Chapter 1: What are the main reasons for AI optimism discussed by Reid Hoffman?
Imagine two brilliant team members. One builds your campaigns instantly. One handles customers 24-7. On brand and always on. Meet Klaviyo's AI agents at klaviyo.com. Today's number, .001. That's the percentage of deep sea ocean floor that explorers have actually seen. Ed, how do you identify the blind man at Anuta's beach? How? It's not hard, Ed.
Listen to me. Markets are bigger than us. What you have here is a structural change in the world distribution. Cash is trash.
Stocks look pretty attractive.
Something's going to break. Forget about it. Ed, how are you? Um, let's see. I'm headed to Florence, Italy. What? !
For what? For a speaking gig. Hold the phone. Now we know why my speaking revenue has crashed. You're taking my gigs. Let me get this. Who's bringing you to Florence? Royal Bank of Canada.
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Chapter 2: How did OpenAI's missed targets affect investor confidence?
RBC is bringing you to Florence? That's right. Florence, baby. I'll be there for one day. So I'll give that, do that, probably try to have some fun as much as I can, but we'll be recording at the same time. Coming back, then going to Texas.
Oh, well, that should be easy. There's a ton of direct flights from Florence to Houston. Oh, my God. You're literally living the life I had in the 20s and 30s. And let me tell you, it's exhausting. Look at me. You're going to look like this. I'm anxious about it for sure. So you are now getting my speaking gigs and your memo to self undermine Ed's credibility.
Begin slowly diminishing his professional standing. Yeah. Kill the prince. Okay, okay. That's great. Congratulations. Thank you.
Yeah, I'm excited.
It'll be fun. That's really exciting. Have you been to Florence before?
No, it's going to be my first time.
I love the tourist places. People say it's so touristy like Venice and Florence. I love highly touristy destinations.
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Chapter 3: What is the significance of the frontier model landscape in AI?
I think they're great. It's a beautiful little city.
Why are you a fan of the tourist traps?
I went to Florence right out of college with a backpack and a Eurail pass. Oh, nice. Do young people do that anymore, or do you just sit at home on their phones?
Yeah, of course. Interrailing. That's what I did, too. Yeah, a little bit. I joined my friends for the latter half, so I was mostly in Eastern Europe. We were in, like, Slovenia, which was not that interesting. But Florence... Would have been fun. I'll probably have like an hour to myself if I had to see one thing in Florence. Do you have any recommendations?
The honest answer is get fucked up eating a lot of good food. I don't have any like cultural landmarks or in the know. Florence with Scott Galloway. I couldn't be less Stanley Tucci. I couldn't be... I'm like, where's the bar? Where's the bar? Yeah, that's not my good, but I'm very happy for you. Should... Oh, we should get on our promo. Ed, how are ticket sales going?
What's going on with our tour?
I believe they're going quite well. I think we're close to sold out in San Francisco. To be honest, I haven't checked, so I'm sort of speaking out of my arse here.
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Chapter 4: What concerns are raised about AI's impact on jobs and wealth inequality?
New York and San Francisco are almost sold out. Very soon we're going to be announcing speakers in L.A., Miami, and... I think we're close to New York. I think we're close. Very excited about that. We're doing kind of business of entertainment or business of nightlife in Miami. We're going to do business of money or business of finance in New York.
We're going to do business of media slash entertainment in LA, business of tech in San Francisco, that kind of stuff. If you are interested in getting a ticket, please go to ProfMarketsTour. ProfGMarketsTour. Thank you. ProfGMarketsTour.com. And we look to see you in either LA, San Francisco, Chicago, Miami, or New York.
Absolutely. And if you're watching us on YouTube and you like what you're hearing, hit subscribe. And if you're listening on Spotify or Apple Podcasts, hit follow. And with that, shall we get into our conversation? Let's do it.
Over the past month, we have focused on the downsides of AI, from real-world violence targeting industry leaders to growing political pushback against data center expansion. So this felt like the right moment to bring on someone who believes that AI can serve the public good. Our guest has spent years making the case that AI will improve our lives.
He has also created, advised, and invested in some of the largest and most successful technology companies in the world. Few individuals sit closer to what might turn out to be the most transformative technology of our time. And so we wanted to find out what is actually happening on the ground.
Here is our conversation with Reid Hoffman, co-founder of LinkedIn, Inflection, and Manus AI, partner at Greylock, and author of Super Agency, What Could Possibly Go Right With Our AI Future? Reid, great to have you on the show. Thank you for joining us. I want to start with some specific news that we saw this week that is making investors a little bit anxious, and that is this open AI news.
specifically that they missed their revenue target last year. They also missed their user target and they're obviously set to go public soon.
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Chapter 5: Why did Inflection merge with Microsoft, and what does it mean for the AI industry?
It should be one of the most important IPOs ever. And suddenly investors are very anxious about this company and anxious that they're actually not growing in the way that they had expected. And you are in an interesting position because one, you're on the board of Microsoft and And Microsoft is obviously one of the largest shareholders in OpenAI, one of the earliest investors.
And also you are an early investor in OpenAI through your VC firm, Greylock. So let's just start with this. What do you make of this news? And should investors be worried about OpenAI? Well, ultimately, as an investor, I'm not worried. I mean, part of it is the company had very aggressive targets.
And so when you have aggressive targets and you kind of go in a little below them, that's actually not the kind of thing that I worry about that much. And classically... The reason why public companies, because most public investors tend to want reliability quarter to quarter, tend to do their best to be on target in terms of what they're saying, because there's this kind of reliability thing.
Whereas I, as a private investor, tend to be the just established, the really, really strong basis. And so for me, the thing I wasn't tracking so much was last year's revenue, last year's User count. Those are great in the making progress. Obviously, we'll need to grow more, too, but a lot more.
But the the real question that I was like very happy to see was, you know, the 5.5 release, because, you know, I think the thing that I'm most tracking is is OpenAI continuing to deliver some of the world's best software. you know, kind of technology frontier models and so forth. And, you know, that's I think the precursor to everything else.
And that from every source I've seen and multiple different benchmarks and multiple different engagements has been, you know, is the kind of the new world leader and quality of model. So that's more of what I pay attention to because it's, you know, with investing, it's downstream effects. It's, you know, what does next year look like is the most relevant question. Does that make you concerned?
It's an interesting point. Early-stage investors, venture capitalists, they're more interested in the technology. They're more interested in what's going to happen 10, 15, 20 years down the line. Public markets investors are more interested in what's happening right now.
I mean, literally, we read quarterly earnings, we look at what happened last year, we look at what happened in the previous quarter.
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Chapter 6: What framework does Reid Hoffman propose for sensible AI regulation?
It's much more of a sort of backward-looking practice. which seems to be like it might be a big problem for OpenAI if they are to go public at the end of this year, because suddenly they will be scrutinized for their financials. And so I wonder if that concerns you, that suddenly the company is...
leaving an ecosystem of people and investors that are okay with financials that might not make sense right now, but then suddenly they're going and pitching the company to a group of investors who care a lot about that stuff. Are you worried that maybe they will get punished or that they will have a hard time in the public markets when that time comes?
Well, if they can't establish themselves, there is a small set of companies, and it isn't just Tesla, which is probably the extreme example of this, which is kind of betting on the future. But Amazon, for a significant number of years, and others that say, hey, I've got a foundational promise of which I'm going. Where investors go, we believe in that future. And so we're buying in that future.
And the fact that the, you know, the current, you know, you know, PE comparables, other kinds of things, you know, don't make sense on a quarter by quarter basis, even factoring in a, you know, an interesting CAGR or anything else. I think it'll be for all of the AI companies that are, you know, kind of, you know, prepping and considering going public.
It'll be important to establish that basis because I think the first year or two will look a lot like, you know, kind of early dot-com companies, some of which were complete flame outs and some of which were enduring institutions, Amazon being an example. What do you make of the unit economics right now?
This is something I've been thinking about when we think about these companies like 10 years down the line, where currently the unit economics don't really make sense. Like the cost to build these models is so incredibly high. And that is why OpenAI and Anthropic, any of the sort of foundation labs are burning a lot of money right now. And it almost seems as if the only way that this works out
from the perspective of the business model, is for these companies to become like essentially utility companies, like almost have these monopolies over compute. And Sam Altman has said this.
He said that eventually he could see OpenAI becoming something like a utility company down the line, which to me is quite an interesting perspective because it's quite different from what we saw in the dot-com boom where we've had You know, a handful of companies, we might call it an oligopoly, but a handful of companies operating in the same space.
I mean, where do you see the business model trending further down the line? Obviously, you know, as you know, you have to distinguish between the training costs and the inference costs. And one of the things when you look at these numbers and the inference costs are actually, in fact, pretty good. economics for inference, but obviously you have an exponentiating training category.
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Chapter 7: How does Reid Hoffman view the competitive landscape of AI companies?
And I think a little bit of the race is a race to that asymptote. Now, where is exactly that asymptote is, I think, somewhat governed by availability of capital from investors believing and then the delivery of revenue. Now, part of the delivery of revenue is not just the
current services where you say, okay, you know, OpenAI is dominating the kind of chat bot and kind of what the consumer and kind of interaction with that service is. Anthropic is doing the coding side, you know, followed by OpenAI, you know, on the coding side in terms of how the API and coding works. But like, this has got to be just the beginning.
Part of how you're looking at these AI models is not just the provision of tokens, which obviously is interesting and across a number of things, but what is the output that comes from now that software engineering can be much more broadly spread and a whole bunch of areas which otherwise wouldn't have been able to afford software engineering can now do them, that can affect what their productivity of their areas, what happens there.
what happens in services firms when a bunch of different services, you know, take legal or accounting or anything else can be done with intensive, you know, kind of amplification of this. And it's not just the question of like, you know, paying seats in the software basis, but what is the delivery in terms of the margins of what the delivery of the services.
And where all those economics play out is I think very early in TBD, And you'd say, well, should we wait to go public for those?
I actually think one of the things when you, you know, you two know this better than I do, but I think one of the things that's been a bit of a kind of call it a social problem over the last few decades has been so much of the growth has been contained only to the private markets because of like, let's delay going public until it's
you know, extremely stable, which means that a lot of folks who cannot participate in the private markets only get exposure to the public markets. And I think that's one of the benefits of having some of these AI companies. But I think it's early relative to what the business model is going to be and then what is that revenue going to be. And now, utility...
I think in terms of the fact that any task that you do with language or information, I think will have AI participation at some level of depth, whether it's complete automation, whether it's augmentation, whether it's assistance in various ways. And so the spread of that is, I think, a good measure on utility.
I think one of the things that's good for society is the fact that we have multiple providers of these frontier models.
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Chapter 8: What solutions are suggested to address the challenges posed by AI?
So I think, and this is to some degree a good news for startups and productive startups, is I think the strongest positions are OpenAI and Anthropic.
I think in the traditional big companies, Gemini is next, and I think we'll have a bunch of different efforts from Meta, Microsoft on its own, obviously used a lot of OpenAI right now, Amazon, who knows where Apple will play out and all this stuff since they still don't seem to realize that Siri is 20 years old in tech terms.
Now, for the Chinese models, the interesting question will be, like, there's a lot of good open source models that pretty clearly have some roots in distillation from China. kind of the major Western frontier models. And I think part of what we're going into is an area where that distillation will get a lot harder.
Now, that being said, the Chinese are building up compute, building up chips, have extraordinary amounts of talent, hardworking, great tech companies. Matter of fact, the C-Dance multimodal video model is, I think, amongst the best in the world. So I think it's already, you know, you already have stuff that's kind of playing there, but like, and then you get kind of a jagged edge.
Like you go, okay, let's go coding specific. So most developers will say cloud code. Part of the reason is because cloud code has got the best model, I think, the best interaction thing for iterating through kind of the amplification of a software engineer.
But then the in-depth engineers that I know who play with them go OpenAI Codex because it spends – it's much more useful at, like, call it, you know, 20, 30, 50, 90-minute reasoning, long tasks that play to harder engineering. And so they prefer that versus cloud code. And right now, at the moment, that's it when you get to, like, people who have exposure to it.
Now, some people then go, okay, I can't afford either of those, so I'll use Quen, you know, the Chinese open source model, which is quite credible. But when you think about the fact that, you know, when you're looking for coding, just as one instance—
you're actually looking for really, part of the reason why you pay software engineers and all the rest, you're really looking for something that's quite good and quite reliable that doesn't introduce bugs. Obviously, we have mythos coming with questions around cybersecurity and what does this all mean there, and that matters too. So I tend to think a little bit on the coding side
For your particular coding problem, it's a little bit like what we call in blitzscaling a Glengarry Glen Ross market, which is, you know, first prize Cadillac, second prize steak knives, second prize steak knives, third prize you're fired. So, roughly.
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