The a16z Show - The $700 Billion AI Productivity Problem No One's Talking About

Episode Date: December 1, 2025

Russ Fradin sold his first company for $300M. He’s back in the arena with Larridin, helping companies measure just how successful their AI actually is.In this episode, Russ sits down with a16z General Partner Alex Rampell to reveal why the measurement infrastructure that unlocked internet advertising's trillion-dollar boom is exactly what's missing from AI, why your most productive employees are hiding their AI usage from management, and the uncomfortable truth that companies desperately buying AI tools have no idea whether anyone's actually using them. The same playbook that built comScore into a billion-dollar measurement empire now determines which AI companies survive the coming shakeout.Timecodes: 0:00 — Introduction 2:15 — Early Career, Ad Tech, and Web 1.03:09 — Attribution Problems in Ad Tech & AI4:30 — Building Measurement Infrastructure6:49 — Software Eating Labor: Productivity Shifts8:51 — The Challenge of Measuring AI ROI14:54 — The Productivity Baseline Problem18:46 — Defining and Measuring Productivity21:27 — Goodhart’s Law & the Pitfalls of Metrics22:41 — The Harvey Example: Usage vs. Value25:18 — Surveys vs. Behavioral Data28:38 — Interdepartmental Responsiveness & Real-World Metrics31:00 — Enterprise AI Adoption: What the Data Shows33:59 — Employee Anxiety & Training Gaps38:31 — The Nexus Product & Safe AI Usage42:08 — The Future of Work: Job Loss or Job Creation?44:40 — The Competitive Advantage of AI53:45 — The Product Marketing Problem in AI55:00 — The Importance of Specific Use CasesResources:Follow Russ Fradin on X: https://x.com/rfradinFollow Alex Rampell on X: https://x.com/arampell Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Totally wrong. Every time I use AI, it's amazing. There's somebody at every big company who has figured out I could do something in one minute that used to take eight hours.

28-year-old guy who was using chat GPT really, really well, and they had him create a 30-slide debt. And they did a global call for everyone in the investment bank for this guy. to spend an hour walking people through how to use chat TV. 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 taking amazing engineers and made them gods.

I report me, I go in for my other four metrics. I have some report of how are we doing those reports. And 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 said 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?

Companies are spending $700 billion on AI this year. Most know there's waste, but don't know how much. And with AI budgets continuing to grow, this is no longer any old measurement problem. It's the measurement problem. The one that will determine whether AI becomes the productivity revolution we are promised are the most expensive placebo in corporate history. Russ Frayden saw this movie once. He was the first employee at the first online ad network in 1996.

when companies were pouring money into digital advertising with no clue if it worked. The industry didn't take off because the ads got better. It took off because companies like ComScore built a boring infrastructure to prove the ads worked at all. Now he's building Laredin to do the same thing for AI. Not to sell you more AI tools, to tell you if the ones you bought actually do anything. The stakes are higher this time. Ad budgets were millions. AI budgets are billions.

And unlike banner ads, AI is supposed to replace how your entire workforce operates. In this episode, Russ and A16Z general partner, Alex Rampel, dig into the paradox at the heart of Enterprise AI. Everyone's racing to adopt it, terrified of falling behind. But almost no one can answer the most basic question. Did it work? I'm excited to be here with my friend Russ Frayden. Yeah, good to see you. I've known you for a long time. I think when I first met you, I still actually remember meeting you the first time. It was from, I think, Josh McFarland. And Josh was at Google. And he was like, yeah, there's this guy, Russ Frayden. He started this company

Adify, and he sold it to Cox for all this money. Back in $300 million was like a lot of money. It is amazing. Now it's like a B route. But like back then, like that was a huge acquisition. And there was like, oh, like Russ, like I had like amazing person that pulled this off. And I think we met in Florida. That's right. On a Silicon Valley bank trip and kind of everything comes full circle in the end. But 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 a nice little segue. It was like ad tech,

you're trying to figure out, does the advertising work? A lot of ad tech is, here's an advertisement, and there's this attribution problem. Yep. Of the sale happened, 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 a machine? So part of ad tech is like on buying ads, that's part of it, but part of it is also did it work? And AI, there's all sorts of stuff around making AI work, which is like technically very, very challenging. But then there's the question of, did it actually yield a benefit? Yep. Which is probably the biggest question for, I mean, there's a lot of like myths on this on both sides,

but we'd love to kind of hear about the origins of Lerden and how you think about even some similarities between the two. Sure. Yeah, there's 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. I mean, Forget the capital markers perspective. It is funny to think about what is defined as big from an exit these days versus five years ago, 10 years ago, 20 years ago. That's kind of its own topic. But just when I moved out here, I moved out to Silicon Valley in 1996 and I was the first guy

at the first online ad network. And 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 like, how? How do I actually plan this? How do I spend this? How do I give tools, right? All of the money lived in TV or in radio and there were these tools like Nielsen, Arbitron, 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 Double Click or Flycast where I was or companies like Omnature building a different part of the stack, companies like Compscore building a different part of a stack. And those companies, obviously, Google and Facebook or two 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. And I really do think we'll see the same thing in AI now. The technology is unbelievable. And my core thesis when I was thinking about starting Laredin after having been, you know, first guy at the first online ed network, and having been maybe the first one of the first two executives at ComScore way, way 25 years ago back in the day, my partner Jim and I sat down and we said,

look, every time there's a tremendous shift in budget, And especially when it happens at a great pace, like what happened from TV to digital advertising, 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, around governance, not with the goal of stopping anything, frankly, with the goal of accelerating it. Because 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 the technology standpoint.

point. It's amazing. It's wonderful. But also, there are 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? And so we really wanted to start a company about how would you build kind of 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, and maybe we can get into how you're doing this, but just to kind of 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. So, you know, software eats the world. This was a thesis that our firm has founded on, but it's eating labor. But it doesn't actually mean that, like, jobs are going to go away. For sure. Largely, what it means is that people are going to be like 10 times more productive where I can't hire anybody to do this job, but I can hire AI to do it. So you have companies.

where their software budget is very, very small, but their labor budget is enormous. And step one 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 like a $10 billion labor budget and like a $1 software budget, you're not going to try to cut, you know, optimize your $1 software budget, but you're really going to say, okay, do I need to hire more people? Can I make people more productive? You know, all these things that are through people's minds right now. And 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.

Sure. Of like the $10 billion of labor, you know, maybe that goes to eight. And now you spend $1 billion on software. So like the net spending for the company is actually lower. The company is more profitable? Productivity gains galore. But then is this productive? Like I always want to know if the humans are productive. But then is the software yielding me more productivity? And how do I measure that. So everybody's excited about this gold rush. I'm going to use these tools, but do they work and how well do they work and what's the baseline? Yes. So I've stolen your framing of it's like if Chase spends $18 billion on software or whatever, and they double that. They need to know if they're getting their money's worth. They need to figure out if this is actually efficient spent. Yes. Look, a thing you

will hear said frequently by the people running the largest AI companies in the world, the people running the largest firms investing in AI in the world is they'll say 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. And let's ignore whether that's true or false. It's certainly the bulk case for Invidia, for Open AI, 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 spends on the order of $18 or $19 billion. And they spend a couple hundred billion a year on people.

So if you really think about that, well, is there IT spend going to go from 18 billion to 180 billion? Seems unlikely in the next couple months, but it's certainly going to go up. And if it's going to go up, what is the CFO need to understand? And 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, right? We've shifted society a million times. We've shifted from farms to city, right? 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 six months ago, right? And so there is an element where everyone needs to figure this out as we go along. So what did we start with as a company, right? 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? and what you find is 80-something percent of our customers

find far more tools being used by their employees than they know about and they've 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. And I don't really say that to our customers or fears 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 and solve is how do we get people using this stuff more in a productive way 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, right? I'm a marketer working in General Mills. How is General Mills going to help me use these tools? And what I've generally found with employees is if you really want to drive employee usage of tools,

you have to make them feel safe so they won't look dumb, and you have to make them understand that they can use this safely without getting fired. Because, again, 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 been, you know, had a 20-something year career and you're working in your job every day, and by the way, you also have things you do at home and you have business travel,

you have all of these things you have to do. Also, you have to become an AI expert. You really would like to not look dumb, and you'd like to accidentally not upload the wrong data and get yourself fired. This is actually a bigger issue in some countries where there's a bunch of EU regulations around AI that do matter.

And if I'm an employee at a company, I don't want to look dumb. So if I'm a CFO, we bought all these tools. What did we actually buy, number one? Number two, how do we get people actually using these tools? Because the usage on these tools in the enterprise is less than people would think today, which makes sense, by the way.

I'm going to get to the productivity thing in a second. But 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? And 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, your intranet software from SharePoint, things like that, are used by a relatively small set of the population that you wish were using it. And so if the goal is to get people, more productive using AI tools, you want to drive actual employee engagement.

So we've built a suite of tools around that. And 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 Larrad. And what I like to think about today is what we're doing today 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. So what we're doing today is we're marrying the behavioral data.

that no one else has, which is, is Alex a heavy user of chat GPT 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. But at the end of the day, I want to understand, did my users in the legal department that were using this expensive legal tool I bought? Are they more productive than my users in the legal department that are not?

Because what I've definitely done is I've driven up my OPEX. I bought this software. I've driven up my OPEC. but are they more productive? Are my marketers that are using Claude or chat GPT actually more productive? And how do you measure that? So today we do it the only way productivity research has ever existed so far, which is we take the normal productivity survey market research

that people have done for 50 years, not ideal, but it is the gold standard. It's McKinsey. It's towers, Watson, it's Accenture. And we lay on top of it proprietary data that other folks don't have, which is actual usage. So the way I think of it is the worst way to measure productivity is I'm going to send a survey to my employees and say,

do you feel more productive today from using chat cheap T? First of all, there's a definition issue. Second of all, people are going to answer the way you hope they'll answer. But third, you have no idea if they're actually using the tools. So a better way to do that. I learned this years ago at ComScore. One of the many things I did at ComScore is I ran our survey market research group. And one of the reasons the ComScore surveys were great is 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 on productivity. the truth with that is 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

kind of like put a finer point on this, so I'm a lawyer, I work at some big company, productivity to a certain extent, like if I only have to work four hours a day versus eight hours a day, like that's great for me. Like I kind of feel like it's a win because I often think about like the principal agent

problem, right? So everybody is an agent, and then there's the ethereal being of the corporation, which is the principal. And it's like, you know, yeah, 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. Like, that's kind of every individual agent's job. And then you have these tools. So, like, theoretically, it's like everybody's going to adopt these things if they get to be lazier. Yep, yep. Everybody wants to be lazier. Sure. Right? They want to be lazier and richer. I feel like these are like the universal like kind of human conditions.

There's some small set that want promotions, but I agree for 90%. But that's richer. Yeah, that's true. That's true. So like if you can get the promotion by doing less work, I'm sure people would opt for that. But I guess like how like think about everybody will use these tools or like I think I was telling you this sad story because our kids go to the same school. It's like younger kid gets busted cheating, right, with chat GPT. Like clearly productivity gain for him.

Right. Right. Because it allowed him to be lazier and, you know, richer with his video game time. until we confiscated this phone. But also a set of rules that you can get in trouble for. But like take that example. Like so you can imagine the individual agent, you know, the human being, the lawyer in this example is benefiting,

you know, but then does the company benefit? Because to a certain extent, like, I'm paying you the same amount of money. I want you to work for eight hours a day. Right. So actually my expectation should be that if you're whatever, I don't know what the lawyer does, but like drafting legal drafts, like if you can now do it in four hours versus eight, you know, and spend four hours playing golf,

like you're thrilled, you got a productivity gain. The company didn't actually benefit. So what you kind of want is you want 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 part to sell. But like, I mean, maybe kind of taking this example and riffing on it,

like now I can do an eight out, like in four hours I can do what used to take me eight. Sure. But how does the company, a company is like, oh, wow, you're still, you're operating. But actually, you should be able to do twice as much with this tool. So I guess, like, how do you define the baseline? How do you address that problem? Sure.

Am I framing it the right way? I think you're framing it. I think you're certainly framing it all right way for certain size of companies, right? We all know for Silicon Valley what you're going to have just because of the competition and the equity form of compensation. What you'll have is, if I can get done in four hours, what I could have done in eight, I'm just going to work four more hours and then another four. And that's very different.

That's, there's some subset of workers at all. all-sized companies, probably a larger percent in Silicon Valley, but smaller percent in at GE, right? 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 is management going to evolve overall, right?

I think behind all this, the first question is, the first question we're trying to solve is, like I said, do people use these? And from a corporation standpoint, for our measures of productivity, which is we're defining it with each of our customers. For our measures of productivity as we ping 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. What we want to measure with that is then some concept of raw tonnage of work, right? The ultimate, there's this lingua franco when we talk about kind of employees of FTE, right?

And we all know that you work different than I work and then, you know, various people work. And we all know that. Yet if I'm the CFO of, I don't let's pick on J.P. Morgan again. I'm the CFO of J.P. Morgan. I have a fundamental, you know, horse sense for what a thousand FTE do versus 500 FTE versus 2,000 FTE. And AI is going to break all of that for sure. And so our main goal today is just to build the baseline for our customers, which is at the end of the day, are the people using these tools fundamentally more productive than the folks that aren't? Layer on top of that tonnage of amount of time worked. You can get a pretty good, it's never perfect. People are on vacation. You have to measure this as groups, right? any given person was out sick one day or was on a flight one day or was at a training one day that's impossible to measure. And it seems from a system standpoint they weren't working, they actually were working. They were doing a training, right?

So think of this as at the aggregate data. It's never useful. None of this data is ever useful at the Russ Frieden level. Right. I mean, to get existential, was I productive yesterday? It's is 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 is there some correlation between specific use of these tools on an advanced side, light side, heavy side, heavy user of the tool, lighter use of the tool, were the users more productive in their job, or the employees more productive in their job? And then measure on top of that amount of time those segments of workers were actually working. Because the goal if I'm a CFO today is not to understand did Ben do a good job and did Tina do a good job. The goal is to understand, I have definitely been asked to spend 50% more on OPEX.

Right. Did I drive something? Right. And then, by the way, there are interesting questions. We know this around staffing size and will companies get more done because people will actually work eight hours. Look, it will turn out. I suspect it is true that this is one of the things managers do.

I suspect it is true over time if it becomes clear that all of your employees are now working four hours a day instead of eight, you will probably decide to have fewer employees and the remaining employees will work six hours a day. So I'm not sure I really buy in the next couple of years. You will see people in large companies actually just working half as much. You know, sole proprietor is what it is. If I were a sole proprietor lawyer, kind of my only measure of productivity today is to myself anyway, right? It's how hard do I want to work and how much money do I want to do? Well, the principal is the agent. Right. So that's fine.

But this is why it's so important. And this is why, I mean, candidly, I love what you do. Sure. Obviously, I love what you do. That's where you're here. But you, there is no baseline. Like, did this work? Well, first I have to know how, like, you have to define the outputs.

You have the inputs, which are largely just like time and money. Right. And then you have the outputs. And part of it is actually it is kind of complicated to come up with an output. For sure. Do you know a Goodhart's Law? Go ahead. So Goodhart's Law, I love this one.

It's like when a target becomes a measure, sorry, when a measure becomes a target, it is no longer accurate as a measure. Yes. Right? So if I say, okay, I'm going to judge you based on, I'm going to, like, how many emails are sent every day? Well, that's a measure. But once it becomes a target, it's like, I want you to send more emails.

Well, you're no longer, like, the measurement gets corrupted. Because now people decide to do more things to hit this target, and it's no longer an objective measure. So, you know, part of it is if I'm trying to figure out, like, okay, there's a product called Harvey. A lot of people love Harvey, and it seems to make people a lot more productive, but compared to what?

Right. And so to me, the only way you answer that, we'll talk about Harvey, nothing gets Harvey, I'm sure Harvey's amazing. To me, the only way to really understand this, and that's why I think the traditional way company is doing this just doesn't work at all, which is, hey, let's survey the people that use Harvey

and ask them if they were productive. And by the way, they will all say yes because no one ever answers they weren't, number one. And number two, my boss paid for the product. 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. So that's wonderful. So I think all you can actually do is, it's why I think the traditional way of measuring this is broken. Look, it's why we started learning it. All you can really do is understand, without asking people, how much usage of Harvey are these people actually doing, right?

We have five people, we have six people, whatever they'd say on a survey. Two have never logged in, right? We've all seen the joke about, you know, your project is due, it's due in an hour, you said you were caught up, and then you, oh, shit, I have to ask permission for this Google back, right? So there's two of the six, I'm making these numbers up, of course, two of the six people actually signed up for Harvey the day they were told and then never went back to it all.

They're very happy with the way they work. They work that way all day every day. Two of the six log in and use a little bit, and two of the six use it all the time. The only way I can even begin to understand if that software is valuable is by knowing that data passively without asking those folks a question. Yeah. and then asking everyone the same questions about productivity and measure it with amount of work actually output.

And if I take those three things together, then I can begin to form an understanding of, was Harvey useful? You and I had a discussion with someone where they were talking about one of the ways they incent their engineers at their company is they have a leaderboard of the amount of money each engineer spends on ClaudeCode.

And 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 CloudCode. Why aren't you spending any money with Cursor? I really don't get what's going on.

And so that was an example of for these companies where they're very developer heavy, you probably don't need us. If you're a very developer heavy company, probably measuring amount of money spent on Cursor plus your kind of normal management understanding of is this person actually working. If they come in for two hours a day,

you may be happy with that. You may not. that's going to be company-specific and 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, right? We have these metrics.

The issue, though, is we see this explosion of there's hundreds of AI tools. And companies have hundreds of roles. And so that's why we want to try and replace, you know, the McKinsey corporate health index or the Tows Watson or the Accenture Survey with some real useful data around AI. But I think that kind of cursor example really crystallized in my mind what you'd want to be able to do. for a whole company, which is how much did this person work?

So I have that quantitative judgment. Qualitatively, as a manager, right, we're not replacing this. Did they do a good job? And then fundamentally, did they use the tools? And when you take those three things together, that's the only way you're going to have measurement. And like I said, when you think about my, you know, micro world of if you really think JP Morgan is going to go from spending 18 billion or an IT to 30 billion or 40 billion,

the CFO is not just going to say no problem, right? Today our customer is a CIO. I think over time our customer becomes a partnership with the CIO and the CFO. The numbers are just big. It's like cloud span. The numbers are just so big people are going to pay attention. It's going way beyond experimental. And obviously the companies themselves, like if you ask any company that is trying to sell you anything

and you ask that does your product work, they will probably 99 times out of 99 say, like, of course it does. Sure. That's the best. It's the best. You need to have an independent arbiter. and that's where you guys come in but kind of like double-clicking on this point before. It's almost like reinforcement learning

at a company-wide level, right? Of what is the outcome that I'm looking for? And sometimes it's clear, right? So, and this is where the measurement and target thing is also relevant because it's like, I want you to write more lines of code. If that's, there's a measurement of like how many lines of code were written,

but if it becomes the target, then you're just like writing gobbly good code and like you're, for sales, it's very easy. Right. I want you to sell more sales. stuff. But there's a lot of latency between, like, you go talk to a customer and then you go collect money. So you might have targets in between. You might have measurements in between. If you're a lawyer, draft more contracts. So I guess how do you try to define the goals?

Because some of them are just like, it's kind of, I think of it as like background information that's going through. It's like emails that are being sent or, you know, slacks that were sent or Google Docs that were edited. Like there are these key, like there are these very, very clear measurements, but those aren't necessarily outputs. So first to your point on measurement, this is why I said earlier, if you think about any time true third-party measurement exists, there's this interesting dynamic,

and we saw this at ComSquare, but everyone has seen this anytime they tried to build a third-party measurement company Omnature saw this in the early days. Google at some point fought it and then actually bought Urchin and built Google Analytics, right? Because it turned out it's actually good when your customers can track value if what you do is actually valuable.

And so my general perspective is, I think today a lot of the AI companies probably look askance at us. But I think over time, certainly the AI tools that actually provide value are going to love us, right? The way you will ultimately unlock real enterprise budget is because people believe these tools are actually valuable. So what we do today, and this is a journey, right, the company is about a year old. So what we do today is we work with all of our customers say, look, here are the baseline productivity questions that are gold standard that people have asked for 70. years. You know, there's pros and cons to them, but this is, you have to start somewhere. This is where we start. And 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 good hearts 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. So there is an element of, 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? And so what we found is one of the things we found is just almost an intra-departmental SLA,

which is it turns out 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? Turns out companies don't really like firing people. Companies do fire people if they have to, but I've actually never met. CFO that got excited about firing 30% of the workforce. Outside of call centers, that's a different issue. We could talk about, like, companies treat their call center employees different from the rest of their employees. But outside of call centers, I've never met a CFO who is, if you went to a CFO and said, you can fire half your FPNA people.

He doesn't 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. And 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 just did this raise or lower the interdepartmental responsiveness? So our measure would be, am I now comfortable sending more things to legal? Right? 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, whereas a different measure of productivity right there, cost centers, not profit centers. So one thing to do it is, did over time, because my

lawyers are now more productive? Or other departments asking them more questions? Are they getting their responses faster? When I'm in product and I'm asking for input from engineers, are they responding more quickly, right? That is a good way for me to see behaviorally. We become more productive. That's not lines of code. Now, 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 map, which of my departments use these tools more? And do they become more responsive to my other departments? Because there's an element of when you're at a big company, People know this.

One of the reasons small companies do so well in innovation. There's just a giant coordination problem for all of these companies. And we know this. And, you know, 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.

They now do have a giant bureaucracy. Right. And, you know, kind of that's kind of a good segue into perhaps the state of AI enterprise. Right. So you went on this whole, like, listen. When you talked about 350 people? Yeah. We interviewed 350 heads of IT at major companies.

And across the whole gap, right? It wasn't just, you know, kind of Silicon Valley companies that's... Not, I mean, in all honesty, my whole career, basically, I spent a couple years helping my friend at Carman and I spent a year trying to fix wine.com. But 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 a very, very quickly. But if you're in the Fortune 500, you are going to be 20 plus years old,

99% of the time. Right. And so 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, you know, people have seen this before. I actually don't think of this. You'll see people turn this into kind of click-bady fear-mongering things. I don't really think of it that way. So first of all, we saw them. Like, we know this from Gardner, there's like $700 billion being spent in enterprise AI. It's growing very, very quickly. It's going to keep growing quickly. And one of the things we found is something like 70% of 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 bosses, their PE owners, gave them five things they had to do this year. And one of the five was adopt AI across the organization. And he said, every board meeting, I go in for my other four metrics,

I have some report of how are we doing those reports? And on AI, all I have is the amount of stuff we bought. It's not... So yes. Yes, I'm doing great. We have a large family of AI. We adopted all these AI children. It's all great.

But it turns out we were. want to actually do it. And so what we found is these leaders like, maybe they are right that 70% of their projects are failing. Regardless of their right, it's a giant problem they feel that way because they have no system to figure it out in the first place. 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 ask? actually drive value for me. And we just don't really have that in AI, like I said, outside of some

very, very specific verticals. And so really the biggest thing we found was kind of three things. One, you saw the AI span. Two, something like, I said, they believe 70-something percent of AI projects are wasted. But the other thing we found is basically 80, 85 percent, I can't remember, 80, 85 percent 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. So I think one of the things, one of the reasons you've seen this giant unlocking budget is, there's tremendous anxiety at these enterprises going like, we're going to lose if we don't adopt this stuff.

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, a forgotten group in the company for all of this AI. From my last company, we built a very, very large HR technology company. We sold into heads of HR. It touched all the employees in the company, but we sold into heads of HR.

And so as we've talked to a lot of our old customers, so aren't really our customers today, but they're influencers. What they will all say at all of these large companies is, hey, our employees are really worried. It's not even they're worried they're going to lose their job.

There's a base level of worry about AI and the economy, all that stuff. It's not even they're worried they're going to lose their job. It's just they're getting told to use a new system all day every day. Generally, if you work in a large company, there's one or two new systems initiatives

a year. Now there's 20 new tools. They don't know, they don't know what they're allowed to do and they have no training. How do they actually, how do I get people using these tools? And so you have this weird,

you have this weird almost perfect storm it's why we're excited about Laird and it's why you're excited about Lared and you have this perfect storm of tremendous growth in budget tremendous anxiety that none of it is working tremendous anxiety from their employees

about what they're even allowed to do and so 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 about like 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 and that that kind of last point is super interesting as well, because it's like there's the did it work, how well did it work,

you know, what are the measurements, make sure that the measurements don't become targets, like all the stuff that we just talked about. And then to use the metaphor of my son who cheated on his math homework, there are people that are just like, wow, like they're the go-getters in the company.

This is actually why I am convinced that AI is underhyped. Yes. 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 wrong. Every time I use AI, it's amazing.

because you go, it does, it has not diffused. You have like the 19 year old kid or, you know, my 13 year old son, I was like, wow, normally homework would take me two hours. Right. Now it takes me one second. Right. And obviously that's bad, right? Like I'm not using him as the, that's why we confiscated his iPhone, right? 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 sometimes, again, not to like oversimplify human behavior, but it's like, I want to be lazy and I want to be rich. Yes. Right? Like, these are the two things that are motivating people. And, like, I found this tool that allows me to be lazier and richer that actually helps the company.

Yes. So not the math, not the cheating, right? It's like, you're getting, like, I can now, I know that my boss was thinking this would take eight hours. I've figured out a way to do it in five seconds. And it's really good, by the way. It's really good. And the worst thing that can happen.

This is like the inverse of everything that we just talked about. The worst thing that can happen is that guy can happen is that guy can. keeps it a secret. Right. Right. Because like, and he might be afraid. It's like,

oh, am I allowed to use this? But what you should do, this is how AI will go from underhyped to like correctly hyped and correctly diffused is it's like, there's somebody at every big company who has figured out this like, I could do something in one minute that used to take eight hours.

We need to like make this person a hero, like memorialize this and push it out through the entire company. So like how do you do that? This was my point on the, what we're doing on the AI engagement side. And that's a great question. This is my point on what we're doing on the engagement side. This is one of these areas where everyone's interests are aligned.

The employee that's working very hard loves recognition, and by the way, he'd like his coworkers to come up to speed. The employee that's scared wants support and wants training. And 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 like I said, I have yet to find the CEO in all of our I've spent 30 years selling things to CEOs. I've 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 property. He wants more revenue. But contrary to popular leap, they want more employees. They like running big companies. They do. You can find old interviews of Larry Page

talking about his plan for how Google was going to have a million employees one day. And he was spending a lot of time thinking about self-driving cars to move the cars around the parking lot because this is before remote work. And literally where were the million employees going to park all the cars? And I remember I read that 15 years ago. Like, that stuck in the back of my mind of, I have never met a CEO who wants to run a smaller company. It's one of the reasons, by the way, the totally unrelated point in the capital markets,

one of the reasons if you ever made 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 run, I've had my companies grow. They're fun when they're bigger. It is. It's super cool. And so from an employee standpoint, what we built with this Nexus product is effectively a product where we said,

or I'll use an anecdote. I was in the UK in July, and I went on a bunch of sales calls, and I was talking to someone at a bank, very large, very regular, European Bank is among the most regulated folks in the world, least, you know, fast to adopt new technology for good reasons, honestly.

And they were telling me a story about how they had, it was a 28-year-old guy, I don't remember what level that makes you an investment bank. So let's say a director, but 28-year-old guy who was using chat GPT really, really well, in the investment banking side of the business. And they had him create a 30 slide deck, and they did a global call for everyone in the investment bank

for this guy to spend an hour walking people through how to use chat GPTO. 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 that, you know, the secret to a lot of LMS is other than things you must do or you will lose your job,

like sexual harassment training, hippotraining in certain orders. No one, no one does it. They just don't go. And so how do I actually get people using these tools? And this is my point from earlier is you want to help them, A, not look dumb, and B, no, they won't get fired. And so what we effectively did is built these wrappers that exist around the models.

So we don't tell people to use quad or to use Gemini or to use Chachy Pee. Right. Then the other thing we built, because, again, people are also worried about getting fired. They're worried about getting fired because the economy, because whatever, it's a new tool. I would like to think I get fired. By the way, when you're talking about European banks is, a lot of regulation that is a legitimate concern of if our employees do the wrong thing, we will get fined.

Forget whether you fire them. These companies don't want to get fined. And so the other thing we did is we basically trained our own kind of customized Lama model to block people from asking questions that are illegal or the company doesn't want you to. So we're not talking about hackers here. True bad actors in the company is plenty of security solutions. What we're really talking about is the X percent of the people who I'm in people

ops in HR at a large company, I'm supposed to do a workforce analysis. Am I allowed to go into chat GPT 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. And by the way, I think it's incumbent on the company to say to our employees, here is a safe space. Nothing you can do here is going to get you fired. So we, oh, Alex, you're not allowed to up. That has social security data. Don't, don't share that. You're not allowed to ask that prompt because in Europe, we're not. allowed to use HR, we're not allowed to use AI to write employee reviews. I don't know if that's a good law or 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, you know, prosecute that. Our read of the regulations, 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, I want my employees using AI, I have to block them from using it for those use cases. And so what we've tried to build is this almost like harness to say you can be more productive. You're not going to look dumb. You're going to be more productive. And you're not going to make any mistakes that get you fired. And so what we found is that actually drives more AI usage, surprising literally no one. And 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 my company. It's a total unlock. And same thing on the coding side, right? You know, cursors taken mediocre engineers and made them good, but it's taking amazing engineers and made them gods, right? And so our goals 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, I mean, this is a little bit philosophical, but future of work. Because to a certain extent, like, all right, you're the, like, if you're the measurement,

sure, like the measurement inevitably will become a little bit more of a target. And I always like to kind of, remind people that I think it was 97, 98% of Americans when the Constitution was ratified were farmers. Right. And they all lost their jobs due to these pesky things like, like the tractor and fertilizer and all these things. And I think the average life expectancy was like 35. And most children died in childbirth or shortly thereafter. It's like, you know, we, things have changed, but this is what, this is what technology brings you. I mean, nobody knows the answer to this,

but given that you're in charge of a company that's measuring AI productivity and human productivity and kind of AI and humans working together. I mean, what's your timetable for like how fast things change? Are we going to see, you know, net new jobs create? And by the way, like behind every one of these, there are all sorts of jobs that start becoming around that didn't exist before. So maybe that's kind of part two of the question. Because like the job that we have right now, like, you know, filming a podcast, like that wasn't a job like 200 years.

Like there's so many jobs that just like nobody could even. think of. So I guess like where do you think things are going and like what types of maybe to put a nuance on it? Like what types of like future jobs do you see in and around this like new stuff? So I don't buy for a second there's going to be large scale job loss because of AI. Frankly, because of what we've seen through all of history, which is just flat out capitalism, if my two choices are I can maintain my base level of productivity but fire a bunch of my employees and be more profitable. That is a fine idea in the short term. If I'm, there's probably a good idea for a P.E. firm to go around and buy a bunch of minorly profitable companies,

fire half their employees and make them more profitable. But that's what P. Firms have done for a long time for non-competitive companies anyway, yet employment has still increased. Right. So you can argue we've always had a function, or for the last 50 years, you can argue we've had a function whose goal is to take underperforming companies and fire a bunch of employees, right? And let's say that that's what P.E. firms have done and that's, you know, what AI could theoretically do. Yet, has increased. So I don't buy an AI. And look, it's philosophical and, you know, 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 is not 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. I mean, 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 competitor's opportunity to be less profitable and compete with you. So other than some very niche monopolistic, I can fire everybody, you know, one man firm. Like, will we have one woman firms that do a billion in revenue? Probably. But today we have very

profitable, you know, one man, 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. So that's amazing. And there'll be a ton of opportunity to be more successful solo entrepreneur. So I absolutely believe there'll 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 and cut all the people will no longer be in the Fortune 500. So I just flatly, because we live in a competitive world, it's, we haven't seen any proof yet that the economy is zero sum. Right. Maybe, right, 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 is generally grown. I just don't know why you'd believe that this time is different because of the 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 is 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 Cowan 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, hey, I 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 it's going to destroy your company. And we all know this. So I don't, it's one of these things of like, you know, there's a lot of fun headlines about OAI and then you have to have the counterpoint of, oh, it's going to take the jobs and oh, kids these days. I don't know, like when you, we all know this, we've all seen this. You can find articles about when the TV came out.

It was the end of reading. When newspapers came out, it was the end of conversation. So I do think it is scary that new tools are coming out and it is impacting the entire globe of all knowledge workers everywhere. So what? So I think there'll 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. Like someone is going to have to scrub the toilets in the space station and someone is going to have to pilot the plane to pilot the ship to the space station. The self-driving.

Yeah, of course. Self-driving spaceships. So, by the way, there is a chance I will turn out to be wrong. And 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 Glaser, I think he's at Harvard, and he was saying, I asked him this question. It's like, you know, what's going to happen with jobs and, you know, how do you

compare this to everything else? And it's like, well, what's really interesting is that this is arguably the first time that the job. losses might be born by like white-collar super-educated people? Sure. That's why everybody gets scared. Well, but so he actually had a different framing on it. So, so yes, agreed.

But almost tautologically, hyper-educated people are hyper-educated. Sure. So should be able to rejigger themselves and do something else versus in all of these previous revolutions where it's like you have somebody that really has no skills and just like showed up at work with no skills and got paid. Yes. And there are a lot of jobs that look like that when there's tremendous labor shortages. So if you were doing anything in 1849, apparently, in California, like, boom, right?

It's like, there's just like, oh, you're a human, you have a pulse? We need someone with a pick. Like, go. Like, go do this. Or just like, you know, you see that line over there? Like, yeah, like straightening it out. So, but what's different is that, yes, it's scary for some people. But like everything right now, and maybe robots will work better in the future.

But like 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. It might not, like this does not mean that like, you know, what happened to Detroit? Sure. Right. It's like that actually wasn't about automation. That was about like the Japanese built better cars. Right.

There were a lot of reasons why that happened. But what do you do with somebody who had like a very, very high-paying job but actually didn't have that many skills? and now they lost that job, well, 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. And I do think there's an element of,

look, 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, 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, or frank, there's 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. But at the end of the day, to your point, yes, there's a set of people who've probably been slowly working less and pushing themselves less and now they have to push themselves more and that just is.

My joke all the time, I won't say the company I use, but part of my sales pitch for Laredon, when I'm talking about this, I say, look, when we talk to employees, you know, look, your average employee is a 42-year-old associate brand manager. And if you ask them what they want out of AI, they do want it to go away. Their number one wish would be that would just go away. I'd like that because I liked yesterday. But we'd make a lot of money if we had the power to make AI go away,

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 and help you be more productive and help you as a manager, or understand if your team is using these tools better. And so, you know, my macro point is I just don't really believe there will be wide-scale mass unemployment. Might an individual have to push themselves more? Yeah, for sure.

And some of those will be sad about that. And like, that same thing is true in the entertainment industry, right? The jobs have moved and jobs have shift and people don't watch movies the way they used to. And, you know, TV seasons used to be 22 episodes, and now they're eight because consumer preferences have changed. And so that is probably, lousy if you were a guy who played a part on law and order, right?

That probably is uncomfortable. I'm not being calisive. Sure, there's many ways it will impact my life negatively. But I just don't buy that they won't be more educated. Yeah, a lot of this actually predates AI. Sure. There's this great article or interview with the CEO of Waste Management before ChatsypD came out.

And he was saying, I have unlimited, like, I get resumes every day from somebody who has an MBA and wants to work like in our office. for, and like the key, they're like negotiating against themselves. Like the price keeps going down there. So 100 jobs for, 100 applications for every opening or I forgot what he said. I need to hire truck drivers. Somebody who, and not just like self-driving, like I need somebody who actually is collecting the trash.

Like that's what waste management does. For $150,000 a year, I can't find them. Right. So it is kind of interesting how things have flipped. But I guess the other thing that I would say is I would almost argue that a lot of of AI's problem right now in terms of diffusing into the workplace, it's almost a product

marketing problem. Right? Where it's like, okay, AI can do anything, right? But I'm not looking for anything. Like I say like, hey, I can do anything. And you're like, you're like, oh, I don't need it. It's like, no, I could do this one thing very, very, oh, you do that? And

like that needs, like, this is kind of, I think, once you have more of these articulations of what can be done. And like, the things that have really kind of gone hyper growth, it's like, oh, like I have AI does everything. Oh, I will help you code. I will help you code better. Oh, I want that.

I have this chat bot for, yeah, look, a long, long time ago, it's funny when you get old. A long, long, long time ago, I was, like I said, it was the first guy at Comscore. And Comscore's sales pitch in the early days. Comscore, for those that don't know, right, basically had all the data for everything that was happening on the Internet. And so the founders were true geniuses, and they basically knew everything that was happening everywhere on the Internet. And our sales pitch in the early days would be, we know everything.

I mean, obviously not. But would it basically be like, we know everything? What would you like to know? And it turned out that wasn't a really good sales pitch. You would sometimes accidentally run into someone who would go, oh, God, I need to know this. Can you do this? And we'd say, yes, we could.

And there you go. But it turned out we only had a couple sellers that could figure that out in real time. And then it turned out if we said, we can tell you the market share for Visa versus MasterCard versus others in Japan. Turns out Visa really wants to know that. But I can also tell you the share for your pharmaceutical drug

versus others in research online. Turns out they also want to know that. Right. After, I'm going to get it wrong, but Ford had some giant issue with Bridgestone tires setting on fire. It turns out they really do want to know that did employees, did search for Ford get worse because of Bridge Town, right? So people do want to know these specific. Like I said, I think that's exactly the right way to think about it

is you need this product marketing problem. Look, it's why we're so focused on what's happening. Are they more productive? Right. 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. I mean, 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 chat GPT, right? So one in a million times it does work out, Facebook, chat GPT on the consumer side. Well, just like there, it's just like it's magic. Yeah, it's amazing. 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. But if you ever watch Seinfeld, there's this great episode where Jerry buys his father, a sharp wizard, which was like an early Palm Pilot. It was like this early smart computer like in the 1990s. Never went on to great things. But it did everything. It was like, you could run these applications.

And Jerry's trying to explain it to his dad. And he's like, well, I don't get it. What does he do? It was like, well, look here. It has a tip calculator. It's like, oh my God, a tip calculator. And then he explains it to all of his friends. It'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.

So I know we're... 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... No, look, I think I'll leave you with two thoughts. One related to something you brought up in the conversation and the professor, but one related to Laredin.

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? And, you know, there's a ton of business there. And then I'll leave you with a, we'll leave you with an unrelated thought to your, to your Harvard professor. So as you said, our kids go to the same school and my oldest kid is in 12th grade and just got into college and he got into his top choice. And he's very proud of himself and and it's a very highly rated school and I'm very happy for him. And he came home and he said, Dad, look at this ranking.

And he showed me the new U.S. News World Reports ranking that showed the different rankings. And I was like, his name's Henry. I said, Henry, I'm very proud of you. There's going to be a lot of different rankings over a lot of different years. And there's only one thing you have to know for sure. Whatever the ranking says, everyone knows number one is Harvard. And 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 one. I did not go to Harvard. Thanks for listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcast, and Spotify. Follow us on X at A16Z and subscribe to our substack at A16Z.com. Thanks again for listening, and I'll see you in the next episode.

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