00:37:21 · Molly O'Shea, Parag Agrawal
[00:37:21] S1: Do you think bots should address themselves? [00:37:24] S2: I have two contradictory feelings about this. I do think good bots should declare themselves, so that others can recognize them as good bots, and treat them differently from bots that might be adversarial. And we keep using the word bots, I would like to use the word agent. [00:37:46] S1: Okay, well, every time we use bots, I really think
00:37:48 · Parag Agrawal
[00:37:48] S2: use bot has a more negative connotation, while an agent is a more appropriate word. I think agents are perhaps the good bots. Mostly. Yeah. The... So, yeah, I think they should declare themselves. I know there's like a bunch of protocols now being done around declaring identity or the world at large. The core challenge there, why the other side is like, there's a conceptual question. Should your agent have the same access as you? Should it inherit your access or should it have only a part of your access? Now, would really, it would be so much better if we could figure out a way to get rid of the bad bots, but live in a world where we could enable our agents to have the access we have and grant that privilege. Today it's strange, right? Like I can't get my instinct or muse to have the access I have on many websites because they got blocked there and they will get even more blocked if they started declaring themselves. Yeah. And that is why I think we're in this moment of transition where these products have these rough edges, partly because we haven't done incentive alignment. But if we started just declaring them everywhere and they've started declaring themselves everywhere, like we don't participate in that space, but we started doing that. A bunch of people today, we haven't done the work to do business model alignment are going to start blocking this. Yeah. And so these products then will look worse and be less useful and that sucks. So to me, the existential problem is how do we drive alignment between agents and everything on the web.
00:39:44 · Molly O'Shea, Parag Agrawal
[00:39:44] S1: Do you think everyone will need alignment and safety on their team? Is that the next higher class? [00:39:49] S2: Yeah, I think how do you not deal with the problem? Like we, if you think about us, we are the web layer. The web is the dangerous place. Anyone can post anything malicious anywhere. Right? A lot of that content will flow into an agent's context. So you can do injection attacks. To the credit of model builders in the last two years, models have made very real strides on not being very vulnerable. But I think this whole world will rely on defense in many, many layers. So almost every layer has to obsess about this problem. You can't say, models will solve it. We have to really focus on it to solve it at our layer before we get into the models. Then models have to really focus on it. Then the app builders who are building with models and with us have to really focus on it at their layer. So we have to deal with adversaries at every layer and that is actually what gives you true defense. Still the occasional thing will leak out.
00:40:59 · Molly O'Shea
[00:40:59] S1: Merkur's ivity Index, or APEX, tracks it with every major Frontier model release. Built with partners like Harvey and Cognition, see where the Frontier actually stands at merkor.com/apex. Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at getvcx.com. Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets and it brings AI into investing in a way I've honestly never seen before. Here's how it works: You type in an idea like AI powered supply chain companies with positive free cash flow or defense tech companies growing revenue over 25% year over year. Publix AI then dispatches a swarm of agents that scan every single US stock, evaluates them, and instantly builds a custom index around your thesis. What really stands out is how clearly it explains why each stock is included. And before you invest, you can even backtest your idea against the S and P 500, so you're making decisions with real context, not just guessing. And beyond generated assets, Public lets you invest in stocks, bonds, options, crypto all in one place. They'll even give you an 1% match when you transfer your investments over from another platform. If you want to build a portfolio that actually reflects your thesis, visit public.com/sorcery, paid for by Public Investing. Full disclosure's in the description. Founders scale faster on Deal. Set up payroll for any country in minutes, hire anyone anywhere, get visas handled fast, and get back to building. Visit deal.com/sorcery. That's deel.com/sorcery. How did you recruit for your team, especially as this is moving so fast? So you have global offices now.
00:42:47 · Parag Agrawal, Molly O'Shea
[00:42:47] S2: Yes. How [00:42:49] S1: big is the team and how have you recruited? What are the main roles? [00:42:52] S2: The team is about 120 people now. I think we started the year close to 30. This has been a year of explosive growth. Lot of that growth has been because we had no GTM at the beginning of the year. We were pure tech play. Now, the last three months, six months, we've built a pretty sizable GTM team, and that's been done because we are seeing all of these, almost every business that buys inference needs to buy web search. So we're finding really large, like whatever, the top five five hundred hundred companies in the world, starting to really spend on tokens, and starting to really buy us alongside those tokens to make them more effective. So, a lot of our growth is the sales team, which [00:43:50] S1: has How been do they know to buy you it alongside the tokens?
00:43:50 · Molly O'Shea, Parag Agrawal
[00:43:50] S1: has How been do they know to buy you it alongside the tokens? [00:43:54] S2: They run into problems. Of the common things people run into is people start using a model. That model perhaps bundles itself with a web search. Okay. And they start building an application with that. Now, sometimes it's not good enough and the team realizes that it's the web search component which is causing it, despite the model being great, which is causing it not to be capable enough to solve the problem they're solving. Which is perhaps how we started first working with some of the AI legal, AI finance, AI science kind of customers. The second point at which they really need us is they decide to use multiple models. So we're like, okay, we want to switch across multiple proprietary models on a dime, or we want to start adopting, open weight models. And at that point, having the highest quality web search which works with every model actually enables model independence for you to have flexibility to build your application. So that's the second reason people buy us. The third reason is when people have really scaled use cases and now start optimizing for measuring ROI, optimizing for end to end costs. Because our search is so high signal and token efficient, the models do half the number of searches, use up far fewer tokens, end to end are cheaper, faster. That's the third reason people buy us, which is like, I'm spending too much on tokens. It works. It's useful. Can you help us be more efficient? [00:45:37] S1: So it's not just routing. You also help with token optimization?