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

Alex Lieberman and CJ Hess join swyx at AI Engineer New York to discuss Tenex’s approach to AI-native engineering, paying for outcomes, MetaHarness, enterprise AI adoption and rebuilding SaaS. Show notes: Tenex: https://www.tenex.co/ Building an AI-native SDLC: https://www.tenex.co/blog/building-an-ai-native-sdlc Earlier Tenex episode: https://www.latent.space/p/10x-ai-engineers-with-1m-salaries Kill My SaaS: https://www.youtube.com/watch?v=zlD-CkLVKtU Full video: https://www.youtube.com/watch?v=Vki6cklFqxo 0:00 Cold open 0:26 Introductions 0:51 What Tenex does 1:32 Why CJ joined Tenex 3:17 Paying engineers for outcomes 7:20 Inside MetaHarness 10:25 Models and long-running tasks 15:11 Token spending and ROI 19:29 The enterprise AI adoption gap 23:22 Why agent deployment is hard 24:42 Vertical AI and enterprise software 30:32 Kill My SaaS 35:23 AI and jobs 38:18 New York’s AI ecosystem 42:28 Hiring at Tenex This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe https://www.latent.space/subscribe?utm_medium=podcast&utm_campaign=CTA_2

• 0:00Speaker 00

  And I just felt my output getting better and faster and ultimately getting paid less like I was charging hourly to my clients. I phrase it to the team is like, you can't automate a process that does not exist. So.

• 0:26Speaker 01

  Okay, we're here at AI Engineer checking with with 10x. Welcome back, Alex.

• 0:31Speaker 02

  Thanks. Thanks for having us.

• 0:32Speaker 01

  You brought your Director of Engineering, CJ. Welcome to the pod.

• 0:34Speaker 00

  Special Projects Lead at 10x.

• 0:36Speaker 01

  Okay, special projects. All right, you're the SWAT team.

• 0:39Speaker 00

  Yes, yes.

• 0:40Speaker 01

  So you've got, you've been on the pod before. We in, you know, people are vaguely familiar with 10x story. But can you sort of reintroduce from the top? What is the 2026? How you introduce yourself?

• 0:50Speaker 02

  Yeah, very simply, 10x is an applied AI partner for some of the biggest companies in the world to help them go from AI absent to 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. 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.

• 1:18Speaker 01

  Yeah, yeah. We have the explicit tag on Apple Podcasts. Although I did recently discover that YouTube doesn't like it.

• 1:24Speaker 02

  Oh, really?

• 1:24Speaker 01

  So like, yeah, you can't say the F word.

• 1:27Speaker 02

  So did I just mess up your video? What are you gonna do?

• 1:29Speaker 01

  I don't care.

• 1:29Speaker 02

  Okay.

• 1:32Speaker 01

  CJ, what drew you to 10x?

• 1:34Speaker 00

  So I was actually the first hire. I don't even know if Alex and Armon had the name 10x quite yet. But I have like a graveyard of startups I tried to start before 10x, ultimately culminating in like my own kind of engineering consulting company. 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. 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.

• 2:13Speaker 00

  And I really liked kind of the ethos of 10x that Alex had first posted when I saw it on Twitter about kind of looking to reward engineers for their output as opposed to rewarding them for their time. I think, you know, fast forwarding two years, you can see like how engineers can now parallelize a ton of work and just have a ton more output. At the time with, you know, Sonnet 3.5, Gemini 2.5 Pro, 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. And since then, I've just like fell in love with that model.

• 2:55Speaker 00

  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 going to pay based on outcome and they're not going to want to pay based on the time that's required to build something.

• 3:09Speaker 01

  Yeah. So for more detail on that, people can refer back to our previous pod where you literally pay people by story points.

• 3:14Speaker 02

  Yep.

• 3:15Speaker 01

  And you just have to agree what the story points are. But that also means that some 10x engineers can literally make millions of dollars a year. And some others may not make as much just because they're not as productive.

• 3:24Speaker 02

  Yeah, exactly. Like the thesis is that AI is really just a leveraged bet on a human's intellect and work ethic. And so you're just going to have more variance than ever before in earning capacity. And our view is, is our model isn't for everyone, but for the person who basically wants to go long themselves, who has this incredible like foundation in software engineering, but also is constantly pushing themselves to leverage the frontier to stretch their time as much as possible while having quality output. There's no better way to earn.

• 3:53Speaker 01

  What's the state of like, last time I talked to you, I think you had like maybe two or three people making a million a year or over a million a year? Is there, is there an update on that or?

• 4:01Speaker 02

  Yeah, yeah. So, so just to paint a picture, we started 10x 18 months ago. 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 coalmine for AI. And part of the impetus was my co-founder Armon, 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. And so we're like, why don't we just supercharge a company's engineering org who isn't adopting this? 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

• 4:45Speaker 02

  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 og and AI engineering. They were asking for like AI transformation as a service. Today, the business is at 75 people. 90% of those 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. It's probably something around 4x difference.

• 5:24Speaker 01

  Oh, not 10x?

• 5:25Speaker 02

  Not 10x difference. Yeah, exactly. 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 would be limits and how we think about that moving forward. 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 going to either sandbag story points or they're just going to ship a bunch of bad code.

• 5:58Speaker 01

  Like barely check the box.

• 5:59Speaker 02

  Exactly.

• 6:00Speaker 01

  Right? But not actually do the job.

• 6:01Speaker 02

  Yeah, exactly. And I would say those two concerns have not been concerns at all because of our AI strategists. We incentivize, I think, perfectly at odds with engineers where AI strategists are incentivized for like customer retention and growth. Actually, the biggest challenge has been how do you compensate engineers for all the time that's not spent coding? Whether it's architecting, meeting with clients. 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.

• 6:32Speaker 01

  Anything to add?

• 6:33Speaker 00

  Yeah, on that, I spent a lot of time thinking about this because if I think about my tenure at 10x, I was coding a lot, coding a lot, coding a little less. And now we're kind of getting to the point where the models are more or less given the right context and can get you to 90% of the solution. 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 going to start to look more at like true, what this output is, how it works, how it looks

• 7:15Speaker 00

  as opposed to, you know, kind of that traditional story point model, but still anchoring to those as the unit.

• 7:20Speaker 02

  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 that has allowed us to do, I think, like really meaningful work on long running tasks. Do you want to just talk through like what our process is? Because I feel like

• 7:38Speaker 00

  Yeah.

• 7:38Speaker 02

  people would find that interesting.

• 7:39Speaker 00

  Yeah. So we basically started trying to kind of standardize a set of skills. I think most engineering orgs have done something similar by this point. 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 kind of 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. 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.

• 8:22Speaker 00

  But I'd say as the models have gotten better, we've actually reduced the enforcements there in the structure.

• 8:29Speaker 00

  Kind of similarly across all of these, 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.

• 8:40Speaker 01

  Everything checked into git.

• 8:42Speaker 00

  Yeah, all of this is checked in. All of this is really kind of marked down for me now more HTML. I like to see these picture books that the agents spit out. 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. 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. 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.

• 9:23Speaker 00

  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 having conversations or having their heads. And our goal is to kind of 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.

• 9:44Speaker 01

  Yeah, amazing.

• 9:45Speaker 02

  And one, one way we even measure, like 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?

• 9:56Speaker 01

  Right. And what do you think it is, let's say compared to start of this year?

• 10:02Speaker 02

  I don't know. What do you think? Do you think?

• 10:04Speaker 01

  Plus fifty?

• 10:05Speaker 00

  I think so. It really depends because we have some people that 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. I'd really say overall, it's been like a two X increase this year in particular.

• 10:25Speaker 00

  I think basically, if you think of December as the like Opus four, five moment where the models really started to take on these long running tasks. To today, we're back to Opus, but Opus five, five. I mean, that's just a magnificent model that can really do a ton of work. 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.

• 10:50Speaker 01

  Is a single story point worth the same now as it was a year ago?

• 10:54Speaker 00

  We're we're always trying to calibrate. It's a tricky equation.

• 10:57Speaker 01

  Is there like inflation or deflation, right? I don't know.

• 11:00Speaker 02

  I mean, in theory, in theory, there should be deflation.

• 11:03Speaker 01

  Right?

• 11:03Speaker 00

  Yeah.

• 11:03Speaker 02

  Yeah.

• 11:04Speaker 00

  Yeah. And that's that's roughly how we think about it. I would say at least once a quarter, we kind of sit down and evaluate like, what is our criteria? For these story points, how are we actually estimating these projects? 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 kind of calibrate like the value that we're creating.

• 11:27Speaker 00

  So overall, like I would say roughly that two X 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.

• 11:40Speaker 01

  Yeah. Well, so there's a couple of ways you can take this, but I'm just going to check in on the sort of model preference stuff since it's always a topic of conversation. You, I think you just let people choose whatever the tools they like. But then you maybe you have some statistics on what is preferred in the office. It seems like Opus is leading.

• 11:58Speaker 02

  Yeah, what's the vibe?

• 11:59Speaker 01

  Can you like care to estimate like?

• 12:00Speaker 00

  The vibe.

• 12:01Speaker 01

  Market share, whatever.

• 12:02Speaker 00

  The vibe right now is Opus five, five, I think.

• 12:05Speaker 01

  No Fable?

• 12:07Speaker 00

  I don't particularly use Fable, and I think a lot of people right now are just

• 12:10Speaker 01

  Like Opus is good enough.

• 12:12Speaker 00

  Opus is so good, it's fast, it's cheap, it kind of hits all my checkboxes. 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. 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. 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 five, five. Using a Codex to review and having them actually work back and forth on your machine, I think still produces the best results.

• 12:55Speaker 00

  But right now we're pretty aligned on those two.

• 12:57Speaker 02

  And are you still are you still AMP maxing or no?

• 13:00Speaker 00

  Oh, I am AMP maxing.

• 13:02Speaker 01

  So you're using Opus through AMP?

• 13:04Speaker 00

  So, yeah, they recently.

• 13:06Speaker 01

  That's the default model for for AMP.

• 13:07Speaker 00

  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.

• 13:15Speaker 01

  Because that'll really cut your cost by like eighty percent.

• 13:18Speaker 00

  Totally. And like, I mean, I love AMP because it takes everything off my computer. They have this like cloud cloud setup, the orbs. We actually co-hosted a happy hour with them last week, so it was cool to meet the team and orb out. But but.

• 13:35Speaker 01

  That's never going to be a thing.

• 13:36Speaker 00

  Oh, yeah. I want to make it a thing.

• 13:38Speaker 02

  This may be the first time in history that any human has said.

• 13:40Speaker 00

  We're orbing out, we were orbing out.

• 13:42Speaker 01

  No, no, they do say it's orbiting time.

• 13:44Speaker 00

  Yeah.

• 13:44Speaker 01

  That's that's.

• 13:44Speaker 00

  Yeah. But yeah, that's my that's my interface of choice and models are really between that if I'm leaning on Codex, it's so six one.

• 13:52Speaker 01

  Isn't it? Did you give Cloud Tag a shot? Like, isn't it weird that Anthropic never captured the remote environment thing in a way that Orbs did?

• 13:58Speaker 00

  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. Tools like Claude Code in the Cloud and Claude Tag, I think, provide value for teams that are really trying to like collaborate maybe on non-technical tasks. 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 in the Cloud, a Claude Tag.

• 14:39Speaker 01

  Yeah, yeah, fair enough. Another thing I always track, 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. So the roughly, most people, most companies are at maybe, let's call it ten to two hundred dollars in spend per employee. The more sort of AI native ones would would sort of convert that to a percentage, so roughly ten percent. The average AI startup employee is about one X in terms of employee spend and token spend. And then it goes up to maybe five to to ten X in terms of token spend. Now, where are you guys in that range?

• 15:17Speaker 00

  I actually don't have a number off the top of my head. I'd say we're somewhere between, if we're talking like raw tokens, somewhere between one to one and maybe like

• 15:28Speaker 01

  Really?

• 15:29Speaker 00

  Two to one.

• 15:29Speaker 01

  That's pretty high.

• 15:30Speaker 00

  And we, 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.

• 15:40Speaker 00

  But I will say recently our costs have been driven just way down from

• 15:43Speaker 01

  Because of the subscription thing.

• 15:45Speaker 00

  We kind of had an era of like everyone was using Astra and Fable and they just gave the best results. But ultimately, those models are.

• 15:52Speaker 01

  And like a lot of like, you know, since the last time we talked, a lot of people had this sort of token maxing error, token leaderboards, everything. And then they were like, oh, no, no, no, no, now we have too much cost. And like, what is the most obvious like predictable thing in the world? Did you ever fear about it or like you were just getting good ROI regardless?

• 16:07Speaker 02

  Yeah, I mean,

• 16:10Speaker 02

  there's 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

• 16:21Speaker 02

  I I think also like,

• 16:24Speaker 02

  even though our team, I would say like, I don't I don't feel like anyone on our our team ever really ascribe to like token, the token maxing zeitgeist, because.

• 16:34Speaker 00

  I had a few moments.

• 16:36Speaker 01

  Yeah, me too. And but it's it's like, well, if the the boss is going to pay for it, like, I'm just going to go nuts, right? Because then then you you do the weird stuff so that you can scale back. But you have to do the weird stuff first to discover the lessons that you can take with you.

• 16:49Speaker 02

  Yeah, totally. I mean, this this may sound crazy, but and I'd be curious your thoughts, but like, I was talking to someone earlier about the the debate that continues around like, basically, I promise this is related, like, I was talking to someone earlier around the data companies, right? Like, um, Micro One, Merkle, Handshake, and just like, what is like, what does the long term for these businesses look like, right? Because like ninety ninety plus percentage of their their revenue is from the labs. And a number of these founders would argue that they think it's going to flip flop, where in the next few years, it's going to be enterprise driven. And then like the thing that comes to mind, like the next thing, I assume you have to believe if enterprises are going to be the core buyer of that data, is that enterprises are going to be doing a lot of fine tuning on their own models.

• 17:34Speaker 02

  And then the question becomes, where is that going to make sense? It's either you think there's going to be better quality, which again, like, the bar is just going to 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, the reason we're not concerned internally is because I it almost feels like we we've kind of hit this like perito point of like costs are coming down at a per task basis, then it is it is way harder today for someone to to token max and and like really F the company in terms of cost than it was six to eight months ago.

• 18:18Speaker 02

  Like, I almost think six to eight months ago was the the point at which like you'd be really concerned.

• 18:22Speaker 01

  Yeah.

• 18:22Speaker 02

  Do you agree with that?

• 18:23Speaker 00

  Yeah, I think there's a couple of ways like the future might play out there, like if there's a world where there's a Astra six, five, a Fable five, five, 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, today. And it's on the frontier, it's dirt cheap, the competition is heating up. And but 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 Opus five, five.

• 19:05Speaker 00

  So, you know, it's it's that cliff of like, would a ten times smarter model really justify the cost of like moving some cells in a spreadsheet? Probably not.

• 19:14Speaker 02

  Right.

• 19:16Speaker 01

  So coming back to your customers and what you're what you're seeing from the market, 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?

• 19:28Speaker 02

  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 AIE, when I tell people about what percentage of companies have reached these milestones, they're kind of like mind blown by it. 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 hundred enterprises we're talking to today.

• 20:01Speaker 02

  Ninety percent have rolled out either Claude or Codex to their employees. 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, but is arguably more important or as important as the technology itself. I would say that's at like sixty percent.

• 20:21Speaker 01

  Oh, that's pretty good.

• 20:22Speaker 02

  Yeah, yeah, like there's some group there of companies that are like, our people will just figure it out. But then there's like a good chunk that is like, okay, we need to teach our people what skills are. We need to teach our people the features and functionality of these surfaces. Then the next, after training and enablement within a general purpose LLM is coding agents. And the crazy thing is like, 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? And most people are like, it has to be seventy or eighty percent. The number is closer to fifty percent. 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.

• 21:08Speaker 02

  Then the next, and this is where like the biggest, I would say, drop off in like 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 five percent. It's like, I mean, you're in these conversations all day long, but like most enterprises that we talk to that have, let's just say more than a thousand employees, do not have an agent in production that is doing work that is not building software.

• 21:37Speaker 02

  And so I think there's a few things. 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 in 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 on to the things that are most in the zeitgeist. I guess now for the last year and a half, people have been saying it's the year of the agent or however long it is, every year is the year of the agent. Like enterprises are like, can you help build us an agent for some back office task within the organization? 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

• 22:21Speaker 02

  outside of the Norg for companies for repeatable work that they're doing. The second is permissioning and like our back. Basically, as every employee in a company has Claude or they have Codex and they need access to certain data, it is turned into an absolute nightmare for CIOs and CISOs. And they don't have basically a good gateway for easily doing almost like a control panel for data permissioning.

• 22:49Speaker 01

  Literally just what can you read, what can you not read?

• 22:51Speaker 02

  Exactly. And that is a huge issue. So that's like something we spend a lot of time on.

• 22:55Speaker 01

  You make them for people or do you like buy them off, buy something off the shelf?

• 22:59Speaker 02

  So basically, we started by building bespoke for companies and now we actually productize that where we have our own gateway, our gateway solution. Yeah, exactly. Is there anything else that comes to mind that's like, we're hearing a lot from customers?

• 23:12Speaker 00

  I think that covered what we're hearing when it comes to like particularly the agent deployments. I think one of the pains that just always gets discovered, I phrase it to the team is like, you can't automate a process that does not exist. So when you have forty people that are all roughly doing the same function, but they're all doing it a little bit of a different way. There's actually, it's way harder to build an agent that's kind of proactively doing that in the background. 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, everyone's been talking about becoming an agent manager.

• 23:58Speaker 00

  And I think we're on the cusp of that. 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 going to be impossible for the agent to know whether it's doing it right, for us to build evals around this system and actually deliver the value that an agent that is doing this function would have.

• 24:27Speaker 02

  And even one thing I'd be curious your thoughts on, we were literally talking about this right before is,

• 24:34Speaker 02

  and like CJ will be, okay, let me share, this is like the, we were talking about like vertical specific AI solutions and like what does the future look like for these solutions? And we were generally 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. 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. And also when a buyer out of a vertical solution is expecting truly a one of one experience,

• 25:15Speaker 02

  like we're in this, we are in an age where people are expecting one of one software solutions. And so if you're expecting that, it there's, 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 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 expects a one of one solution? And also, why should we not expect like frontier models to just gobble them up?

• 25:59Speaker 01

  Yeah. So to give some concrete examples, you're talking like Harvey for lawyers.

• 26:04Speaker 02

  Yep.

• 26:05Speaker 01

  You're talking open evidence for doctors. These are the classic examples of people that are winning their domains. And you're bearish on them?

• 26:12Speaker 02

  I would say generally bearish on them. Correct.

• 26:14Speaker 01

  Well, so far you've been wrong.

• 26:17Speaker 01

  And you know, I think the answer very simply is like access to data that chat GPT is not going to have, which is like very common. That's, that is a moat. Obviously, Anthropic and OpenAI are also acquiring those same data sources, but they're just not as dedicated, you know, as, you know, these guys are going to be. They do have innovations. If you've like tried it, Harvey, they have like the tabular review, Legora also have them. We've had the CTO on the podcast where they have affordances and UIs and all that, that like is just customized because they only serve them.

• 26:50Speaker 02

  The pitch is focus, basically.

• 26:51Speaker 01

  Focus affordances, maybe like in a way, in a way that you care about the last mile for your customers, they care about their last mile in terms of the software. And as long as people train in that software, like it never goes away. Like as a banker or, 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 fifty thousand dollars a year for Bloomberg? 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 whatever. Or like it's tied into my trading, trading system or my chat or whatever, my news system.

• 27:29Speaker 02

  No, I think people went for Bloomberg is the social network. I think people love the DMs in Bloomberg.

• 27:33Speaker 01

  There's that, there's that. But as a trader, I know, I actually use that for.

• 27:37Speaker 02

  Yeah, yeah, yeah.

• 27:38Speaker 01

  So, so I mean, I think there's a, there's a bunch of integrations and tools. And like, I think ultimately it's hard to be bearish on any of them, mostly because the demand for software, the demand for tooling to do your job is just huge. And some of it, yes, is going to be fulfilled by horizontal generalized tools like the clause and chat GPTs and some of it, you're just going to want something better. And you know, it's just, I always think about maybe the success of Superhuman as an email client. Everyone has Gmail, but here's just premium Gmail with like more stuff for people who live in email all day long. And like, that's a market. It maybe wasn't the largest startup in the world, but I think reasonable people can disagree how large each thing is.

• 28:17Speaker 02

  Yeah, totally.

• 28:18Speaker 01

  I think, I think what's not to be disagree with is just the total market is going to grow and all rising tide is going to lift all boats. And then you're just talking percentage pressure market share at that point.

• 28:29Speaker 02

  Yeah, yeah, agree with that.

• 28:30Speaker 01

  Totally agree.

• 28:30Speaker 00

  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. I don't even spend a ton of time in email, but anything that has.

• 28:44Speaker 01

  You pay thirty bucks a month just to.

• 28:46Speaker 00

  Anything that has cool hotkeys, I'm into.

• 28:50Speaker 00

  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, unless there is some very strategic compliance data, whatever it might be moat, I think those are going to struggle going forward 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.

• 29:24Speaker 00

  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 those will be successful.

• 29:38Speaker 01

  Yeah. Oh, okay. Okay. So so there's this, there is a related view about this that maybe isn't quite as spicy as yours. 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? So everyone is like sitting on like some kind, some part of your data and they're like, use my agent, please, please pay me that twenty dollars a month. But you you can't just like stack up all these ten dollars a month subscriptions, like ultimately, like whoever, like probably a small set of companies are going to own the interface and then everyone just plugs into that.

• 30:11Speaker 02

  Yep.

• 30:12Speaker 01

  That overall agent, which I, which I agree with. Then 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 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. Yeah, which is kind of cool. Okay, so I actually wanted to also walk through one example. So I just released my results for the my Kill My SaaS competition where we replace a mid-tier SaaS. I call them mid-tier basically in the sense that it's probably not that trivial, but it's not Salesforce. It's somewhere in the middle. And I think there's a lot of mid-tier SaaS out there that people are always talking about like the death of software, right?

• 30:51Speaker 01

  It's, we recorded forty thousand dollars a year. We spent a thousand dollars to replace it. It's it's gonna, it's going to production next, for the next conference. So almost one, like, we are that customer that says like, I want the one-on-one tool and I'm expecting all these things and we're building a domain specific or vertical specific thing for our business. And like, so far it's going okay. Tell me where it's going wrong. Also, I'm wondering how many story points that that is.

• 31:14Speaker 02

  Yeah, well, CJ can size up the story points on it. I mean, my general view is, I think the the ripping out of, my my general view is like, the more you move up marketing business, the harder the proposition of ripping out and building from scratch gets. I agree with you, like, part of the reason when, as people have said, like, a very small group have said, like, Salesforce is really at risk, SAP is really at risk, like, don't agree with that at all. It's just like, those businesses in my mind just accrue value in this moment in time. 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 or billions of dollars.

• 31:57Speaker 01

  You're gonna be risk averse.

• 31:58Speaker 02

  Yeah, exactly. I think for mid-tier, it really,

• 32:04Speaker 02

  it it actually comes down to what is the size of the opportunity? And is that ever going to be like the the the worthiest opportunity to rebuild inside of a company? Like, I 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. 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 a 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,

• 32:43Speaker 02

  the thing that gets companies outside of like capability, like, you know, we we talk about how, I forgot who who created the repo that is basically the reverse engineer repo where. Yeah, exactly. Like, everyone's talking about that right now and even at ten X, like, we we like, we built our own like CRM from scratch. We we, what have we built? That our OS we built from scratch 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,

• 33:28Speaker 02

  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? Evidently, like, I think for for your business, the difference of basically thirty nine thousand dollars a year, you're you're implicitly saying that it is worth it to have.

• 33:50Speaker 01

  It is not just about costs.

• 33:52Speaker 02

  Yeah.

• 33:52Speaker 01

  It is, that's defense and I also think about the offense.

• 33:54Speaker 02

  Yep.

• 33:55Speaker 01

  Of like, no, actually, our experience could be a lot better for literally every party concerned.

• 33:59Speaker 02

  Totally, totally. And so, yeah, I think a part of the the the thought there is, is actually just, there's a significant, basically, it's not just that. So there's two pieces to it. 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 times. What would the ten X better version of this product look like?

• 34:17Speaker 01

  Because we are the customers and we can iterate with like minutes and not months.

• 34:20Speaker 02

  Totally. I think, I actually think what's interesting is, I think startups and like even like, high-growth mid-sized companies can be nimble in that way. I think for enterprises where like they're so entrenched in a in a certain capability, rarely is that going to be the the thing that, rarely are they gonna say like, oh, we can create a ten X 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.

• 34:50Speaker 01

  For sure.

• 34:50Speaker 02

  Yeah.

• 34:50Speaker 01

  Yeah, it's been discussed. I I actually, I was, I would say that, you know, you talk about automation. The the more spicy thing is, are they automating humans, right? And like, once everything is automated and and smooth sailing, do we, you know, replace those jobs? I don't know how much you can speak to that.

• 35:08Speaker 00

  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, it will absolutely change and displace, but I think it's more of a movement as opposed to just like cutting off work.

• 35:25Speaker 01

  Yeah.

• 35:26Speaker 00

  I think A16Z posted something about how like call centers are truly now on the decline and AI is 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. 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. 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

• 36:10Speaker 00

  that cycle kind of continues forever. There's always the next problem if I think about how we've grown.

• 36:15Speaker 01

  Infinite demands.

• 36:15Speaker 00

  There's all, there's always been another problem. 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 salin spreadsheet and more about how these teams are working together. Ultimately, in the 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 when they you, you know, when you're thinking about someone that's in like a finance division,

• 36:56Speaker 00

  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?

• 37:03Speaker 02

  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. 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.

• 37:21Speaker 01

  You can adjust.

• 37:22Speaker 02

  Yeah, people really should be thinking, like, when I think about, like, enablement or people re-skilling, I think it is the job of an employer because they want to drive growth in their business, but it is also the job of an employee. 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.

• 37:50Speaker 01

  Yeah.

• 37:50Speaker 02

  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.

• 38:10Speaker 01

  Yeah.

• 38:10Speaker 02

  Yeah.

• 38:11Speaker 01

  I think that's a good message for people to reflect on. The last piece I wanted to touch on before I let you guys go is just New York. You know, greatest city in the world, according to some people, some people. What's the mood generally among the people that you talk to, whether it's at work, personally? What's the feeling, right? Like, I I live in San Francisco all day long. Everyone's just in agents all day long, like, you know, it's, the discussion is instinct or grok or muse. It's not like, are you, do you like AI or not, right? And I I feel like I want to step out of that bubble for a bit or, but it's still New York. It's still progressive. It's still forward looking.

• 38:45Speaker 02

  Yeah. I mean, what I'd say is, I feel like, basically, I would just say, there's a bubble in New York that is a smaller bubble than exists in SF.

• 38:54Speaker 01

  Okay.

• 38:54Speaker 02

  If SF is like a big bubble, I think we at ten X feel like, you know, we are in a, what I would say is like a microcosm of San Francisco because of the people that self-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. 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.

• 39:38Speaker 02

  Like, I feel like SF is similar to AI. I mean, it's like, I probably like the the overlap of AIE generally and and people who have lived at some point in their life in SF is probably a very.

• 39:49Speaker 01

  Oh, yeah, yeah.

• 39:49Speaker 02

  Large overlap.

• 39:50Speaker 01

  We're roughly one percent of the city now.

• 39:53Speaker 02

  Actually?

• 39:54Speaker 01

  Uh, for Wolf's Fair, yeah.

• 39:55Speaker 02

  That's crazy.

• 39:55Speaker 01

  Yeah.

• 39:56Speaker 02

  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 ten X 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 going to be a place where diffusion of the technology actually has such a meaningful impact on the GDP of this country. I think the bubble is smaller now and we take it almost as like a charge of ours to increase our bubble. Maybe, 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.

• 40:37Speaker 01

  There's a physical, like, you see it on the billboards.

• 40:40Speaker 02

  Yeah.

• 40:40Speaker 01

  It's slowly creeping in.

• 40:41Speaker 02

  Oh, totally.

• 40:41Speaker 01

  People used to, remember the friend campaign where people were like spraying graffiti on that? That was, I think that was like a little bit of intentional rage bait, but now you have the unironic AI billboards and people are like, I guess that's part of life now.

• 40:53Speaker 02

  A hundred percent. 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.

• 41:03Speaker 02

  Just like the the living in the future nature that SF has created because of just like the economic climate that exists or because of venture, because of tech companies, then because of the people that are attracted there. 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.

• 41:30Speaker 01

  Yeah, totally.

• 41:30Speaker 02

  And so I I actually.

• 41:32Speaker 01

  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 customers and also.

• 41:40Speaker 02

  Exactly.

• 41:40Speaker 01

  Like, get a reality check.

• 41:41Speaker 02

  Totally. And I think, I actually think there's a really interesting arbitrage there.

• 41:45Speaker 01

  Yeah.

• 41:45Speaker 02

  Yeah.

• 41:46Speaker 01

  Yeah, totally. Okay, awesome. Uh, call for hiring. I know you guys are always hiring.

• 41:49Speaker 02

  Yep.

• 41:49Speaker 01

  What are you hiring for? Uh, what's changed?

• 41:51Speaker 02

  Yep, we're hiring, uh, for deployed engineers at 10x. Just go to 10x.co. 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. I promise we'll do better work than, not going to name them, but a lot of other, uh, traditional consultancies.

• 42:23Speaker 01

  Ooh, spicy. Uh, CJ?

• 42:26Speaker 00

  Yeah, I mean, I echo all of that. I'm particularly looking for engineers.

• 42:31Speaker 01

  What's a special project? What what is that?

• 42:34Speaker 00

  A special project involves anything outside of kind of our client work. 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.

• 43:04Speaker 01

  Yeah.

• 43:04Speaker 00

  Um, and solve a lot of those like messy IT problems that that we hit on earlier.

• 43:10Speaker 01

  Dude, uh, when you have some solutions, uh, we take guest posts on the on the Lean Space blog. Um, I'd love to welcome some sharing of knowledge, but otherwise, thank you so much for your time.

• 43:20Speaker 02

  Thanks, man.

• 43:21Speaker 01

  And good to see you again in New York.

• 43:21Speaker 02

  Yeah, likewise. Appreciate it.

• 43:23Speaker 01

  Once a year, once a year.

• 43:24Speaker 02

  Hell, yeah.

• 43:24Speaker 00

  Thank you.