As the field of artificial intelligence moves forward, applications have graduated from buzzword-laden projects to fully developed tools impacting a wide range of industries.
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While science fiction-esque fears of robots taking over are still overblown, the potential for AI tools to significantly augment human ideas and workflows on an everyday basis is more tangible than ever. This is evident in products like the collaborative effort of Microsoft and OpenAI to produce the AI programming tool Copilot, to OpenAI’s language generator GPT-3 and image creation platform DALL-E.
Now, the field is poised to move into the next stage, says OpenAI CEO Sam Altman. From massive leaps forward in large language models and multimodal models that move between image and language, to applications that significantly extend the capabilities of scientists, Altman sees artificial intelligence as the foundational platform from which numerous advancements will be made across all industries.
“If you just think about that alone as a way to unlock the applications people will be able to build, that would be a huge victory for all of us and just a massive step forward and a genuine technological revolution,” says Altman. “I think that these powerful models will be one of the genuine new technological platforms, which we haven’t really had since mobile. And there’s always an explosion of new companies right after.”
Altman joined me for a far-reaching discussion on the current state of AI and what’s to come next during Greylock’s Intelligent Future event, a day-long summit hosted by myself and fellow Greylock general partner Saam Motamedi. The summit featured experts and entrepreneurs from some of today’s leading artificial intelligence organizations. You can listen to this interview here, or wherever you get your podcasts. You can also watch the video of this interview on our YouTube channel here.
Episode Transcript
Reid Hoffman:
So all right, let’s start a little bit more pragmatic, but then we’ll branch out. So one of the things I think a lot of folks here are interested in is, based on the APIs, that very large models will create, what are the real business opportunities? What are the ways to look forward? And then given the APIs will be available to multiple players, how do you create distinctive businesses on them?
Sam Altman:
Yeah. So I think so far, we’ve been in the realm where you can do an incredible copywriting business or you can do an education service or whatever. But I don’t think we’ve yet seen the people go after the trillion dollar take on Google. And I think that’s about to happen. Maybe it’ll be successful. Maybe Google will do it themselves. But I would guess that with the quality of language models we’ll see in the coming years, there will be a serious challenge to Google for the first time for a search product. And I think people are really starting to think about “How did the fundamental things change?” And that’s going to be really powerful.
I think that a human level chatbot interface that actually works this time around, I think many of these trends that we all made fun of were just too early. The chatbot thing was good. It was just too early. Now it can work. And I think having new medical services that are done through that, where you get great advice or new education services, these are going to be very large companies.
I think we’ll get multimodal models in not that much longer, and that’ll open up new things. I think people are doing amazing work with agents that can use computers to do things for you, use programs and this idea of a language interface where you say a natural language – what you want in this kind of dialogue back and forth. You can iterate and refine it, and the computer just does it for you. You see some of this with DALL-E and CoPilot in very early ways.
But I think this is going to be a massive trend, and very large businesses will get built with this as the interface, and more generally [I think] that these very powerful models will be one of the genuine new technological platforms, which we haven’t really had since mobile. And there’s always an explosion of new companies right after, so that’ll be cool.
RH:
And what do you think the key things are, given the large language model we provided as an API service? What are the things that you think that folks who are thinking about these AI businesses should think about as to how you create an enduring differentiated business?
SA:
I think there will be a small handful of fundamental large models out there that other people build on. But right now what happens is a company makes a large language model (API enabled to build on top of it), and I think there will be a middle layer that becomes really important where… I’m skeptical of all of the startups that are trying to train their own models. I don’t think that’s going to keep going. But what I think will happen is there’ll be a whole new set of startups that take an existing very large model of the future and tune it, which is not just fine tuning, all of the things you can do.
I think there’ll be a lot of access provided to create the model for medicine or using a computer or a friend or whatever. And then those companies will create a lot of enduring value because they will have a special version of it. They won’t have to have created the base model, but they will have created something they can use just for themselves or share with others that has this unique data flywheel going that improves over time and all of that. So I think there will be a lot of value created in that middle layer.
RH:
And what do you think some of the most surprising ones will be? It’s a little bit like, for example, a surprise from a couple years ago – we talked a little bit to Kevin Scott about this morning as we opened up – which is train on the internet, do code. So what do you think some of the surprises will be if you didn’t realize it reached that far?
SA:
I think the biggest systemic mistake in thinking people are making right now is they’re like, “All right, maybe I was skeptical, but this language model thing is really going to work and, sure, images, video too. But it’s not going to be generating net new knowledge for humanity. It’s just going to do what other people have done. And that’s still great. That still brings the marginal cost of intelligence very low. It’s not going to cure cancer. It’s not going to add to the sum total of human scientific knowledge.” And that is what I think will turn out to be wrong that most surprises the current experts in the field.
RH:
Yep. So let’s go to science then as the next thing. What are some of the things – whether it’s building on the APIs, or use of APIs by scientists – what are some of the places where science will get accelerated and how?
SA:
So I think there’s two things happening now and then a bigger third one later. One is there are these science dedicated products, like AlphaFold. And those are adding huge amounts of value, and you’re going to be seeing this way more and way more. I think if I had time to do something else, I would be so excited to go after a bio company right now. I think you can just do amazing things there.
Anyway, there’s another thing that’s happening, which is tools that just make us all much more productive that help us think of new research directions that write a bunch of our codes so we can be twice as productive. And that impact on the net output of one engineer or scientist, I think, will be the surprising way that AI contributes to science that is outside of the obvious models. But even just seeing now what I think these tools are capable of doing, CoPilot is an example. There’s much cooler stuff than that. That will be a significant change to the way that technological development, scientific development happens. So those are the two that I think are huge now and lead to just an acceleration of progress.
