When you think about OpenAI and Anthropic, we tend to want to tell these stories as cage matches. Well, one of them's going to win and the other one's not. And you're like, well, actually in fact, there's a lot of room for both of them to win, incredibly. If you believe that AI has the impact of applying intelligence with the scale and price of electricity across everything, these are going to be two of the major providers of that.
Like almost every dinner conversation I'm at, the big question is, can you make sense of the valuation of these companies? So, what should my answer be?
Think of it as a little bit like internet valuations. Some of them will have turned out to be insane and go to zero, and some of them will have turned out to be way too low and have gone up a lot higher. The trick is, which ones?
Reid Hoffman is one of the leading tech investors of our time, and I'm lucky to call him a mentor and a friend. He's co-founder of LinkedIn and has backed so many influential startups. Plus, he served on the board of Microsoft for a decade. He's recently said he's turning his focus to Manifold AI, his AI drug discovery company. With Anthropic and OpenAI on the path to going public and SpaceX surpassing a $2 trillion valuation after its IPO, I needed a check-in with Reid. So, we hopped on to talk about what's signal and what's noise in the AI race and so much more.
Hi Reid, welcome again to Pioneers of AI.
It is always so great to have you on the show. It's been a while since we last chatted, and as with everything in the AI world, lots of updates. So, I'm excited to catch up.
I want to start with some personal updates on your end. So, after a decade of serving on the Microsoft board, you just departed from the board.
Actually, I just said I wouldn't stand for re-election, which means I'm on it through the end of the year, but yes.
Okay, okay, okay, okay. But you largely did that because you are going back to founder mode and focusing on Mana. So, tell us more about that. I'm actually really intrigued by that.
You know, when Ujjwal and I had been doing the work in the last couple months and we had been beginning to see some small molecule proposals from our AI drug discovery engine that our computational chemists are looking at and going, 'Oh my god, that's really interesting.' And that could work. And I said, 'Okay, I've really got to make sure that this thing is navigating and building these things really matters.' And so, I got to focus on this and talked to Satya and said, 'Look, it's been a long time. I'm always a friend and an ally. We'll continue to help even afterwards, but I'd really like to spend my time more on building, on being a founder than essentially being a governance person.' And I can still help when, as a matter of fact, Satya and I were just on the phone today talking about some piece of strategy.
As you reflect on your tenure on the Microsoft board, any things that stand out, any takeaways, any reflections, insights?
It's been a couple things. One of the things is obviously the LinkedIn acquisition is one of the epic M&A of history in terms of growth and impact and revenue, having high operating margin, etc. Also continuing to be successful, right? LinkedIn is thriving still, and that's not to be taken for granted. And then, helping navigate the organization to buy GitHub, which was bought well over revenue in terms of comparable prices, but the strategic part of it and how that plays into the whole coding revolution now is really key. Facilitating conversations of trust between OpenAI and Microsoft. Those are some fun highlights.
Yeah, so amazing. So incredible. Okay, so I want to spend a little bit of time on the big news in AI. The SpaceX IPO was on June 12th. It's kind of interesting. It was only in February when SpaceX and xAI merged, right? And a big part of SpaceX's IPO story is actually an AI strategy and an AI narrative. And it seems that they want to vertically own and integrate the AI supply chain, whether it's compute, data centers, the model, Grok, Cursor's acquisition, which as we record this, was announced just 48 hours ago. I would love your take on what that all means.
There's a sense in which SpaceX talking about itself as an AI company is somewhat honest by the fact that we bought xAI. And what we're going to do is use our market cap to try to buy our way into being an AI company. It's essentially what it's trying to do. And so it's not surprising that, X days after the IPO, we're using our market cap to buy Cursor. And I wouldn't be surprised if there were others as well because SpaceX isn't an AI company. xAI is, as Elon himself has described, a complete train wreck for its building of foundational models and other kinds of things. All the founders have left. It's on its third restart. And if you look at the revenue in SpaceX, it's 'oh, we're going to be charging Anthropic a lot for compute.' Like, 'oh, you're a premium CoreWeave. I get it.' Which is not an AI company. The reason why they're saying they're an AI company is because they're going to go buy a bunch to try to become an AI company. I think what investors are buying or holding on in this was that they're going to go spend a lot of equity capital on buying a bunch of AI assets and seeing if they can be cobbled together. You could almost think of it as the IAC of AI in terms of how it's playing. So, I'd say it's TBD, watch this space, but use the market cap to buy AI companies and try to buy your way into relevance.
So, Anthropic and OpenAI have both filed for IPO with the SEC. And both are expected to also be historically giant offerings. Now, you're an investor in both of these companies. So, I think my first question is, do you think the order and the timing of these IPOs, both of them, are important in relation to each other?
I'm sure the two companies do, right? There's definitely a lot of rivalry between the companies. And there's the theory of who gets out first and do you absorb the capital? I don't think it matters. I actually think there's a lot of room for both. I think they naturally have categories that they're very strong in. With everything that's going on with code and its applications from Anthropic, and Anthropic's clearly testing the waters on expanding in design and legal, and OpenAI with ChatGPT is clearly the front-end search Google equivalent. And it's also testing the waters on other things and is coming up strong on Codex, which is actually insufficiently talked about as a really compelling coding product. In fact, it's stunning how much cloud code and Codex are. One of the questions in the SpaceX acquisition of Cursor is that Cursor seems to have had its bright star some number of months ago and seems to be fading over the horizon. So what the theory is that it doesn't completely fade over the horizon is an interesting one. We tend to want to tell these stories as cage matches. Well, one of them's going to win and the other one's not. And you're like, well actually in fact there's a lot of room for both of them to win, incredibly.
Like almost every dinner conversation I'm at, the big question is, how can you make sense of the valuation of these companies? So what should my answer be when I get asked this question?
Think of it as a little bit like internet valuations. Some of them will have turned out to be insane and go to zero and some of them will have turned out to be way too low and have gone up a lot higher. And this is what happens when you have high growth, hot, fast moving tech companies, of which AI is most of it. Look at what Google became from being an internet company, and then other things bombed for being too early. And so I think there'll be a number of these valuations that'll just be like, 'oh yeah, you pay a lot for that and that turned into zero.' But I think there will be others. For example, when you think about OpenAI and therapeutics, why should it be valued in the same trillion-dollar company category as these others? The answer is, if you believe that AI has the impact of applying intelligence with the scale and price of electricity across everything, and these are going to be two of the major providers of that. And what's more, you can already see some of the revenue really well. And frequently the revenue comes later. Like in the OpenAI category, advertising. For example, Google's early theory of revenue was we're going to sell enterprise servers. And that didn't work. And then AdWords, the best business model invented in human history. If you say all the valuations are crazy, you're wrong. If you said some of the valuations are crazy, you're right. And the trick is which ones.
Which ones? Yeah. I think the other question that comes up a lot is, do we care whether these companies are profitable or not? Is venture at the moment and maybe eventually the public markets, are we subsidizing the cost of AI and the cost of tokens? How do you make sense of that?
Well, we're definitely subsidizing it for growing. A modern exemplar, speaking of trillion-dollar companies, is Amazon, which was unprofitable and barely profitable for a very long time. And yet has an amazing strategic position and continues to be that. So, it doesn't matter if they're profitable at IPO. It does matter that companies become profitable and then eventually become seriously profitable. But part of valuations, which most people don't appreciate, is future expected terminal value. So, you go, well, this thing is so strategically important, it's going to exist for a very long time and continue to grow and have an important market position even if its revenues are modest or its profitability is wobbly. That still makes a very valuable company.
The learning too is you're not always able to predict what these revenue models or business models are going to look like anyway, right? It's going to evolve. It is evolving.
Part of the thing is, is there some possibility? Because if you said there's a 3% possibility it becomes an AdWords, that's huge.
Right. All right, so let's talk about what this all means for early stage AI startups. Back to these IPOs and where this is going. How does this change how we evaluate defensibility in these AI innovation ecosystems and also what should we be looking for?
The change of defensibility and the change of what kinds of things lead to sustaining value. People tend to be overly dramatic and say it all goes out the window. That is certainly not the case. But on the other hand, a bunch of things that used to be strong defensive modes don't. Part of the reason why people were talking about the SaaS apocalypse is because they went, well, the previous dynamic with SaaS companies was it cost a billion dollars to build the baseline tech that everyone would use. You chip away and eventually get an ecosystem and customer base, and then challengers, it's very expensive to come because you're continuing to grow your customer base while they have to spend a billion dollars and then get the first couple customers and move on. And then you go, well, if all of a sudden it doesn't cost a billion dollars to make one of these things, the product feature set of what's useful to them may change radically because individual companies may want things deeply personalized in ways that they can do it with the coding agents themselves. So the economic model, which was have a high operating margin because switching is very expensive, it's like, oh, I don't want to pay you that operating margin anymore and competitors can come in. Hence, SaaS apocalypse. Now, the reason why the SaaS apocalypse is overly stated and it plays the defensive moat question that you have. The real question is not short all SaaS, the real question is short any SaaS that's not aggressive and driven and committed to becoming AI native, and buy the SaaS that is, as a direction. That then gives you some code also what moats might be. There's a bunch of standard defenses that I think will continue in the current age. Now, the interesting question is which things will add in. And I wouldn't be surprised if there were new kinds of moats. People have speculated, will there be data moats? Maybe, especially on things that are real-time oriented or you can only serve the product if you have this particular data versus training data moats, which I tend to think I'll be a little bit more skeptical of. How do network effects play in? I think there's a set of different questions there. There's the kind of in some senses brand and trust matters a whole lot more. So, if you've established a brand and trust and gotten through the noise, how does that matter? So, I think there's a stack of things that matter there. I do think one of the things that you and I love about both being founders and investors is that hey, when you're throwing up all the cards in the air, it takes taking intelligent probability risk-adjusted bets, working with speed, making it happen, and that's fun.
Yeah, that is fun. But we still believe that there is a lot of potential in vertical AI startups. So, startups that are coming in with deep domain expertise to solve and reimagine workflows in antiquated industries. But, or maybe it's an and, I think of the legal industry for example. Three or four years ago we saw a whole bunch of legal AI companies and they've been quite successful. But then recently Anthropic just released their legal agents essentially which is essentially what these companies were doing. And so I don't know if I would invest in a legal AI startup today. And I think that applies to many, in fact it applies to a lot of these vertical industries. So that is definitely kind of a question mark.
