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

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 phrased it to the team as, like, you can't automate a process that does not exist. So

Okay, we're here at AI engineer, checking in with with, uh, Tenex. Uh, welcome back, Alex.

Thanks, thanks for having us.

Uh, you brought your director of engineering, CJ. Welcome to the pod.

special projects lead at Tenex.

Okay, special projects lead. All right, you're the SWAT team. Uh, so you've got you've been on the pod before, uh, we and, you know, people are vaguely familiar with Tenex story, but can you sort of re-introduce from the top, what is the 2026, how you introduce yourself?

Yeah, uh, very simply, Tenex 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. And so our view is to act as the player to help enterprises to figure all of this, can I curse?

To figure all this shit out.

yeah, yeah, yeah, we we have the explicit tag on Apple Podcasts, 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, uh, or they will should I just sort of like,

mess up your video? turn it or What are you gonna

don't, don't, I don't care. do? Okay, Whatever, else, uh, CJ, what drew you to Tenex?

Um, so I was actually the first hire. Um, I don't even know if Alex and Armand had the name Tenex quite yet, but I have, like, a graveyard of startups I tried to start before Tenex, um, ultimately culminating in, like, my own kinda engineering consulting, um, 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.

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. Um, and I really liked kind of the ethos of Tenex 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 two years, you can see, like, how engineers can now parallelize a ton of work and just have a ton more output.

Um, at the time with, you know, Sonnet three five, 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. 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, you people can refer back to our previous pod, where you literally pay people by story points, and you just have to agree what the story points are. Uh, but that also means that some Tenex engineers can literally make millions of dollars a year, and some others will 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. 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.

what's the state of, uh, like, last time I talked to you, I think you were had, like, maybe two or three people making a million a year or over a million a year. Is uh, is there, is there an update on that, or is it no longer the case?

So, so, just to paint a picture, uh, we started Tenex eighteen 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 coal mine for AI. And part of the impetus was my co-founder Armand, 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 supercharged 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 comp our customers are fortune 2,000, and then 50% are mid-market, doing a hundred to 500 million in revenue. They weren't just asking for, basically, staff aug and AI engineering, they were asking for, like, AI transformation as a service. Today, the business is at, uh, seventy five 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 Tenex and the lowest paid engineer at Tenex. It's probably something around four x difference,

Oh, not Tenex?

not Tenex difference.

That's the dream.

Yeah, yeah, exactly, 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 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 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.

yeah, exactly, and I would say those two concerns have not been concerns at all because of our AI strategists. We in 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, architecting, Meetings, meeting with clients, 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 spent a lot of time thinking about this, cause if I think about my tenure at Tenex, 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. Um, and I do think there is some opportunity here to think about, like, really, the value is sitting down with the 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, kind of that traditional story point model, but still anchoring to those as the unit.

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 MetaHarness, which is, like, the the engineering workflows at Tenex that we use that has allowed us to do, I think, like, really meaningful work on long-running tasks. Do you wanna just talk through, like, what our process is, cause I feel like people would find that interesting.

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 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.

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, 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, everything checked into especially,

Git?

yeah, all of this is checked in. Um, all of this is really kind of markdown for me now, more HTML. I like to see these picture books that the agents spit out. 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. 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.

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 up front.

Yeah, amazing.

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

I don't know, what do you think? Do you Plus think? Ah,

fifty?

I think so, it really depends, cause 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. Um, I'd really say, overall, it's been, like, a two x increase this year, in particular. Um, 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.

Um, I mean, that's just a magnificent model that can really do a ton of work. 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? we're Or,

we're always trying to calibrate. Um, it's a tricky equation is there like there.

inflation or deflation, right? Like, I don't know, I mean, in what's the

theory, in the theory, there should be deflation. Yeah,

I don't know.

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? 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. Um, 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.

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 of preference stuff, since that's always a topic of conversation. Um, you, I think you just let people choose whatever they tools they like, 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 D. Can

the vibe? you, like, what's the vibe care to estimate, checking in on? like, The vibe

market share, whatever?

the vibe right now is Opus five five. I think

No fable?

I don't particularly use Fable, and I think a lot of people right now are just like, Opus is defaulting to WaveNet. good enough, yeah, Op Opus is so good, it's fast, it's cheap, it kinda hits all my check boxes. 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 kinda 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. 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 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. Um, but right now, we're pretty aligned on those two.

And are I don't think you still, there's too much are you still amp else.

maxing or no?

Oh, oh, I am amp maxing. So you're using Um,

Opus through amp? so, yeah, they recently, Or, like, th- that's the default model for for for amp?

yeah, they recently launched, like, the ability to hook in your cloud code subscription, so that's been a pretty big game changer for me. Um,

Cause that'll re really cut your cost by, like, 80%.

to, uh, totally, and, like, I I mean, I love amp, cause it takes everything off my computer. 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 but, you that's never know, that,

gonna be a thing. Yeah,

oh yeah, I I wanna This may be make it a thing.

the first time in history that any human Orb. We're has said

orb-ing out, we were orb-ing out.

they they do say it's orb in time. Yeah, that's that's, uh, yeah, um, but yeah, uh

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.

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?

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 Cloud Code in the Cloud and Cloud 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 Cloud Code in the cloud, a Cloud tag.

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. Uh, 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 ten to $200 in spend per employee. 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 one x in terms of, uh, employee spend and token spend, and then it goes up to maybe five to to Tenex in terms of token spend. Now, where, where are you guys in that range?

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,

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 five five. subscription We kinda thing, yeah, had an era of, like, everyone was using Astra and Fable, and they just gave the best results, but ultimately, those models are expensive.

in in, 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 ops like, predictable thing in the world. 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

I had a few moments.

Yeah, me too. And but it's it's like, well, if if the the boss is gonna pay for it, like, I'm just gonna go nuts, right? 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 to your thoughts, but, like, I I was talking to someone earlier about the the debate that continues around, like, b- basically, I promise this is related. Like, I was talking to someone earlier around the data companies, right? Like, um, micro one, mercur, handshake, and just like, what, I like, what does the long term for these businesses look like, right? Cause, like, not ninety plus percent 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.

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. And then the question becomes, where is that going to make sense? 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 cost 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, 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.

Like, I almost think six to eight months ago was the the point at which, like, you'd be really concerned. 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 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, yesterday or today, and it's on the frontier, it's dirt cheap, the competition is heating up, and both 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. 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.

um, so, coming back to, uh, your customers and what you're, where you're seeing it 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 AI. E. 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 a hundred enterprises we're talking to today, 90% have roled rolled out, e- e- 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, but is arguably more important or as important as the technology itself. I would say that's at, like, 60%. There's,

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, 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 engineering product org in their company?

And most people are like, it has to be seventy 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%.

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 a thousand employees, do not have an agent in production that is doing work that is not building software. Um, 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, um, or however long it every year is, every year

is the 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 Tenex 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 a company has Claude or they have Codex, and they need access to certain data, it has turned into an absolute nightmare for CIOs and CISOs, and they don't have basically a good gateway for easily doing almost like a con, like a control panel

for, uh, data provisioning.

Literally, just what can you read and what can you E- exactly, not read?

and that is a huge issue, 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? What's So the story there?

basically, we started by building bespoke for companies, and now we actually productize that, where we have our own gateway,

Build a platform?

our gateway solution. Yeah, solution, Yeah, 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. 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 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 n it's. 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, e- Everyone's been talking about becoming an agent manager, 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 gonna 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.

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 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 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, 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 of a vertical ex- uh, uh, solution is expecting truly a one of one of experience, like, we're we're in this, I 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. 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, whe- 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, four vertical-specific solutions, either one, how will they be able to do this at software margins if everyone expend- uh, expects a a one of one solution? And also, why should we not expect, like, frontier models to just gobble them up?

yeah, um, so, uh, to give some concrete examples, are you're talking like Harvey for lawyers? You're Yep, that talking like

would be an example.

open evidence for doctors. These are the the classic examples of, uh, people that are winning their domains. Uh, and you're bearish on them?

I would say generally bearish on them, correct?

Wow, uh, well, so far, you've been wrong. Uh, and, uh, you know, I think 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, o, obviously, Anthropic and OpenAI are also acquiring those same data sources, but they're just not as dedicated, uh, you know, as as, uh, you know, these guys are gonna be. Um, they do have innovations, uh, if you look, if you've, like, tried it, Harvey, um, they have, like, the table review that Legora also have them.

We 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 s they only serve them, whereas

The pitch is focus, basically.

focus affordances, maybe, uh, that like, like What do you in the way,

mean by affordances?

in the 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, eh, 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- $50,000 a year for Bloomberg?

While 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, or, like, it's tied into my trade, uh, trading system, or my chat, or whatever, my news system.

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

There's that, there's that. But as a trader, I know, I I actually used to use, use, use that for, um, so so, I mean, I think there's a there's a bunch of integrations in in in tools. And, like, I think ultimately, it's hard to be bearish on any of them, mostly because the the demand for software, the demand for tooling to do your job is just huge. And some of it, yes, is gonna be fulfilled by horizontal, generalized tools, like the Claws and ChatGPTs, and some of it, you're just gonna want something better.

Um, and, uh, you know, it's just, uh, 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? I? 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. I think you, I think what's not, uh uh, eh?

To be disagreed with is just the the total market's gonna grow and all rising tide lifts

totally agree with that.

all boats. Uh, and then then you're just talking percentage of market share at that point.

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

pay thirty, thirty bucks a month just to anything not worry about that has cool it. 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, um, but anything that has this, like, app and tool built around it, kinda did 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, so so, um, 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. W- 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 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 $20 a month subscriptions, like, uh, ultimately, like, whoever, like, like, probably a small set of companies are gonna own the interface, and then everyone just plugs into that, uh, that overall agent, which I which I agree with. Um, then it's it's interesting that Salesforce, you know, being the one of the system of record companies, the the biggest, probably, or the most well known, um, is embracing that path as as, like, uh, like, you will not use our agents, like, we will just provide APIs and MCPs for you to plug into

our data. our stuff. Yeah, Yeah,

which is kinda cool. Um, okay, so, uh, I actually wanted to also, uh, walk through one example. So, um, I just released my results for the my kill my SaaS competition, where we replace, uh, a mid-tier SaaS, 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. 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, it's, uh, we were quoted $40,000 a year, we spent $1,000 to replace it, it's it's gonna, it's going to production next, uh, for the next conference. So, uh, 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?

yeah, well, uh, CJ can size up the story points on it. 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. I agree with you, like, part of the reason when, as people have said, like, a very small group, uh, 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 the 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 or billions of dollars.

you're gonna be risk averse. Yeah,

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? 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. 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, like, you know, we we talk about how I forgot, uh, who who created the repo? That is basically the reverse engineer repo, where, uh, yeah, exactly, like, everyone's talking about that right now. And even at Tenex, like, we, we like, we built our own, like, CRM from scratch.

We we, what did we build, uh, 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 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 th- basically $39,000 a year, you're you're implicitly saying that it is worth it to it is, it have.

is not just about cost, it is it, that's defense, and I also think about the offense of, like, no, actually, our experience could be a lot better for literally every party concerned.

totally, totally. And so, yeah, I think, uh, part of the the the thought there is, is actually just there's a signifi- 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 time. What would the Tenex better version of this product yes, because look like?

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

Totally. 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 in a in a certain, uh, capability, rarely is that going to be the the thing that they're rarely are they gonna say, like, oh, we can create a bet Tenex 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. Uh, I actually, I w- I was, I would say that, you know, you talk about automation, 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, eh, I don't know how much you can speak to that.

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. Um, I think a sixteen z 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. 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.

Um, so our approach really is about what can we replace, and then what are the next biggest problems to look at? And the I'd make an argument in most large companies, that that cycle kind of continues forever. There's always the next problem, if I infinite think about how we've grown, demand. 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 cell in a spreadsheet, and more about how these teams are working together.

Um, 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, 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. 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 really should be thinking, 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. 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. 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,

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 world, according to some peop- some people. What's the mood, uh, generally, uh, among the people that you talk to, whe- whether it's at work, personally, uh, 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 it.

The discussion is instincts or grok 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, it's still forward-looking.

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. If SF is like a big bubble. I think we at Tenex 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-tell 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's someone who similar to, like, AI. 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, AI is like that on steroids.

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, we're large overlap.

roughly 1% of the city now. Actually, Uh, for Wall Street, yeah,

that's crazy. 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 Tenex 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. I think the bubble is smaller now, and p- we take it almost as, like, a charge of ours to increase our bubble. Maybe ne- 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.

the there's a physical, like you see it on the billboards, like, slowly creeping in. People used to there. You remember the friend campaign, where people would, like, spray-paint some 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, Oh, I guess that's part of life now.

100%, 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, and 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.

A and so I I actually

the 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, like, get a reality check.

totally, and I think I actually think there's a really interesting arbitrage there. Yeah,

yeah, totally okay, awesome. Uh, call for hiring, I know you guys are always hiring. What are you hiring for? Uh, what's changed?

yep. We're hiring, uh, f- for deployed engineers at Tenex, just go to Tenex dot co. Uh, we're also always hiring AI strategists, which are basically technical consultants who are leading client engagements. Um, and the team is seventy five 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 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's what is that?

a special project involves anything outside of kind of our client work. So 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.

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

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

Thanks, man.

and, uh, good to see you again Yeah, likewise, in New York. Uh, once,

appreciate it.

a once a year, once a year.

hell yeah.

awesome, thank you.