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

Building an applied AI partner

0:00And 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 as like, you can't automate a process that does not exist.

0:26Okay, we're here at AI Engineer checking with 10x. Welcome back, Alex. Thanks. Thanks for having us. You brought your Director of Engineering, CJ. Welcome to the pod. Special projects lead at 10x. Okay, special projects lead. All right, you're the SWAT team. Yes, yes. So you've been on the pod before, and people are vaguely familiar with the 10x story, but can you sort of reintroduce from the top what is the 2026, how you introduce yourself? Yeah, very simply, 10x is an applied AI partner for some of the biggest companies in the world

0:57to 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. Yeah, yeah. Yeah, 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.

1:27So did I just mess up your video? What are you going to do? I don't care. Okay. We're for adults.

Rewarding output over time

1:32CJ, what drew you to 10x? So I was actually the first hire. I don't even know if Alex and Armand 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

2:06and ultimately getting paid less. Like, I was charging hourly to my clients. 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,

2:38it 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. 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 an outcome and they're not going to want to pay based on the time that's required to build something.

3:09Yeah, so for more detail on that, people can refer back to our previous pod where you literally pay people by story points. Yep. 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. 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 our model isn't for everyone, but for the person who basically wants to go long themselves,

3:40who 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.

Scaling up the team

3:52What'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 an update on that? Yeah, yeah, yeah. 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 coal mine for AI. And part of the impetus was my co-founder, Armand, who's technical by trade,

4:23was, 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 upmarket from startups and, like, mid-market companies to now, you know, 50% of our customers are Fortune 2000, and then 50% are mid-market, doing $100 to $500 million in revenue. They weren't just asking for,

4:53basically, staff org 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.

5:24Oh, not 10x? Not 10x difference, yeah. That's the dream. Yeah, exactly. But everything is 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

5:55a bunch of bad code. Like, barely check the box. 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 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.

6:26And 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, 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

6:57to 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, as opposed to, you know, kind of that traditional story point model, but still anchoring to those as the unit.

Engineering workflows and harnesses

7:20I think one thing you'd also, I don't know if we've talked about this before, but, like, this idea of, like, the meta-harness, which is, like, 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 people would find that interesting. 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,

7:51a 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,

8:22but I'd say as the models have gotten better, we've actually reduced the enforcements there in the structure, 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. Everything checked into Git. But 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 fit out. But all of that gets checked in,

8:52and 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.

9:23So 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. 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 up front. Yeah, amazing. And 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

9:54of every engineer in the company? Right. And what do you think it is, let's say, compared to started this year? I don't know. What do you think? Do you think... Plus 50? I think, so, it really depends because we have some people that I... There's some picture that I saw on Twitter of a guy over, like, a wizard orb and he's got these, like, things in the sky. And that's how I think of some of our engineers when they're whipping agents around.

10:19I'd really say, overall, it's been, like, a 2x increase this year in particular. I think, basically, if you think of December as the, like, Opus 4.5 moment where the models really started to take on these long-running tasks to today we're back to Opus, but Opus 5.5. 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.

10:50Is a single story point worth the same now as it was a year ago? We're always trying to calibrate. It's a tricky equation here. Is there, like, inflation or deflation, right? Like, I don't know. I mean, in theory, there should be deflation, right? Yeah. Yeah. Yeah. I don't know. And 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,

11:20as the models get better, we do need to be able to kind of calibrate, like, the value that we're creating. Yeah. So overall, like, I would say roughly that 2x has played a similar role in, like, how we do these estimations, but, I mean, if I just look at some of the features and things people are building and pushing out now versus this time last year, I mean, it's insane what the difference has been. Yeah.

Model preferences and token costs

11:42Well, so there's a couple ways I can take this, but I'm just going to check in on the sort of model preference stuff since it's always a topic conversation.

11:50I think you just let people choose whatever tools they like, but then maybe you have some statistics on what is preferred in the office. It seems like Opus is leading. Yeah, what's the vibe check right now? Can you, like, care to estimate, like, market share, whatever? The vibe right now is Opus 5.5, I think. No Fable? I don't particularly use Fable, and I think a lot of people right now are just defaulting away from it. Like, Opus is good enough. 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

12:20depending 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 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.

12:38And really, workflow-wise, I think using both is still the best option, whichever your driver is, which I think for most of our team is Opus 5.5. Using a Codex to review and having them actually work back and forth on your machine, I think still produces the best results. But right now, we're pretty aligned on those two. And are you still AMP maxing or no? Oh, I am AMP maxing. So you're using Opus through AMP? So, yeah, they recently... Or, like, that's the default model for AMP. Yeah, they recently launched, like,

13:09the ability to hook in your Cloud Code subscription. So that's been a pretty big game changer for me. Because that'll really cut your cost by, like, 80%. Totally. And, like, I mean, I love AMP because it takes everything off my computer. Yeah. They have this, like, 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, you know... That's never going to be a thing. Oh, yeah. I want to make it a thing. This may be the first time in history

13:39that any human has said... We're orbing out. We were orbing out. No, no, they do say it's orbing time. Yeah. That's that's... Yeah. But, yeah, that's my interface of choice. And models are really between that. If I'm leaning on codecs, it's Sol6-1. Isn't it... Did you give CloudTag a shot? Like, isn't it weird that Anthropic never captured the remote environment thing in the 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. Tools like CloudCode in the cloud and CloudTag,

14:11I 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 CloudCode in the cloud, a CloudTag. Yeah, yeah. Fair enough. Another thing I always track,

14:42I'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 roughly, most people, most companies are at maybe, let's call it $10 to $200 in spend per employee. The more sort of AI native ones would sort of convert that to a percentage, so roughly 10%. The average AI startup employee is about 1x in terms of employee spend and token spend. And then it goes up to maybe 5 to 10x

15:14in terms of token spend. 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 1 to 1 and maybe like 2 to 1. That's pretty high. And 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. But I will say recently, our costs have been driven just way down from Opus 5.5.

15:45We 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 expensive. 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, well, it's the most predictable thing in the world. Did you ever fear about it? Or like you were just getting good ROI regardless? Yeah, I mean, there's a few ways that I think about it. It's like we were for sure, we were for sure worried about it

16:15just as like our headcount was growing. And I think also like, even though our team, I would say like, I don't feel like anyone on our team ever really ascribed to like token, the token maxing zeitgeist because. I had a few moments. Yeah, me too. But it's like, well, if the boss is going to pay for it, like I'm just going to go nuts, right? Because then you do the weird stuff so that you can scale back. But you have to do the weird stuff first to discover the lessons

16:47that you can take with you. Yeah, totally. I mean, this may sound crazy, and I'd be curious your thoughts, but like I was talking to someone earlier about 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 Micro One, Mercor, Handshake. And just like, what does the long term for these businesses look like, right? Because like 90 plus percentage of their revenue is from the labs. And a number of these founders

17:18would 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. 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

17:50continue to go down on these models. And so I guess part of my, the reason we're not concerned internally is because it almost feels like we've kind of hit this like Pareto point of like costs are coming down at a per-task basis, that it is way harder today for someone to token max 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 point

18:20at which like you'd be really concerned. Yeah. Do you agree with that? Yeah. I think there's a couple ways like the future might play out there. Like if there's a world where there's a Astra 6.5, a Fable 5.5 and they're super expensive but they get the best results, I could see us trending back that direction but generally I agree that you know, you look at something like Muse Wonder that they launched yesterday, today and it's on the frontier, it's dirt cheap, the competition

18:50is heating up and 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 5.5. So you know, it's that cliff of like would a 10 times smarter model really justify the cost of like moving some cells in a spreadsheet? Probably not.

Enterprise milestones and agent bottlenecks

19:14Right. So coming back to your customers and where you're seeing it 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? 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

19:45but it's like, okay, first milestone in the enterprise AI race is just rolling out a general purpose LLM to your people and I'd say of the, you know, let's call it 100 enterprises we're talking to today, 90% have rolled 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

20:16more important or as important as the technology itself I would say that's at like 60% Oh, that's pretty good 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

20:47of 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 70 or 80% the number is closer to 50% like every other enterprise that we speak to either hasn't rolled out a coding agent or what they are using is closer to not having a coding agent than having a coding agent then the next and this is where like the biggest I would say drop off in like is where like the biggest diffusion gap is from like raw intelligence to enterprise usage is agents

21:18in 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 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 sure 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

21:49what 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 onto 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

22:20production outside of the entor for companies for repeatable work that they're doing the second is permissioning and like RBAC 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 literally just what can you read what can you not read

22:51exactly and that is a huge issue so that's something we spend a lot of time on so you make them for people or do you buy something off the shelf so basically we started by building bespoke for companies and now we actually productize that where we have our own gateway solution exactly is there anything else that comes to mind that's like we're hearing a lot from customers I think that covered what we're hearing when it comes to like particularly the agent deployments I think one of the pains that just always gets discovered

23:21I phrase it to the team is like you can't automate a process that does not exist so when you have 40 people that are all roughly doing the same function but they're all doing it a little bit of a different way 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

23:52throwing it back and you become more of this like you know 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 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

24:22actually deliver the value that an agent that is doing this function

Vertical software and market viability

24:35like 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 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 the guts of enterprises there is so much nuance and so much inertia and so many systems you have to wrangle and people you have

25:05to wrangle and also when a buyer of a vertical solution is expecting truly a one-of-one experience like we're in an age where people are expecting one-of-one software solutions and so if you're expecting that 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

25:36not traditionally expected from software and so I think it's really interesting to think about four 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 frontier models to just gobble them up yeah so to give some concrete examples you're talking like Harvey for lawyers these are

26:06the classic examples of people that are winning their domains and you're bearish on them I would say generally bearish on them correct wow well so far you've been wrong and you know I think the answer very simply is access to data that chat GPT is not going to have which is very common that is a moat obviously Anthropic and OpenAI are also acquiring those same data sources but they're just not as dedicated as these guys are going to be they do have innovations if you've

26:37tried it Harvey they have the tabular review we've had the CTO on the podcast where they have affordances and UIs and all that that is just customized because they only serve them the pitch is focus basically focus affordances maybe what do you 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 it never goes away as a banker or in my finance

27:07career I could always access the same exact information from the NASDAQ from the New York Stock Exchange but why do I pay $50,000 a year for Bloomberg well it's what all my peers

27:25into my trading system or my chat or whatever my news system I think people play for Bloomberg is the social network I think people love the DMs in Bloomberg there's that but as a trader I actually use that for so I think there's a bunch of integrations and tools and I think ultimately it's hard to be bearish on any of them mostly because the demand for software 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

27:55some of it you're just going to want something better and I always think about maybe the success of superhuman as an email client everyone has Gmail but here's just premium Gmail with more stuff for people who live in email all day long and that's a market maybe wasn't the largest startup in the world but I think reasonable people can disagree how large each thing is I think what's not to disagree with is the total market is going to grow and then

28:26you're just talking percentage market I agree with that I think there's a differentiation between some new app built from the ground up with AI involved that's what comes to mind I love super human I don't even spend a ton of time in email but anything that has cool hot keys I'm into but when I think about products where they are really pushing some agent as the product that is doing work

28:57in 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 quite literally the ability to either spin up your own really quickly continue to compound as the models get better or just those tasks start to fall into the world of the models just generally but anything that has this app and tool built around it to my previous

29:27point changes the process and has an agent on top of it and I think that builds on the same philosophy and I think that can still be super helpful and those will be successful yeah oh okay okay 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 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

30:01a month but you can't just stack up all these $10 a month subscriptions ultimately a small set of companies are going to own the interface and then everyone just plugs into that overall agent which I agree with then it's interesting that Salesforce being one of the system of record companies the biggest probably or the most well known is embracing that path as you will not use our agents we will just provide APIs and MCPs for you to plug into your stuff which is kind of cool I

30:33actually wanted to walk through one example I just released my results for my kill my SaaS competition where we replace 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

30:57for the next conference so we are that customer that says I want the one one tool and I'm expecting all these things and we're building a domain specific or vertical specific thing for our business and so far it's going okay tell me where it's going wrong also I'm wondering how many story points that is CJ can size up the story points on it I mean my general view is I think the ripping out

31:27harder 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 of dollars you're going to be risk averse yeah

31:58exactly I think for mid-tier it really it actually comes down to what is the size of the opportunity and is that ever going to be the worthiest opportunity to rebuild inside of a company I'm trying to even think like in an enterprise let's just say there's some mid-tier sass that is a few million bucks a year I think the first question is is like from a percentage of revenue or cost perspective

32:29is that going to ever move the needle enough to basically allocate engineer resource to I think it's going to be case by case I think for some enterprises the answer may be yes for other

32:47how I forgot 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 10x like we we like we built our own like CRM from scratch we what did we build 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

33:17it 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 your business the difference of basically $39,000 a year you're implicitly saying

33:49that it is worth it it is not just about costs 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 part of the thought there is is actually just there's a significant basically it's not just that so there's two pieces to it

34:13what would the 10x better version of this product look like because we are the customers and we can iterate with minutes and not months totally I think what's interesting is I think startups and even high growth mid-sized companies can be nimble in that way I think for enterprises where they're so entrenched in a certain capability rarely is that going to be the thing rarely are they going to say oh we can create a 10x better version of this SaaS

34:43we use and we're going to spin it out potentially as its own thing or whatever it is like I just don't think enterprises function that way for sure

Job displacement and reallocation

34:50yeah it's been discussed I actually I would say that you talk about automation the more spicy thing is are they automating humans right and once everything is automated and smooth sailing do we replace those jobs I don't know how much you can speak to that yeah I mean I think there's there's two pieces here I really deeply believe in the like AI will produce good for humanity it's going to augment people's jobs it will absolutely change and displace but I

35:21think it's more of a movement as opposed to just like cutting off work yeah I think A16Z posted something about how 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 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

35:51think any business would claim 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 what are the next biggest problems to look at and I'd make an argument in most large companies that that cycle kind of continues forever there's always the next problem if I think about how we've grown there's always been another problem and actually taking those people that may have this super

36:22deep subject matter expert and bringing them above water to start to maybe work more across teams or think about parts of the business less so on the level of a 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 the big meaty problems and not the drudge of most people's day to day when you're

36:52thinking about someone that's in a finance division 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 job displacement or job reallocation we talk about it as this thing 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 have agency in their own lives meaning I think you can adjust yeah

37:22people really should be thinking like when I think about like enablement or people reskilling 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 going to replace all of the work that people don't enjoy doing I think that's a little bit of cope for the reality of 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

37:52to the extent that it's possible should have agency in saying to themselves okay what is the stability or replaceability of my job over the next few years and if I try to skill

38:12people to reflect on the last

The AI bubble in New York

38:15piece 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 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 live in San Francisco all day long everyone's just in agents all day long like you know the discussion is instincts or groc or muse it's not like do you like AI or not right and I feel like I want to step out of that bubble for a bit but it's still New York it's still progressive it's still forward looking yeah

38:45I 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 10x feel like you know we are in a what I would say is like a microcosm of San Francisco because of the people that 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

39:16than 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 like I feel like SF is similar to AI I mean it's like probably like the overlap of AIE generally and people who have lived

39:46at some point in their life in SF is probably a very large overlap we're roughly 1% of the city now actually for World's Fair yeah that's crazy yeah but like to that point I just think like it is in the culture and the DNA of SF where a very big population of people live in the future I think you can find that group in New York and I think we at 10X have found that people and part of our goal is to drive like AI as a hub or New York as a hub for AI in the US

40:17because 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 there's a physical like you see it on the billboards it's slowly creeping in oh totally people used that remember the friend campaign where people would spray all sorts of graffiti on that I think that was like a little bit

40:47of an intentional rage bait but now you have the unironic AI billboards and people are like I guess that's part of life now 100% yeah so I think it's like I think there's an opportunity to bring kind of like the edge and like the just like the living in the future nature that SF has created because of just like the economic climate that exists there because of venture because of tech companies then because of the people that are attracted there but I I also think of us as like we're in a

41:18it'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 commercial value yeah totally and so I actually the way I put it is like the reason I do AI in New York is I bring all the people from San Francisco to come here and meet their customers and also exactly like get a reality check totally and I think I actually think there's a really interesting arbitrage there yeah yeah yeah totally okay awesome call for hiring I know you guys are always hiring

41:49yep what are you hiring for what's changed yep we're hiring forward deployed engineers at 10x just go to 10x.co we're also always hiring AI strategists which are basically technical consultants who are leading client engagements and the team is 75 strong in New York City we're continuing to make New York City our home and then if you 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

42:20than not going to name them but a lot of other traditional consultancies spicy CJ yeah I mean I echo all of that I'm particularly looking for engineers what's a special project 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

42:50how well they can do it and then also internally working on the platform that Alex mentioned big goal there diffuse these background agents and actually get them out to enterprises in a way that's truly truly helpful to them and solve a lot of those like messy IT problems that we hit on earlier dude when you have some solutions we take guest posts on the Latent Space blog I'd love to welcome some sharing of knowledge but otherwise thank you so much for your time and good to see you again

43:21in New York likewise appreciate it once a year hell yeah thank you thank you thank you thank you