The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:39:34 · Brendan Foody, Harry Stebbings
[00:39:34] S0: So I agree with this in part, but the caveat I would give is I think it's arguably more the forward deployed motion rather than the go to market. And forward deployed motion being the post sales, go to market being the presales, because ultimately, say you're just really good at sales, and then you provide a SaaS product, and you have a savvy customer who's spending a million dollars a year on the SaaS product, and they realize they could just like tell Claude to copy it and they'll get the same exact thing, it feels very difficult to maintain your pricing power, even if you're the best in the world at sales. Whereas on the other hand, if you have a great forward deployed motion, where you're going deep with a customer, you're training the agents based on all of this tacit knowledge within the company so that it understands how to perform effectively, that feels incredibly differentiated and hard to recreate. And that's also the reason that we see obviously the labs at Anthropic investing so much in this for deployed motion. And so I think that the Sequoia article that services are the new software resonated a lot in that these software boats are whittling away, and it's the ability to layer services on top of software to meet customer where they're at and go the last mile that is creating stronger defensibility. Do you buy this new sexy category? [00:40:48] S1: I mean, venture investors are wonderful people, but this new sexy category that AI enabled services is like the future gold mine.
00:40:55 · Brendan Foody
[00:40:55] S0: I think in a large way I do. You do? I think the key thing is that you need to make sure that they're actually going to leverage AI. I think there are a lot of companies that are just building services and not gaining a significant competitive advantage from AI and using that to that's the thing you've got to be careful about, but I think it's very rational. I'll give an example in the context of Merkor, which is that within this process of turning human time from the talent network into building these super rich environments that mirror everything that people could do in their jobs, there is a lot of human coordination of how do we answer people's questions, how do we track the KPIs of the project and manage it effectively, how do we build the bespoke tooling for that project, and we have about 100 people, or call it 150 people, in our delivery organization that do that for deployed work of helping to go the last mile for the customer. But now we have an AI project manager that just completed its first project managing that entire thing end to end, where it's able to hire the experts, it's able to answer their questions, it's able to build the annotation tool using its coding tools within our platform and produce the end data type. And the experts all had a really good experience on the project reporting to the AI project manager that was running it. And so I think we're seeing in real time that services are getting automated and that that is going to be this extraordinary transformation in the economy.
00:42:26 · Harry Stebbings, Brendan Foody
[00:42:26] S1: One thing that powers obviously the agents that we use is the tokens that power them. And I the whole point was that we have increased token efficiency and token costs come down. Token costs are rising for everyone. Mhmm. Help me understand how you see token costs changing in [00:42:40] S0: the next six to eighteen months and why that is? Well, it's a fascinating case study in Siobhan's paradox, similar to what we were talking about in the context of making humans more efficient leading to more jobs, right? When we make models improve by 10x year over year, that has just been causing the total consumption of the models to go up and up and up as the cost per performance go down. I think insofar as how it's going to develop is that this trend is going to continue very, very significantly before we start seeing any leveling off of token consumption within the enterprise. Like right now, we're spending more on tokens for our internal agents than we are on employee headcount. And I think most businesses are gonna look like that in [00:43:26] S1: But you're spending more on tokens for agents than you are in headcount. Exactly. So your token spend on agents is more than salaries.
00:43:32 · Brendan Foody
[00:43:32] S0: That's correct. Incredible. And so the way we manage it is that we have a variety of these key workflows throughout the company, where we have an AI project manager, as I was describing, that manages operations. We have our interview question agent where we've done over 5,000,000 interviews and asked all the questions in the interviews. We have our interview ranking, or the broader candidate ranking, where it helps to assess all of the candidates and figure out who we should be hiring. We have agents for accounting automation. We have agents fraud detection, etcetera. And corresponding to each of these agents, we have an eval that tells us which model is best to use for this given use case, and what is the Pareto frontier of price performance for that specific use case. And that eval allows us to make the decisions around where should we be allocating our inference spend, what providers should we be using, etcetera. And I believe that over time, this is going to develop to look very similar across every Fortune 500, where they'll need to have this system of record for evaluating and specifying agent behavior across every workflow in their business. And they're going to use that to commoditize the model layer because they want to enable perfect competition for the models having zero switching costs. And so we've been growing extremely quickly with the enterprise and helping them to populate the system of record and building out those evals for each of
00:44:52 · Harry Stebbings
[00:44:52] S1: the use cases that they have throughout their business. Do you think you will see that commoditization at the model layer, whereby enterprise clients are able to really efficiently package the workflows that they do, so it does commoditize the model layer. Because right now it's not commoditized quite.
00:45:07 · Brendan Foody
[00:45:07] S0: Yeah, so I think the key distinction is that I think the API layer will get commoditized. You can definitely build stickiness and workflows that people people have on top of those APIs. Like for example, I have all of these routines running in Cloud Code, and I feel like it would probably be difficult, or I at least wouldn't put in the time to move those routines over, and I have a bunch of similar things running in ChatGPT. So I think that there's going to be various ways that people can build stickiness, but for pure API based products, where it's like if we are just spending $10,000,000 a year on a specific workflow, obviously we're gonna have an eval for that. And every time a new model comes out, we're gonna benchmark that and understand exactly how we should be hot swapping between models and distilling models. Why does the API layer get commoditized? Because the switching costs are zero. Like when the switching costs are zero, there's a new frontier model every two months. That means that we very quickly are going to swap them out, right? And ultimately the decisions that we make boil down to the score on the eval corresponding to that workflow. And so it's very easy to compare model to model one for one in a perfectly hot swappable way, which is almost the definition of a commodity.