20VC: Enterprises Will Not Adopt AI without Forward-Deployed Engineers | Who Wins the Data Labelling Race: How Does it Shake Out? | Lessons Learned Hitting $200M ARR with Matt Fitzpatrick, CEO of Invisible Technologies

23:53And we're able to do with one to two person, small FDE teams a lot. And so once that's set up and running, then we do have ongoing software that is, you know, I think the paradigm that we're evolving from is over the last 20 years, you had kind of the system of record layer was where a lot of the value is at. And what we're building is hyper-personalized system of agility layers, kind of what sits atop that. I think the Accenture paradigm is what people are afraid of. And it's very hard to convince somebody you're going to pay time and materials until it gets working. And so I spend less on sellers and more on forward deployed engineers.

24:24That's my simple math. I always think the biggest mistake that people have is they don't put the hat on of their customer. Yeah. Yeah. I think the reason the show has been successful is because I put the hat on of different customers. A lot of the customers that we have is startup founders who create amazing products and everyone wants to sell into enterprise. That's where the money is. Yeah. If I'm a startup founder thinking, huh, do we need FDs? How do we do FDs? How do we move into an FDE model? What would you say to them that they should know if they're thinking about starting that model or potentially needing that model, knowing all that you know?

24:56I think it depends a lot on the nature of the business and what you're trying to build. If you're trying to build a knowledge management system of public filings for finance, for example, you don't need FDs because what you're building there is a repository of information that people can access. You've seen similar things in healthcare, for example, if you're trying to change workflows, you do need FDs. I think that's the simple paradigm difference in my mind is if you're building something where the hardest part is getting adoption and workflow embedding, and you need to actually change the way a company works, then yes, forward deployed engineers are the only way to do it.

25:28It's interesting. There aren't that many folks that have expertise doing that. So it's a hard thing to train and learn, but I do think it is the only way to get the enterprise working. You've said several times, hey, don't pay until you prove that it works. And you said earlier, pay as it works. That's not the SaaS business that we've been trained on, Matt. And I'm a SaaS investor. How does the pricing model of the future look in this very new environment? Let me step in for a second. I think an interesting thing, if you look at the economics of SaaS and enterprise five to 10 years ago, and I think it's an interesting, look at any large public enterprise software business, and then look at how much of their revenues actually services.

26:06And I think you could kind of argue that out of the box software has always been a lie to some degree. It's a weird thing to say, but they always had a ton of configuration and they just dressed it up to some degree. I think SaaS was even more challenging than that because often the unit economics of SaaS, you're selling a much smaller cost per customer. The SaaS business that worked was actually about selling something where the out-of-the-box setup was quick enough that you could make it work with the sales team where you didn't have to do lots of configuration. Because the minute you had to bring in FDs in a SaaS context, your economics broke instantly, right?

26:36And what I'd say then on the enterprise side, the way people made it work was that's why Accenture grew so much. That's why Cognizant, that's why TCS grew so much is, I'll give an example, like if you take InsurTechs as an example, right? Every one of the major InsurTechs, like a Duck Creek, like what they have is a set of core data schemas, a series of analytical logic and a front end. And the ones that did really well had momentum and push from the SIs that got them going. And so their economics were geared by having somebody else do all your services around what you did. And then you've got something up standing up at the end that worked.

27:07I think the The challenge with Gen AI is that motion doesn't really work because what ends up being built at the end of the day is something that is hyper-specific to that customer. If you actually think about the nature of fine tuning an LLM or creating a knowledge management system, it's not a box. It's not. It is something that uses a lot of different consistent tooling, but it has to be customized. The way we do that is we stand that up, we get it working, and at the end of it, usually two to three months in, the payment happens when we pass user acceptance testing and validation, and it works.

27:38And here's the other thing I'll say is we use SaaS as a paradigm because that's how software has worked. But machine learning has been around the enterprise for, I was building machine learning miles 10 years ago. That's always been a motion that looked like this. So what's happening now is we're starting to realize that the Gen AI adoption paradigm in the enterprise works the same way that ML did. When we look at the different products that we have today, the expert platform is one I think that gets a lot of attention. How much of the business today is the expert platform. I find companies are lumped into categories.

28:07It's easier. And you have your McCaw's, your Surge's, your Invisibles. And you're all kind of put in this, like, are all just talent marketplaces? And no one wants to be a talent marketplace, it seems. And I'm like, how much of your revenue is the talent marketplace? And why does no one want to be a talent marketplace? I actually think the AI training space has many different players that have many different business models within it. There's four to five, but actually they're all quite different. I think of us much more of as an AI training platform than just a talent marketplace. Meaning we have 1.3 million experts that come through the marketplace, but a lot of the expertise we've built over the last 10 years is the ability to, here's the simplistic question I think that AI training asks.

28:45You have to be able to source any expert in the world in 24 hours notice. You have to be able to source a PhD in astrophysics from Oxford, put them into a digital assembly line, in four days later, generate perfect statistically validated data that will be compared head to head to somebody else's data and make sure that that is perfect at the end. That is an incredibly difficult thing to do. And so actually a lot of what I saw when I took over Invisible was that motion was incredibly applicable to actually the next phase of the enterprise as well, which is the fine tuning motions, the training, the ability to statistically validate for an enterprise use case like claims processing.

29:21It's the same motion. I actually think AI training will be used next in banking and healthcare. And then after that, in many other different enterprise contexts. And so the historical business I took over in 2024 was pretty materially weighted to the AI training side of the house. But I came in with a thesis that enterprise would be a huge source of growth. And I think as you see next year evolve, I think we've confirmed 12 enterprise deals in the last 45 days. So we see pretty good momentum on that side of the business. And I think that's where we will evolve as to doing both. I think the five core platforms we have allow us to serve a whole host of different end markets.

29:55And I do think that's very different than the other AI training players you mentioned. I think we're the only player that spans that broad-based view in the same way. On the talent marketplace side, how much of the business is that today then? I won't say an exact number, but it was a pretty material percentage of 2024. Okay, got you. So it's a pretty material percentage. The one thing that's also striking is the concentration of revenue to a couple of core players. When you look at other providers, it's like two players that make up more than 50 % of revenues for pretty much every provider. Is that the same for you?

30:25And how do you think about what that revenue makeup will be given the enterprise diversification that you're talking about? Yeah, I do think for, this is a space where there are not that many players that are actually building LM. So by definition, the whole space has concentration. I think I would not disagree with that. I do think that's one of the really interesting things for us on the enterprise side is we have materially more diversification now in the number of customers we serve on a whole different range of topics. I also think you're seeing more kind of early stage model builders as well that are building hyper-specific topics.

30:58And so that's the other part of where we see expansion in the total customer base. When you come to negotiations with a client, given the revenue concentration, how do you play that staring contest? Because essentially they go, we know that we are one of your core customers and we will squeeze you on price. And you go, I know I'm one of your core data providers. I will stand firm. How do you handle that negotiation? Because it is a staring contest of sorts. I think people are willing to pay for good data. That's my simple framework. If you think about the importance of these models, if you think about the cost of compute, that is actually a huge chunk of the cost base.

31:35If you think about one week of bad data burns a lot of compute. I think what we've seen, the reason it's been the same four to five players and market for a couple of years now is it's really hard to do well and so people are willing to pay for good data and so i think we we have a very collaborative dynamic with all of our customers on that front you know i i think that when you provide a service that's helpful people are willing to pay for it and if you provide a service that doesn't work people don't pay for it and so the interesting thing i would say on that front is the discussion topics anchor around again proven value so we'll get a topic that'll come in like a multimodal audio model for example and we'll go head to head with somebody on that that week and at the end of it we win or lose And so if you win and your data is way better, people are willing to pay for that.

32:14I had a chat last night with a board member of another of the companies in the space, and he said two things that really stood out to me. He said, I'm just drastically shocked at the lack of price sensitivity for the core customers. They're willing to pay pretty much anything. Is that the case or is that a bit of an exaggeration? I think that's an exaggeration. I think if you think about classic economics, people are willing to pay a fair price for good data. And so I don't think we operate in a model of trying to give anything unreasonable. I think there's actually fairly standard price bounds across all the players here.

32:44Is data commoditized? When I think about like pricing power, I'm a massive fan of Hamilton Helm's Seven Powers. It's an amazing book. Yeah, great book. Yeah, when you think about like pricing premiums, you get that through not being a commodity, through owning supply of a rare asset. Is there commoditization of data and we're kind of in a race to the bottom on the pricing of that data? Or do you own the supply of VET workflow data for surgeons in Oklahoma. It's very fun. Yeah, so let me take that. I'll actually start with the market context and then I'll actually use Seven Powers. It is a great book.

33:18I'll use one of his frameworks for that. Like, I think the market context that is somewhat misunderstood here is the way that human data becomes more and more important over the next decade. And I think the reason for that is if you thought of the different types of things you could train off of. So synthetic data gets mentioned a lot, but like most of the time, synthetic data is useful for things like let's say base truth information, like math, where there is a clear output that is right or wrong. Now let's take all of the different reasoning tasks, like a multi-step reasoning task. Like, I mean, even a simple one, like what movie would I select based on, you know, these five preferences.

33:51And then let's take that question and add into it audio, video, multimodal language, the ability to do it in 45 language, language context. So the ability to think about computational biology in Hindi versus French versus English versus English with a Southern accent. That paradigm is actually incredibly hard to train on. And we're still in the first inning of a lot of those permutations of complexity is what I would say. And so for a multi-stage reasoning test that requires a PhD in multi-different languages, human feedback is going to be important in that for the next decade. I have a strong belief on that.

34:28And that was actually one of, when I chose to take this job, that was actually one of my core convictions is the enterprise is going to need that too, because actually a lot of you take legal services, for example, a lot of the way you're going to validate that is with legal expertise. There's no corpus of information you can train from. So I would start with the idea that I think the market tailwind for the next 10 years, we're actually in the first inning because there's the LMs, then there's the more sophisticated enterprises, and then there's everyone else that needs to train, validate, and move to fine tuning.

34:55So again, contrasting, there's like the pre-training and LM work, but then to fine tune a model to a specific context, most companies don't even know what that is in the enterprise yet and that whole process we're in the first inning of so i think the market demand is going to continue to grow pretty materially for a decade the hamilton helmer framework is an interesting one because he my favorite example is uh he talks a little about what he calls institutional memory he mentions the toyota production system as an example right where toyota would literally say to people this is exactly how our factories are set up and nobody could replicate it right i think the interesting thing about this space and why you've had a consistent set of folks doing it for a while is to go through the process of every week having to spin up.

35:35We have 1.3 million active agents or kind of experts that come into the pool. At any given week, we have 26 ,000 of those that we've selected that have to start in 24 hours and produce perfect data. Think about the challenge of scaling an organization that for five years can do that at really high quality and consistently turn and evolve to the different permutations of the market, new ideas of training. It's really hard to do. And I think that was what got me most excited when I took the invisible job was the question of can you make AI work in a really complicated context? Very few companies know how to do that on the enterprise side or on the training side for that matter.

36:12And so I thought that was a really unique institutional memory context. It is a digital assembly line, no different than an auto factory. And I think that is a hard thing to replicate. The other really interesting area that this board member said to me was, he very much agreed with you he said exactly the same words as you in terms of first innings of data in terms of just how much market size will increase he said the other thing i really didn't understand when i made the investment was the specialization of data and how we are moving into the acquisition of this kind of insanely niche data supply pools where it's not like cat hedge zebra crossing zebra crossing is a what would you guys call it a pedestrian pathway yeah i did not see the specialization in the unbundling.

36:56Is that something that you see too in terms of these very micro niche specialized data requirements? Absolutely. I think five years ago, this space was what I would call cat dog, cat dog commodity labeling. I don't think anyone, and I think there was a lot of Google Sheets in that era, and you've seen some comments on that. This sector has evolved the same way most technology sectors do, where it started with Google Sheets and cat dog labeling and it's evolved to real digital assembly lines huge velocity of expertise and incredibly specific expertise so like you know we have to give a funny example we have to be able to validate an architectural expert on 17th century french architecture who speaks french i mean that is a that is a complex thing to do on 24 hours notice right and so the ability to source assess validate and i think one of the advantages for us is because we have five years of data on who's been good at what task, there's real institutional data memory in how you do that selection and assessment.

37:49I think that's one of the core advantages we have from that. How important is pay? I think a couple of other providers have said that bluntly it's about how much you pay. You pay more than the others, you'll get a good talent. So a weird analogy, I think of our business like Uber. We source talent at the price at which people will do the work that is asked of them, right? So the same way I do that, if you're standing on a street corner, your question is, can I find a ride that will pick you up at this moment within three minutes? And that's a different price if it's raining. That's a different price if you're in Rio de Janeiro versus London, right?

38:20The price depends on the market context and the specific place you are. I think expert pay is the same dynamic, really. A lot of what we're doing is what I call price discovery. And so the nuance I would add to what you're saying is you can overpay a really bad expert, and that is a total waste of everyone's time. And so what I think our customers appreciate is we can tell you between$150 expert and$130 expert, the difference in expertise you get. Do you think you have control of a finite supply of data providers? If you look at the Seven Powers and Hamilton Hamill, one of them is like acquiring finite supply.

38:53So I actually don't think finite supply matters. And what I mean by that is I think the expertise needed varies so much month to month that if you tried to do a world where you bottled up whatever supply it is, it would change in three months. And we actually relish that concept. I actually think the dynamic, again, why I would use Uber and Lyft, you could use Airbnb and VRBO as the same context is I don't think experts go on five platforms, right? I think actually what you want to be is this is a two-way marketplace where you need enough demand for people to be interested and you need enough expertise that many experts.

39:22And I think the reason we get 1.3 million in balance is because of that kind of supply-demand balance. So I don't think this moves to a world. And actually, I would never say it moved to a world where there is one player coming out of this. I think there's benefits to everyone to having numerous players that do AI training. And so it's a question of being one of the players that has that balance. You said there about kind of the switching of preference of like, oh, three months ago it was this that you want, now it's something definitely different. Switching cost is another. When you have data providers in this way, are there inherent barriers to switching?

39:54Is there any loyalty? Yeah, no, I think that if you've learned how to do a certain data task really well, there's incredible value in that. And let's take the enterprise context again, because I do think it's a good one. So I'll give you an example. We're doing a lot of fine-tuning on some pretty interesting topics. One example, we worked with SAIC, Vantor, and the U.S. Navy on fine-tuning a model for underwater drone swarms. And so the question on that, if you think about... Niche. Very niche. That's why I use this as an example to answer your question. So if you thought of in that context, you've got a bunch of underwater unmanned vehicles, and they're getting all the drone and sensor data from the interaction patterns of those vehicles.

40:30And what they want to know is, you know, an object is in the water near them. What do they do? Do they react? Do they pull back? Do they alert another drone? Do they engage? What are the topics of that? So fine-tuning a model to take in all that complex sensor data, fine-tune it, train it, and build a decision-making framework for those drones, there's a lot of logic built into that. And I think that's why it's been a great partnership with SAIC and Vantor because we built logic on how to do that. And it's, you know, I think that there is real sustainability and expertise you build up. And so the way I think about like our enterprise motion, for example, is every sector is led by somebody with deep, deep sector expertise.

41:07And we do build real logic on those topics. And I think the same is true for multimodal video and audio. It's true for legal. I actually think a lot of the training work, even at the model builder side now, one interesting view I have is people talk a lot about the public benchmarks. That tends to be one question you get a lot is like, are we reaching a point where models are not improving? I actually think about it very differently, which is the models are now all moving down hyper-specific things where there's not a public benchmark for them by definition, right? Like they're moving to more very specific tasks that are very different and not something you can publicly benchmark in the same way.

41:42And that's where we do see more and more model improvement every day, but both in model builders and enterprises on these specific tasks. You said about kind of the benchmarks, I'm just so interested. Gemini 3 killed it. It's the best ever and then yesterday opus 4.5 killed it it's the best ever next week sam's gonna release one does it matter like are we in a world of such transient and flux where really we should detach ourselves from these bluntly updates that last for days look i i think the benchmarks are a useful framework for society to gauge progress on this topic and it's a very it's a very often discussed topic so people want a way to answer the question about are the models improving and i can tell you like unequivocally the answer is yes.

42:23I mean, I think by every measure you look at, they are, and you know, they're not only improving on the benchmarks, but even on specific tasks, like research for investments, for example, you can see the models are much better at doing certain tasks. And I think what you're seeing start to happen is people, and we're doing this as well, are building very specific work-based benchmarks to calibrate certain things. Like how well does the model do on building an LBO model, for example, and you're going to see more and more benchmark cited. Now the complexity then becomes if you move from five main benchmarks, like SweetBench and others to 600 benchmarks, then you kind of lose track of what's doing, who's doing well and which things.

42:58But I think my interesting view on that would be, I'm not sure the benchmark progress is what determines enterprise adoption. And what I mean by that is if you take the fact that the models have improved exponentially over the last couple years, and you say consumer adoption has been massive, right? Like KBMG had this report that 60 % of consumers use this on a weekly basis. The adoption curve on enterprise is not going to be a question of generalizability. It's gonna be a question of hyper-specific performance on a specific task, right? And so there isn't actually a benchmark for that. Like if, you know, let's take a investment summary document for a private equity firm, right?

43:35There's no benchmark to say, firm one, this is how you write investment committee memos. Does this generate something that looks with 99 % precision like something you would roll out? There's no benchmark to do that. And so that's where what I see as the adoption curve is actually the fine tuning and inference layer of actually testing that, getting into a place where that firm could say like, this looks good. I'm okay with this. You've tested it. Like machine learning has a context. I don't know if you've heard of it. The banks do this thing called model risk management, where they actually do a whole host of validation and testing on things like redlining before they roll a model out.

44:09That's what the enterprise is going to have to do. And so it's not that the model improvement doesn't matter. I actually think the benchmarks are a good way to get some sense of model improvement, but they're almost orthogonal to enterprise uptake. I think enterprise uptake depends on trust and precision on specific tasks at 99 % accuracy, not generalizability. If those specific tasks are removed in the way that you said, like summary docs for investments, often it's done by more junior people in the earlier stages of their career when they are building and kind of scaling those skills. Do you think we will have a talent pipeline problem if we do remove a lot of those junior roles, which we are seeing in certain cases already, and I think we'll continue to see, where we won't actually have the graduation pathways that lead to the leaders that we have today because we've removed those junior roles?

44:55I don't, actually. So I think one of the challenges is that the adoption curve of this stuff is going to take a lot longer than people expect. So I do think, you know, I said this to you earlier, like I think on enterprise, this is a five to 10 year adoption journey, not one to two. And so I think you have a dynamic where people have a lot of time to react and to think about what's useful as, you know, in addition to that. And so I actually find a lot of the people coming out of college right now are some of the highest adopters of this and the most useful for these kinds of tools. And so we're hiring more and more people that profile, not less.

45:25But I think the usage curve of that group of people, certain tasks will not be done, but there will be many more. So I'll give an example, accounting. If you worked at a bank example or any accounting firm in the 1980s, this is absurd to think about, but you literally calculated revenue and financial statements with a slide rule. Like people literally would sit there and they would generate a financial statement manually on paper with slide rule. And that was how people did accounting. Now, Excel comes around. That becomes the main tool everyone uses to do accounting. And so in theory, you'd have less accountants because you went from manual generation of slide rules to Excel, which actually makes it way easier to do that.

46:03You look today, we have about the exact same number of accounts, in fact, the same number of junior accounts. And what's happened is people do way more sophisticated accounting scenarios with the tools they have. It's this old idea of Jevons paradox, which is you increase consumption with advanced technology. And so the number of accounts didn't go down. You actually had way more accounting. In fact, every FP &A function is probably larger now than it was 25 years ago because the work people do is more sophisticated. I do want to go back to, we said about kind of market composition and how we see the different players.

46:32is this a market where you said like Uber and Lyft, is this a market where there's one and two players and they take the dominant market share and then there's everyone else? Is it a cloud market where it's much more evenly distributed? How do you project that out in, say, a 10-year horizon? In both AI training and in enterprise, I don't think the answer is one player. You know, I think actually interestingly in the enterprise, historically, there's probably, it's been Palantir and not many others. So that's kind of, I think, why you've seen more, people want alternative options to that. I think that's part of the reason you've seen so much excitement on enterprise AI.

47:02recently. I think most of these markets end up with three, four or five players. I don't actually think it's even two. And I think the choice in consumers is markets tend to create that. And that's a good thing, right? Like I think you'll have some specialization on certain topics, you know, maybe some better at coding, some better at specialist tasks, some better at PhDs, but I think it'll stay with a fair amount of choice. When you look at the landscape, who do you most respect and what do you learn from them? I would say Palantir is the company I probably respect the most in enterprise AI. It's really interesting.

47:31You see them as a competitor more than a Serge or McCoy or a Charing or any of the others. I think they are both competitors in different ways to different parts. All of those players are competitors in different ways to different parts of our business. I think I call out Palantir because I think they realized 10 years before the rest of the kind of tech market that forward-deployed engineering customization would be important. And I think that was a very counter-cultural leap at the time. Because look, I mean, I spend a lot of time running forward-deployed engineering teams, and most of what I saw with players like Accenture, what was called tech services back then was not a place that anyone wanted to play in.

48:06And so Palantir spent a decade before anyone realized it was important building good tech, right? And so I have a ton of respect for that and the culture they built out of that. I think on the training side, I won't comment on anyone specific. I think all the players in the space are good and they all do different things well. There are large revenue numbers thrown out. Yeah. Are they revenue? Because I've done shows before with them and I got battered. bluntly, when people are like, oh, it's not revenue, Harry, and you can't categorize it as revenue. Is it GMV, not revenue? Are we playing fast and loose with the truth on revenue versus kind of bookings?

48:40I think it is revenue. I think that the rate you get on every project is different. The margin you make on every project is different. So I do think it is revenue. And I think that the Can you help me understand? Sorry, and I'm very naive. If I'm acquiring amazing talent, and I get paid for that. And then I have to pay them. And then I get my take at the end of that. How is that different than booking on Airbnb, where I get my take from a location, but I have to pay out to the owner? Oh, good question. Well, I think Airbnb has one consistent fee. That's the difference. There's actually a fair amount of variation based on the skill side of the expert.

49:13You don't have a consistent rate relative to the booking amount. That's the biggest difference. So there's huge variety depending on the project, the expertise type, the expert type of what you book on that. Are there any other big misnomers that you think are pronounced in the industry where you consistently are, I wish people would change the way they think about it? Look, I think the biggest one is just the view that when I first started this job, the main pushback I always got was that synthetic data will take over and you just will not need human feedback two, three years from now. And it's interesting.

49:44I don't, from first principles, that actually doesn't make very much sense if you think through it, right? If you think about the diversity of tasks that exist in the world and then how long it would take you to get comfortable with the accuracy, it doesn't make any sense. I'll take legal services because it's a really interesting one. A lot of the legal data in the world exists with big law firms. It doesn't even exist in the public industry. So if you take the corpus of publicly available information, that's been commoditized for years at this point. And so most of the logic is incredibly contextual to language, culture, multimodal context, and the information stored in individual companies, as an example.

50:20And so the only way to actually do the fine-tuning process consistently and to get it accurate for any specific context is RLHF. And I actually think in my decade, in my McKinsey days, McKinsey-Guan and Black days, that was the thing I realized was different about traditional ML models versus Gen AIs. In machine learning, you can backtest, you can get to a really clearly statistically validated outcome without any human intervention. I think on the Gen AI side, you are going to need humans to loop for decades to come. And I think that is something that most people are starting to realize. I think it's always confusing to people when they hear like, oh, that's how models are trained on the back end.

50:54I didn't realize that's how the statistical validation works. And so I think that's been an interesting evolution curve as people started to realize that. You're profitable, right? This year, we have started to invest a lot more. So I think one of the big differences, historically, Invisible had only raised$7 million of primary capital in its entire nine-year journey. We initially announced$100 million. Actually, right now, we raised$130 million. And so I'm investing very heavily in technology. So we will not be profitable this year now. Can you just take me to that decision? Because this was going to be my question, which is that's a very clear decision to be profitable.

51:23And profitability comes often at the extent of growth, naturally. Can you just take me to that decision-making for you and how you thought about it? Yeah, look, I mean, to me, it was a simple one, which is if you think about the dynamics of return on capital, you can either harvest capital or invest capital. And your decision to invest depends on the growth you see as a result of that investment. And I think we're in the greatest environment for growth that has ever existed. I think Invisible is really uniquely positioned to capitalize on that growth, too. And so I think of our five core platforms, I think of the growth vectors across both AI training and enterprise.

51:56And there were just way too many different things I thought were interesting to invest in. was the clear best use of capital. And look, I'm trying to build this for the next 10 to 20 years. And I think if you want to build enterprise value for 10 to 20 years, now is the time to invest and build. And I hope we never get to the harvest stage, but definitely not now. Where are you not investing that you want to be investing? I think the simplest answer is actual physical world interactions. So what I mean by that is I think a lot of the most interesting data that we don't even really have access to yet is things that exist in the physical world that are more complicated to acquire and organize.

52:32So I'll give you an example. We're serving one of the largest agricultural conglomerates in the US on herd safety. So actually monitoring risk factors, when should you send a vet for their herd of cows basically. That whole process relies on us actually sending forward deployed engineers to farms, dropping Starlink terminals into those farms and building out custom computer vision models in those contexts. And I think there are so many different physical world contexts that become really, really interesting. But it does take cost and capital to build those out. Like, you know, I think oil and gas, oil rigs are an interesting one as an example.

53:03And so I think physical world interaction patterns are some of the most interesting growth vectors for this, but they do take time and money to invest in. Robotics being another big part of that. One area of investment I think is interesting is brand. How do you think about Invisible's brand today? Well, it's interesting. When I took over, we had, if you looked at the entire public internet, I think there was one article available. And so we've definitely spent a lot more time this year thinking about - Was that a deliberate decision? I think so to some degree. I think Invisible has a culture of, you know, we believe in doing great work for customers and we were kind of not really focused on telling the whole world about that.

53:39Does that become detrimental to the business at some point there? Yeah, look, I do think branding matters a lot. My view now is that it's been very helpful for us to spend time where I spend a lot, I spend about 70 % of my time on the road and I go to a lot of conferences, things like that. And I think building a brand is really important for trust, for awareness, for engagement. And so, and I think also how you tell that story is really important. So I'm very much a believer. One of my favorite quotes, like Mark Andreessen has this idea that when private and public narratives diverge, that is the risk or the opportunity.

54:10So meaning if you say things you don't believe to be true, or if everyone's saying things that don't believe true, then what is the actual private narrative? So I think it's been very important to me to make - Can you just help me understand that? Yeah. So hypothetically, if I was going around saying we have an out of the box agent that does everything and then that wasn't actually true, that's what either creates opportunity for others or risk for us. That's how I think about it. And so I think what's been very important for me. Is that not our industry? I'm sorry. I don't mean to pick a fight with Marc Andreessen, but like, hello, Marc.

54:40Like our job is to sell and then deliver later. I'm thinking, well, I'm fucked. Well, you know, I guess it's all a question of degrees. And I think in my mind, like I want to say things where the narratives are the same to the public and to what our team thinks and what our customers experience. And so I think that's part of why I have focused on saying some of the nuances of what's not working and not claiming everything works out of the box. And I think that is that is a different approach. But it's been a core to how we've thought about building the brand is we are building this around trust where like I want a company we work with to know that if I say this will work, it will work.

55:12And I think you only get one chance to do that right. Do you agree with fake it till you make it? That's such an interesting question. I think it depends on what faking it means, right? And one of the things I think is really complicated about Gen AI is it's non-deterministic, right? So like if you've never built a machine learning model to do pricing in industrial manufacturing, you can still understand what data is available, understand how the price is being set today and get pretty comfortable that what you build, if you say you will build it, will work. And I think that is okay. I think the challenge of non-deterministic systems is there is more risk to faking until you make it.

55:46Meaning if you can kind of go out and say your agent will do anything, then you actually have to deliver an agent that works. Right. I think that's part of the interesting, you're asking about accounting dynamics. I think it's part of the interesting dynamic of like a lot of the contracts that people will sign right now are like, I'll sign for 50 agents to be delivered. But then the question is, do you deliver the agents? Do they work? And so I think that is a different thing than SaaS. To go back to your earlier question, if I deliver a SaaS box, I know it will work. If I deliver an agent, in the current world, there was actually a report AWS came out with today.

56:18It's interesting. Like 70 % of agents are actually not even AI agents, as you think of it. Most of the agentic processes today are actually traditional script writing and just traditional automation, right? And I think that's why I don't self-identify as an agent company, actually, at all. I think we do AI agents. We do AI workflows are a core part of what we do. But we do data. We do training and fine-tuning. and agents are one tool in the toolkit because I think too much emphasis on them a lot of the time won't work. Did you see the video of the robot going around the house recently? And it was like the worst thing ever.

56:50It was like 11 minutes to take out a glass and then at the end it was like, and this was controlled by Simon in the back room and you're like the shittest robot ever was then controlled by some weird dude in your back bedroom. Like this is so shit. I do think that is, yeah, I did see that. And look, I think robotics is another one that will take longer but will be really interesting when it works. But by the way, I think even in that case, you'll need more task-specific robotics, not just broad-based. Have you ever faked it till you make it and been caught out? And did you learn anything from it?

57:21So when I first started working and it wasn't even called AI back then, it was kind of data analytics was what it was actually called. This is probably 12 years ago or 13 years ago now, probably 12 years ago. And I think the firm gave me a pretty interesting purview to try and explore where I could build out AI offerings across different sectors and customer bases. And I don't think I knew what I was going to build, candidly. I think that the interesting dynamic is I had a lot of conviction that, and partly because some of the things I'd done before, that I could be really useful on a whole host of things from inventory forecasting to pricing to credit underwriting.

57:55If you just thought intuitively of the sources of data, the fact that 70 % of the software in America is over 20 years old. Most of that data is massively fragmented, not clean. And so a lot of the decisioning that happens in the enterprise is done in a really fragmented way. And this is what I did know. I did know that like, you took your average sales rep making a call. Most of the time they're like Googling some stuff to try and figure out what information, not now, but this was 12 years ago. They had very little information on the script to say customer information, what they might sell. So I had a lot of conviction that that would work.

58:26I did not know what would be most interesting. In fact, there were areas I thought would be really interesting like banking that were actually much harder to do this inconsistently. It was somewhat you mentioned earlier, like bank. So the average bank spends 93 % of its cost, of its tech cost on maintain initiatives. 7 % go into building new things. It's my favorite thing with people that I just had one of the CEOs of a big vibe coding platform on. And he was like, if SAS is dead, we're going to build our own SAS products. I heard this episode. Yeah. Yeah. And I'm just like maintaining, provisioning, updating.

58:58Are you high? Yeah. If you've never gone through InfoSec and approval of the bank, like the banks are banks. And look, for very good reason, banks are much more complicated to do a bill like that in, right? And so I think what was - This event that I was at last week was a bank. They have 6 ,500 people in KYC alone. 6 ,500 people. It's a great example. And so I think when I was doing that in the early days, partly because there was very little media coverage or interest in it, I was kind of figuring stuff out from first principles. And so I think the degree to which I faked it when I make it was I had to figure out other people I worked with and customers that trusted me enough to allow me to co-iterate and develop stuff with them.

59:35I had to figure out a way to recruit really good people. That was actually like, I actually think if you take any business very simplistically, it's a question of can you build trust with customers and co-iterate to develop and make things work? And then can you recruit unbelievable people to deliver that? And it actually really comes down to recruiting in a lot of ways. I think that that's actually the number one thing we focus on. I think of us as a talent company as much as anything else. Like you could, you could argue that like, not to use a sports analogy, but like Nick Saban did not build Alabama football with the process.

1:00:02He built it with recruiting the best football players in the country. And I think about that the same way is like, you have to recruit great people. So in some degree, in the early days of that, you know, 10, 12 years ago, I was setting a vision and trying to figure stuff out and actually iterating a lot of stuff. And I do think we ended up building a lot of things that really worked, but it took time and it took iteration as much as anything else. It took iteration and trust. So I would say the counterintuitive thing is I didn't fake it. And then I never told people would definitely work. I would actually, my entire approach would be to say, I think this would work.

1:00:33This is my reasoning why I think it would work. And let's build this. And that actually, a lot of people were very comfortable with that. I think if you go in and say, I have an out of the box AI that solves all your problems, people are pretty skeptical. I do just want to stay on recruiting. Cause again, I always, again, I think it shows successful because you put on the hand and you're like, as a startup CEO, one of your biggest jobs is to recruit great people. Yeah. Having recruited people across different companies now, both McKinsey and now obviously Invisible, what would you advise startup CEOs in the earlier stages, knowing all you know now and what it takes to be great at recruiting, acquiring, and retaining great talent?

1:01:07Yeah, it's probably the topic. I spent an enormous amount of time focused on that. It's probably the topic I think about the most because I actually do think if you get amazing people, everything else will follow from that. So you agree with the moniker of like hire great people and let them do that work Because people have kind of pushed back on that now. Yeah, I think not just hire, hire, retain, and evolve great people. Because I actually think you have to give them a platform that they enjoy day-to-day. I think the two things that I believe that are somewhat counterintuitive is when you recruit a great person, I don't think about role most of the time.

1:01:36Meaning I think people are very role-focused of like, I will hire this person and they will only do oil and gas as an example, right? But the reality is like really good people will run five to six different roles across your group, they'll run seven, eight different products. Particularly on the business side, you may have somebody that does everything from delivery to sales to account lead. And you can be comfortable with that if you hire great all around athletes in a lot of ways. And I think the second thing is it has to be fun. My view on one of the narratives that has gotten a bit lost in the last couple of years is if you have a culture that is brutal to work at, people will leave.

1:02:11They might stay around as long as your stock's high, but they're not going to day i think you have to create an environment where people really enjoy going to work every day where they're intellectually challenged and where they feel like they can unleash creativity and so i think that's i spent a ton of time thinking about that can you just i don't want to argue back but i i want to build great companies myself yeah i'm trying to the 20 vc and i try to build good cultures revolute is a brutal culture to work out famous for it but nick has famously always told me culture's fucking bullshit. Winning is what matters.

1:02:42When people win, they learn more, they earn more, and they grow. Yeah. And that really is culture. Brutality in bounds drives humans. Is that wrong? No, I think it's actually right. And let me caveat what I said is I think it's also the nature of the business I'm in being AI. Meaning I actually think that's a very true statement. If what you're trying to do is scale a relatively consistent business model to do one or two things, then that is a function of execution and hiring people to go in very specific roles and do very specific things well. And I actually, sorry, let me caveat my prior comments on that.

1:03:15I think the difference is a lot of what we do is research and exploration fundamentally, right? And so in the AI world, it is a different dynamic in that you're trying to figure out very specific problems with customers to solve and build really unique tech. And so I think in that world, you do have a different cultural dynamic. It is a research culture as much as it is an implementation culture. Is that difficult then? We do a show every Thursday, which is blown up, which is incredibly nice for us as a business. But essentially we have Jason Lemkin and we're at Driscoll, two VCs. And we talk about news and we talked about Sam Altman and war mode.

1:03:51Can you do a war mode then in the culture of research and AI where it's maybe more thoughtful? Does that work? Yeah, there are definitely parts of our, I think if you take our delivery and operations team, they're in warm mode quite a bit of the time. So I think, again, I'm more describing general, I think, countercultural beliefs I have on how to hire certain sets of great people. I don't think it applies to every single function of the company. I would agree with that. I think there are definitely, you have to be able to push really hard to deliver certain outputs. And I think we do a great job of that.

1:04:19But I also think, you know, there have been ideas of like every great engineer should be able to spend 30 % of their time on new projects as well as sprinting on the existing ones. I think it's paradigms like that that are important. What decision are you scared to make, but you think about it often? Yeah, I think the simplest answer I'd have to that is that growth in this industry relates to the amount of capital you raise. And, you know, your earlier question on investment. I do think there's a world in which if you pursue hyperscale growth, it is possible, but you have to invest a lot more to do that.

1:04:49Like every new company, every new customer you onboard, it does cost money to do the forward-deployed engineering work. You invest more in your tech. and so there is an interesting like do you run a business for consistent steady growth for 20 years or do you try and build something that gets to 50 to 100 billion dollars and becomes game changing and we have very much tried to operate in a way where i think we have a path to profitability everything else but we are going to invest in the near term because i think it is it is a very interesting time to do that i know you don't like to name names but i can because like when macaw raises like two billion dollars and you're like fuck we need to raise more fucking money It's interesting.

1:05:25If you look at the players in our space, there have been very different levels of capital raised and people had success more and less. I actually think a lot of our investment is in different areas than many of our peer set in AI training are focused on. A lot of it's in things like the enterprise, it's in core software platforms that are maybe a little bit different than what others are focused on. So I think you can raise a lot of money and the question is where you spend it. Again, I actually think most of the capital we need in the next five years is more enterprise focused. I think we've actually built something on the IT side that I feel very, very good about.

1:05:54We were talking about recruiting before I went off on a tangent there. You now have offices despite being a remote company for several years. Does remote not work? Yeah, so we were a fully remote company for nine years until I took over. We've now gone largely in person. We do have some folks that work remote, but we now have offices in New York. We took the old Pinterest space in San Francisco, London, Paris, Poland, DC, and just opening Austin, Texas now. And I think the interesting thing I've experienced is that is I do think remote, you really struggle to build culture in the same way. So I think that the things I've experienced as we remote is just a way stronger positive culture of co-location, which I think people enjoy their work and get to know their coworkers a lot better as a result of it.

1:06:39I think it gives us a lot more depth with customers to be co-located in cities where we spend time with them. I mean, like if I take London and Paris, we need to be co-located with the customers there. It can't just be, you know, someone in a Zoom screen in New York. Do you see productivity increase? Exponentially, yes. I think if you take engineering as an example, like I think you can execute engineering tasks remotely, but the process of working through really thorny problems, like we, so I've tripled the size of the engineering team. And what I can tell you is interesting thing is vast majority of those people wanted to be in person.

1:07:09Now, I'm not saying that's true of all engineers, but it was interesting how many people, particularly the younger tenures were like i want to be co-located i want to work through things and so i don't even mandate office attendance i just have it in those offices and we have huge appetite like i was with our we have 40 people in our london office i was with many of them last night they were all commenting on how many of them come in voluntarily even on like a friday where they might not need to because they like being around their peers i think that i would actually bifurcate two separate things and i don't think they're related one is the hours you work seven days a week, very flexibly, depending on what client needs exist from physical co-location.

1:07:46I actually don't think they're related. Meaning I think the benefit of integration is if stuff comes up on a Saturday or you're pushing on a new product bill, like you will work on that Saturday. But if you do that from your home, that's totally fine. I think office culture to me is like, if you took a hypothetical thought experiment and you said over a year, I think there is a diminishing return from being in the office all the time where you lose flexibility. So as an example, if I said we were physically, we're remote 100 % of the time, that would not work at all. If I said we were physically in the office six days a week, I think that is overkill and you lose great people, particularly senior enterprise folks don't want to be in the office on Saturdays.

1:08:23But what I think we found a nice balance between is people come to the office most days, people really enjoy being with their colleagues. They work most days, but they can do it from their own home on the weekends. And I think that sort of flexibility is good. Final one before you do a quick fire. What did you believe about management that you now no longer believe? I think two things I would highlight. One is that I think control is a bit of a fallacy depending on the volume of things you have going on. Meaning I actually think to the question earlier on hiring great people, if you're serving several, let's say a couple of years from now, you're serving a couple of hundred customers on different topics, you actually need to have values, consistent tooling, consistent approaches, but you need to empower all those teams at the edge to operate and do what they will.

1:09:09And so actually one of the big focuses I've made over the course of the year is to reduce a lot of our hierarchy, make the organization way more flat so that companies, that people at the edge serving customers are empowered to make decisions. They have decision-making frameworks, they have consistent tooling, but they are empowered. I think trying to control that centrally maybe works in like a manufacturing business, but you you lose a lot of latency of decision-making. So, you know, I think if you look at like, there's a lot of, interestingly, military history that would say the same thing that it's like, actually, if you look at the function of an army, at some point it moves into people in the field, make the decisions.

1:09:43And so you have to have the training, the strategy, recruiting to do that. And then you have to empower your teams to work. And I think, I think about a lot of that very similarly. And the second thing I think a lot about is in the AI world, at least strategy is a somewhat overrated concept. And what I mean by that is I think actually all strategy, I was talking to a CEO in the biotech space, and he was saying that strategy is very important for them because every time they make a capital decision, it's a seven-year capital cycle, right? And so in that case, strategy makes a lot of sense. But in the AI world, one thing that's been interesting to me is every three months, the entire world changes.

1:10:14And I just had to get very comfortable with that dynamic. You have to think about your investment life cycle as core beliefs you have, and then 30 % to 40 % of things that you iterate constantly based on new tech. So there is tech that you're going to build, like a new voice agent comes out that will become obsolete. and you have to just be very comfortable that you're building an interoperable set of frameworks that you can integrate the new tech into and that has to be a core function of the business is five year strategic planning is not a useful exercise right now in a lot of ways i think you want to think about five years in terms of the cultural context you build the organizational like the institutional memory to use the seven powers framework but the actual iteration cycles are much much faster and i think if you don't react quickly that that does not sustain and now i think The interesting flip side of that is enterprise sales cycles, for example, are much longer.

1:11:02So it's not like you can't survive unless you're making decisions. But I do think the big thing is a lot of the tech being developed changes every two to three months. And so you need to be constantly incorporating that into what you build. Final, final one, I promise for a quick fire. You said about always being traveling and you mentioned a girlfriend earlier. How do you make that work? And what would you advise me as like, hey, tips and tricks to not have a severely pissed off girlfriend most of the time? I think the first thing is to find a great girl who understands that you are really passionate what you're doing and is supportive of that.

1:11:33I think my girlfriend Claudia has been great on that front. I am very appreciative of that. But look, I mean, it's tough. I'm on the road probably 60 % of the time. If you look at my last four or five weeks, Riyadh, Geneva, Paris, Berlin, London, San Francisco, Boston, Singapore, now London again. So I mean, that's a - Do you enjoy this? I do in some ways. I think that I feel very lucky to be building something at this particular time and with a group of people I love working with. You know, this happens to be what I spent my last decade doing. And it happens to suddenly now be what a lot of people want to do, which is great.

1:12:08And so I feel very lucky because of that. And so every day I wake up and see what else can I do to kind of push that forward. And so I do kind of live on the road. But look, I think some of the things I've tried is like, you know, you figure out things like FaceTime. you make sure you keep the cadence interaction high because being on the road is tough but i also don't think it's forever i also think i'm in that fun stage of trying to take something to like we kind of went zero to one and now we're trying to go one to end but we're not yet you know fully mature public company or anything like that and so i think she's been very understanding throughout that process are you ready for a quick fire round yeah okay open ai at 500 billion or anthropic 360 billion which would you rather invest in i do not comment on any uh any players in the model their space for a variety of reasons.

1:12:53You can see what is the discomfort around. What's the most underrated infra company today? Databricks, which is you're going to be like, well, they're very rated, but look, I think their tech is great. And I think that it's interesting in a lot of ways, the most useful foundation for AI is really good Databricks infrastructure. I think when I hear a customer has them, I'm always very happy. What's the best advice that you've been given that you most frequently go back to? We kind of talked about this a little earlier, but a CEO that I respect a lot, when I took the role, I asked him his advice.

1:13:23They're like, what's the best way to think about a team? And he said, look, your job as a CEO is to do three things really well. Recruit great people, create a culture where they love working together and build great things and try and make them all extremely rich. And I think it's a funny framework, but I think an interesting way to think about, like that is my responsibility to employees. I want to find great people, help them enjoy each other, and then build something that becomes big and helps all of them achieve their dreams. What's one widely held belief about AI that you think is completely wrong?

1:13:53That out-of-the-box agents will solve everything with a push of a button. That is, I think, the biggest misconception now is that I think many people were hoping the adoption curve will be, I buy something, I just push it in my business and it takes a whole process and fixes it. And I think they're realizing it requires training, fine-tuning and a whole host of process redesign and business ownership. You are me today. You have a new$400 million fund and you're a partner in the fund with me. where should we be investing when most people are not? Because everyone is investing in agents out of the box.

1:14:22Well, yeah. Look, I think it's an interesting question because I think a lot of the reason people are investing in the agents out of the box is that they're trying to apply a SaaS paradigm of what's worked historically to AI, which is challenging. The model building layer is clearly producing amazing returns. I think the AI agent layer is more complicated now. Where I think that's also complicated is the application layer is tricky too. And I think you hear a lot of commentary on this. Many of the applications may or may not work. They're not really getting full workflow embedding. They're more of nice to have in workflow context.

1:14:53So actually my counterintuitive take would be one interesting question of the paradigm now is whether new companies built around AI get distribution faster than big companies figure out how to adopt AI. I think that's the interesting paradigm for our society. And so I think some of the most interesting new businesses are actual businesses using AI in the physical world that are AI native and that will be highly disruptive. So you mentioned Revolut in banking, for example, or you could go into like loan servicing. There's many different areas where people are standing up new businesses. One of the most interesting stats I've heard recently is if you look at Y Combinator's recent class, I think it's like the largest, it's 2x the revenue of any prior class.

1:15:34And many of those are businesses that are actually serving a customer need, not selling that customer software, if that makes sense. And so I think from an adoption standpoint, one way to do this is to bet on AI agents, which are more of like a SaaS paradigm who will sell stuff to customers. And the other way to think about it is what are business models that will change because of this? I think there's a whole list of like, you know, Gen AI native services businesses are very like, you know, tax accountancies, et cetera, are really interesting examples of that. Again, you're a partner with me in the fund.

1:16:04Do we just get used to a world of lower margins? Is that how this business plays out? Is the world of 70, 80 % software margins over? First of all, challenge that 70, 80 % software margins actually ever existed. What I mean by that is there's the gross margin. But if you look at profitability in public software multiples, it's fascinating. In the last two years, you've seen public software multiples go from 20x to 10x, partly because of growth changes and partly because as they move profitable, their growth slows materially. And what you realize is, I actually would take the flip side of this, which is the integrated units will be very, very profitable because the way they grow, they'll be able to acquire customers faster, build them things that are good faster.

1:16:43And so they won't have the box stickiness, but they'll also, I would argue a lot of those software companies below the line were not that profitable. When you look forward to the next 10 years, final one, what are you most excited about? Like, you know, for me, my mother's got MS. I look at potential advancements in MS, drug discovery, treatment pathways. What are you most excited for? I like to end on a tone of optimism. Yeah. You know, I think despite some of my, what I call realism on enterprise adoption, I actually am an AI optimist. And I actually think that the current narrative on some of the risks are far outweighed by some of the benefits.

1:17:15And like, just to give a couple examples, right? And I'll go through four, including healthcare. If you take energy as an example, right? There's a lot of question around like data center implications for energy, but do the math right now, data centers are about 1 % of total global electricity usage. AI data centers are 0.25 to 0.5 % of that. So actually really small. I don't is 14 to 20 % of global electricity usage. AI has so many different ways of grid optimization cooling where, I mean, the World Economic Forum just came out and so it's going to be massively net positive from a environmental impact standpoint.

1:17:49So I think energy is one where if you think about all the energy needs we're going to have and the investment now going into clean energy because of all this, I think we'll actually be in a much better place 10 years from now. I think healthcare is another interesting one. If you look at US healthcare, we spend$14 ,000 per capita per year on patients in the US. So that's like a rough spend. That's two and a half to three X, like Germany and Canada spent as an example. If you then break down the context of that, you know, 9 % of that roughly is administrative, something like 25 % of it's waste.

1:18:18And then actual cost of care is like really challenging. I mean, Johns Hopkins has released a stat that 250 ,000 deaths a year happen because of avoidable errors. You see things in AI like 20 % better identification of breast cancer risk, for example. So I think actually healthcare is another one where the cost framework for healthcare has been not good over the last 20 years. And the cost of care improvements will be really material if AI works well. So I think that's another one. I think the one I'm probably most excited about is education. If you're a kid growing up in any socioeconomically disadvantaged city in the world, your ability to learn about any topic on earth incredibly quickly is better now than it has ever been at any point in history.

1:18:58You can take any topic on earth and with just an internet connection, learn, you can go through, and you can pick your topic. And I think one of the reasons that's particularly important is educational system we've had for the last 10, 15 years, actually 50 years, doesn't really work. I mean, we have massive K through 12 challenges with STEM topics in the US, for example. We have huge learning gaps, largely driven by sociodemographic context. And most of our educational system is based around like teach people biology, English, and history, and like not teach them a base on things like FICO scores or how to do coding.

1:19:31And to add to all that, the college system has created a student debt crisis where way too many people are going to colleges that are not worth going to for and taking on enormous amounts of debt to do it. So I actually think, again, I think the way our educational system will shift, will function, will shift material. We're a talent assessment company. An enormous amount of people we bring in did not go to college. And we assess them on cognitive aptitude and skill. And so I think the really positive note I would leave on is I think the way that people learn, the topics they learn, the way we look at resumes and how to screen and assess people will move in a really positive direction.

1:20:05And I think a very different one than we've had the last 100 years. Absolutely thrilled to hear that there is value in non-college or dropouts as a dropout myself. This has been so much fun to do, Matt. Thank you so much for being so flexible with the topic type. You've been fantastic, dude. Thank you for having me. But before we leave you today, are you drowning in AI tools, chat GPT for writing, notion for docs, Gmail for email, Slack for comms, and you're constantly copy pasting between them all, losing context and losing time. This is the AI productivity tax, and it's killing your output. At 20VC, we're all about speed of execution, and Superhuman is the AI productivity suite that gives you superpowers everywhere you work.

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