This transcript is generated with the help of AI and is lightly edited for clarity.
REID:
I’m Reid Hoffman.
ARIA:
And I’m Aria Finger.
REID:
We want to know what happens, if in the future, everything breaks humanity’s way.
ARIA:
Typically, we ask our guests for their outlook on the best possible future. But now, every other week, I get to ask Reid for his take.
REID:
This is Possible.
ARIA:
So Reid, last week on Possible, we spoke to Sean White, who is a longtime friend of yours, and lovely new friend of mine. But I would love to hear from you: what was the decision making in your mind behind bringing Sean in to sort of lead this new iteration of Inflection?
REID:
So it’s kind of obvious from our conversation. Sean and I have known each other for decades sharing a kind of a humanist view of computational technology. And so when we made the decision that Inflection should pivot from a B2C agent app to a B2B enabler, and you know, Mustafa was like, “well, but I really want to be building the agent that could be everyone’s individual companion.” We said, okay, well we need a CEO who has the same characteristics that Mustafa has, that I have, that Karén has of being a humanist technologist, to whom this mission would be super exciting. You know, on one hand we, of course, we talked about Inflection and AI and LLMs and Earth Species Project, but Sean has been the lead singer of a band when he was at Stanford. He has worked in climate change with a, getting a PhD in that. But it’s all of that of how does technology help us amplify our humanity by our interaction and place with it. Those are the things that led Sean to be my first call. And fortunately for Inflection and for me, my only call.
ARIA:
Sort of to bring it back to the field at large. Like Inflection is an example of a startup that pivoted away from relying solely on building its own LLM and towards partnerships with Microsoft, with, you know, 13,000 companies using its API, et cetera. So I think a lot of people are asking the question — in the beginning all the talk was about people building their own LLMs. That was a huge part of the conversation. Do you think that it’s possible for a startup today, like someone just getting started, to say, “we’re going to build our own LLM and have its own personality” or whatever it might be? Or is that just like too resource intensive? Is that a strategy now? Or is that something that we’re just going to see the existing players investing in?
REID:
I think actually the vast majority of startups will do some of their own model training and LLMs, but it’s the next level of detail that matters. So if your strategy is how do you build the most hyperscale model to, to potentially compete with the hyperscalers, that is I think very, very difficult. And you need to have a very unique strategy for a product, for revenue, for financing, for the market, for the whole thing. And it’s not impossible, but it’s maybe so low-probability as to be functionally impossible. But that’s only a particular model and a particular thing. And one of the things that I think that people don’t really fully track about, like for example, you know, this element of the agentic universe and all these different agents is it’s not going to be like a model per agent. Actually, I think there’s going to be multiple models in composition that get to agents.
REID:
And that is part of the reason why there’s multiple models to get that blend of capabilities, which already happens within these agentic models, using a mixture of experts kind of combination, actually in fact is part of what brings a high cognitive performance characteristic. Now, once you realize that it’s going to be the offering of agents, the offering of AI services and everything else — is going to have a, a combination of models including specialized models, some smaller models, some compute, you know, efficient models, et cetera, as part of what they’re doing, then that suggests to you, and that will then mean there will be room for startups as they’re figuring out their unique product, their unique go-to market, their unique industry, their unusual, you know, approach to transforming an industry. And so actually, in fact, I think there will be their own LLMs, but the particular thing that I think gets extremely tricky is if you say, “well, I’m going to be trying to match the hyperscalers in the billions of dollars of spend on the computers and, and training runs.” And instead your startup strategy should be — how do I leverage those hyperscale models in what I’m doing? And the fortunate thing for startups is, from what I can see today, there’s at least going to be three and probably more like five to seven of these hyperscale models. So it gives you a lot of competition, things for accessibility, pricing, different capabilities, et cetera. So I think that’ll be very good. But I do still think startups will in various ways create their own LLMs.
ARIA:
Even if GPT-7 existed, it would be a really stupid business decision to have GPT-7 servicing all my queries because some of my queries would only need the power of GPT-3. Some of my queries would be perfectly well serviced, both in terms of cost, resources, time. Like you want to be, you know, a quicker-servicing LLM. So can you talk a little bit more about that, about how different LLMs are going to come in to service one consumer or one company and, and then perhaps more in the enterprise space.
REID:
Yeah, so, so you already gestured at one of the really fundamental things, which is the larger models will always be not just more expensive to train, but more expensive to run, slower to respond, et cetera. And like for example, there’s a whole set of circumstances where very quick responses are actually in fact more helpful than being 5% better [laugh], right? Especially if it’s a shorter or kind of other response. Think about like, actually in fact, you know, from the very earliest days at Google, one of the things that they discovered in measuring search quality was the speed of getting search quality results under 200 milliseconds increases the perception of quality. So even if you kind of presented the two things, and the other one like independently was considered to be better, but that one took a second to do, and this one took 200 milliseconds — people were like, “oh, the 200 milliseconds is, is better quality search results.”
REID:
So that’s, you know, that kind of performance characteristic is one. But the other thing that people I think — we’re just, you know, tip dipping our toes into the ocean here about how LLMs work — is that large language models, they get trained in certain directions. Now people overly generalize, “well, they are only a product of what their data is.” That’s actually not true because there is data, which of course is an important ingredient. There’s different algorithms and training processes, there’s different human factor and other kinds of post-training. All of that goes into different shapes of these things. And they’re large complicated things. Like one of the reasons why, you know, even the smaller models you can say, “I can’t fully predict all their behavior,” is because they’re a complex piece of software that’s done a bunch of learning. And so they’ll have different personalities and shapes.
REID:
One of the things that, you know, I’ve been playing with for this last summer is there’s almost like AI psychology, which is if you’re prompting — say you’re, you’re saying, “Hey, give me a birthday wish for Aria,” you get one thing. But if I say to the prompt, say, “Okay, consider the Zen aphorism. What is the sound of one hand clapping? Now give me a birthday wish for Aria.” I’ll get a different and interesting thing. There’s like almost like a psychology to them. And what that means, of course, is that’s part of the thing that underlies the multiple models composing into these agents because you can make incredibly different, cognitively-rich leaning in different directions. The podcast I did with Allie Miller with a bunch of custom ChatGPTs where they’re, they’re each framed in a different way to understand that there’s like, there’s almost like the same mixture of experts that helps a, a scale LM work.
REID:
Well, the same thing. We’re going to have a set of different agents, going to have a set of different models to bring in a certain amount of cognitive diversity to saying, “well, what’s the best solution to this particular circumstance, problem, challenge, dance move, et cetera.” And so all of that — not just the, the economic capabilities, but also the result capabilities and the thing that makes something better. And then the very last moment that I’ll note is that, you know, part of the thing that people should track about why it is, “oh, you know, we are just going to fire all marketers and replace them with GPT-6 or 7?” And the answer is then it’s all vanilla and it’s all the same. Part of the role for humans is trying to figure out what is that competitive differentiation. And so that’s part of the reason why the person plus the machine, I have informed belief about why that’s a persistent, you know, kind of workforce trend and productivity trend.
ARIA:
So leave us with your, your hopes and dreams for Inflection’s Pi and, and for personal intelligence more generally.
REID:
There’s a couple of different hopes and dreams here. I mean, I think, you know, part of the whole business deal that we did with Microsoft is I think there’s still a, how does everyone have a personal intelligence that helps them? I think that will continue. I think what Inflection will do is say, well, how do we make that universe available to a whole set of other companies that have their own particular product domain, their own particular service, their own particular zone of human enhancement, human service — and to bring those same issues around eq, personal intelligence amplification to them, and have that be, you know, kind of specifically delivered. So it’s kind of almost like a, you know, one version of impact is the scaling of a particular agent and the other version of impact is to bring this technological base across a number of different, you know, compelling, innovative product services companies. That’s what my hope for Inflection’s Pi is.
ARIA:
Fantastic. Reid, thank you so much.
REID:
Thank you.
REID:
Possible is produced by Wonder Media Network. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Katie Sanders, Edie Allard, Sara Schleede, Adrien Behn, and Paloma Moreno Jiménez. Jenny Kaplan is our executive producer and editor.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Ben Relles, Parth Patil, and Little Monster Media Company.