The Exchanges

Q Right, is that like a natural selection effect?

A Yeah, you don't, you can't vary radio circuits and then suddenly get a computer out of that or radar. You can't vary air piston engines in 1930 And get a jet engine out of that. These things come along as completely new combinations using new principles, and that keeps adding to your Lego set. And that starts to explain why there's a controversy or a question, say, in the 19 twenties, anthropologists were asking, why don't you have trams and steam engines in the Trobriand Islands? And they began to say, well, it's not because the Islanders are stupid. It, It's because they don't have these building blocks to build it out of, and that in turn has many implications. One of them is if you get a region like Silicon Valley with an enormous number of these building blocks, and more important, it has the people who understand the craft to put all this together, not just the science, but what parameters, then it can very quickly keep coming up with new combinations.

AI assessment note: “Yeah, you don't, you can't vary radio circuits and then suddenly get a computer”

Q I mean, I would agree, but is that the big transformation now, that we have the modern tech equivalent of the printing press?

A What's gone external now is not information, what's gone external is intelligence. I may be driving in a convoy of 50 driverless cars, And the whole idea of the car adjusting, the car is talking to roadside sensors and servers, it's talking to other cars, it's talking to the highway patrol servers and so on, and it's basically farming out its intelligence into this other economy, and then getting back intelligent actions in return. So it's a bit like phone a friend, Only the friend is incredibly smart, and the friend consists of, again, these hundreds of thousands of servers talking to each other and then adjusting what you do. So suddenly intelligence doesn't just exist on human beings. Suddenly intelligence exists in the cloud or in this autonomous economy, and we can format not just getting information, But getting smart moves back, and this is making all the difference.

AI assessment note: “What's gone external now is not information, what's gone external is intelligence.”

Q Right, is that like a natural selection effect?

A Yeah, you don't, you can't vary radio circuits and then suddenly get a computer out of that or radar. You can't vary air piston engines in 1930 And get a jet engine out of that. These things come along as completely new combinations using new principles, and that keeps adding to your Lego set. And that starts to explain why there's a controversy or a question, say, in the 19 twenties, anthropologists were asking, why don't you have trams and steam engines in the Trobriand Islands? And they began to say, well, it's not because the Islanders are stupid. It, It's because they don't have these building blocks to build it out of, and that in turn has many implications. One of them is if you get a region like Silicon Valley with an enormous number of these building blocks, and more important, it has the people who understand the craft to put all this together, not just the science, but what parameters, then it can very quickly keep coming up with new combinations.

AI assessment note: “Yeah, you don't, you can't vary radio circuits and then suddenly get a computer”

Q So speaking of it being a different game, you know, we have a lot of entrepreneurs that listen to our podcast. How does it change the game? Because people always use the phrase game changing very freely.

A Well, first of all, entrepreneurs in Silicon Valley are really smart. And they didn't exactly get all these ideas from me. I'm not being modest, I'm just being realistic. When I brought out this theory, it kind of corroborated their intuition. So, what I'd say is if you are thinking in standard terms, go back to brewing beer or a company like General Foods, if you want to make profits, you're thinking of getting production, Up and running properly, getting your costs down, making sure everything's terribly efficient. The game was different in tech. The whole game was to try to, early on, grab as much advantage as you could, and I remember that I wrote a paper on this, the Harvard Business Review, in 1996, and as that paper got circulated very widely in Silicon Valley, I remember hearing one story that Sun Microsystems had developed Java, and naturally that cost a huge amount of money, so the guys with the green shades, the accountants, were saying, naturally enough, we should charge a huge amount of money for anybody who buys this, and the other people who had read this theory said, no, no, no, no, no, give it away, give it away, give it away for free, and there was a tremendous Hullabaloo over this, and finally somebody took my article and just slammed it in Scott McNeely's desk, and it was game over. He got the point immediately that what you do in an increasing returns marke…

AI assessment note: “what you do in an increasing returns market is you try to build up your user base”

Q I mean, I would agree, but is that the big transformation now, that we have the modern tech equivalent of the printing press?

A What's gone external now is not information, what's gone external is intelligence. I may be driving in a convoy of 50 driverless cars, And the whole idea of the car adjusting, the car is talking to roadside sensors and servers, it's talking to other cars, it's talking to the highway patrol servers and so on, and it's basically farming out its intelligence into this other economy, and then getting back intelligent actions in return. So it's a bit like phone a friend, Only the friend is incredibly smart, and the friend consists of, again, these hundreds of thousands of servers talking to each other and then adjusting what you do. So suddenly intelligence doesn't just exist on human beings. Suddenly intelligence exists in the cloud or in this autonomous economy, and we can format not just getting information, But getting smart moves back, and this is making all the difference.

AI assessment note: “What's gone external now is not information, what's gone external is intelligence.”

Q So speaking of it being a different game, you know, we have a lot of entrepreneurs that listen to our podcast. How does it change the game? Because people always use the phrase game changing very freely.

A Well, first of all, entrepreneurs in Silicon Valley are really smart. And they didn't exactly get all these ideas from me. I'm not being modest, I'm just being realistic. When I brought out this theory, it kind of corroborated their intuition. So, what I'd say is if you are thinking in standard terms, go back to brewing beer or a company like General Foods, if you want to make profits, you're thinking of getting production, Up and running properly, getting your costs down, making sure everything's terribly efficient. The game was different in tech. The whole game was to try to, early on, grab as much advantage as you could, and I remember that I wrote a paper on this, the Harvard Business Review, in 1996, and as that paper got circulated very widely in Silicon Valley, I remember hearing one story that Sun Microsystems had developed Java, and naturally that cost a huge amount of money, so the guys with the green shades, the accountants, were saying, naturally enough, we should charge a huge amount of money for anybody who buys this, and the other people who had read this theory said, no, no, no, no, no, give it away, give it away, give it away for free, and there was a tremendous Hullabaloo over this, and finally somebody took my article and just slammed it in Scott McNeely's desk, and it was game over. He got the point immediately that what you do in an increasing returns marke…

AI assessment note: “in an increasing returns market is you try to build up your user base”

Q So what does that mean for jobs? In this podcast we talk a lot about how whenever industries are changed in this way, you know, through tech and other shifts, that other new jobs, classic examples include more designers in the age of Adobe design, that new jobs never existed before, like social media managers that can only exist today. What's your take here?

A So what I'm seeing is about 90 years ago or so, John Maynard Keynes pointed out that he thought by A hundred years time, 2030, we'd be in an economy where the production problem was largely solved. There'd be enough in principle to go around for everyone. There might be plenty, in principle, goods and services around, but getting access to them meant you needed wages, which you needed a job for, and that was not possible. I think that what Cain said in that regard is becoming true. In other words, the trough is full, but how do the piggies get their share of the trough? So we're now in a new distributive era. What counting is not how much is produced, but who gets what. The whole question of Growth and getting more economic product out there, physical product and services. That's a job for entrepreneurs, and it's a job for engineers. Who gets what is much more a political issue, and that's not quite a job just for politicians, but it's a job for society to solve. And we haven't solved it in Europe or anywhere else, so it's a new era.

AI assessment note: “getting access to them meant you needed wages, which you needed a job for”

Q you know when you're seeing the beginning of one of these revolutions that it's a morphing in the making? Is this sort of a hindsight view? Because you are sort of seeing it early with everything else. What are the signs that tell you this is a morphing, this is a big theme that's emerging, that gives you the confidence to say that about, say, deep learning or CRISPR even?

A I think that a change is usually quite well underway before People pick it up. You wake up one day and you say, oh my god, the game has changed. In the case of sensors, I remember in 2010 or so, sitting down with the CTO of Intel, and I asked him, can you tell me when the average sensor is, for example, at a parking meter that might sense a car being at the meter, the average sensor is going to drop below about 10 cents per unit. And he said, yeah, that'll be around 2013, 20 15. He knew pretty well exactly, and so I thought that's going to be a game changer because we will now know what's happening everywhere. What I didn't see at the time was that the ubiquity of sensors would bring in big data. Some of us saw that in advance, but the big data didn't see would bring in all these Smart algorithms. Right. And so it's the combination. Is there a way to see these new things coming along? Yeah, if you're waiting for them.

AI assessment note: “a change is usually quite well underway before People pick it up.”

Q you know when you're seeing the beginning of one of these revolutions that it's a morphing in the making? Is this sort of a hindsight view? Because you are sort of seeing it early with everything else. What are the signs that tell you this is a morphing, this is a big theme that's emerging, that gives you the confidence to say that about, say, deep learning or CRISPR even?

A I think that a change is usually quite well underway before People pick it up. You wake up one day and you say, oh my god, the game has changed. In the case of sensors, I remember in 2010 or so, sitting down with the CTO of Intel, and I asked him, can you tell me when the average sensor is, for example, at a parking meter that might sense a car being at the meter, the average sensor is going to drop below about 10 cents per unit. And he said, yeah, that'll be around 2013, 20 15. He knew pretty well exactly, and so I thought that's going to be a game changer because we will now know what's happening everywhere. What I didn't see at the time was that the ubiquity of sensors would bring in big data. Some of us saw that in advance, but the big data didn't see would bring in all these Smart algorithms. Right. And so it's the combination. Is there a way to see these new things coming along? Yeah, if you're waiting for them.

AI assessment note: “Is there a way to see these new things coming along? Yeah, if you're waiting”

Q it almost sound like it's an accident that there's a winner-take-all effect, but is there some way of knowing early on the entrepreneur who maps out the future, who knows the ecosystem, how do we sort of know that these are the ones that will Figure out how to tip the market in their favor. What are some of the indicators? It's not an accident. Like, they're pulling levers strategically.

A Let me give you an analogy. Shows how hard this is to predict. I remember sitting in 1991. I was invited to the Senate building to brief Al Gore, who was the senator then. It was an afternoon. It was quite hot. And they were all sitting there. Everybody was a little bit sleepy. And the Gore says, can you give me an example I can latch onto? And I said, yeah, presidential primaries. And they got it immediately. The phenomenon I'm talking about, you know, if something gets ahead, it tends to get further ahead. It's true in presidential primaries. That if some candidate pulls ahead, they get more financial backing, they can be more visible. The more visible they are, the more likely it looks that they might win the presidency. So they get further ahead and more backers. You have to be quite a way into the game before it's pretty clear. That's the best I can do on that, meaning sometimes if there's a very early tilt, like within a few months, it's pretty clear what's going to take over, but it can be very much like presidential primaries. It's all the same mechanism, and predicting exactly who that's going to be, Might look easy afterwards. But on the spot, it's very difficult to do.

AI assessment note: “predicting exactly who that's going to be... on the spot, it's very difficult”

Q it almost sound like it's an accident that there's a winner-take-all effect, but is there some way of knowing early on the entrepreneur who maps out the future, who knows the ecosystem, how do we sort of know that these are the ones that will Figure out how to tip the market in their favor. What are some of the indicators? It's not an accident. Like, they're pulling levers strategically.

A Let me give you an analogy. Shows how hard this is to predict. I remember sitting in 1991. I was invited to the Senate building to brief Al Gore, who was the senator then. It was an afternoon. It was quite hot. And they were all sitting there. Everybody was a little bit sleepy. And the Gore says, can you give me an example I can latch onto? And I said, yeah, presidential primaries. And they got it immediately. The phenomenon I'm talking about, you know, if something gets ahead, it tends to get further ahead. It's true in presidential primaries. That if some candidate pulls ahead, they get more financial backing, they can be more visible. The more visible they are, the more likely it looks that they might win the presidency. So they get further ahead and more backers. You have to be quite a way into the game before it's pretty clear. That's the best I can do on that, meaning sometimes if there's a very early tilt, like within a few months, it's pretty clear what's going to take over, but it can be very much like presidential primaries. It's all the same mechanism, and predicting exactly who that's going to be, Might look easy afterwards. But on the spot, it's very difficult to do.

AI assessment note: “predicting exactly who that's going to be, Might look easy afterwards. But on the spot, it's very difficult to do.”

Q So what does that mean for jobs? In this podcast we talk a lot about how whenever industries are changed in this way, you know, through tech and other shifts, that other new jobs, classic examples include more designers in the age of Adobe design, that new jobs never existed before, like social media managers that can only exist today. What's your take here?

A So what I'm seeing is about 90 years ago or so, John Maynard Keynes pointed out that he thought by A hundred years time, 2030, we'd be in an economy where the production problem was largely solved. There'd be enough in principle to go around for everyone. There might be plenty, in principle, goods and services around, but getting access to them meant you needed wages, which you needed a job for, and that was not possible. I think that what Cain said in that regard is becoming true. In other words, the trough is full, but how do the piggies get their share of the trough? So we're now in a new distributive era. What counting is not how much is produced, but who gets what. The whole question of Growth and getting more economic product out there, physical product and services. That's a job for entrepreneurs, and it's a job for engineers. Who gets what is much more a political issue, and that's not quite a job just for politicians, but it's a job for society to solve. And we haven't solved it in Europe or anywhere else, so it's a new era.

AI assessment note: “Who gets what is much more a political issue”