Inside Ode with Anthropic, the private equity-backed startup betting AI services are the future of enterprise

Can a handful of engineers really do the work of an army of consultants? That’s the bet behind Ode with Anthropic — the joint venture dedicated to embedding forward-deployed engineers in enterprise firms, backed by Anthropic, Blackstone, Hellman & Friedman, Goldman Sachs and others. On this episode of TechCrunch's Equity podcast, Rebecca Bellan sits down with Ode’s leaders Chris Taylor and Eddie Siegel, who founded Fractional AI, the applied AI services startup that Ode acquired earlier this year to serve as the new venture’s core. The three discuss why so many enterprise AI pilots never make it to production and why they think AI-native services are about to become one of the biggest categories in tech. Listen to the full episode to hear more about: The case for AI-native services, and why Ode believes small teams of applied AI engineers can unlock hundreds of millions of dollars in business value. Why the company is hiring experienced generalists and former founders instead of AI researchers, and how it's turning them into elite FDEs. Whether an AI services company can scale like software, yet maintain a boutique feel, and why Taylor believes Ode could one day become a trillion-dollar business. Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. Chapters: 00:00 Intro 00:30 Fractional AI becomes "Ode with Anthropic" 1:13 Why non-AI companies are the real AI winners 2:04 Working with Blackstone, Anthropic, and beyond 3:05 Inside a real project: fixing LogicGate's bottleneck 7:29 How long does it take from hypothesis to production? 9:19 Measuring ROI: revenue, efficiency, and evals 16:37 Model choice vs. workflow redesign, and why it's Claude-first 23:10 Hiring generalists over specialized AI talent 26:39 Can this scale without turning into another consulting firm? 30:49 Outro Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Rebecca Bellan?

  Hello and welcome back to Equity Tech Crunch's flagship podcast about the business of startups. I'm Rebecca Boulon and this is the episode where we bring on industry experts to help us explore a trend in the tech world and dive deep. Today, we're joined by Chris Taylor and Eddie Siegel, former co-founders of Fractional AI. And I say former because they're now working on a new venture. It's still in the nascent AI services industry, but this time it's backed by heavy hitters like Anthropic and Blackstone.

• 0:31Rebecca Bellan?

  Chris, Eddie, welcome to the show.

• 0:32Speaker 01

  Thanks for having us.

• 0:33Speaker 02

  Thank you very much. Great to be here.

• 0:34Rebecca Bellan?

  Fractional is the company that is on your email still, but you were recently acquired by JV between Anthropic, Blackstone and some others. And you've got some news to share about that JV today. Well, can you give us the news?

• 0:50Speaker 01

  Yeah, so we are launching under our official name, which is Ode with Anthropic. Very excited to put the Fractional AI chapter behind us, clear up the confusion that has been created by the lack of having a name for this JV and put a real name to it.

• 1:07Rebecca Bellan?

  Okay, so tell us about the company and what the mission is and why Fractional is at its center.

• 1:13Speaker 01

  The basic founding belief of Ode is that non-AI companies are going to be among the big winners of this whole AI moment if they adopt the technology the right way. But adopting AI is very hard and companies need a lot of help to do it right. You know, you're essentially taking this magic hallucinating ingredient and trying to rewire your core business operations or your core customer experiences with it. That requires top caliber applied AI talent, which is not something that the most companies have. And so Ode is being founded to close that gap and help companies with that adoption. I think this is an emerging market that you're hearing more and more about that's this being referred to as AI native services.

• 1:56Speaker 01

  And I think with Ode and with the close relationship that we have with Anthropic, we're aiming to build the defining company of this AI services market.

• 2:04Rebecca Bellan?

  Okay, so what is the breakdown of your companies like or your customers, I should say? Like, who are you working with? What kinds of companies are you helping? Are they all Blackstone portfolio companies or?

• 2:14Speaker 01

  This is a big mix of clients that we're working with. We are, we are working with many Blackstone portfolio companies as well as other portfolio companies from across the consortium of investors that we have. So Helman and Friedman, and we're now exploring engagements with Goldman Sachs portfolio companies and many others. We're also working with many Anthropic clients. And so there's a close collaboration between our team and the Anthropic commercial team. And they're bringing us into some of these really exciting opportunities where there's an opportunity to use AI to innovate and drive outsized business impact.

• 2:49Rebecca Bellan?

  Okay, so then what is Ode actually building and delivering, right? Eddie, I'm curious, like from a CTO's perspective, what are you doing? Like, how is it different than like this is just some API calls and we're helping set up an agent or something like that?

• 3:05Speaker 02

  Yeah, it's a great question. I mean, I think to Chris's point, part of what, you know, sort of got us into this market to begin with is we just saw the really early days of this like gap starting to grow between like, you know, the these frontier models, the API calls you can make and the sort of answers you can get back are getting like increasingly incredible, like really smart models that are getting smarter and smarter and smarter. And yet the ability to sort of achieve real business results with those models is staying really hard. And so what we're doing is we're going embedding with these companies and like really studying their problems and then building these custom bespoke pieces of software that fit into their problem, that fit into their infrastructure and can use the new technology to sort of solve these problems.

• 3:46Speaker 02

  And so there's a mix of really challenging engineering and really complex systems. It's also a bunch of really complex understanding of bespoke processes. Like one example of a project we're working on recently is for this, you know, company that makes software in like the governance, risk and compliance space. So picture like complicated B2B software that in order to sell it to their clients, the very first thing they have to do is a bunch of like customization and configuration to make their software useful to their clients. It's like very, very customized software. And so they have this kind of team of people internally that has to go understand their clients' needs, apply a bunch of configuration and customization.

• 4:28Speaker 02

  It takes months and it really delays, you know, how long it takes for those customers to be able to get value. And so we went in there, we spent a bunch of time in there trying to help them, you know, make this whole process better with AI where we are now, you know, building this, this system that can take as input a bunch of, you know, sort of things that happen in their sales process, right? It's like calls with customers and PDF documents describing processes and email exchanges, things like that, and help them do a bunch of their work much, much faster where like it can sort of pre-plan how to configure the software for them. It can make, it can, it can apply that configuration internally. And so going into that is like a lot of ingredients, right? We have to like build it pretty hard to build system.

• 5:10Speaker 02

  We have to stand up a bunch of evals to help us measure how well is our system performing? Like versus what their team would be doing without AI? Like, are we performing as well or are we not performing as well against like a bunch of real complicated use cases? It has to integrate with all their internal systems. You know, they have complicated software and like, if you want to automate the configuration, you have to be making lots of internal API calls into their, their systems. And you have to sort of tune for accuracy, you have to tune for cost.

• 5:34Speaker 01

  So this is an example that we see across a handful of clients, both software companies and tech-enabled services companies, where implementation of their software or the configuration of their software is the bottleneck for their, for their business. So the particular example that I believe Eddie was referencing here was Logic Gate. The CEO of Logic Gate called us up and, and basically came to us with the idea that, hey, I think there's an, there's an opportunity here to automate many of the tasks involved in implementing our software. This implementation process is the key bottleneck of our business. Revenue is, you know, six months out while we're implementing software for our clients.

• 6:20Speaker 01

  How can we pull that revenue in, help clients get more value out of the, out of this software and un-bottleneck the growth of our business from the, the growth of this implementation team? And, and the, the conversation was essentially like, you are, you are correct. This is a massive opportunity. Many of these tasks are automatable, but it's a lot more complex than, than simply rolling out Claude license to, to your team. And, and so we went, we went in and designed a custom piece of software, working hand in hand with their implementation team that was aimed at automating many of these tasks and creating a lot of leverage for their team.

• 6:58Rebecca Bellan?

  How long does this take? Like if I'm a company that, you know, is trying to boost my whole workflow with AI, I know there's a problem, right? I know, I know there's ways that AI can speed things up or automate certain processes, but I don't know how to do it, right? So I call you guys. How long will it take for you to get, like, how long, like end to end, does it take? How involved is the process? Do you, like, I'm imagining an army of FDEs for deploy engineers just like hovering behind people as they do their jobs to see how it works? Like, what does this look like?

• 7:29Speaker 02

  The, the process is very hands-on and it changes throughout the lifecycle of an engagement, right? It usually starts with someone coming to us with some set of hypotheses, but that are not totally well-understood, right? They're not, they think they know where sort of AI can help, but they don't necessarily have every detail in place. And so we spend a bunch of time with them sort of first just solidifying the business case with them, right? We want to be building things that actually have business impact, that are actually going to see the light of day, that are going to remove the needle. And so we spend time with them refining their business case, understanding things like, is this possible? Can AI really do this? Is your data available? Like, will your teams be able to accomplish this kind of thing? And then from there, when we move into implementation, you know, then there's a lot of building and that, that looks like what you were describing, right?

• 8:09Speaker 02

  Sitting next to their teams, multiple times a week, talking to the folks that, that really know their processes really well and kind of collaborating on the right things to, to, to do. How long does it take question is, is hard because the, this, often this exists on some long-term, you know, sort of roadmap and, and the endpoint isn't necessarily clear. So like in the, in the software configuration example, the first thing we wanted to build was something that their team could use to speed up a subset of implementations within a few months. Like wanted something in production useful to them, speeding them up in short order. But the long-term goal is to build something that can become so robust, they can actually make it a product feature and give it to their customers to do some of their own configuration work.

• 8:52Speaker 02

  And that, that might take, you know, a year or more to actually get, you know, to see the light of day. But like in, in the interim, we want to be shipping something of value. So like one rule of thumb we have is like, It may be part of a long-term big vision, but we want to make sure that something is in production adding value, like measurable business value within three to six months. Otherwise, I think there's risk that it can become, you know, this sort of perpetual endeavor that never shows ROI. We want to make sure we're actually moving the needle on something.

• 9:19Rebecca Bellan?

  Yeah. Well, how do you like, what is a metric that you use to tell you that this is succeeding? Right? Is it adoption? Is it EBITDA? Is it headcount reduction? You know, is there revenue growth? Like, how do you know that there's an ROI for this? Like, are you matching like a, like a software developer's salary spend? Like, what are we talking about here?

• 9:38Speaker 02

  Chris, you can like share more about the, the kinds of metrics we see, but I think it is like the, the range of stuff you're thinking about there is the range of, of stuff, right? We want it to be revenue and not that we successfully ship the chatbot, right? We want it to be like something that is a business case that like actually impacts the business. More often than not, it takes the form of, you know, revenue or growth or some sort of efficiency. Like in the case of the, the, the GRC software, like they are measuring their time to value for new customers, right? How quickly can a new customer get on boarded and actually see value? And that, that, that time should, should shrink. And they want to see how many new customers can be served by their stretched way too thin implementation team. And they should see that that same team should be able to handle

• 10:22Speaker 02

  more and more customers, whether they should be able to engage in more and more sort of like high leverage work. So different, different projects are different. I think a lot of times there's sort of like AI enthusiasm that is driving the, this sort of like investment of the technology. That enthusiasm is usually rooted in like a real opportunity. Like they see that like AI can really help with this part of our business that's driving us crazy every day. Like this, this thing used to be like an immovable object and now, now AI has like changed that equation. But sometimes then the, the measurement of it is immature or like, you know, different people at the company will disagree about how to measure the results. And so we try and kind of get involved in shaping it into like, okay, if we accomplish this thing, like that'll show up in the share price of this company or,

• 11:04Speaker 02

  you know, that, that'll show up in, in like, you know, their ability to grow revenue.

• 11:08Speaker 01

  Yeah. More, more often than not, it's revenue growth with, with some efficiency gains. You know, it's, it's removing that primary bottleneck of the business to unlock more revenue growth. And then if you grow that revenue, you don't have to grow expenses at the same pace. And so ultimately you end up with a business that's making more money, both top line and, and EBITDA.

• 11:29Rebecca Bellan?

  Yeah. I mean, well, it's still such early days, right? So I imagine you have to do this on like a quarter by quarter basis, these kinds of check-ins to make sure that a lot of the tools that you're, that you're creating are, are working. And I imagine that because you have so much experience working with different enterprises, like you have a really good bird's eye view into why so many enterprise pilots are failing and not making it to production. Like, what do you think is the biggest bottleneck?

• 11:54Speaker 02

  Yeah, we, we're not checking in quarter by quarter. We're checking in every two or three days. So it is a very, very kind of, I don't know, we're, we're very hand to hand in glove with these clients where, you know, it's, it's very iterative. It's our kind of AI expertise meets their domain expertise that actually makes these, these things successful. For every one of our engagements, like we're very religious internally about tracking a north star for every project, having, once a week, we check in on that north star. We make sure it's correct. We think about the blockers that north star. We design, you know, with all these AI systems, like evals are absolutely critical. So the fun thing about these AI projects is like, when you think about how AI can be a solid,

• 12:37Speaker 02

  you know, used to solve these problems, usually everybody's got a million ideas for things you can do. You can use this model. You can use that technique. You could build a system this way. It's like very, there's a lot of enthusiasm. The way to sort of rein that in is to measure it. You start with something really simple, people the simplest possible system. And then you build some system for measurement where you can say, okay, today my system does this thing. You feed it input. Here's the output it gives you. And then you compare that to known good output for input. And if you can build that, you can then measure consistently over time. You're sort of up into the right trajectory for how well you're, you're sort of performing at, at these tasks. And so we are like measuring every couple of days, you know, we, we're at some threshold. We like use these evals

• 13:20Speaker 02

  to spot some edge cases. We bring those edge cases to the team doing the work at the customer and we talk to them about it. We're like, hey, you know, GRC software company, you, you know, we found this like edge case. It's like really hard implementation thing. Like this is what our system did. How would you have done it? And we use that to sort of check in on the, on, on the project. And usually some part of those evals is either directly measuring or it's like highly correlated with the business impact. You're measuring, well, if you can drive this up, like it is one for one with like revenue you're going to drive, or it's one for one with time you're going to save. And so you try and measure things that are not just accuracy, but that are like very tied to, to the ROI.

• 13:54Speaker 01

  And if, if I zoom back to something he said at the top there, you know, it's, it's the combination of the applied AI experience and expertise to, to build these systems with the deep subject matter expertise of the client and working in close collaboration that makes these projects successful. Ultimately, you also are, are needing the senior level buy-in of the company because you're trying to change how a company does business. And, and so you need, you need all of those things to be successful. A lot of the work that we're, we're doing is the top one or two priority for the CEO of the company. It's the most important product feature that the company is going to, to build over the course of the next two years, or it's reworking the most important business process they have. And so it's, it's really exciting work. You know, it's, it's the, the team that

• 14:38Speaker 01

  we have gets to go work on the most exciting project at every one of our clients with that senior level support, with the deep subject matter experts on the other side as, as thought partners in building these systems.

• 14:50Speaker 02

  And we're in like a super fortunate position, right? Like Ode kind of fundamentally exists in this market where there is a lot more demand than there is supply. And so it means that we get to be somewhat choosy about which projects we take on, you know? One of the, the primary lenses we're applying is what is the size of the impact we think we can have here? And so we're trying to take on these top couple priorities of a CEO of a big company where we think AI could provide huge, you know, impact if you can get it right. And then usually getting it right is bottlenecked by having the best applied AI team, you know, super talented engineers, super talented, you know, forward deployed product managers, folks that, that actually have experience doing these projects. That's like the sort of the magic set of ingredients that we see work for us.

• 15:33Rebecca Bellan?

  Yeah. So then how much of, of enterprise AI success today comes from choosing the right model versus redesigning these workflows, right? Like, cause we know, we're always, everyone's so excited about the next model that comes out. This one's going to be a game changer, et cetera. But it sounds like maybe that's not the most important thing? Or is there a combination?

• 15:50Speaker 02

  I mean, I think that the improvement of frontier models and seeing them get smarter is a rising tide that's sort of like helps with all of this stuff. And so it is very exciting when, when new models come out that, that make new things possible, specifically for the category that we're talking about here of like bespoke implementations where you're building a custom thing to solve a unique business problem. I think model selection matters, but it's, it's, it's not where like the majority of calories are spent. You know, like you spend a lot more time. It's one ingredient in a, in a system that has to be engineered. It's like the choice of programming language when you build a piece of software, right? Like you don't want to make a crazy choice in programming language. And like for some projects, it matters more than others, but I would not define an enterprise transformation

• 16:33Speaker 02

  in terms of like whether they choose, chose like Python or Java for something.

• 16:37Rebecca Bellan?

  So Ode by Anthropic is Claude first, right? You're always going to choose Claude first if you can. Otherwise you're model agnostic? Or how does that work exactly? Like what happens if, you know, in a few months, like GPT-6 just way outperforms anything that Claude does? Like, will you always pick Claude first if it, if, if it works well enough?

• 16:58Speaker 02

  I think there's more to think about here than just the model. When you think about Anthropic, like an example here is like, you know, we recently, you know, started becoming very heavy users of like Claude tag, right? Which is not a model. It's an application. If you're not familiar with it, it's, it's sort of the ability for Claude's models to sort of live inside of your Slack and independently.

• 17:18Rebecca Bellan?

  A Claude tag, yes.

• 17:19Speaker 02

  Yes. Run a bunch of workflows for you. It, it acts as like you're a coworker that's sitting inside of Slack with you. And it is using Anthropic models under the hood. By the way, this has been like a really cool experience for us. Like, it's taken a bunch of work for us to sort of get it into a place where it's running these workflows. But there's like all these like business processes we run internally where like, you know, we have like our ops channel or like people come with, with operational requests. And like, it is the primary individual responsible for these requests now internally. You know, sometimes it's got to delegate to a human, but it's delegating to us. Like it's not, not the other way around. And, and so like that sort of thing, I think is like what happens when you have this sort of like.

• 17:58Speaker 02

  Magical kind of like close collaboration between us and Anthropic, close collaboration between the application layer, the model layer. There's sort of this coming together of all these capabilities that yields a really good outcome for the business. And so like, those are the things we're, we're thinking about every day.

• 18:11Rebecca Bellan?

  So, you know, we're seeing forward deployed engineer model is suddenly everywhere, right? So Anthropic's doing this as like a separate unit, OpenAI, AWS, Microsoft, everyone's doing this. I mean, Palantir is the first one that popularized it, but never really talked about it. Why do you think it's worth like having a new company for this? Like what gap exists to justify it? Or what, or you know, what did Deloitte and Accenture get wrong, right? About deploying generative AI?

• 18:39Speaker 01

  So I would say the, the need in the market for, for this and the, the reason you're seeing this category explode, there's just overwhelming demand for AI native services and for, for the types of teams that we are deploying. What it takes to, to, but the, the skillsets that are on these teams that, that are ultimately successful. It's a combination of like an entrepreneurial spirit and ability to, to be a flexible thinker, understand how a system works today, rethink it with, with a, you know, the AI fluency to rethink it, the flexible thinking to rethink it, and then the builder mindset to go actually bring that, you know, bring that into, into real life, despite whatever technical challenges you encounter

• 19:22Speaker 01

  and whatever organizational challenges you, you encounter. And, and so that, that type of skillset is not really the, the thing that the traditional services businesses were, were built for. And so the, the, that, that is why this AI native services category is, is exploding at the moment. And why you're seeing, you know, all of these companies pop up. It's because companies, the enterprises out there are reaching for exactly this type of solution. They're not getting it from the, the traditional players in the space. And so there's just a overwhelming demand for, for these types of FDE teams.

• 20:02Speaker 02

  The, the engineering degree of difficulty also tends to be very high in a way that it is not in many other types of services. Like our team is predominant, like, or at least our engineering team is like predominantly, you know, very experienced engineers, they tend to be like generalist elite software engineers, right? Like our, our headquarters are in San Francisco and in New York. These are like top tier experienced generalist engineers. Over half of them are former founders. And so the, the kinds of folks that can sort of juggle both a really challenging technical problem, but also own something end to end.

• 20:36Rebecca Bellan?

  So not necessarily like a whiz kid that just knows how to play with the tools, right? They're people who have experience hands-on with, with this kind of thing.

• 20:42Speaker 02

  Correct. Especially like, you know, i- i- in this environment, I think what is key is keeping the teams that you're deploying pretty small. And so the, these, these folks are, are tasked with owning in one brain, like the engineering challenges, the deployment challenges, the cultural challenges, the business challenges, the UX challenges. Like they're thinking about all of this stuff. And part of that is because like the trajectory we're on is one where like you can take, and I think this is, this is true in some sense today, and it's getting more and more true every day. You can take like a team of four of the right people, put the right models and technology in their hands, put them into a company that needs some transformation and has the right appetite for it.

• 21:26Speaker 02

  Like, you know, they're, they're sort of CEO or board level sort of buy into this. And that team of four might be able to have like a hundred million dollar impact on that business. That's like a pretty unique world that I think didn't, other businesses weren't designed for. And so we're trying to design for that.

• 21:39Rebecca Bellan?

  It's really interesting because we were talking about this before, about this new trend of engineers. You know, talent is scarce, but engineers are increasingly not just looking to the top AI labs and, and working on the frontier, but they're interested in, in deployment. So I'm kind of curious if you could talk a little bit about, about that.

• 21:56Speaker 02

  What I hear from engineers like all day is like, what they see is the world just changed. Like some very impressive transformative technology was just invented. And as a result, like the two things that make sense to them for their career are like, one, you can go work on that technology. You can be building the models and the applications right above them. You can go work for, you know, go work for Anthropic. Or you can own, you know, full business challenges end to end. You can own outcomes and go use this technology to create outcomes that weren't possible before. It's like, those are increasingly the two categories that are exciting to engineers. And they look at this sort of middle layer of like, you know, like it used to be really, really exciting to join a SaaS company. It's now really, really scary.

• 22:40Rebecca Bellan?

  Yeah.

• 22:40Speaker 02

  Like it, it makes less sense in this context. Like you wonder if those businesses are, are going to be okay versus like, you know, will, will the model sort of subsume some of this stuff?

• 22:48Rebecca Bellan?

  Yeah, the old death of SaaS. So then how do you guys, I mean, you're, you're a startup, but you're not a startup, right? You've got the backing of Anthropic and Blackstone, et cetera.

• 22:57Rebecca Bellan?

  Like, are you finding it difficult to acquire the, you know, elite engineers during these poaching wars with the crazy insane comp packages you're seeing from the top labs?

• 23:10Speaker 01

  So it's a different talent that we're going after than some of the, the talent wars you're referring to. You know, the, the talent wars are, have generally been about the top researchers. And, and we're going after the top applied AI talent. The, the key challenge that we have with that is that there isn't enough experienced applied AI talent out there to meet the needs of the market.

• 23:30Rebecca Bellan?

  Right.

• 23:31Speaker 01

  And in response to that, our strategy here is to hire the best generalists. You don't have to have AI experience. And then we try to create the best environment to learn applied AI here on the job. You get to work on AI projects, get hands-on experience. You're surrounded by top caliber AI engineers and you can learn from all of your teammates. We try to build systems and, and knowledge sharing rituals that allow every team member to learn from every single project that we're doing and, and really lean into this as, as a, as a way of enabling us to hire generalists and help them very quickly become the world's leading applied AI experts to, to really meet the need. Cause there's no other way to do it. There just simply isn't enough applied AI expertise out there.

• 24:15Rebecca Bellan?

  And there's also probably increasingly there'll be less generalist expertise, right? Like when we think about the next generation, they haven't had their hands in enough stuff to be generalists, right? And they're coming in at a time when they're not going to be learning necessarily the skills you need to like build the blocks up to that. So are there any moves from you guys or anything that you've thought about or anything you've seen to help increase that supply, like with helping with upscaling? Like, for example, a couple of weeks ago, we had on Pim Duit from General Intuition. And he talked a lot about how his company is, you know, trying to create job opportunities for people who are, who are being displaced by the AI revolution. So I'm kind of curious how you guys think about that.

• 24:56Speaker 02

  I'm not sure if I agree with the assertion that there'll be fewer generalists in the world. I think it has never been an easier time to become an entrepreneur. Like AI is making it really easy and increasingly appealing as like a first career path to go, you can go start a company and go try and have an impact on the world. And I think you learn so much by like trying to own problems end to end, like going to try and get product market fit, move the needle on a business. You learn a lot of stuff there that you don't learn from just solving sort of a narrow problem. And like, that's, that's the skillset that fits really well within Ode is, is these, you know, over half of our team are former founders. Like that's not, it's not to say the only founders fit in here, but I think it's like a sign that like, it's a sign of the types of, you know, sort of like attributes that work well, right?

• 25:39Speaker 02

  Like broad experiences, broad end-to-end ownership, sort of hunger to, to move the needle on something real and, and see an actual outcome in the world.

• 25:52Speaker 02

  So I, I, I'm actually excited about the sort of supply of, of entrepreneurship that's, that's going to come into the world. To some of your point though, like, yes, it is challenging in this environment to build a super elite team of engineers like this. But I don't think that actually is the environment that makes it hard. I think it's always been hard to build really, really talented teams, especially in the face of overwhelming demand, right? Like we see so much opportunity to grow and scale. And like keeping culturally the, the, like our obsession is quality. And like keeping that obsession as you have pressure to kind of serve all this demand is really challenging. And it'll be interesting to see like how all the players in the market sort of where they land and, you know,

• 26:34Speaker 02

  and the sort of quality spectrum as we all like kind of face this unlimited demand sitting in front of us.

• 26:39Rebecca Bellan?

  When we think about scaling this, like consulting doesn't scale like software, right? Do you imagine this right now is sort of like a boutique services firm? Like, are you, how are you, how are you thinking about scaling and avoiding becoming just like another labor intensive consulting firm?

• 26:53Speaker 01

  I mean, the ambition here is very much to become a scaled provider of AI native services that can meet the overwhelming demand of the market. And, you know, we've, we've, we've always been aimed at taking a very ambitious swing. We've long talked about building a hundred billion dollar plus business in this category. I actually think that in the, in the grand scheme of things, that's maybe not even ambitious enough. I think we could potentially, it's pretty easy to imagine this is a trillion dollar company someday if we execute well. The key challenge of the business is how do you go through that phase of hypergrowth without losing the emphasis on quality that Eddie was just talking about? Yeah, we have a key cultural value here of over delivering for our clients. It's something we talk about all the time internally.

• 27:39Speaker 01

  And, and, and I think the, the key tension that we face every single day is trying to over deliver on that next project is hard. And where, where do the resources for that next over delivery come from?

• 27:50Rebecca Bellan?

  Right.

• 27:50Speaker 01

  If you just add. 300 new people tomorrow and you deploy them all on projects. Those projects aren't going to go well. That's not going to be a great experience for those 300 new, new people on the team. Not going to be, not going to be a great experience for anybody. And so the, the kind of, the things that we invest in every day are the things that are going to allow us to absorb new, new folks faster, allow them to become a plot AI experts faster, to create, to over deliver on that next project faster. That's a combination of investing in technology, learning and development and, and other things. But, but ultimately the ambition here is large and I think, you know, navigating that tension isn't going to be easy, but I, but I think we're up for the challenge.

• 28:31Speaker 02

  I, I think AI really does change like some of the laws of physics about like what it means to be a services business, like the, first of all, we get tons of efficiency internally by leveraging AI to help us do a lot of our work, right? It, it can help us with our, you know, the sort of oversight of our projects. It can help us with consistency. It can help us learn across projects. It can help us do, you know, sort of check ins and there's like lots of things, lots of sort of operational pieces of our business that get, get easier with AI. There's lots of reusable building blocks and sort of componentry that can be built to make these engagements go faster. But I also think like the biggest sort of like Uber is, is the thing that I was talking about earlier where like the impact you can have in a, in, in, inside of a company with a really small team is just crazy high with AI.

• 29:14Speaker 02

  Like if you look at the really successful previous generation services businesses, I mean, there are a number of them, but a lot of them are like the size of a small country and they're doing like deployments that have like hundreds of people on them. I think that, that the opportunity to like at scale, be doing four person engagements where those four people can transform a whole business is an AI phenomenon. And so, you know, you still need to scale headcount to become large, the same way every, every kind of like, you know, every software company has to do that too.

• 29:46Rebecca Bellan?

  Right.

• 29:47Speaker 02

  But not size of a small country, large, just like normal software company large.

• 29:51Rebecca Bellan?

  Right. So like a lot of the, the process of FDEs is being automated already. Do you imagine that you could create and like a, an FDE agent eventually, like learning from the way that all of your employees help other companies implement AI workflows?

• 30:11Speaker 02

  I mean, I think we see like sort of the sidekick version of that all the time internally. You know, I talked about Claude Tag before, but like, you know, Claude lives right, right alongside us in, inside of our Slack discussions and is very aware of how we do these engagements and is helping us all the time. I think those capabilities are going to get better and better and better. I think that's just going to create room for even larger outcomes and make us value sort of judgment even more. And so I think it's an accelerant to, to all this stuff.

• 30:38Rebecca Bellan?

  Okay. Well, we are probably just about out of time, but this was really interesting to learn about where can our listeners connect with you online if they want to learn more or get in touch or apply to be an FDE?

• 30:49Speaker 01

  So our site is o.com. You can go there and apply or you can find us on LinkedIn.

• 30:56Rebecca Bellan?

  Okay. Well, thank you again so much for joining us on the show. To our listeners, you can find me on LinkedIn as well or X and you can find Equity on X and threads at Equity Pod. Talk to you next time.

• 31:07Rebecca Bellan?

  Equity is hosted by TechCrunch senior reporters and produced by Teresa Locan Solo with editing by Cal. Subscribe on YouTube or wherever you get your podcasts and find out what's next at techcrunch.com/events. Thanks so much for listening and we'll talk to you next time.