In this presentation from the American Dynamism Summit, a16z General Partner Martin Casado lays out the case for AI as a driving force behind incredible advancements in technology, creativity, and the human experience — not to mention efficiency improvements on par with, if not greater than, those delivered by the internet and the microchip.
Here is a transcript of his presentation:
I’m going to be covering this AI stuff everybody is talking about. I’m going spend about 10 minutes to talk about why we’re so excited about it, and then Senator Todd Young is going be up here and we’re going to have a discussion.
I probably took my first AI course in the late ’90s. The stuff has actually been around with us for a very long time. And during that 70 years, it, by every metric, has been a huge success. It’s been up and to the right.
We’ve solved a number of problems we didn’t think computers were good at solving. So, for example, expert systems in the ’50s and ’60s we used for medical diagnosis. We got very good at beating Russians at chess in the ’80s and ’90s. We’re good at image detection. We’re good at robotics. We’ve solved a lot of problems that originally we thought computers were just like large calculators.
On top of just solving these problems, for decades, a lot of the solutions are actually better than humans. Like, we’re better than humans at handwriting detection, we’re better than humans at identifying objects and images. And with all of this magic, we’ve actually been able to add a lot of value to large companies, right? Every time you go to Google and you get a search, this is using AI. Anytime you get some personalization, this is AI, right? So, this stuff is like magic, right? It’s been around for a long time. It’s solved all these problems.
So, there’s been this huge conundrum in the investment community. And the conundrum is the following. If this stuff is so magic and it solves all of these problems, why haven’t we seen a platform shift in the same way we saw a shift with mobile or with the internet? Like, why hasn’t this happened? And we’ve actually done a lot of research with this as a firm, and the answer is that even though the capabilities have been fantastic like I talked about, the economics just haven’t been there in the same way. There’s a number of reasons for this. I won’t be exhaustive, but I’ll just cover a few of them.
So, one of them, a lot of the solutions just tend to apply to niche markets. There’s not a lot of broad market appeal. The second one is probably the most important in nuance, which is a lot of the use cases that we apply it to, correctness is really important, like robotics, but getting something absolutely correct is very, very hard and requires a tremendous amount of investment. So, a number of the solutions require hardware.
And finally, you know, the competition for AI. It’s not another computer, it’s actually a human brain. And, you know, maybe it’ll be better, maybe it’s not as good. The human brain is incredibly efficient and it’s incredibly cheap. And one of the best examples of this is autonomous vehicles or robotaxis. So, when I joined Stanford to do my Ph.D. in 2003, Sebastian Thrun had just won the DARPA Grand Challenge, right? So, he had driven a van autonomously across the desert and won this. And we were like, “Great news, exciting.” Like, autonomous vehicle is a solved problem back in 2003.
Now, if we go 20 years later, we’ve invested $75 billion as an industry. And while we do have autonomous vehicles on the road and they’re great and they’re solving real problems, the unit economics are still worse than say Uber and Lyft because they’re competing against the human brain. So, while this is very important technology, to date, it’s really remained in the realm of large companies, right, that can sync these types of investments.
So, the AI learnings of the last couple of decades is not that technology can’t be built or even that we can’t monetize it, we’re actually good at all of that, it’s that this is very hard for startups to build businesses around. So, the reason that we’re so excited and the industry is changing so quickly is this wave is very, very different on exactly this issue, economics.
So, when I talk about kind of this wave, I’m talking about the emergence of what we call large models or foundation models or state-of-the-art models. These are pieces of software that you put in text or you put in an image and out comes something. Out can come an image or text or a conversation, right? There are this kind of, like, very, very smart pieces of software that you can ask questions and they provide answers to.
And they’ve already entered a number of problem domains that we just haven’t cracked in computers and certainly in AI, right? The AI has not been able to do this piece before. So, for example, creativity. You know, these models are better than humans at creating images or creating music or creating, you know, voice imitations. It actually turns out they’re great at natural language reasoning as well, right? They’re great conversationalists, they’re great friends, they’re great romantic partners, they’re great therapists, and they also are now serving this thing which we call co-pilot, which is this catch-all phrase that they’re pretty good at, like mean online tasks. And by mean I mean average, right? So, if it’s something that you do a lot of it can kind of get the hang of it and do it as well.
Now, remember when I said, like, traditional AI the economics didn’t work and there was a set of reasons? So, those reasons just don’t apply to this set of tasks, right? Like, these markets are enormous, like, whatever. Video games and movies alone are, like, a $0.5 trillion market. In many of these use cases, correctness isn’t an issue, right? I mean, like, there’s no formal notion of correctness of creating, like, a fantasy image or, like, creating a sonnet or something like that. The use cases are primarily software.
And the last point is the one that I couldn’t have predicted, and it’s the most surprising that it turns out that for these tasks, the one that we think of as very human, like, you know, communication and social interaction and creativity, the computers are far cheaper and far better than humans are. I wanna give you a very specific example. It may be silly, but it actually generalizes.
So, let’s just say that I, Martin, wanted to create a picture of me as a Pixar character. So, if I had one of these AI models do it, the actual inference cost, the cost of doing that is about 1/100 of a penny, and it takes about a second. And, I mean, this is what we did here, and this is the quality that you get. If you were to compare that to hiring a graphic artist, a graphic artist is what, let’s say 100 bucks in an hour. It actually is much more. I’ve gone down this road before. So, the AI, you know, it’s just not a little bit better. It’s not, like, 20% better, it’s 4 orders of magnitude cheaper and faster.
This isn’t limited to images, this is also the truth for, like, any sort of language understanding. So, like, take a complex legal document. I can take a complex legal document, I can feed it into an LLM and I can ask questions. If you compare the analog would be for me, like, whatever, like working with my lawyer. So, like, the lawyer would have to read it, would have to understand it, you know. I don’t know how much, you know, the average lawyer cost is, but, like, you know, $500 tends to be pretty standard. And so again, to use an LLM is four to five orders of magnitude cheaper and faster.
And it’s exactly because of this that we as venture investors and we on the private market side are so exciting because we’re seeing the fastest growing companies we’ve seen in the history of the internet, including, by the way, the internet itself. And this is by measuring for revenue or the number of users, etc., right? It’s exactly economic dislocations that create new startups, not just new technology.
So, if you take a step back, historically when marginal costs have dropped this much, this is what creates platform shifts and has changed the industry entirely. It’s happened twice. You know, pretty concretely, I wanna walk through both of those.
So, the first one was compute. So, in the creation of the microchip, it brought the marginal cost of compute to zero. Like, so before you had the microchip, calculations were done by hand, right? So, it was like people doing logarithm tables, you know, in large rooms. And then ENIAC was introduced, which was four orders of magnitude faster, and then you have the computer revolution. And here came, you know, IBM and HP and everything else.
The internet brought down the marginal cost of distribution to zero, right? So, before, like, whatever you’d send, you know, a box or you’d send a letter and then the price per bit dropped and you could send it over the internet. By the way, also four orders of magnitude improvement. And this ushered in kind of the internet revolution, right? This is kind of Amazon and Google and Salesforce.
So, it’s pretty clear if you just take the fundamental economic analysis that these large models bring the marginal cost of creation to zero, like creating that image and language understanding, like reasoning over those documents. And these are very, very broad areas that they can be applied to.
Now, whenever we talk about economics, we always kind of talk about job dislocation as well. It’s very, very important, especially with an economic dislocation of this size. We can learn from the last two epochs, both the microchip and the internet in that if demand is elastic, so, for example, the demand for compute seems kind of unlimited and the demand for distribution seems kind of unlimited, that even though the costs drop, the total throughput, the total use increases by a lot because it becomes more accessible. And so, rather than removing jobs or removing value, these tend to expand growth. Like, the internet almost certainly expanded growth in the United States. And so, we think the same thing is gonna happen here as well.
So, get ready for a new wave of iconic companies. It’s almost certainly gonna happen. It’s not just the technology which is solving problems that have never been solved before, but the economic case is absolutely there.
You know, when this happened with the internet, like, we didn’t really know what was gonna be on the other side of it. Like, we couldn’t have predicted Google and we couldn’t have predicted Yahoo, but we knew it was gonna be something. And it’s kind of one of those moments, but we have some glimpses, right? We know, like, the social order is changing. We know, like, this is a very real use case that’s monetizing today and people are using.
We absolutely think that creativity itself is going to change. You know, productivity, this kind of mean workers, like, this is happening as well. And if you really want some prognostication for where this is all going, I mean, for the first time, I say there is actually real line of sight to embodied AGI. And by embodied AGI I mean something that’s economically viable so you don’t have a bunch of robots that can’t work because they’re too expensive. Like, actually, like solving problems that we need to have solved.
And so, like, with that, listen, there’s a lot to do. This is gonna be a major value driver. I think it requires a ton of partnership from the VC community, from the tech community, and certainly from D.C. So, I appreciate all of you being here today, and your patience with my talk.
