没人谈论的7000亿美元AI生产力难题

85% of the companies we talked to said they really believe they only have the next 18 months to either become a leader or fall behind. We have our little group chat where we have another friend who's like, “Oh, all this stuff is overhyped and it's going to zero.” Every time I use AI, it's amazing.

There's somebody at every big company who has figured out, “I could do something in 1 minute that used to take 8 hours.” A 28-year-old guy was using ChatGPT really, really well, and they had him create a 30-slide deck. They did a global call for everyone in the investment bank for this guy to spend an hour walking people through how to use ChatGPT.

But that's absurd. That's an absurd way to hope people adopt world-changing technology. Cursor has taken mediocre engineers and made them good, but it's taken amazing engineers and made them gods. Every board meeting I go in, for my other 4 metrics, I have some report of how we're doing. On AI, all I have is the amount of stuff we bought. When a measure becomes a target, it is no longer accurate as a measure.

Even though we thought we had our quota set and we thought everyone was productive, it turned out we thought we were productive, and actually it turned out we could be much more productive.

But compared to what?

I'm excited to be here with my friend, Russ Fradin. I've known you for a long time, and I still actually remember meeting you the first time. It was from—I think Josh McFarland. Josh was at Google, and he was like, “Yeah, there's this guy, Russ Fradin, and he started this company, Adify.” And he sold it to Cox for all this money. You know, back then, $300 million was a lot.

It's amazing.

Now it's like a B round, but back then, that was a huge acquisition. And there was, like, “Oh, Russ—an amazing person who pulled this off.” And I think we met in Florida.

That's right, on a Silicon Valley Bank trip. All things come full circle in the end. But, you know, AI is probably the hottest thing in the history of the world. But you also worked in what was the hottest thing in the history of the world in Web 1.0.

Yeah.

But now there's this big question, actually. It reminds me of ad tech. I think it's kind of a nice little segue, because ad tech, you're trying to figure out: Does the advertising work, right?

A lot of ad tech is: Here's an advertisement, and there's this attribution problem.

Yep. Did the sale happen? Who is responsible for that sale? Was it the banner ad on Yahoo? Was it the last click that happened on Google? Was it the coupon site that stuffed a cookie on your machine?

Part of ad tech is just: How do I figure this out? I'm buying ads. That's part of it, but part of it is also: Did it work?

Yep, which is probably the biggest question. I mean, there are a lot of myths on this on both sides, but I would love to hear about the origins of Laridan and how you think about even some similarities between the 2.

Sure. Yeah, there are a lot of parallels, really, to what happened in the ’90s with advertising and the growth of the internet and what we're seeing with AI. Forget the capital-markets perspective. It is funny to think about what is defined as big from an exit these days versus 5 years ago, 10 years ago, 20 years ago. That's kind of its own topic, but when I moved out here—I moved out to Silicon Valley in 1996—I was the first guy at the first online ad network.

In the early days, it was just: There are websites; we should put ads on them. Great. How do we do that at scale? Great. What are the metrics we should capture? Then you saw the growth of things like comScore or Nielsen as they moved into television to figure out: How do I actually plan this? How do I spend this? How do I give tools?

All of the money lived in TV or in radio, and there were these tools like Nielsen, Arbitron, and IMS Health on the pharmaceutical side. There were all these tools to help people understand what they were getting when they advertised on television. You had to build that entire stack for the internet.

You had companies like DoubleClick or Flycast, where I was, or companies like Omniture building a different part of the stack. You had companies like comScore building a different part of the stack. Those companies—obviously, Google and Facebook are 2 of the most amazing companies ever built—but if it wasn't for all of that infrastructure, their revenue just wouldn't have grown as quickly.

I really do think we'll see the same thing in AI. The technology is unbelievable, and my core thesis when I was thinking about starting Laridan, after having been the first guy at the first online ad network and having been maybe the first or one of the first 2 executives at comScore 25 years ago, was this: Every time there's a tremendous shift in budget, especially when it happens at a great pace, like what happened from TV to digital advertising or what's happened in a lot of categories from client-server to cloud, anytime that happens, people need to rebuild all of the infrastructure.

There's a great opportunity to build all of these tools around measurement and governance—not with the goal of stopping anything, frankly, but with the goal of accelerating it. If I am a large company, yes, I'm going to experiment a ton with AI today. It's the most exciting thing that's happened in the last 20 years from a technology standpoint. It's amazing. It's wonderful.

But there are also very boring but important questions. I have 35,000 people in my workforce. They can't all get retrained all at once with perfect knowledge and perfect security. How does it affect my D&O insurance? Was the project ultimately valuable?

We really wanted to start a company around how you would build the measurement and governance set of tools—not to be a gatekeeper, but to empower more of this spending. I think as we grow, we will be the best friend to all of the AI companies.

Yeah. Maybe we can get into how you're doing this, but just to level-set a little bit—and I love this framing that you gave me. I've stolen it. When I steal a phrase, it's the most sincere form of flattery, of course—but I just released a little video about how software is eating labor.

Software eats the world. This was a thesis that our firm was founded on, but it's eating labor. It doesn't actually mean that jobs are going to go away. Largely, what it means is that people are going to be 10 times more productive, or I can't hire anybody to do this job, but I can hire AI to do it.

You have companies where their software budget is very, very small, but their labor budget is enormous. Step 1 of the mega-opportunity that excites us as a firm is that people say, “Oh, I'm going to start hiring software,” but now that means that your software budget is enormous.

Yes. Because right now, if you have a $10 billion labor budget and a $1 software budget, you're not going to try to cut or optimize your $1 billion software budget. You're really going to say, “Okay, do I need to hire more people? Can I make people more productive?” All these things are going through people's minds right now.

This is yielding a lot of the mega-growth curves of the AI software companies. But now, this chart is going to be a little bit more balanced. Of the $10 billion of labor, maybe that goes to $8 billion, and now you spend $2 billion or $1 billion on software. So the net spending for the company is actually lower, the company's more profitable, and productivity gains are galore.

But then, is this productive? I always want to know if the humans are productive, but then is the software yielding more productivity, and how do I measure that? Everybody's excited about this gold rush. I'm going to use these tools, but do they work? How well do they work? What's the baseline?

Yes. So, I've stolen your framing: If Chase spends $18 billion on software, they need to know if they're getting their money's worth. They need to figure out if this is actually efficient spend.

Yes. Look, a thing you will hear said frequently by the people running the largest AI companies in the world, and the people running the largest firms investing in AI in the world, is something along the lines of, “Today, global IT spend is $1 trillion, and we think because of AI and agents that could go to $10 trillion.”

Let's ignore whether that's true or false. It's certainly the bull case for Nvidia, for OpenAI, and for all of the other things that we spend all of our time doing.

And so when you think about it, I think we said—if I remember correctly—JPMorgan Chase's global IT spend is on the order of $18 billion or $19 billion, and they spend a couple hundred billion a year on people. So if you really think about that, is their IT spend going to go from $18 billion to $180 billion? Seems unlikely in the next couple of months, but it's certainly going to go up.

And if it's going to go up, what does the CFO need to understand? At the same time, because of the pace, a way I like to frame this that I think everyone knows, but I think it's important to say out loud, is: Yes, there have been tons of shifts. We've shifted society a million times. We've shifted from farms to cities. We all know all of these examples, but we've never had a time where we've expected the entire global workforce of knowledge workers to be retrained immediately on a new set of tools that didn't exist 6 months ago.

Right? And so there is an element where everyone needs to figure this out as we go along. What did we start with as a company? Our first set of tools is just: What do you have in your company, and are people flat-out using it? You've spent all of this money. Are people using it?

What you find is 80-something percent of our customers find far more tools being used by their employees than they know about and have licensed. That doesn't mean it was bad, by the way. Some of those tools are dangerous, and they should worry about that. Some of those tools might be very popular, and they need to bring them into the fold and understand what's happening.

But from an IT standpoint, you normally don't allow software to just be used across your organization, with access to your organization's data, and have no idea what's happening. We're letting that happen in AI all the time. I don't really say that to our customers as a fear sell. It's to be expected. Things are moving quickly. You have to know what's going on.

So we start with the baseline of just flat-out what's happening. The second set of things we try to solve is: How do we get people using this stuff more productively on the AI side, on the agent side? How do we get people using this in their workflow? I'm a marketer working at General Mills, or something like that. How is General Mills going to help me use these tools?

What I've generally found with employees is that if you really want to drive employee usage of tools, you have to make them feel safe so they won't look dumb. You have to make them understand that they can use this safely without getting fired. It's one thing if you're 22 years old and you've been using these tools effectively your entire life since high school. But if you're a 42-year-old person who's had a 20-some-year career and you're working in your job every day—and you also have things you do at home, business travel, and all of these things you have to do—you also have to become an AI expert.

You'd really like not to look dumb, and you'd like not to accidentally upload the wrong data and get yourself fired. This is actually a bigger issue in some countries where there are a bunch of EU regulations around AI that do matter. If I'm an employee at a company, I don't want to look dumb.

So, if I'm the CFO, we bought all these tools. What did we actually buy? Number 1. Number 2, how do we get people actually using these tools? Because usage of these tools in the enterprise is less than people would think today, which makes sense, by the way.

Anyone listening to this, if you've ever been a part of any software rollout at any enterprise ever, a very boring but very important question is: How do we drive actual usage? Sure, everybody uses email. People use Workday because if you don't use Workday, you're going to get fired, right? You're not going to get your paycheck. But most enterprise software—from intranet software to SharePoint and things like that—is used by a relatively small set of the population that you wish were using it.

If the goal is to get people more productive using AI tools, you want to drive actual employee engagement. So we built a suite of tools around that. Then you have to get into productivity, which is: Did this get people actually more productive? Is my organization actually more productive?

So today, I know where I want to go with Larin. What I like to think about today is that what we're doing on the productivity side is not as far as I'd like it to go, but it's certainly better than anything that exists in the market.

What we're doing today is marrying the behavioral data that no one else has—which is, is Alex a heavy user of ChatGPT or not, just flat-out? We're not doing it at the individual level, but we'll use that example for the podcast because we have to worry about the employee privacy concerns that companies have for their own employees.

At the end of the day, I want to understand: Did my users in the legal department who were using this expensive legal tool I bought—are they more productive than my users in the legal department who are not? Because what I've definitely done is driven up my opex. I bought this software. I've driven up my opex. But are they more productive? Are my marketers who are using Claude or ChatGPT actually more productive?

How do you measure that?

So today, we do it the only way productivity research has ever existed so far, which is that we take the normal productivity survey market research that people have done for 50 years. It's not ideal, but it is the gold standard. It's McKinsey, Towers Watson, and Accenture. We lay proprietary data on top of it that other folks don't have, which is actual usage.

The way I think of it is that the worst way to measure productivity is to send a survey to my employees and say, "Do you feel more productive today from using ChatGPT?" First of all, there's a definition issue. Second, people are going to answer the way you hope they'll answer. Third, you have no idea if they're actually using the tools.

A better way to do that—I learned this years ago at comScore—is to marry the behavioral data with the actual survey responses. One of the many things I did at comScore was run our survey market research group, and one of the reasons the comScore surveys were great is that we had the behavioral data married with the actual survey responses. We're doing the same thing here.

Where I ultimately would like to get to is full passive measurement of productivity. The truth is that, for enterprises, that's going to require a level of additional data sharing that we're not getting yet from customers. We will eventually get there.

But to put a finer point on this, I'm a lawyer. I work at some big company. Productivity, to a certain extent—if I only have to work 4 hours a day versus 8 hours a day, that's great for me. I kind of feel like it's a win, because I often think about the principal-agent problem.

Everybody is an agent, and then there's the ethereal being of the corporation, which is the principal. I guess if I own stock in my corporation, I want it to be more profitable, but really, I want to work as little as possible and get paid as much as possible. That's kind of every individual agent's job, and then you have these tools.

So, theoretically, everybody's going to adopt these things if they get to be lazier. Yep. Like everybody wants to be lazier.

Sure.

Right. They want to be lazier and richer. I feel like these are the universal human conditions.

There's some small set that want promotions, but I agree: for 90%, richer.

That's true. That's true. So, if you can get the promotion by doing less work, I'm sure people would opt for that. But I guess, how do you think about everybody using these tools?

I think I was telling you this sad story because our kids go to the same school. Our younger kid gets busted cheating with ChatGPT—clearly a productivity gain for him, because it allowed him to be lazier and have more video game time until we confiscated his phone.

But there's also a set of rules that can get you in trouble.

But take that example. You can imagine the individual agent—the human being, the lawyer in this example—is benefiting. But does the company benefit? To a certain extent, I'm paying you the same amount of money. I want you to work for 8 hours a day, right?

My expectation should be that if you're drafting legal documents, and you can now do it in 4 hours versus 8, and spend 4 hours playing golf, you're thrilled you got a productivity gain. The company didn't actually benefit, right?

So what you kind of want is for both parties to benefit, which is always tough, because sometimes it's very, very hard to sell products to people that eliminate their jobs.

Sure.

That's probably the hardest thing to sell. Maybe taking this example and riffing on it: Now, in 4 hours I can do what used to take me 8. How does the company say, "Oh, wow. You're still operating at your baseline, but actually you should be able to do twice as much with this tool"?

I guess, how do you define the baseline? How do you address that problem? How do you think about it? Am I framing it the right way?

I think you're framing it the right way for certain sizes of companies. We all know, in Silicon Valley, because of the competition and the equity form of compensation, what you're going to have is: If I can get done in 4 hours what I could have done in 8, I'm just going to work 4 more hours, and then another 4.

That's very different. There's some subset of workers at companies of all sizes—probably a larger percentage in Silicon Valley, but a smaller percentage at GE. There are people at GE who want to one day become the CEO of GE, and those people will work as much as they possibly can. So there's some subset of workers there.

For the rest, look, there's an interesting question about how management is going to evolve overall. I think behind all this, the first question we're trying to solve is, like I said, do people use these? From a corporation standpoint, for our measures of productivity—which we're defining with each of our customers—as we survey folks, is there a difference in productivity between the heavy users and the lighter users?

What we want to measure with that—we're not doing this today—is some concept of raw tonnage of work. Ultimately, there's this lingua franca when we talk about employees: FTE. We all know that you work differently than I work, and various people work differently, yet if I'm the CFO of—I don't have to pick on JPMorgan again—if I'm the CFO of JPMorgan, I have a fundamental horse sense for what 1,000 FTE do versus 500 FTE versus 2,000 FTE.

AI is going to break all of that, for sure. Our main goal today is just to build the baseline for our customers: At the end of the day, are the people using these tools fundamentally more productive than the folks who aren't?

Layer on top of that the amount of time worked. You can get a pretty good measure, but it’s never perfect. People are on vacation. You have to measure this in groups, right? Any given person was out sick for 1 day, was on a flight for 1 day, or was at training for 1 day. That’s impossible to measure from a system standpoint—they seem not to have been working, but they actually were. They were doing training, right?

Think of this as aggregate data. It’s never useful at the individual level. I mean, to get existential, was I productive yesterday? It’s unknowable. I can’t know if I was productive yesterday.

I think you were.

I was all for it. But what we’re trying to do at the systems level for companies is understand whether there’s some correlation between specific use of these tools—advanced use, light use, heavy use, heavy users of the tool, lighter users of the tool—and whether those users were more productive in their jobs.

And then measure on top of that the amount of time those segments of workers were actually working. Because the goal, if I’m a CFO today, is not to understand whether Ben did a good job or Tina did a good job. The goal is to understand: I’ve definitely been asked to spend 50% more on opex. Did I drive something with that?

There are interesting questions around staffing size. Will companies get more done because people will actually work 8 hours a day?

Yeah, look, it will turn out—I suspect it’s true—that this is one of the things managers do. I suspect it’s true that, over time, if it becomes clear that all of your employees are now working 4 hours a day instead of 8, you’ll probably decide to have fewer employees, and the remaining employees will work 6 hours a day.

I’m not sure I really buy that, in the next couple of years, you’ll see people in large companies actually just working half as much. For sole proprietors, it is what it is. If I were a sole proprietor lawyer, my only measure of productivity today is myself anyway, right? It’s how hard I want to work and how much money I want.

Well, there, the principal is the agent. This is why it’s so important, and this is why, candidly, I love what you do. Obviously, I love what you do. That’s why you’re here.

There is no baseline. First, you have to know how to define the outputs. You have the inputs, which are largely just time and money, right? Then you have the outputs, and part of it is actually complicated to come up with an output. Do you know Goodhart’s Law?

Go ahead.

Goodhart’s Law—I love this one. When a measure becomes a target—

Yes.

—it is no longer accurate as a measure.

Right. So, if I say, “Okay, I’m going to judge you based on how many emails are sent every day,” that’s a measure. But once it becomes a target—“I want you to send more emails”—the measurement gets corrupted because people decide to do more things to hit the target, and it’s no longer an objective measure.

So part of it is, if I’m trying to figure out whether a product like Harvey is valuable—a lot of people love Harvey, and it seems to make people a lot more productive—compared to what?

Right. And so, to me, the only way you answer that—we’ll talk about Harvey; nothing against Harvey, I’m sure Harvey is amazing—is to understand this passively. I think the traditional way companies are doing this just doesn’t work at all: “Let’s survey the people who use Harvey and ask them if they were productive.” They’ll all say yes, because no one ever answers that they weren’t productive. Number 2, my boss paid for the product, so I’m going to say it was a good product, right?

Unless we all universally hate it—which I assume is not true of Harvey, because everyone seems to like Harvey—that’s wonderful. But I think it’s why we started measuring it. All you can really do is understand, without asking people, how much they’re actually using Harvey.

We have 5 people, or 6 people, whatever they said on a survey. Two have never logged in. We’ve all seen the joke: Your project is due in an hour, you said you were caught up, and then, “Oh, I have to ask permission for this Google Doc.” I’m making these numbers up, of course, but 2 of the 6 people actually signed up for Harvey the day they were told to and then never went back to it at all. They’re very happy with the way they work; they work that way all day, every day.

Two of the 6 log in and use it a little, and 2 of the 6 use it all the time. The only way I can even begin to understand whether that software is valuable is by knowing that data passively, without asking those folks the question, then asking everyone the same questions about productivity, and measuring it against the amount of work actually output. If I take those 3 things together, I can begin to form an understanding of whether Harvey was useful.

Right? You and I had a discussion with someone who was talking about one of the ways they incentivize their engineers. At their company, they have a leaderboard showing the amount of money each engineer spends on cloud code. The founder was talking about how he went to one of his best engineers and said, “I don’t understand what’s happening. You’re one of our best engineers. Why aren’t you spending any money with cursor? I’m sorry—not cloud code. Why aren’t you spending any money with cursor? I really don’t get what’s going on.”

Right. You probably don’t need us. If you’re a very developer-heavy company, you probably don’t need us. You can probably measure the amount of money spent on Cursor, plus your normal management understanding of whether this person is actually working. If they come in for 2 hours a day, you may be happy with that, or you may not. That’s going to be company-specific, lifestyle-specific, and culture-specific.

But you’re in the office; I see you’re there. You’re not spending any money on Cursor. What’s up? We have these metrics. The issue, though, is that we’re seeing an explosion: There are hundreds of AI tools, and companies have hundreds of roles. That’s why we want to try to replace the McKinsey Organizational Health Index, Towers Watson, or the Accenture surveys with real, useful data around AI.

I think that Cursor example really crystallized in my mind what you’d want to be able to do for a whole company: How much did this person work? I have that qualitative judgment as a manager—we’re not replacing that. Did they do a good job? Then, fundamentally, did they use the tools? When you take those 3 things together, that’s the only way you’re going to have measurement.

Like I said, when you think about my micro-world—if you really think JPMorgan is going to go from spending $18 billion in IT to $30 billion or $40 billion—the CFO is not just going to say, “No problem.” Today, our customer is a CIO. I think, over time, our customer becomes a partnership between the CIO and the CFO. The numbers are just big.

Yeah. It’s like cloud spend. The numbers are just so big that people are going to pay attention. It’s going way beyond experimental.

Well, obviously, the companies themselves—if you ask any company that is trying to sell you anything whether its product works, they will probably 99 times out of 100 say, “Of course it does.” It’s the best. It’s the best. You need to have an independent arbiter.

And that’s where you guys come in. But double-clicking on this point, it’s almost like reinforcement learning at a company-wide level: What is the outcome that I’m looking for? Sometimes it’s clear.

This is where the measurement-and-target thing is also relevant. It’s like, “I want you to write more lines of code.” If there’s a measurement of how many lines of code were written, but it becomes the target, then you’re just writing gobbledygook code.

For sales, it’s very easy: I want you to sell more stuff. But there’s a lot of latency between when you go talk to a customer and when you go collect money, so you might have targets in between and measurements in between. If you’re a lawyer, draft more contracts.

So I guess, how do you try to define the goals? Some of them are just background information that’s going through—emails that are being sent, Slacks that were sent, or Google Docs that were edited. There are these very clear measurements, but those aren’t necessarily outputs.

So, first, to your point on measurement, this is why I said earlier that anytime true third-party measurement exists, there’s this interesting dynamic. We saw this at comScore, but everyone has seen this: Anytime they tried to build a third-party measurement company, Omniture saw this in the early days. Google, at some point, fought it and then actually bought Urchin and built Google Analytics, because it turned out that it’s actually good when your customers can track value—if what you do is actually valuable.

My general perspective is that today, a lot of the AI companies probably look askance at us, but over time, certainly the AI tools that actually provide value are going to love us. The way you will ultimately unlock real enterprise budget is because people believe these tools are actually valuable.

What we do today—and this is a journey; the company is about 1 year old—is work with all of our customers and say, “Look, here are the baseline productivity questions that are the gold standard, that people have asked for 70 years.” There are pros and cons to them, but you have to start somewhere.

This is where we start: let's define a set of metrics for each of your departments. One of the things we've found that actually seems to matter—not as a metric that companies share with their employees, because then you have the Goodhart's law problem, but as an actual reality on the ground—is fundamental responsiveness.

There is an element of, “I spend some amount of money on my legal department, and I am happy with the amount of productivity they do today.” Unless I'm trying to fire lawyers—which I'm not—you can argue, how would I measure the value of software? I guess my lawyers might be happier, but I don't have a churn problem there. So, frankly, why should I do this?

One of the things we found is just almost an interdepartmental SLA. It turns out that if I roll out these tools and I'm not firing employees—because one way to look at this is, “Could I fire half my lawyers?”—companies don't really like firing people. Companies do fire people if they have to, but I've actually never met a CFO who got excited about firing 30% of the workforce.

Outside of call centers, that's a different issue we could talk about. Companies treat their call center employees differently from the rest of their employees. But outside of call centers, I've never met a CFO who, if you went to them and said, “You can fire half your FP&A people,” would want to fire Tina. He knows Tina. He's met Tina's husband and children. He doesn't want to fire Tina.

He'd like Tina to be happier and more productive. Actually, he'd like her to do a great job and never quit, right? Companies don't really like churn. So, one of the metrics we found that people seem quite excited about is simply: did this raise or lower interdepartmental responsiveness?

A measure would be, am I now comfortable sending more things to legal? If I'm going to keep my legal department the same size, I'm not going to start suing more people. We're talking about companies here, not law firms, where there's a different measure of productivity. They're cost centers, not profit centers.

One thing to ask is: over time, because my lawyers are now more productive, are other departments asking them more questions? Are they getting their responses faster? When I'm in product and asking for input from engineers, are they responding more quickly? That is a good way for me to see, behaviorally, that we've become more productive. That's not lines of code.

By the way, I agree: if you expose the metric and say, “Hey, you better be responsive,” people can lie. They can send Slack messages back and forth. But what I'd really like to understand is, as a result, which of my departments use these tools more? Do they become more responsive to my other departments?

There's an element of, when you're at a big company, people know this. It's one of the reasons small companies do so well in innovation: there's just a giant coordination problem for all of these companies. We know this.

In Silicon Valley, it's fun to make fun of these companies, but actually, every entrepreneur's secret dream is to become so large that they have a giant bureaucratic company. Of course, Google did not plan to have a giant bureaucracy 30 years ago. They just became so successful that they now do have a giant bureaucracy.

Right. And that's a good segue into perhaps the state of AI in the enterprise. You went on this whole—listen, you talked to 350 people.

Yeah. We interviewed 350 heads of IT at major companies.

And across the whole gamut, right? It wasn't just Silicon Valley companies.

Not—not, I mean, in all honesty, I spent a couple of years helping my friend at Carbon, and I spent a year trying to fix Wine.com. Other than that, my whole career has been selling software to large companies—mostly large companies or older companies.

Yes, there's the occasional Silicon Valley company that grows very quickly, but if you're in the Fortune 500, you're going to be 20-plus years old 99% of the time, right? If you're going to sell to someone with more than 1,000 employees, they're almost by definition an older company.

Yeah. So, maybe give us the highlights of what you learned.

Sure. We saw a bunch of different things, and people have seen this before. I actually don't think of this that way. You'll see people turn this into clickbaity, fear-mongering things. I don't really think of it that way.

First of all, as we know from Gartner, there's something like $700 billion being spent on enterprise AI. It's growing very, very quickly, and it's going to keep growing quickly. One of the things we found is that something like 70% of the leaders we talked to said, “We are sure we are wasting money here.” It's being spent so quickly. And, by the way, shame on us: we had no system to measure this in the first place.

I'll get back to the report in a second, but I was talking to a customer today. Why did we sign them as a customer? They're a very profitable business owned by a PE firm, and their PE owners gave them 5 things they had to do this year. One of the 5 was to adopt AI across the organization.

He said, “Every board meeting, I go in for my other 4 metrics, and I have some report of how we're doing against those metrics. On AI, all I have is the amount of stuff we bought, right? It's not.”

So, yes, yes, I'm doing great. But it turned out—

We have a large family of AI. We adopted all these. It's all great, but it turns out we want to actually do it.

What we found is that these leaders may be right that 70% of their projects are failing. Regardless of whether they're right, it's a giant problem. No one believes 75% of their ad spend is failing. It's not because their ad planners are smarter than their AI buyers. It's because there are 20 years of systems in place to help me understand: when I buy this ad campaign, when I spend this money, when I do this app install, whatever it is, did it actually drive value for me?

We just don't really have that in AI, like I said, outside of some very, very specific verticals. The biggest thing we found was basically 3 things. First, you saw the AI spend. Second, something like 70-something% of them believed their AI projects were being wasted.

The other thing we found is that basically 80% to 85%—I can't remember—of the companies we talked to said they really believe they only have the next 18 months to either become a leader or fall behind. I think one of the reasons you've seen this giant unlock in budget is that there's tremendous anxiety at these enterprises: “We're going to lose if we don't adopt this stuff yet.”

So, we're adopting it quickly. We have no particular idea if it's succeeding. Our employees aren't really using it. By the way, employees are a forgotten group in the company for all of this AI.

At my last company, we built a very, very large HR technology company. We sold into heads of HR and touched all the employees in the company, but we sold into heads of HR. As we've talked to a lot of our old customers, who aren't really our customers today but are influencers, what they will all say at these large companies is, “Hey, our employees are really worried.”

It's not even that they're worried they're going to lose their jobs. There's a base level of worry about AI and the economy and all that stuff. It's not even that they're worried they're going to lose their jobs; it's just that they're being told to use a new system all day, every day, right?

Generally, if you work in a large company, there are 1 or 2 new systems initiatives a year. Now there are 20 new tools. They don't know what they're allowed to do, and they have no training. How do I actually get people using these tools?

You have this weird, almost perfect storm. It's why we're excited about Laren. It's why you're excited about Laren. You have this perfect storm of tremendous growth in budget, tremendous anxiety that none of it is working, and tremendous anxiety from employees about what they're even allowed to do.

What we're trying to do is—I don't think we solve all of that. That would be an absurd thing to say. But I think we really help with all of that: what is your plan to measure this in the first place? Did anyone use it? Did they become more productive when they did? How do you give them the tools to use it more?

Yeah. Well, that last point is super interesting as well, because there's the question of whether it worked, how well it worked, what the measurements are, and making sure that the measurements don't become targets—all the stuff that we just talked about.

To use the metaphor of my son, who cheated on his math homework, there are people who are just like, “Wow, they're the go-getters in the company.” This is actually why I am convinced that AI is underhyped.

You know, we have our little group chat where we have another friend who's like, “Oh, all this stuff is overhyped and it's going to zero.”

Totally. Every time I use AI, it's amazing.

Because you go, it hasn't diffused. You have the 19-year-old kid, or my 13-year-old son. I was like, “Wow, normally homework would take me 2 hours.”

Now it takes me 1 second.

And obviously that's bad, right? I'm not using him as the example of that. That's why we confiscated his iPhone. But there are these productivity unlocks where it's probably not going to happen top-down.

Sure.

It's like somebody in the company—and, again, not to oversimplify human behavior—but it's like, “I want to be lazy and I want to be rich.”

Yes.

Right? These are the 2 things that are motivating people. I found this tool that allows me to be lazier and richer and that actually helps the company.

Yes. So it’s not the math, not the cheating, right? It’s like, I now know that my boss thought this would take 8 hours. I’ve figured out a way to do it in 5 seconds.

And it’s really good, by the way.

It’s really good. The worst thing that can happen—and this is like the inverse of everything we just talked about—is that the guy keeps it a secret.

Right.

Right. Because he might be afraid: “Am I allowed to use this?” But what you should do—this is how AI will go from underhyped to correctly hyped and correctly diffused—is recognize that there’s somebody at every big company who has figured out, “I could do something in 1 minute that used to take 8 hours.” We need to make this person a hero, memorialize this, and push it out through the entire company. So how do you do that?

This was my point on what we’re doing on the AI engagement side, and that’s a great question. This is one of these areas where everyone’s interests are aligned. The employee who’s working very hard loves recognition. And, by the way, he’d like his coworkers to come up to speed. The employee who’s scared wants support and training.

By the way, companies actually want their employees to be more productive. I know it’s a fun thing for a subset of people to tweet about, but I’ve spent 30 years selling things to CEOs and have yet to find the CEO who wakes up in the morning and wants to run a smaller company. He wants more employees, and he wants more profit. He wants more revenue. But contrary to popular belief, they want more employees. They like running big companies.

You can find old interviews of Larry Page talking about his plan for how Google was going to have 1 million employees one day. He was spending a lot of time thinking about self-driving cars to move the cars around the parking lot, because this was before remote work. Literally, where were the 1 million employees going to park all the cars? I remember reading that 15 years ago, and it stuck in the back of my mind: I have never met a CEO who wants to run a smaller company.

It’s one of the reasons, by the way—a totally unrelated point in the capital markets—if you ever meet a CEO of a conglomerate, they never want to break up the conglomerate because they like running bigger companies. It’s more fun, right? I’ve had my companies grow. They’re fun when they’re bigger. It is. It’s super cool.

So, from an employee standpoint, what we built with this Nexus product is effectively a product. I’ll use an anecdote. I was in the UK in July, and I went on a bunch of sales calls. I was talking to someone at a very large, very regulated European bank. It’s among the most regulated institutions in the world and among the slowest to adopt new technology, for good reasons, honestly.

They were telling me a story about a 28-year-old guy—I don’t remember what level that makes you in an investment bank, so let’s say a director, but I don’t know—who was using ChatGPT really, really well on the investment banking side of the bank. They had him create a 30-slide deck and held a global call for everyone in the investment bank for this guy to spend an hour walking people through how to use ChatGPT.

I’m sure that was very cool for him, but that’s absurd. That’s an absurd way to hope people adopt world-changing technology. Another absurd thing to do is to go out and buy some LMS course that HR is going to buy. The secret to a lot of LMSs is that, other than things you must do or you will lose your job—like sexual harassment training, HIPAA training, and certain other required trainings—no one does it. They just don’t go.

So how do I actually get people using these tools? This was my point from earlier: You want to help them, first, not look dumb, and second, know they won’t get fired. What we effectively did was build these wrappers that exist around the models. We don’t tell people to use Claude, Gemini, or ChatGPT.

The other thing we built—because people are also worried about getting fired—is support for that concern. They’re worried about getting fired because of the economy, because of AI, because of whatever. It’s a new tool; I would like to not get fired.

By the way, when you’re talking about European banks, there’s a lot of regulation. It’s a legitimate concern: If our employees do the wrong thing, we will get fined. Forget whether you fire them; these companies don’t want to get fined. So the other thing we did is we basically trained our own customized Llama model to block people from asking questions that are illegal or that the company doesn’t want them to ask.

We’re not talking about hackers here. True bad actors in the company have plenty of security solutions. What we’re really talking about is the X% of people who are just trying to do their jobs. If I’m in People Ops, in HR, at a large company, and I’m supposed to do a workforce analysis, am I allowed to go into ChatGPT and load in our full employee database with race and gender? I don’t know. I would like to not get fired. Maybe I’m allowed to, and maybe I’m not.

By the way, I think it’s incumbent on the company to say to its employees, “Here is a safe space. Nothing you can do here is going to get you fired.” So we can say, “Oh, Alex, you’re not allowed to upload that; it has Social Security data. Don’t share that. You’re not allowed to ask that prompt because, in Europe, we’re not allowed to use AI to write employee reviews.”

I don’t know if that’s a good law or a bad law. I didn’t write the law. But there are companies that look at the EU AI regulations and say to themselves, “Our read of the regulation—I’m not going to litigate that—is that we believe it’s illegal, and we will get fined if our employees use AI tools to do employee reviews.”

So, great. If I am a European-based company and I want my employees using AI, I have to block them from using it for those use cases. What we’ve tried to build is almost like a harness to say, “You can be more productive, you’re not going to look dumb, and you’re not going to make any mistakes that get you fired.”

What we found is that this actually drives more AI usage, surprising literally no one. From a company standpoint, what do you want? A, I want the usage, and B, I want to build up that IP of what really works for my company. It’s a total unlock.

Same thing on the coding side, right? Cursor has taken mediocre engineers and made them good, but it’s taken amazing engineers and made them gods, right? Our goal should be: How do we help people get much more productive with all of this? How do we help them use Cursor more effectively, Harvey more effectively? We started with all of the LLMs more effectively.

Yeah. So maybe we could talk about the future of work. This is a little bit philosophical, but to a certain extent, you’re the measurement. If you’re the measurement—

Sure.

—the measurement inevitably will become a little bit more of a target. I always like to remind people that I think 97–98% of Americans were farmers when the Constitution was ratified, and they all lost their jobs due to pesky things like the tractor and fertilizer. I think the average life expectancy was around 35, and most children died in childbirth or shortly thereafter.

Things have changed, but this is what technology brings you. Nobody knows the answer to this, but given that you’re in charge of a company that’s measuring AI productivity, human productivity, and AI and humans working together, what’s your timetable for how fast things change? Are we going to see net new jobs created?

And, by the way, behind every one of these, there are all sorts of jobs that start becoming available that didn’t exist before. The job that we have right now—filming a podcast—wasn’t a job 200 years ago. There are so many jobs that nobody could even think of. So where do you think things are going, and what types of future jobs do you see in and around this new stuff?

I don’t buy for a second that there’s going to be large-scale job loss because of AI, frankly, because of what we’ve seen throughout all of history, which is just flat-out capitalism. If my 2 choices are to maintain my base level of productivity or fire a bunch of my employees and be more profitable, that’s a fine idea in the short term.

It’s probably a good idea for a private equity firm to go around and buy a bunch of marginally profitable companies, fire half their employees, and make them more profitable. That’s what PE firms have done for a long time for noncompetitive companies, anyway. Yet employment has still increased.

You can argue that, for the last 40 years, we’ve had a function whose goal is to take underperforming companies and fire a bunch of employees, right? Let’s say that’s what PE firms have done and that’s what AI could theoretically do. Yet employment has increased.

I don’t buy it. And, look, it’s philosophical, and I don’t have any special expertise because I’m building a measurement company. But I don’t buy it because your competitor across the street isn’t going to fire all those employees. He’s just going to do more with those employees, and he’s going to kill your business.

Right? This is the Jeff Bezos “Your margin is my opportunity” line. To the extent that AI is going to drive up your margin, that will be all of your competitors’ opportunity to be less profitable and compete with you. So, other than some very niche monopolistic cases where I can fire everybody—one-man firms, one-woman firms that do a billion dollars in revenue probably—but today, we have very profitable one-man and one-woman operations. Not many people work at the Joe Rogan podcast. I don’t think that many people work for Ben Thompson Incorporated, and yet I imagine those are quite profitable businesses, the best I can tell.

That’s amazing, and there will be a ton of opportunity to be more successful as a solo entrepreneur. I absolutely believe there will be even more entrepreneurs. But at a very high level, I just don’t believe the Fortune 500 will employ fewer people in 30 years than they do today, because the ones that try to cut all the people will no longer be in the Fortune 500. So, I just flatly don’t believe it, because we live in a competitive world. We haven’t seen any proof yet.

Yeah.

That the economy is zero-sum, right? Maybe—you can argue that—but we haven’t seen any proof yet. GDP keeps increasing. It increases slower in some places and faster in other places, but it’s generally grown. Employment has generally grown. I just don’t know why you’d believe that this time is different. From a competitive point of view, the tech is different. The tech is amazing. But fundamentally, what will almost definitively happen. There is an interesting theoretical question that's more like an Ivy League grad-school discussion about wouldn't it be more fun as a society? Wouldn't we all be happier if everyone agreed we'd work half as much and be just as productive as we are today? I don't know, maybe. But that's not human nature, right? And so, I'm not even sure that's true. I tend to believe in the Tyler Cowen point that all that really matters is growth. And so, my general perspective is you as a VC would just never get excited if one of your companies came in here and said, “Hey, we got to $100 million in revenue. And you know what? Because AI tools are so good, we're going to fire 90% of our employees and we're going to make $90 million in profit.” You would not be excited with that entrepreneur because you know that Sequoia is going to fund a direct competitor to that company who's going to keep hiring, who's going to be happy with 10% margins and is going to destroy your company.

There are a lot of fun headlines about AI, and then you have to have the counterpoint that it’s going to take the jobs, and, “Oh, kids these days.” I don’t know. We’ve all seen this. You can find articles about how, when TV came out, it was the end of reading, and when newspapers came out, it was the end of conversation.

I do think it is scary that new tools are coming out and impacting the entire globe of knowledge workers everywhere. So, I think there will be opportunities to be podcasters. There probably will be more plumbers. There will be a lot more employment around building data centers, right? There’s going to be a whole set of engineers. Maybe we will need a lot more astronauts. Elon says we’re going to Mars. Someone is going to have to scrub the toilets in the space station, and someone is going to have to pilot the ship to the space station.

The self-driving spaceships.

So, by the way, there is a chance I will turn out to be wrong. In that case, I don’t know—maybe I’ll spend more time on vacation.

Well, it’s interesting. I talked to this economist, Ed Glaeser. I think he’s at Harvard, and I asked him what’s going to happen with jobs and how you compare this to everything else. What’s really interesting is that this is arguably the first time that the job losses might be borne by white-collar, super-educated people.

That’s why everybody gets scared.

Well, but he actually had a different framing on it. So, yes, agreed.

But almost tautologically, hyper-educated people are hyper-educated.

So, they should be able to rejigger themselves and do something else.

Versus all of these previous revolutions, where you had somebody who really had no skills, right?

And just showed up at work with no skills and got paid.

Yes.

And there are a lot of jobs that look like that when there are tremendous labor shortages. So, if you were doing anything in 1849, apparently, in California, it was a boom, right? There was just, “Oh, you’re a human. You have a pickaxe? Go do this.” Or, “You see that line over there? Straighten it out.”

What’s different is that everything right now is bit manipulation, going after or augmenting—I would argue more augmenting—white-collar, hyper-educated people by virtue of the fact that they’re hyper-educated. Maybe robots will work better in the future, but this does not mean that what happened to Detroit was about automation. It was about the Japanese building better cars. There were a lot of reasons why that happened.

What do you do with somebody who had a very, very high-paying job but actually didn’t have that many skills, and now they’ve lost that job? Because they don’t have any skills, they can’t find another job. Whereas, if you are highly skilled, you will find something else to do.

I do think there’s an element of this: There are certainly a set of people who were pretty highly educated. They were in good classes, they got into a good school—whatever that meant—and they got a good job. They worked pretty hard in their 20s, a little less hard in their 30s, and a little less hard in their 40s, but they’re paid pretty well. And those people probably are a little uncomfortable today because their career, frankly, there are some professions that just require continuing education. By the way, if you’re an electrician or a plumber or a doctor or a lawyer, some of these professions just require constant upkeep and constant education. That’s not true in a lot of professions. There are a lot of jobs where you get to 40 or 50 and you can keep doing a good job, but you don’t really have to learn much new. You can just keep doing a good job doing what you’re doing, and you don’t have to learn much new. And that’s probably quite uncomfortable. I acknowledge it is quite uncomfortable for those people. But to your point, they’re educated. They have skills. We have much more of a knowledge economy. So I don’t necessarily have the issue of—I literally have this house in Detroit. The jobs are now in Knoxville, right? Forget Japan. The jobs are now in Knoxville. I don’t want to move to Knoxville, right? We all know the data on mobility and housing costs and all that. So, sure.

So, sure.

But at the end of the day, to your point, there is a set of people who have probably been slowly working less and pushing themselves less, and now they have to push themselves more. That just is uncomfortable. My joke all the time—I won’t say the company I use, but part of my sales pitch for Laridan when I’m talking about this is: Look, when we talk to employees, your average employee is a 42-year-old associate brand manager. If you ask them what they want out of AI, they do want it to go away. Their number-one wish would be that it would just go away. I like yesterday, too, because we’d make a lot of money if we had the power to make AI go away. It would be a super-big blackmail business. But we don’t have that power, so all we can do is give you the tools to use these things better, help you be more productive, and help you, as a manager, understand whether your team is using these tools better.

My macro point is that I just don’t really believe there will be widespread mass unemployment. Might an individual have to push themselves more? Yeah, for sure. Some of them will be sad about that. The same thing is true in the entertainment industry: Jobs have moved and shifted, people don’t watch movies the way they used to, and TV seasons used to be 22 episodes and now they’re shorter because consumer preferences have changed—a part on Law & Order, right? That probably is uncomfortable. I’m not being callous. There are many ways it will impact my life negatively. But I just don’t buy that people won’t be more educated.

Yeah. A lot of this actually predates AI. There’s a great article or interview with the CEO of Waste Management.

Okay.

This was before ChatGPT came out. He was saying, “I get resumes every day from somebody who has an MBA and wants to work in our office,” and they’re negotiating against themselves. The price keeps going down. It was something like 100 applications for every opening, or I forget what he said.

I need to hire truck drivers. Somebody who is actually collecting the trash—that’s what Waste Management does—for $150,000 a year. I can’t find them.

So, it is kind of interesting how things have flipped. I would almost argue that a lot of AI’s problem right now, in terms of diffusing into the workplace, is almost a product-marketing problem.

Sure.

Right? AI can do anything.

Right. But I’m not looking for anything.

Like I say, “Hey, I can do anything,” and you’re like, “I don’t need you.” It’s like, no, I could do this one thing very, very well. “Oh, you do that?” Once you have more of these articulations of what can be done, and the things that have really gone hypergrowth, it’s like, “Oh, I have AI. It does everything. Oh, I will help you code.”

I will help you code better. Yeah, look, a long, long time ago—it’s funny when you get old—I was, as I said, the first guy at comScore. comScore’s sales pitch in the early days, for those who don’t know, basically had all the data for everything that was happening on the internet. The founders were true geniuses, and they basically knew everything that was happening everywhere on the internet.

Our sales pitch in the early days would be, “We know everything.” I mean, obviously not, but it would basically be like, “We know everything. What would you like to know?” It turned out that wasn’t a really good sales pitch. You would sometimes accidentally run into someone who would go, “Oh my God, I need to know this. Could you do this?” And we’d say, “Yes, we could,” and there you go. But it turned out we only had a couple of sellers who could figure that out in real time.

Then it turned out that if we said, “We can tell you the market share for Visa versus Mastercard versus others in Japan,” Visa really wants to know that. “But I can also tell you the share for your pharmaceutical drug versus others in research online.” It turns out they also want to know that.

I’m going to get it wrong, but Ford had some giant issue with Firestone tires setting on fire. It turns out they really do want to know: Did employee searches for Ford get worse because of Firestone? Right? So people do want to know these specifics.

Like I said, I think that’s exactly the right way to think about it: You need to solve this product-marketing problem. It’s why we’re so focused on what’s happening. Are they more productive? How do you get them to use it more? We can actually do a lot of things, but you can’t sell things that way. That’s more like general entrepreneurial advice, but it turns out building something amazing that people don’t know how to use mostly doesn’t work—unless it does, which is ChatGPT, right? So, 1 in a million times, it does work out.

ChatGPT—it’s just like magic. If you show somebody a magic trick or you get somebody addicted, right? And you’ll guess which one I’m referring to for which.

If you ever watch Seinfeld, there’s this great episode where Jerry buys his father a Sharp Wizard, which was like an early PalmPilot kind of thing. It was like this early smart computer in the 1990s. It never went on to great things, but it did everything. He could run these applications.

Jerry’s trying to explain it to his dad, and he’s like, “Well, I don’t get it. What does it do?” He’s like, “Well, look here. It has a tip calculator.” He’s like, “Oh my God, a tip calculator.” And then he explains it to all of his friends. He’s like, “Look at this. My son—he’s a comedian, he’s doing great—he got me a tip calculator.” And Jerry’s like, “No, it does other things.”

And it often ends up being frustrating for the company that does the other things, because they aspire to have this more broad, horizontal platform. But what we kind of need is more of these tip-calculator things.

Yes, thank you. But actually, why don’t we just— is there anything that we haven’t talked about that you want to get in, like a little soliloquy that you can offer?

No, look, I think I’ll leave you with 2 thoughts. The 1st is related to something you brought up in the conversation and the professor, and the 2nd is related to Laridan.

So, look, my general perspective is: Anytime you see some giant shift in budget, you’re going to build a set of very important but very boring tools. What’s actually happening? Are people more productive? How do I get them to use it more? There’s a ton of business there.

Then I’ll leave you with an unrelated thought to your Harvard professor. As you said, our kids go to the same school, and my oldest kid is in 12th grade and just got into college. He got into his top choice, and he’s very proud of himself. It’s a very highly rated school, and I’m very happy for him.

He came home and said, “Dad, look at this ranking.” He showed me the new U.S. News & World Report rankings, and I said—his name’s Henry—“Henry, I’m very proud of you. There are going to be a lot of different rankings over a lot of different years, and there’s only 1 thing you have to know for sure: Whatever the ranking says, everyone knows number 1 is Harvard. So it doesn’t matter. Don’t get excited. Whatever it says, wherever it puts your school, everyone always knows, whatever the rank is, Harvard’s number 1.” I did not go to Harvard.