AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

0:00 And I just felt my output getting better and faster, and ultimately getting paid less. Like, I was charging hourly to my clients. Um, I phrase it to the team as, like, you can't automate a process that does not exist, so- [upbeat music] Okay, we're here at AI Engineer, checking with, with, uh, 10X. Uh, welcome back, Alex. Thanks. Thanks for having us. Uh, you brought your director of engineering, CJ. Welcome to the pod.

0:35 Special projects lead at 10X [laughs]. Okay, special projects lead. [laughs] All right. You're the SWAT team. Yes, yes. Uh, so you've got- you've been on the pod before. Uh, we, and, you know, people are s- vaguely familiar with 10X story- Yeah... but can you sort of reintroduce from the top what is the 2026, how you introduce yourself? Yeah.

0:51 Uh, very simply, 10X is an applied AI partner for some of the biggest companies in the world to help them go from AI apps into AI native. Our belief is that AI is incredible, but it is raw intelligence, and the hardest problem to solve is the final mile to actually diffuse it through the economy. Yeah.

1:10 And so our view is to act as the player to help enterprises to figure all of this... Can I curse on? To figure all this shit out. Yeah, yeah. Yeah. Yeah, we, we have the explicit tag on Apple Podcasts. [laughs] Although I did recently discover that YouTube doesn't like it. Oh, really? They're so, like, yeah, you can't say the F word, um, or they also- Should I just mess up your video? What are you gonna do?

1:29 I don't, I don't care. Okay. [laughs] We're for adults. Uh, CJ, what drew you to 10X? Um, so I was actually the first hire. Um, I don't even know if Alex and Arman had the name 10X quite yet, but I have, like, a graveyard of startups I tried to start before 10X, um, ultimately culminating in, like, my own kinda engineering consulting, um, company.

1:51 And I spent some time there doing traditional engineering. Eventually moved over to doing some of this, like, AI consulting and just, like, building more and more with it. Um, I'm a software engineer by trade, and I just felt my output getting better and faster, and ultimately getting paid less. Like, I was charging hourly to my clients.

2:12 Um, and I really liked kinda the ethos of 10X that Alex had first posted when I saw it on Twitter, um, about kind of looking to reward engineers for their output as opposed to rewarding them for their time. Um, I think, you know, fast-forwarding [laughs] two years, you can see, like, how engineers can now parallelize a ton of work and just have a ton more output.

2:33 Um, at the time with, you know, Sonnet 3.5, Gemini 2.5 Pro, um, it might not have been that obvious. But I basically took the bet that I think with my skill set and leaning more into AI, I could have much higher output, and ultimately have, like, a higher earning capacity to do that. Um, and since then I've just, like, fell in love with that model.

2:55 I think that's, like, what the future of engineering really looks like, and ultimately when AI is solving all these tech problems, companies are gonna pay based on outcome, and they're not gonna wanna pay based on the, the time that's required to build something. Yeah, so for more detail on that, it- people can refer back to our previous pod, where you literally pay people by story points. Yep, yep. And you just have to agree what the story points are.

3:16 Uh, but that also means that some 10X engineers can literally make millions of dollars a year, and some others w- may not make as much just because they're not as productive. Yeah, exactly. Like, the thesis is that AI is really just a leverage bet on a human's intellect and work ethic, and so you're just gonna have more variance than ever before in earning capacity.

3:35 And our view is, is our model isn't for everyone, but for the person who basically wants to go along themselves, who has this incredible, like, foundation in software engineering, but also is constantly pushing themselves to leverage the frontier, to s- stretch their time as much as possible while having quality output- Yeah...

3:51 there's no better way to earn. What's the state of, uh... Like, last time I talked to you, I think you had, like, maybe two or three people making a million a year- Yep... or over a million a year. Is, uh, is there, is there an update on that, or- Yeah... just an update? Yeah. Yeah, yeah. So, so just to paint a picture, uh, we started 10X 18 months ago.

4:08 When we started the business, really we started as, like, an AI-powered dev shop. Really, like, the original idea was engineering is kind of like the canary in the coal mine for AI, and part of the impetus was my co-founder, Arman, who's technical by trade, was, like, starting to use coding agents a lot, and then he had some people he knew that were still typing into their ID, like, not adopting the technology at all.

4:31 And so we're like, "Why don't we just supercharge a company's engineering org who isn't adopting this?" Um, so we started there. Then as we started moving up market from startups and, like, mid-market companies to now, you know, 50% of our com- our customers are Fortune 2000, and then 50% are mid-market doing 100 to 500 million in revenue, they weren't just asking for basically staff aug and AI engineering.

4:55 They were asking for, like, AI transformation as a service. Today, the business is at, uh, 75 people. Um, 90% of those, uh, employees are either engineers or AI strategists, which are basically think of as, like, technical consultants, and I think the thesis has proved out true, which is there's a very wide dispersion in earning capacity between the highest paid engineer at 10X and the lowest paid engineer at 10X.

5:21 It's probably something around 4X difference. Oh, not 10X? [laughs] Not 10X difference. That's the dream. Yeah. Yeah, exactly. Yeah. Um, but everything has proved right about the thesis. I do think, like, there's been interesting learnings around our model of paying based on story points, where there are limits to the model that we knew w- would be limits and how we think about that moving forward.

5:42 But overall, I think the concerns that people had around this model of, like, paying people based on output, like, the classic concerns are, well, story points are subjective. People are gonna either sandbag story points, or they're just going to ship a, a bunch of, um, bad code- Like barely check the box. E- exactly. Right? But not actually do the job.

6:01 Yeah, exactly, and I would say- Those two concerns have not been concerns at all because of our AI strategists we ins-incentivize, I think, perfectly at odds with engineers, where AI strategists are incentivized for, like, customer retention and growth. Um, actually, the biggest challenge has been how do you compensate engineers for all the time that's not spent coding, whether it's, uh- Meetings... architecting, meeting with clients.

6:27 Um, and so that's where, like, we've had to find nuance in the comp model, but it's still largely driven based on output. Anything to add? Yeah. On that, um, I spend a lot of time thinking about this 'cause if I think about my tenure at 10X, I was coding a lot, coding a lot, coding a little less, and now we're kinda getting to the point where the models are more or less given the right context and can get you to 90% of the solution.

6:53 Um, and I do think there is some opportunity here to think about, like, really the value is sitting down with a customer, hearing everything that they can brain dump, and then just providing that to the model to actually get closer to the solution. So I imagine, you know, in the future, we're gonna start to look more at, like, true what this output is, how it works, how it looks, as opposed to, you know, kinda that traditional story point model, but still anchoring to those as the unit.

7:20 I think one thing you'd also... I don't know if we've talked about this before, but, like, the- this idea of, like, the meta harness, which is, like, the, the engineering workflows at 10X that we use- Mm-hmm... that has allowed us to do, I think, like, really meaningful work on long-running tasks. Okay. Do you wanna just talk through, like, what our process is 'cause I feel like- Yeah... people would find that interesting.

7:39 Yeah. So we basically started trying to kinda standardize a set of skills. Um, I think most engineering orgs have done something similar by this point. Um, and for us, that grew into what is actually, like, a very structured set of documentation within a repo, and then a CLI that's around it that kinda the models are using to actually drive the work that's being done, whether it's creating a plan, a visualization, you know, creating some readout for a client.

8:05 Um, effectively, like, building all the pieces of the process around actually writing the code to just happen more automatically and standard across our team. So we have a couple of things. Like, one is this, like, big canonical plan document that has some similar structure, but I'd say as the models have gotten better, we've actually reduced the enforcements there in the structure, um, kinda similarly across all of these.

8:31 But once you have your plan, you actually build it out, and we have a very opinionated way on how agents will go and log their decisions and log what they're doing and just keep track of what they're up to, especially- Everything's checked into Git? Yeah. All of this is checked in. Um, all of this is really kinda marked down for me now, more HTML. I like to see these picture books that the agents spit out.

8:50 Um, but all of that gets checked in, and then it serves as this surface where not only do you have the code as the source, you know, I think there's a debate on, like, do you even need docs? Like, the code can actually be the memory of a code base, um, and I do think you do. You need to know why you made a decision. You need to know the context around the conversation that was had.

9:09 Um, but ultimately, that plus the code base. If there's a question, if there's something we need to answer, if we need to look back on why we did something, we just always have that in some durable record, and it's never, like, in someone's head. It's always in the repo. So that just gives, like, our agents and our tools these superpowers to have all the context that I think a lot of engineering teams still just have in conversations or have in their heads.

9:34 Um, and our goal is to kinda continue to push that forward so that all the context is in one place, and more and more of that work is just driven by the agent as opposed to us having to provide everything upfront. Yeah, amazing. Um- And one, one way we even measure, like, i- if any of these kind of, like, processes are working, is are we seeing an increase in trend in average story points per month of every engineer in the company? Right.

9:57 And what do you think it is, let's say, compared to start of this year? I don't know. What do you think? Do you think a- Plus 50? I think-- So it really depends 'cause we have some people that- [laughs] I, I... There's some picture that I saw on Twitter of a guy over, like, a wizard orb, and he's got these, like, things in the sky, and that's how I think of some of our engineers when they're whipping agents around.

10:18 Um, I'd really say overall it's been, like, a 2X increase this year in particular. Um, I think basically if you think of December as the, like, Opus 4.5 moment where the models really started to take on these long-running tasks, to today we're back to Opus, but Opus 5.5. Um, I mean, that's just a magnificent model that can really do a ton of work.

10:39 Um, over that period, I've seen, like, the actual output of engineers, I would say roughly double in terms of, like, what they were able to do to what they're able to cover, you know, in the same time span now. Is a single story point worth the same now as it was a year ago? Or- We're, we're always trying to calibrate. Yeah. Um, it's a tricky equation. Is there, like, inflation or deflation, right?

10:59 [laughs] Like, I don't know what the- I mean, in theory, in th-theory, there should be deflation. Right? Yeah. Yeah. Yeah. I don't know. [laughs] And that's, that's roughly how we think about it. I would say at least once a quarter we kinda sit down and evaluate, like, what is our criteria for these story points? How are we actually estimating these projects? Yeah.

11:15 Um, there's a fine balance to be made between, like, still rewarding this output, but also as the models get better, we do need to be able to kinda calibrate, like, the value that we're creating. Yeah. Um, so overall, like, I would say roughly that 2X has played a similar role in, like, how we do these estimations, but I mean, if I just look at some of the features and things people are building and pushing out now versus this time last year, I mean, it's, it's insane what the difference has been.

11:41 Yeah. Um, well, so there's a couple ways we can take this, but I'm just gonna check in on the sort of model preference stuff since that's always a topic of conversation. Um, you-- I think you just let people choose whatever the tools they like. Yeah. But then you-- maybe you have some statistics on what is preferred in the office. Uh, it seems like Opus is leading. Yeah, what's the vibe?

11:59 What's the vibe check right now? Can you, like, care to estimate, like- The vibe-... market share, whatever... the vibe right now is Opus 5.5, I think. No Fable? I don't particularly use Fable. Yeah. And I think a lot of people right now are just defaulting away from it. Like Opus is good enough. Yeah. O- Opus is so good. It's fast, it's cheap. It kinda hits all my checkboxes.

12:17 Um, I would say I previously have had, like, two to three models, depending on whether they're cheap and fast, whether they're really smart, like, whether they can solve specific problems, and now Opus is kind of covering my, my full range. Um, I think a lot of the team still reaches for tools like Codex when we need to do something maybe more in the computer use world.

12:37 Um, and really workflow-wise, I think using both is still the best option, whichever your driver is, which I think for most of our team is Opus 5.5. Using a Codex to review and having them actually work back and forth- Yeah... on your machine, I think still produces the best results. Um, but right now we're pretty aligned on those two.

12:57 I don't think there's too much- And are you still, are you still Amp maxing or no? Oh, oh. [laughs] I am Amp maxing. Um- So you're using Opus through Amp? So yeah, they recently- More like th- that's the default model for, for, for Amp. Yeah. They recently launched, like, the ability to hook in your Claude Code subscription, so that's been a pretty big game changer for me.

13:15 Um- 'Cause that'll r- really cut your cost by, like, 80%. To- uh, totally. [laughs] And like, I, I mean, I love Amp 'cause it takes everything off my computer. Yeah. They have this, like- Orbs... Cloud, Cloud setup, the Orbs. Um, we actually co-hosted a happy hour with them last week, so it was cool to meet the team and Orb out. Um, but- [laughs] But yeah, they're- That's never gonna be a thing. Oh, yeah.

13:37 [laughs] I, I don't wanna make it a thing. This may be the first time in history that any human has said that. Orb out. We're Orbing out. We were Orbing out. Uh, no, they, they, they do say it's Orbing time. Yeah. That's, that's it. Yeah. Um- Okay. But yeah. That's my, that's my interface of choice. Yeah. And models are really between that if I'm leaning on Codex, it's Sol 6.1. Yeah. Isn't it-- Did you give Claude Tag a shot?

13:54 Like, isn't it weird that Anthropic never captured the remote environment thing in the way that ours did? Yeah, I think there's a piece of that, I think, on the Amp side of them being very opinionated and not trying to build this general solution. Um, tools like Claude Code and the Claude and Claude Tag, I think provide value for teams that are really trying to, like, collaborate maybe on non-technical tasks.

14:20 But part of the reason I love Amp is that it has all these cross-model functionalities, and I can do that. Opus writes it. You know, I can even hop to another Orb and say, "Look at that Orb," and review it, and all of those things still come together to basically replicate what I'm doing on my machine, and I just couldn't quite get there with, you know, a Claude Code and the Claude, a Claude Tag.

14:39 Yeah. Yeah. Fair enough. Um, another thing I always track, uh, I'm starting to track this year, is percent of, let's say, token spend to employee spend. I don't know if you have that number. Uh, so the, the roughly, uh, most people, most companies are at maybe, let's call it 10 to $200 in spend per employee.

14:58 Um, the more sort of AI native ones would, would sort of convert that to a percentage, so roughly 10%. Um, the average, um, AI startup employee is about 1x in terms of, uh, employee spend and token spend, and then it goes up to maybe 5 to, to 10x in terms of token spend. Now, where, where are you guys in that range?

15:17 I actually don't have a number off the top of my head. [laughs] I'd say we're somewhere between, if we're talking, like, raw tokens- Yeah... somewhere between one to one and maybe, like- Really?... two to one. That's pretty high. And we- That's pretty high... what, what we think about a lot is, like, when to reach for the biggest model. I think every company is starting to think about that. Um, but I will say recently our costs have been driven just way down from- Because of the-... Opus 5.5... subscription thing. Yeah.

15:45 We kinda had an era of, like, everyone was using Astra and Fable, and they just gave the best results, but ultimately those models are expensive. They-- And, like, a lot of, like, you know, since the last time we talked, a lot of people had this sort of token maxing era, token leaderboards everything, and then they were like, "Oh, no, no, no, it's, now we have too much cost," and, like, well, it's the most obvious, like, predictable thing in the world.

16:04 Did you ever fear about it, or, like, you were just getting good ROI re- regardless? Yeah, I mean, there's a, a few ways that I think about it, is, like, we were for sure, we were for sure worried about it just as, like, our headcount was growing and, um, I th- I think also, like, even though our team, I would say, like, I don't, I don't feel like anyone on our, our team ever really ascribed to, like, token, the token maxing zeitgeist, 'cause [laughs] I had a few moments. [laughs] Yeah, me too.

16:36 And, but it's, it's like, well, if the, the boss is gonna pay for it, like, I'm just gonna go nuts, right? Yeah. Because then, then you, you do the weird stuff so that you can scale back, uh, and but you have to do the weird stuff first to discover the lessons that you can take with you. Yeah, totally. I mean, th- this may sound crazy, but, and I'd be curious your thoughts, but, like, I, I was talking to someone earlier about the, the debate that continues around, like, ba- basically, I promise this is related.

17:02 Like, I was talking to someone earlier around the data companies, right? Like, um, MicroOne, Mercore, Handshake. Yeah. And just, like, what i- like, what does the long term for these businesses look like, right? 'Cause, like, nine- 90-plus percentage of their, their revenue is from the labs, and a number of these founders would argue that they think it's gonna flip-flop, where in the next few years it's gonna be enterprise driven.

17:24 And then, like, the thing that comes to mind, like, the next thing I assume you have to believe if enterprises are gonna be the core buyer of that data, is that enterprises are gonna be doing a lot of fine-tuning on their own models.

17:34And then the question becomes Where is that going to make sense?

17:36 It's either you think there's gonna be better quality, which again, like, the bar is just gonna keep moving on the frontier, so that's, like, a hard bet to make. Then it has to be price. The price one feels hard to me because it's like we're just seeing token costs continue to go down on these models. And so I guess part of my c- the reason we're not concerned internally is because I-- it almost feels like we- we've kind of hit this, like, Pareto point of, like, costs are coming down at a per task basis, that it is, it is way harder today- Mm-hmm...

18:11 for someone to, to token max and, and, like, really get- F the company in terms of cost than it was six to eight months ago. Like, I almost think six to eight months ago was the, the point at which, like, you'd be really concerned. Yeah. Do you agree with that? Yeah. I think there's a couple ways, like, the future might play out there. Like, if there's a world where there's a Astra 6.5, a Fable 5.5, and they're super expensive, but they get the best results, I could see us trending back that direction. But generally, I agree that, you know, you look at something like Muse Wonder that they launched yesterday- Yesterday... today, and it's on the frontier. It's dirt cheap.

18:50 The competition is heating up, and bo- both in open source and closed source, and I think that just will continue to drive costs down. And when you really look at it, a lot of tasks that are economically valuable can be done with- Totally... Opus 5.5. So, you know, it's, it's that cliff of, like, would a 10 times smarter model really [chuckles] justify the cost of, like, moving some cells in a spreadsheet? Probably not. Right.

19:15 Um, so coming back to, uh, your customers and- Yep... what you're, what you're seeing from the market, um, what is the most common ask that you guys get? And then what is something that people come to you with and you're like, "No, we don't actually want that job yet"? Yeah. So one way I think about this is I think about enterprises adopting AI as almost like these different, like, milestones in a race. And I think, especially at AIU, when I tell people about what percentage of companies have reached these milestones, they're kinda like mind-blown by it.

19:45 But it's like, okay, first milestone in the enterprise AI race is just rolling out a general purpose LLM to your people. And I'd say of the, you know, let's call it 100 enterprises we're talking to today, 90% have rolled, rolled out, uh, either Claude or Codex to their employees. Uh, then the next milestone is, like, actually teaching people how to use it, like training and enablement, which, like, never gets the love that it should get- [chuckles]...

20:15 but is arguably more important or as important as the technology itself. I would say that's at, like, 60%. There- Oh, that's pretty good. Yeah, yeah. Like, there, there's some group there of companies that are like, "Our people will just figure it out." But then there's, like, a good s- a good chunk that is like, "Okay, we need to teach our people what skills are.

20:31 We need to teach our people the features and functionality of these surfaces." Then the next, after training and enablement within a general purpose, um, LLM, is coding agents. And the crazy thing is, like, I, I was asking a few people here, like, "What percentage of enterprises do you think have rolled out coding agents to the engine product org in their company?"

20:52 And most people are like, "It has to be 70% or 80%." The number is closer to 50%. Like, every other enterprise that we speak to either hasn't rolled out a coding agent or what they are using is closer to not having a coding agent than having a coding agent. Then the next, and this is where, like, the biggest, I would say, drop-off in, like, uh, is, where, like, the biggest diffusion gap is from, like, raw intelligence to enterprise usage, is agents in production for non-engineering work drops to, like, 5%.

21:22 Wow. It's like, I mean, you're in these conversations all day long, but, like, most enterprises that we talk to let- that have, let's just say, more than 1,000 employees do not have an agent in production that is doing work that is not building software. Sure. Um, and so I think there's a few things.

21:40 One is there is, because, like, there's just this whole wave is driven by a combination of some small group of people understanding what is possible from this technology and a way larger group of people driven by FOMO. And by the way, FOMO isn't bad. It creates some forward momentum, but it also means that people will latch onto the things that are most in the zeitgeist.

22:01 I guess now for the last year and a half, people have been saying it's the year of the agent, um, or however long it is. Every year it's the year. [chuckles] Every year's the year of the agent. Like, enterprises are like, "Can you help build us an agent for some back office task within the organization?"

22:14 Like, that is one of the most common things, and it's why now a lot of the work we're doing at 10X is deploying agents into production outside of the eng org for companies for repeatable work that they're doing. The second is, uh, permissioning and, uh, like, RBAC. Uh, basically, as every employee in the company has Claude or they have Codex, and they need access to certain data, it has turned into an absolute nightmare- [chuckles]...

22:40 for CIOs and CSOs, and they don't have basically a good gateway for easily doing almost like a con- like, a control panel for, uh, data permissioning. It literally just what can you read and what can you not read? Uh, exactly. Yeah. And that is a huge issue. Yeah. So that's, like, something we spend a lot of time on. So you, you make them for people, or do you, like, buy them off, buy something off the shelf?

22:59 What's the market there? So, so basically, we started by building bespoke for companies- Yeah... and now we actually productize that, where we have our own gateway- Gateway platform... our gateway solution. Yeah. Gateway solution. Exactly. Is there anything else that comes to mind that's like we're hearing a lot from customers? I think that covered what we're hearing when it comes to, like, particularly the agent deployments.

23:18 I think one of the pains that just always gets discovered, um, I phrase it to the team as, like, you can't automate a process that does not exist. So when you have 40 people that are all roughly doing the same function, but they're all doing it a little bit of a different way- Mm-hmm... there's actually n- it's, it's way harder to build an agent that's kinda proactively doing that in the background.

23:41 You can totally sit down with them, give them the tools, walk them through how they could augment their own workflow. But when you really start to get the leverage is when you have these agents that are surfacing work to you. You're reviewing it. You're throwing it back, and you become more of this, like, you know, e- everyone's been talking about becoming an agent manager, and I think we're on the cusp of that.

24:00 But when you zoom out, oftentimes our recommendation to companies is, like, "Hey, first, we need to build a process around this organization within your company, and then here are the steps for us to actually hand off that work to an agent in the background." But if we don't have some form of standardization, it's gonna be impossible for the agent to know whether it's doing it right for us to build evals around the system and actually deliver the value that an agent that is doing this function would have.

24:27 And even one thing I'd be curious your thoughts on, we were literally talking about this right before, is- And like, uh, uh, CJ will be okay with me sharing this. This is like the- [laughs] We were talking about, like, vertical-specific AI solutions, and, like, what does the future look like for these solutions? And we, we were generally expression, expressing an overall bearishness with vertical-specific AI solutions in different functions or industries, and part of it is just from our vantage point of spending all day in, like, the guts of enterprises.

24:58 There is so much nuance and so much inertia and so, like, so many systems you have to wrangle and people you have to wrangle. Oh. And also, when a buyer out of, of a vertical ex- uh, uh, solution is expecting truly a one-of-one ex-experience. Like, we're, we're in this... Uh, we are in an age where people are expecting one-of-one software solutions. And so if you're expecting that, it- there's, there's...

25:24 It's no wonder that every software company is basically building up a team of FDEs because there's just this convergence happening of services and software when, when every company wants all this nuance that was not traditionally expected from software. And so I think it's really interesting to think about, yeah, like, for vertical-specific solutions, either one, how will they be able to do this at software margins if everyone expe- uh, expects a, a one-of-one solution?

25:53 And also, why should we not expect, like, frontier models to just gobble them up? Yeah. Um, so to give some concrete examples are you're talking like Harvey for lawyers. Yep. You're talking- That'd be one example... OpenEd for doctors. Yep. These are the, the classic examples of, um, people that are winning their domains. Uh, and you're bearish on them? I would say generally bearish on them, correct. Wow.

26:14 Uh, well, so far you've been wrong. Uh [laughs]- Yep. Yep... and, uh, you know, I think the, the, the, the answer very simply is like access to data that ChatGPT is not gonna have, which is, like, very common. That's a, that, that is a moat. Um, ob-obviously, Anthropic and OpenAI are also acquiring those same- Yep... data sources, but they're just not as dedicated, uh, you know, as, as, uh, you know, these guys are gonna be.

26:34 Um, they do have innovations. Uh, if you look, if you've, like, tried it, Harvey- Yep... um, they have, like, the tablet review. L-Lagora also have them. We've, we've had the CTO on the podcast, um, where they, they have affordances and UIs and all that that, like, is just customized because they- Yeah... they only serve th-them whereas- The pitch is focus, basically.

26:51 Focus, affordances maybe. Uh, th- like l- What do you mean by affordances? In a way, in a way that you care about the last mile for your customers, they care about their last mile f- in terms of the software. Um, and as long as people train in that software, like, it never goes away. Like, as a banker, uh, or i- you know, in my finance career, I could always access the same exact information from the Nasdaq, from the New York Stock Exchange, but why do I pay f- the $50,000 a year for Bloomberg?

27:16 Well, it's just like that's, that's what everyone, all my peers do, and it's all consolidated for me, and it's all specialized for me, so I don't make mistakes or- Mm-hmm... whatever. Or, or like it's tied into my trade, uh, trading system or my chat or whatever, my new system. No, I think people pay for Bloomberg is the social network. [laughs] I think people love the DMs in Bloomberg. There's that.

27:34 There's that. But as a trader, I know- Yeah, yeah... I actually use the, use, use, use that for- Yeah, yeah, yeah... trading. Um, so, so I mean, I think there's a, there's a bunch of integrations and, and, and tools, and like, I think ultimately it's hard to be bearish on any of them, mostly because the, the demand for software, demand for tooling to do your job is just huge, and some of it, yes, it's gonna be fulfilled by horizontal generalized tools- Yep...

27:53 like the Clause and ChatGPTs, and some of it, you're just gonna want something better. Mm. Um, and uh, you know, it's just g- I always think about, uh, maybe the success of Superhuman, uh, as a, as an email client. Everyone has Gmail, but here, here's just premium Gmail with, like, more stuff for people who live in email all day long, and like, that's a market, right?

28:11 It maybe wasn't the largest startup in the world, but, um, I think, uh, reasonable people can disagree how large, uh, each thing is. Yeah, totally. I think you, I think what's not, uh, uh, uh, to be disagreed with is just the, the total market's gonna grow in all- Oh... rising tide lifts- Totally agree with that... all boats.

28:26 Uh, and then, then you're just talking percentage point of market share at that point. Yeah, agree with that. [laughs] Totally agree. I think there's even, there's like a differentiation between some new app built from the ground up with AI involved. That's what comes to mind when I think about Superhuman. I love Superhuman.

28:40 I don't even spend a ton of time in email, but anything that has- Like, you pay 30 to, 30 bucks a month just to not worry about it... anything that has cool hotkeys, I'm, I'm into. Yeah. Um, but when I think about like, I guess like products where they are really pushing some agent as the product that is doing work in some specific vertical, um, unless there is some very strategic compliance data, whatever it might be, moat, I think those are going to struggle going forward, um, as we start to see, like, quite literally the ability to either spin up your own really quickly, continue to compound as the models get better, or just, you know, those tasks start to fall into the, the world of the models just generally.

29:23 Um, but anything that has this like app and tool built around it, kind of to my previous point, changes the process and has an agent on top of it. And I think, like, that, that kind of builds on the same philosophy, and I think that can still be super helpful, and tho-those will be successful. Yeah. Oh, okay, okay.

29:40 So, so, uh, there's this, there is a related view about this that maybe isn't quite as spicy as yours- [laughs]... which is basically it's not like, like nobody wants to use... People don't, probably don't want to use the agent that you built. They want to use the agent that they already have, and they want to plug in your data into their agent, right?

29:55 So everyone is like sitting on like some ki- some part of your data, and they're like, "Use my agent, please. Please pay me that $20 a month." But you, you can't just like stack up all these- Yeah... $20 a month subscriptions. Like, uh, ultimately, like whoever li- like probably a small set of companies are gonna own the interface, and then everyone just plugs into that- Yep...

30:12 uh, that overall agent, which I, which I agree with. Um, then it's, it's, it's interesting that Salesforce, you know, being one of the system of record companies, the, the biggest probably or the most well known, um, is- Embracing that path as, as like, like you will not use our agents. Like, we will just provide APIs and MCPs for you to plug into your stuff. Plug it yourself. Yeah, which is kinda cool. Um, okay, so, uh, I actually wanted to also, uh, walk through one example.

30:35 So, um, I just released my results for the, my Kill My SaaS competition, where we replace, uh, a mid-tier SaaS. So I call it mid-tier basically in the sense that it's probably not that trivial, but it's not Salesforce. It's somewhere in the middle. Mm-hmm. And I think there's a lot of mid-tier SaaS out there that, uh, people are always talking about, like, the death of software, right? Um, [clears throat] it's, uh...

30:52 We were quoted $40,000 a year. We spent a $1,000 to replace it. It's, it's gonna, it's going to production next, uh, for the next conference. So, uh, almost want... Like, we are that customer that says, like, "I want the 101 tool and I'm expecting all these things that..." And we're building a domain-specific or vertical-specific thing for our business.

31:08 And, like, so far it's going okay. Tell me where it's going wrong. Also, I'm wondering how many story points that- [laughs]... that is. Yeah, well, uh, CJ can size up the story points on it. [laughs] Um, I mean, my general view is I think the, the ripping out of... My, my general view is, like, the more you move up-market in business, the harder the proposition of ripping out and building from scratch gets.

31:33 I agree with you. Like, part of the reason when, as people have said, like, a very small group ha- have said, like, "Salesforce r- is really at risk, SAP is really at risk," like, don't agree with that at all. It's just, like, th- those businesses in my mind just accrue value in this moment in time. Um, and part of the reason they accrue value is there's so much money at stake, where if a company gets it wrong ripping them out, you're talking about potentially hundreds of millions- Yeah... of billions of dollars.

31:57 You're gonna be risk-averse. Yeah, exactly. I think for mid-tier, it really... It, it actually comes down to what is the size of the opportunity, um, and is that ever going to be, like, the, the, the worthiest opportunity to rebuild inside of a company? Mm. Like, uh, I'm trying to even think. Like, in an enterprise, let's just say there's some mid-tier SaaS that is a few million bucks a year.

32:24 I think the first question is, is, like, from a percentage of revenue or cost perspective, is that gonna ever move the needle enough to basically allocate engineer resource to? I think it's gonna be case by case. I think for some enterprises, the answer may be yes. For other enterprises, the answer may be no. I think more of it is going to be, like, the thing that gets companies outside of, like, capability.

32:46 Like, you know, we, we talk about how... I forgot, uh, who, who created the repo that is basically the reverse engineer repo- Mm-hmm... where, uh- OA. Yeah. Yeah, exactly. Like, everyone's talking about that right now, and even at 10x, like, we, we like [laughs] we built our own, like, CRM from scratch. We, we... What have we built, uh, that... Our OS we built from scratch. Yeah.

33:09 And, like, one thing I think we realized, and every company realizes this, is one of the beauties of software is not just the utility that you get from it, it is all of the headaches you don't have to think about in maintaining it and improving it when it's not in your core. So I think it basically comes down to is the size of the prize big enough to justify the cognitive load that is going to take from some amount of resource in your company to nurture something that is not core to your business? Mm-hmm.

33:41 Evidently, like, I think for, for your business, the difference of th- basically $39,000 a year, you're, you're implicitly saying that it is worth it to have- It is, it is not just about cost. Yeah. It is... That's defense, and I also think about the offense- Yep... of, like, no, actually, our experience could be a lot better for literally every party concerned. Totally. Totally.

34:00 And so, yeah, I think, uh, part of the, the, the thought there is, is actually just there's a signif- basically, it's not just that... So there's two pieces to it. Yeah. One is, like, not just we're being overcharged for a product, but it's like the product has been mediocre for a long period of time. Yes. What would the 10x better version of this product look like?

34:17 Yes, because we are the customers, and we can iterate with, like, minutes and not months. Totally. Yeah. I think, I actually think what's interesting is I think s- uh, startups and, like, even, like, high-growth mid-size companies can be nimble in that way. I think for enterprises, where, like, they're so entrenched- Yeah... in a, in a certain, uh, capability, rarely is that going to be the, the thing that... They're...

34:39 Rarely are they gonna say, like, "Oh, we can create a be- 10x better version of this SaaS we use, and we're gonna spin it out potentially as its own thing," or whatever it is. Like, I just don't think enterprises function that way. For sure. Yeah. Yeah, yeah, it's been discussed. I, I, actually, I w- I was, I would say that, you know, you talk about automation.

34:55 Uh, the, the more spicy thing is are they automating humans, right? And, like, once everything is automated and, and smooth sailing, uh, do we, you know, replace those jobs? Uh, I don't know how much you can speak to that. [laughs] Yeah. I mean, I think there's, there's two pieces here. I, I really deeply believe in the, like, AI will produce good for humanity. It's gonna augment people's jobs.

35:19 It will absolutely change and displace, but I think it's more of a movement as opposed to just, like, cutting off work. Yeah. Um, I think A16Z posted something about how, like, call centers are truly now on the decline, and AI's starting to handle more and more of that work. And ultimately, if that is a skill you've built up your whole career, that is devastating to have something come in and, and take that function over.

35:41 Um, that being said, part of what we do when we're talking to customers about, "Hey, an agent is now going to do this work," is actually around reallocation. I don't think any business would claim, like, "Once we have this solved, there's nothing for this group of people to work on and try to solve."

35:58 Um, so our approach really is about what can we replace, and then what are the next biggest problems to look at? And I'd make an argument in most large companies that that cycle kind of continues forever. There's always the next problem. Yeah. If I think about how we've grown- Infinite demands. Yeah... there's al- there's always been another problem.

36:18 And actually taking those people that may have this, like, super deep subject matter expert and bringing them above water to start to maybe work more across teams or, like, think about parts of the business Less so on like the level of a cell in a spreadsheet and more about how these teams are working together. Um, ultimately in the r- long run, I think that keeps most people there and allows those businesses to become more nimble and actually tackle like the big meaty problems and not the like drudge of most people's day-to-day.

36:51 When they- you, you know, when you're thinking about someone that's in like a finance division, um, they're mostly in spreadsheets, but how can we elevate them to think strategically about the business and where money is allocated and things like that. Yeah. And I also feel like when we have these conversations around like job displacement or job reallocation, we talk about it as this thing like happening to people.

37:12 And one of the reasons I don't like that is I feel like it doesn't give enough credit to people to like have agency in their own lives. Meaning, I think- You can adjust... yeah, people- Yeah... really should be thinking- Yeah... like when I think about, um, like enablement or people reskilling, I think it is the job of an employer because they wanna drive growth in their business, but it is also the job of an employee.

37:34 And I think when people say that AI is gonna replace all of the work that people don't enjoy doing, I think that's a little bit of like cope for the reality of just people are going to be put out of jobs. Because a lot of people don't have the luxury of liking or not liking a job. They do a job to make money. Yeah.

37:51 And also I think people, to the extent that it's possible, should have agency in saying to themselves, "Okay, what is the, the stability or replaceability of my job over the next few years? And if I am to try to skill up in a way that I'm better protected, what does that look like?" People have agency to say that to themselves and actually take action. Yeah. Yeah. Yeah.

38:11 Um, I think that's a good message for people to reflect on. Um, the last piece I wanted to touch on before I let you guys go is just New York. Um, you know, uh, greatest city in the world, according to some pe- some people. What's the mood, uh, generally, uh, among the people that you talk to? Y- whether it's at work, personally, uh, what's the feeling, right?

38:28 Like I, I live in San Francisco all day long, everyone's just in agents all day long. Like, you know, it's, uh, the discussion is Instincts or Groq or Muse. It's not like are you, do you like AI or not, right? And I, I feel like, um, I wanna step out of that bubble for a bit or, and but it's still New York. It's still progressive. Yeah. It's still forward-looking. Yeah.

38:46 I mean, what I'd say is I feel like basically I would just say there's a bubble in New York- [laughs]... that is a smaller bubble than exists in SF. Okay. If SF is like a big bubble, I think we at 10x feel like, you know, we are in a what I would say is like a microcosm of San Francisco because of the people that sel- self to- select to work at the company, and then also the companies that we're partnered with and like the people that we engage with in New York.

39:12 But I would for sure say also it is true that it is a smaller universe than like in SF, when you go to SF, it feels all-encompassing, where it's like everyone, everywhere you look there is someone who similar to like AIE, how I was saying earlier when I went on stage, like if people go on Twitter or X because they feel like they're on Edge City, they're living in the future, like AIE is like that on steroids.

39:37 [laughs] Like I feel like SF is similar to AI- I mean, it's like I probably like the, the overlap of AI, uh, E generally and, and people who have lived at some point in their life in SF is probably a very- Oh yeah, yeah... large overlap. Like- We're roughly 1% of the city now. Actually? Uh, for World's Fair, yeah. That's crazy. Yeah.

39:56 Um, but like to that point, I just think like it is in the culture and the DNA of SF, where a very big population of people live in the future. I think you can find that group in New York, and I think we at 10x have found that people, and part of our goal is to drive like AI as a hub or, and New York as a hub for AI in, in the US because there is so much commerce here that like it is gonna be a place where diffusion of the technology actually has such a meaningful impact on the GDP of this country.

40:26 I think the bubble is smaller now, and p- we take it almost as like a charge of ours to increase our bubble. Maybe n- I don't know that it's possible to get to SF size, but I do think we can have a meaningful impact on getting more people to live in the future here. There, there's a physical like you see it on the billboards like- Yeah... slowly creeping in. Oh, totally. People used to, they, you remember the Friend campaign where people would like spray- Yeah...

40:45 us with the- Yeah, yeah... graffiti on that. That was, I think that was like a little bit of in- intentional rage bait, but now you have the unironic AI billboards, and the people are like, "Oh, I guess that's part of life now." 100%. [laughs] Yeah, so I, I think it's like, I think there's an opportunity to bring kind of like the, the edge and like the, just like the, the living in the future nature that SF has created because of just like the economic climate that exists there because of venture, because of tech companies, then because of the people that are attracted there.

41:14 But I, I also think of us as like we're in a, it's weird, we're in a smaller pond for AI, but I would argue we're in as big of a pond, if not a bigger pond, for like, uh, commercial value. Yeah, totally. A- and so I, I actually- The way, the way I put it is like, uh, the reason I do AI in New York is I bring all the people from San Francisco come, to come here and meet their, uh, customers. And also- Exactly...

41:40 like get a reality check. Totally. And I think- [laughs]... I actually think there's a really interesting arbitrage there. Yeah. Yeah. Yeah, totally. Okay, awesome. Uh, call for hiring, I know you guys are always hiring. Yep. What are you hiring for? Uh, what's changed? Yep. We're hiring, uh, for deployed engineers at 10x, just go to 10x.co.

41:57 Uh, we're also always hiring AI strategists, which are basically technical consultants who are leading client engagements. Um, and the team is 75 strong in New York City. We're continuing to make New York City our home. And then if you're an, are an enterprise who is looking to actually get AI native and you've tried it, it's not working, hit us up.

42:19 I promise we'll do better work than, not gonna name them, but a lot of other, uh, traditional consultancies. Ooh, spicy. Uh, CJ? Yeah. I mean, I echo all of that. I'm particularly looking for engineers. Um- What, what's a special project? What, what is that? A special project involves- [laughs]... anything outside of kind of our client work.

42:39 So if I think about my brain right now, it is building that meta harness and system internally that basically it's like find the bleeding edge of what the models can do and give them tools to augment how well they can do it. Um, and then also internally working on the platform that Alex mentioned. Um, big goal there, diffuse these background agents and actually get them out to enterprises in a way that's truly, truly helpful to them. Yeah. Um, and solve a lot of those like messy IT problems that, that we hit on earlier. Dude, uh, when you have some solutions, uh, we take guest posts on the, on the LeanSpace blog.

43:16 Um, I'd love to welcome some sharing of knowledge. But otherwise- Sure... thank you so much for your time. Thanks, man. And, uh, good to see you again in New York. Yeah, likewise. Yeah. Appreciate it. Once a year. Once a year. Hell yeah. Awesome. Thank you. [laughs] [outro music]