Output-based pay still has to account for non-coding work

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 3:06

   going to want to pay based on the time that's required to build something. Yeah. 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 along themselves, who has this incredible foundation in software engineering, but also is constantly pushing themselves to leverage the frontier to

2. 3:47

   stretch their time as much as possible while having quality output, there's no better way to earn. 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 an update on that? 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, was like starting to use coding agents a lot.

3. 4:24

   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? 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 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 org and AI engineering. They were asking for like AI transformation as a service.

4. 4:59

   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. Oh, not 10X?

5. 5:25

   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.

6. 5:52

   People are gonna either sandbag story points or they're just going to ship a 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 customer retention and growth.

7. 6:18

   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 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.

8. 6:45

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

9. 7:20

   I think one thing you'd also,