Tim Scarfe: prediction

Transcript context

…So at least in the history of the transistor, I do think we have some good examples that the teams required to develop those innovations have been growing over time. And this is something that Nick Blum in Stanford, he's a professor of economics there, has emphasized. The fact that there's an increasing cost of innovation that let's say to duplicate again, you know, we have larger teams and larger budgets, like it's getting costly and costly and costly. Now some people, you know, have arguments against, you know, his evidence and so forth, but but I think it's the right question to ask because you know, definitely there it looks like there's something in in in that direction. And you can see it in the history of transistor because the first transistors were developed by a team of 3 people, you know. Originally, a team of 2. It was Bratain and Bardin, and then Shockley got jealous and over Christmas developed a transistor design that replaced the first transistor that was developed by his lab assistants with Rear Bratain and Bardin, and that became the point contact transistor in in 1948. But then you had several different transistor designs. For example, Shockley Semiconductor Company was not successful at producing transistors, but when Moore and Noyes moved to Fairchild, then they produced the Mesa transistor I think in 1958. And then they produced a planar design in 1959. And then they produced an integrated circuit in 1959. There was also another team in Texas Instruments that produces, you know, an integrated circuit, Jack Kilby. Jack Kilby was someone that had just joined the company, and because he had just joined the company, the company was kinda like empty, so during the summer he had nothing to do. And he created the integrated circuit as kinda like an experiment on his own, but you see it's a team of 1. Nowadays to produce the next generation of NVIDIA CPUs or you know Intel CPUs or whoever, you probably have enormous teams involved in the design, in the manufacturing. So I think that might be at some point the limiting factor, which is at some point maybe to double again, the teams are gonna get larger than what we're able to coordinate. And if that coordination capacity doesn't get to scale to the amount of knowledge that we would need to generate another doubling, you know, we might see, you know, this curve petering out. Now, is that close to happening? I I don't know. It's it's hard to, you know, bet against a a law that has been stable for so long. I would love it if you read Kenneth Stanley's book, Why Greatness Cannot Be Planned and he's a good friend of mine and I'm gonna tell him to read your book because I think there's a lot of overlap. But his basic idea was that, you know, consensus committee meetings objectives, they they are actually quite toxic for progress because creativity is about following your your own gradient of interest and and basically preserving diversity and having new ideas about things. So, yes, we have a new generation of these large language models. They're getting, know, the 10 times bigger every few years and the the bullish people say, oh, we're on an exponential curve and it's just gonna keep going up. But I think that because there is so much groupthink and it's fundamentally the same technology and the same people and there's no fresh new ideas that in a sense it's converged and it's not disruptive anymore. You know, I I remember using GPT 3, not even Charge GPT or GPT 2 and all of that. And and definitely this has been going for a while. The the the improvement has been sustained not for like 5 years, but for longer. Yeah, like this technology started at a rather simple level of proficiency that has continued to improve. Now, if there are teams that are gonna disrupt that, those are not the teams that you're talking about. Those are the teams that are now flying under the radar. Those are the Edwin Lands before people know about instant photography. Those are Ibukas before Sony makes it big with you know, the transistor radio and so forth. So yes, I agree that there might be incumbents that are really big, those are like the Barnes and Nobles and the Walmarts and you know, of the tech industry. And there might be disruption that comes from teams that maybe have thought of a different way of creating you know, artificial intelligence that might overtake those incumbents and that might be disruptive because maybe it require doing things in a different way. But we're not gonna know until until those curves cross, you know. Usually it's very hard to see those disruptors early on simply because they're not yet getting the attention.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.