E435: Why Most Companies Aren’t Ready for AI

0:00What does it mean for a company to have a moat in a post-AI world? We mapped 550 companies in the 7Power framework, and we mapped them by defensibility of their moat and by how much money they have raised. When you're asking how do you get a moat in AI, it's not just about the moat, it's also about the value that you can generate for your investors. The surprises we saw were two things. One is you'd expect that the more money you raise, the more so you'd be on a kind of a diagonal line. Only OpenAI and Anthropic are on that diagonal, and they are at the very top of the diagonal. So raise the ton of money, really defensible.

0:41Then there's a cluster of companies that have raised a ton of money but aren't defensible because they rely on modes that no longer work in AI, like switching costs or even cornered resources like a data mode. And then there's also a cluster of companies that have raised very little because they're super capital efficient because they're leveraging AI. And they're super valuable because they rely on two modes, one being network effects, which is saying how do you make your product more valuable as people use it. And then the second one is a different kind of mode, which is counter positioning, which is the traditional mode you see when you have a platform shift and you say, I'm going to do the same thing but differently.

1:23And so the modes that work in AI are completely different than the mode that worked in the SaaS era of switching cost and data mode. It's really network effect and counter positioning. How do you stay disciplined in terms of valuation in a time like today? It's easier than you think. We target very specifically serial entrepreneurs who are building a capital efficient company that grows fast and is fairly valued. And you'll be surprised how many serial entrepreneurs want to raise less. You read in the headlines, they raise these massive rounds. But the reality is what's exciting and challenging for many of them is to maximize the use of AI, which essentially means be incredibly capital efficient.

2:12You read about these three-person companies. And so you'll find that a lot of serial entrepreneurs, they want to minimize their dilution to maximize their outcome. and therefore they're quite reasonable on valuations. And the other thing that we're noticing is that these serial entrepreneurs, the calculus they make is the AI platform shift is going to be a long one. It's going to be like 10, 15 years. And so they have a choice to make, which they have to make earlier and earlier now, which is do I start now and go all the way and in 10 years I may get to an IPO knowing that there are fewer and fewer IPOs, or do I go out, exit quickly, and then do it again, and I can do it three times, and I probably make more money this way than if I aim for the IPO.

2:59The IPO market has gone smaller and smaller. I just had the former PM at Thiel Macro, Peter Thiel's hedge fund, and he said that as more money goes into the passive indexing, that's taking away from the active managers, and the active managers are the ones underwriting IPOs, which is why it's harder and harder to pass that threshold of being a public company. That's one of the factors. The other factor is what's happening in the research division of the investment banks, where they have essentially a limited pool of resources, and they're deciding who are we going to cover, which ticker. And they're obviously going from the top and down.

3:38And so what that does is if you go public with a small market cap, you're not going to get covered by analysts. And therefore, your ticker is not going to be actively traded. And therefore, your stock's not going to appreciate. Even like right now, the IPOs, we celebrated our sixth IPO a few months ago, Netscope. It went public for$10 billion. Our first IPO was Amplitude. It went public for$5 billion. And so did DigitalOcean. So the market has shifted to$10 billion and up. But even$10 billion is still a low floor. And so folks who want to go IPO, they need to generate maybe more like$30,$50 billion worth of value before they consider an IPO.

4:24Being a public company CEO is difficult because of Sarbanes-Oxley. You compare that to an M &A or a licensing deal like Grok, right? NVIDIA licensed Grok for$20 billion. That was our most recent exit. Well,$20 billion is a pretty nice outcome. And it's an M &A, so there's a lot less hassle to deal with. So for a lot of these entrepreneurs, that's a true question. Do I even want to go IPO when an NVIDIA could buy me for$20 billion? A lot of VCs, and it's almost become this consensus view, at least on X and on podcasts, is that you need to have these power law outcomes in order to return funds. You disagree.

5:04Why? I disagree because I think it's a strategy that you have to adopt if you have no other choice. So who has to do that are the megafunds. So for them, that's the only way forward if they want to continue to generate returns. I have a massive fund. I divide it in X many slots. Each of my slot is a pretty big ticket. Therefore, each of my ticket needs to go in a company that has the potential to go IPO. It's a strategy that's incredibly constrained. If you're not a manager that runs a megafund, right, If your fund is, say, less than a billion AUM, then you actually have more options. You can divide your fund size into tickets that fit into a strategy that generates M &A as an option.

5:55I just mentioned the$20 billion deal between NVIDIA and GROC. That's kind of an exception. Most M &A happen in the$100 million to$1 billion range. but if you have a fund that allows for tickets that go into a company that's sub 100 million dollars in valuation as long as it exits in that 1 billion range you still make a lot of money for your investors so the power law strategy is a strategy that works but it's one that's so restrictive that you really only want to adopt it if you don't have another choice how has the M &A market changed today in a post ad world so there's a An incredible dynamic at work that's favoring M &A right now.

6:39In the network that we have, which is a very large network of chief product officers and product builders, the segment that we talk to the most about M &A are the chief product officers of Salesforce and Disney and Walmart and Johnson & Johnson. And their mandate from their own CEO is you need to grow fast and find a lot of benefits from AI. So do it quickly and do it big. And that means they cannot do it just organically. So they have to go and acquire AI native companies so that they can grow faster, so that they can change the culture internally faster, overcome resistance, do more product innovation, better and faster.

7:24Which means that two things. One, there's a lot more M &A. You see that in the quarterly numbers that are reported by most of the investment banks. But also the M &A mandate is actually shifting from traditionally finance, right, which is EBITDA driven and financial metrics driven, to product, which is more product innovation driven. How am I going to bring a team like the GROP team into my organization to do more product innovation, better, more disruptively, as opposed to how do I add up revenue numbers and EBITDA to increase my share price? And what happens is both in private markets and in public markets, the biggest driver of stock performance is product innovation as opposed to financial engineering, if you want.

8:13And what drives the stock appreciation is the repeat, predictable, sustainable ability to innovate in product as opposed to the EBITDA margins and so on and so forth. This episode is presented by Juniper Square, the operations partner for private markets. I'm fascinated by your idea of using M &A to bring in culture into large companies. Tell me about that. Imagine what goes on in the head of a chief product officer. Their CEO says, grow faster, innovate, generate more value from your people. And in a kind of a pre-AI world, CFO would make an acquisition based on these EBITDA multiple and then would say, OK, now everybody in the new company, come on over and now you're part of the larger merged companies.

9:06And the chief product officer would inherit some people that had most of the time a completely different way of building products than the existing team. So what would result from that was essentially a culture clash. I don't want to work with these people. I think these people are doing the wrong thing, et cetera, et cetera. And you essentially were getting a pretty high failure rate on your M &As because the people themselves who are in charge of delivering the innovation weren't getting along. versus in this post-AI world where we see product drive a lot more that M &A, where what they look for are in the EBITDA multiple and so on.

9:44They are looking for synergies in culture, in roadmaps, in innovation. Can these teams do one plus one equals three as opposed to just adding up the numbers? And so the fact that M &A is now used as a way to accelerate the AI transformation and shift the culture is a direct result of it being driven by product. So fascinating because to your point, culture was the hardest thing to evaluate in M &A transaction. Yes, maybe these two companies combining their profitability would go up, but if the culture clashed, it would be an issue. In many ways today, the culture clash is the thing. It's the feature.

10:23You want that AI native company, maybe of 25-year-olds going into this large incumbent so that it doesn't get disrupted from the outside. and in many ways gets almost disrupted by its new partner. From the inside. So you're 100 % correct. By the way, a lot of these companies that get acquired, they're not 25-year-old folks. They are folks who have a ton of experience and have decided deliberately to dedicate it to building AI native. And they're coming into the organization and disrupting a lot of things. There's maybe three ways to disrupt just to keep it really simple. One is the, I would say, the good way.

11:02The good way is what happens when the CEO says, I want 20 % efficiency, right? In the age of AI, that's essentially losing. It's just looking at cutting jobs, generating efficiency. It's sort of a finance approach to leveraging AI. The companies that are doing that, they're facing the most resistance, understandably so, because if your boss comes to you, say, find some efficiencies. And by the way, once you find them, I'll cut your job. Everybody's going to resist that. So that's probably the worst way to go about AI because it's incremental as opposed to disruptive, and it generates maximum resistance as opposed to maximum engagement.

11:43Then there's the great way, right? The great way is to say it's a culture shift. So I'm going to focus on changing the culture of my product organization, product as in at large, engineering, design, et cetera, product builders. And by doing so, everybody's going to embrace AI. Therefore, I'll innovate faster and therefore I'll perform. And that works really well. The shift we're seeing, to your point about culture shift, the silos in R &D are being broken. So you no longer have an engineering division and a design division and analytics division and a product division. You kind of merge all of that into a product builder division, and then people kind of get together in pods and innovate faster.

12:32And then you have the best approach. And what they do is they say, I have this amount of resources, people, agents, and money. And I'm going to allocate them to my highest priority problems. So on this particular problem, I'll put three people, 100 agents, this much capital, and it will be done internally. But on that problem, I'm actually going to go and do an M &A, right? So they're taking an allocator view to their resources. And that's where we see the biggest disruption and the biggest results in terms of outcome inside these big companies. I've heard a lot of people argue that the greatest investor of our generation is actually Elon Musk for this very same reason.

13:18He's the best at capital efficiency. He knows when to double down, when to invest within his companies. He famously doesn't really invest outside of his companies, but within his companies, he knows exactly how to allocate among talent, R &D, and all these things, making him really the greatest investor. I don't know, Elon. I will tell you that we talk with literally every single chief product officer in the Fortune 1000. And every one of them is increasingly taking that approach of allocating. And so maybe Elon was one of the first, I can't speak to that. But I can tell you that there's definitely a lot of people who are becoming incredible product investors in the sense of taking product innovation and turning it into alpha.

14:11You also call yourself Mighty Capital product investors. Tell me about that. We use a methodology that we call the product alpha effect, which is essentially saying what we just described earlier, better product innovation, more product innovation is what drives alpha in investment. And then of course, we have proven our strategy and that's how we go to market. We literally look at what product builders are doing based on this very large community that I mentioned, 600 ,000 of them. That's about one in three on the planet. We read their conversations and signals and turn that into investments.

14:55We've been doing that for eight years. We've had six IPOs, six strategic M &As, a very low loss ratio. The strategy itself is definitely outperforming. So you take this lens of we are investing into people that are creating products. You almost take, instead of the CEO approach, you take this product lens into your investing. What we do is we take the conversations that are happening at the edge of innovation between product people. Product people, when you think about it really simply, they're always the first ones to know about innovation because they have to bring it to their corporation to scale.

15:38That's their job, right? So they're always the first ones to see that. And that's where I started my career as a product person. I was early at Facebook. I was also late at Nokia. The challenge that investors have, which I found out when I started investing, is investors are actually the last ones to know about that innovation. And we said at Mighty Capital, we said, we are going to change that. We're going to bring investors to the frontier of product innovation. And so when those 600 ,000 product builders have conversations, it sounds like something like this. Say somebody working for JP Morgan says to appear in another company, the compliance requirements have increased dramatically in my division because there's a lot of cybersecurity attack because of AI.

16:20And so I'm looking for a product that is going to fix this and that. Do you know something? Oh, yeah, I was actually just talking to so-and-so, and they experienced that this new product has this feature X, Y, and Z, and it's priced really well. It's actually really cheap. You should try it. Okay, great, I will. We capture all these conversations across industry, across geography. We have our own homegrown AI infrastructure that we built. Use AI, a combination of machine learning, NLP, and LLM technology to turn that into signals that are literally readable by humans. Because you can imagine the millions of conversations that are happening every single day between 600 ,000 product managers.

17:06No human team can ever read that. But AI can. from those human readable signals, we then apply the investor lens and go through our diligence. I mentioned earlier to you team traction and terms to make an investment decision. And so we're not looking at the product builder who's actually building the product. We're looking at the product builder who's buying the product in order to implement it in their own organization. So those are commercial signals that are driven from the edge of innovation. So for you, the ground truth is the customer actually buying the next generation of product. Exactly.

17:44And we see that before anybody else across all industry. Arguably, the number one most debated question, and I would argue highly political question, is whether AI will disrupt jobs. It's literally debated at the national level in terms of politics. Because it's debated, it's become almost, there's many different narratives for many different reasons. you're on the ground you're seeing this in terms of actual implementation growing up i thought managing money meant paying bills and balancing a checkbook but as you know that's only a small piece of the financial puzzle managing your money takes more than just checking your bank account every once in a while and great financial decisions come from having a complete picture and proactive management for your income expenses and investments take control of your finances with monarch it brings together all of your accounts investments saving goals and spending into one place, making it much easier to understand where your money is going and whether you're actually on track to achieve your financial goals.

18:40What I like most about Monarch is that it doesn't just tell me what already happened and helps me plan ahead. The AI assistant lets me ask questions about my finances in plain English and the AI weekly recap highlights spending changes or upcoming expenses before they become surprises. It's like having a financial advisor in your pocket. Write your own money story with Monarch. Use code invest at monarch.com to get your first year of Monarch core half off at just$50. That's 50 % off your first year at monarch.com with code invest. Growing up, I thought managing money meant paying bills and balancing a checkbook.

19:10But as you know, that's only a small piece of the financial puzzle. Managing your money takes more than just checking your bank account every once in a while. And great financial decisions come from having a complete picture and proactive management for your income expenses and investments. Take control of your finances with Monarch. It brings together all of your accounts, investments, saving goals, and spending into one place, making it much easier to understand where your money is going and whether you're actually on track to achieve your financial goals. What I like most about Monarch is that it doesn't just tell me what already happened and helps me plan ahead.

19:38The AI assistant lets me ask questions about my finances in plain English and the AI weekly recap highlights spending changes or upcoming expenses before they become surprises. It's like having a financial advisor in your pocket. Write your own money story with Monarch. Use code invest.monarch.com to get your first year of Monarch core half off at just$50. That's 50 % off your first year at monarch.com with code invest. Growing up, I thought managing money meant paying bills and balancing a checkbook. But as you know, that's only a small piece of the financial puzzle. Managing your money takes more than just checking your bank account every once in a while.

20:10And great financial decisions come from having a complete picture and proactive management for your income, expenses, and investments. Take control of your finances with Monarch. It brings together all of your accounts, investments, saving goals, and spending into one place, making it much easier to understand where your money is going and whether you're actually on track to achieve your financial goals. What I like most about Monarch is that it doesn't just tell me what already happened, it helps me plan ahead. The AI assistant lets me ask questions about my finances in plain English, and the AI weekly recap highlights spending changes or upcoming expenses before they become surprises.

20:41It's like having a financial advisor in your pocket. Write your own money story with Monarch. Use code invest at monarch.com to get your first year of Monarch core half off at just$50. That's 50 % off your first year at monarch.com with code invest. What are the chief product officers? How are they allocating between token spend and human capital? That's the crux of the question right now. So what are the key metrics in AI that you want to be looking at when you evaluate a company? The first one is a very easy one. It's the revenue per employee. Not as in, I want to keep my revenue constant, decrease my number of employees, but rather, I want to keep my number of employee constant, triple my revenue.

21:25How am I going to get there? Many of our portfolio companies are thinking about it exactly in those terms. They're saying, we have our people, we're going to keep our people exactly as they are, but we're going to empower them with a ton of AI, and we want to triple the revenue in one year at the growth stage or more at earlier stages. And then the second KPI that you want to look at in AI is a product KPI, which is what's the inference cost of a unit of customer value? So if you're delivering, you know, streaming hours, for example, how much does it cost me to produce an hour, a streaming hour?

22:02You don't want to look at the token cost because the token cost is decreasing dramatically. So you looking at the token cost to produce a streaming hour is going to decrease 10x every year just because the cost of one token decreases. You want your inference cost per streaming hour in this example to decrease way more than 10x, like 20x, 30x, so that it's a combination of the cost per token decreasing 10x a year plus the value add that you're creating by leveraging AI more. One thing I've learned from talking to hundreds of investors is that great investment firms aren't built on investment returns alone.

22:39The firms that endure are great at the things most people don't see, their operations, their relationship with LPs, and the quality of information they use to make decisions. And here's what AI has changed. Every firm now has access to the very same models. So the intelligence isn't the edge anymore. The edge is what you could feed it. A firm with its fund operations and data in one connected record can actually put AI to work. A firm running on disconnected systems simply can't. That's why thousands of GPs run their funds on Juniper Square. Juniper Square puts your fund operations, data, and administration together in one connected record.

23:14That means less time managing disconnected systems and more time investing, working with LPs, and building your firm. This episode is brought to you by Juniper Square, the operations partner for private market GPs. Learn more at junipersquare.com slash how I invest. That's junipersquared.com slash how I invest. The best conferences do two things well. The content challenges how you think, and the people in the seats are the ones whose opinions actually move markets. Alpha Summit is AlphaSense's annual user conference, and it's built around both. Join me at the Glass House in New York City, October 5th through 7th, for sessions going deep on where AI, data, and human expertise converge.

23:55The room will bring together over 1 ,000 institutional investors, corporate decision makers, and capital markets professionals from firms like Goldman Sachs, JP Morgan, and the top PE and hedge funds. If you listen to the show, you're already asking the right questions. Alpha Summit is where you go to stress test your thinking with the people working through the same problems at the highest level. Register at alphasummit.ai to secure your seat. And as a How to Invest listener, you will get 50 % off. Check the show notes for your exclusive discount code. I look forward to seeing you there. Outside of M &A, as we discussed earlier, how are the best organizations integrating AI?

24:33It goes back to the simple framework of good, great, best. Good will say, I'm going to take my existing workflows and I'm going to AI optimize them. So if I'm a salesperson and I go to a meeting, I'm going to record the meeting and AI will create the notes itself. That's very incremental, right? It's taking every step in the workflow and trying to put an agent to do it a little better. Great organizations, they're thinking about it as an end-to-end workflow. And they're saying, well, sales used to be done with SDRs and account executives and customer success. And now maybe it's an integrated function where one person can do more with a lot of agents.

25:19Just like I was describing to you earlier, It used to be that R &D was first defined by product managers, then designers would do the mockups, then engineers would build it, and then QA would release it. Well, now the product builder is sort of doing it all with the help of agents. And so engineers, designers, product managers, analysts, QA are all becoming variations of product builders. That's an integrated organization. And that's one of the best ways to adopt AI right now. And then the third way is, like I was sharing earlier, is to teach everyone or at least to teach the leadership of any function to think about resource allocation the way investors think about it.

26:06Do you see the human plus AI almost side by side collaboration being the future of work? That's a great question. And I think it really affects go to market. If you think about it in the SaaS cloud mobile era, the product was the channel. You would have a technology product and you would sell through that technology product. You'd upsell, you'd cross sell, you'd do referrals, etc. So your channel was your product. Your mobile app was how you did the work and sold the work. Which is different. just taking a small trip down memory lane, but then the pre-cloud era in the on-premise time were traditional channels, and then the product was like a disk that you ship, right?

26:57It wasn't a selling tool. What's happening with AI is that the product is no longer the channel for a very simple reason that the user of the product is increasingly an agent. And right now, most people don't trust AI agents for good reasons to make purchases. And so the workflow used to be done by humans. And so at every step in the workflow, you could upsell and cross-sell. And now the workflow is often done by agents who you will not get upsell from. And then periodically checked by humans. What's happening at the same time is that these agents who are executing the work, they are governed by agents who are overseeing the work, right?

27:46because they have to operate within parameters. It's called evals. And these governing agents, they also are monitored with humans. So you have, in a way, from one human user in the cloud era, now you have three different types of users. You have the human in the loop, you have the executing AI agent, and then you have the governing AI agents. And only one of them has buying power, which means that the channel, right, the distribution is no longer the product. The distribution has to be sort of reinvented. And that I think is one of the most exciting opportunities for today's entrepreneurs. Like what is going to be the go-to-market motion in the AI era?