back to the same factors would start influencing kind of other types of research, especially academic research. And so on because when indeed been happening, but at that stage, I was kind of seeing it as a front lines as, as our wave was coming in and of reacting to it.
Yeah. That, that that's fascinating. And, and sort of as, as more, I guess, more fetters were put on researchers at bell labs. Um, did [00:37:00] you, did you have any conversations? So sort of like with, um, The people who were starting to put, like put those pressures on, on the research. Not well, not, not real deep conversations or so no, but Tommy of course we had general conversations about what was being done.
Uh, you know, how the reorganizations, how the reward structure was changing and all these other kinds of things. Yeah. How, how, how did the rewards trust structure change? If I may ask not much more, uh, uh, much more attention was being paid to work, being done for the company. Or interactions that were more closely related to what the company was doing and much less on just pure scientific accomplishments, which might be kind of a recognized on the outside.
As one example, one of the, my colleagues wrote some papers with him. She was also in my department after I was promoted to department head for [00:38:00] many years, very distinguished researcher. He was on the. Few people who was member of national Academy of sciences, national Academy of engineering, and what was then called Institute of medicine now, national Academy of medicine.
So on August bodies, this was all for work. He did on foundations of a kind of computer cat, computer, extra tomography, which is basically. Was working on some mathematical problems of reconstruction from, uh, kind of the images x-ray measurements and so on. So he did do a lot of this work and that had practically no kind of, uh, uh, no.
No connection to bell system might do well, actually there was a little bit, we tried to develop some of these techniques for electrical tomography, for some cables, anomaly [00:39:00] detection, other kinds of things, but basically this was working a few did, but had a great influence. I say it. Improved the health of many people.
And so on, it was widely recognized on the outside that, uh, you know, a bit of some things that belonged to the earlier era of bell labs, not to the final stages of it. Yeah. And, and, and so I guess the, um, sort of implication of that for me is that he then did not like he wasn't really that rewarded. Uh, w w basically he was rewarded because this was the earlier, you know, that he kind of retired when things were really changing and so on.
And. So kind of a long story. He did very well. He was widely recognized. He contributed to many other things too, but this computerized tomography work, that was a major undertaking for him, which took a lot of time. [00:40:00] Yeah. And, and, um, do you have a sense of how long it took him to do that? Like, like how, how much time did he spend just sort of like, uh, Oh, seemingly producing nothing.
Well, no, no, it wasn't not just that these things were getting published in general scientific journals, but they were not kind of relevant to what bell system was doing. Yeah, it's touched for a number of years, I'd say five years, something like that. And so like sort of counterfactually uh, is there a reason why sort of, if he were working today, he would not be able to do that in like a university setting?
Uh, well, uh, again, it depends, so, um, he might be able to do it. Other than you need to make sure that you get some kind of a funding agency recognizing that this is a [00:41:00] promising area. Uh, I don't know, is that right? Once he started doing it, whether NSF or other agencies where knowledgeable knew enough about it and you regard it as promising enough, so not impossible.
So there were some. People who kind of proposed doing this before there was an issue of how do you actually get useful reconstructions, extra imaging that you obtain. And I think that's kind of part of this big change. Now you'll have many more people kind of looking around trying to find something to do and, uh, Again, if you're persuasive enough, uh, and you can convince enough people and you can persuade you the private founder.
Yeah. Shouldn't or maybe you can persuade and some adventure, some national science foundation. So the director to set up a program for fun, you know, a particular type of [00:42:00] research. Yeah. So, so no, I mean, I'm not saying that this new style of research is in capable of producing breakthrough results, but, but you do, you do it, it does seem like there are, there's a sort of different set of constraints on it, right?
Like it's uh, so if you have someone who is, um, sort of antisocial and, uh, bad at bad at sales, basically, um, the chances of them. Being able to create this breakthrough, uh, are probably lower. That's right. So then they succeed on leave. They hook up with somebody who's more of a promoter. Okay. And then was she examples of it?
Some, some people who managed to assemble a group and kind of collaborate.
Be very conducive towards, so, you know, facilitate interactions amongst them and direct them means our rights area. And even you, those are kind of these kind of [00:43:00] very nerdy types and so on. They might still be very effective at coming up with useful products or services.
Yeah. And, um, uh, another area of the paper that I felt was, was very prescient, was you're, you're focused on sort of Japanese, um, sort of the Japanese, uh, economy, and that you pointed out that the Japanese business structure, uh, should be able to enable longterm work. Um, and, and yet it doesn't. Um, and so. Uh, that's, that's sort of a, a counter, like a counterfactual to the argument that like, Oh, we just like don't have long enough timescales and like, uh, like stockholder pressure is, is forcing people to work on shorter timescales.
Um, do you feel like that is still true in, in terms of, uh, like the Japanese, uh, like that Japanese. Um, output has not [00:44:00] created like breakthroughs that we would expect to happen. No, it has not. Again, again, a part of it may have to do with the kind of cultural factors or how their corporations are structured.
And so on. I certain are excellent. They are still pretty top technology sees a world in different areas. On the other hand, when you look at records of some people, there was one particular guy. I forgot his name now. I think the blue laser. I think this was the breakthrough invention and so on, but very hard time, this company was not really properly rewarded or so, and it was almost like a little bit do a skunkworks work and it kind of gets things most other side.
Indeed. I want to be respectful of your time. So the, the closing question I ask everybody is just, what, what should people be thinking about that? They're not thinking about enough right now,
[00:45:00] people shouldn't be thinking about.
Very very hard. Very hard question. I don't think I have a simple answer. Give a complex answer. Welcome to my particular concentration right now is a group thing. Uh, so I think that's, uh, again, I won't say this will be central for everybody and so on, but I think it's a very important question. The degree to which a human, uh, society really depends on groups, to what extent it's actually let us stray, uh, where people disregard are very obvious evidence in order to, uh, adhere to the preferred worldview.
And I'm studying financial manuals, baubles, precisely from that standpoint, how is it that people manage to overlook all those very obvious sides? Yeah. Yeah. [00:46:00] It's uh, one thing, actually, I I'll just, um, what do you, what's your take on the argument that, uh, there are some like, especially like infrastructure, um, things that never would have happened without bubbles.
So, so there's this argument that like, we, we actually would never have the railroad infrastructure or like the telecom communications infrastructure, uh, without bubbles. Yes. I don't think that's true that we'll never have had it. A single comment probably would have come more slowly. Oh, so, uh, I mean, technology will typically these basic technologies have been developed before achieve what led to the bubbles was the appearance of, uh, of the technology in a form that could be deployed.
And that make money, the case, and that gives rise to excessive optimism and, uh, you know, future investment manuals. But I just can't think of anything. That's kind of juice, just the bubbles [00:47:00] by themselves. Like the, that there's there's. So, so you don't think that there's some sort of activation, like if you think about it, like a cat chemical reaction, there's like an activation energy that's actually higher than the sustaining energy of, of the reaction that's provided by a bubble.
Yes. Well, okay. So I think there's some of it and to somewhat, you know, various ways of thinking about barcodes, they lead to faster deployment. Some technologies will be through otherwise they open up people's minds. Um, extent. And to some extent they may also drain of some of the irrational energies, which might otherwise be deployed in more destructive ways.
That case some people regardless, just farfetched, but there are some ways you could think of the Bible says being conducive to human progress.
My key takeaway is that the decline of unfettered research is part of a complex web of causes from incentives to [00:48:00] expectations, to specialization and demographic trends. The submarine consequences that any single explanation is probably wrong. And any single intervention probably won't be able to shift the system.