The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:36:41 · Parag Agrawal, Harry Stebbings
[00:36:41] S0: I think there is another another 10 x possible. That's extraordinary. So you're gonna pay 10¢. Yeah. But you'll do it more than 10 x as much. [00:36:50] S1: So I think, like, Jevan's proud of us to say, this concept obviously very well, like Instinct, I do so much more than a 100 x. When I hear something I do on Instinct, which is absolutely bizarre, every single country in Europe has a company register that you have to register your new company with. I have an automatic alerting system built for every single company registered that has an 25 year old founder who went to a top university through instinct. [00:37:15] S0: Amazing. Do you know how much that requires [00:37:19] S1: for them to do?
00:37:19 · Harry Stebbings, Parag Agrawal
[00:37:19] S1: for them to do? [00:37:20] S0: Yes. But you're on to exactly what I think the future of the web is. Today, if you think of web and web search, we all think of it like what is web search? An agent gives human or agent gives... Search engine a query gets results. So you're pulling information out of the web. What's next for the use case you highlighted? If you're an always on agent that's working on your behalf, it's kinda silly for instinct to wake up every six hours and go do a bunch of searching and bunch of inference to figure out if you need to get pinged about the the the 25 founder that popped up somewhere. You know what's a better way of doing this? I am sitting here crawling all of the web all day, every day at scale. I'm allocating compute every time I find a change in the web, every time something new happens. If I know that Harry wants to know when this happens, I can do it at 100 to 1,000, the compute that Instinct probably uses today to solve that problem for you. [00:38:19] S1: Alright. And how is that? Because they would be constantly monitoring in real time across all these
00:38:23 · Parag Agrawal
[00:38:23] S0: Our crawl would be. So we have a product. It's called the Monitor API. What does this API do? It's... Think of it as Google alerts, except smart. With... In the world of LLMs. So you you tell it your query, which is when a founder under 25 pops up anywhere, give me a call. Now one way of doing this is the default implementation is run an agent, which is somewhat smart to go search the web. Did someone pop up and remember who previously existed? See if anyone new popped up and then find a delta, send it out. So now every six hours you're spending some money. Now what we can do is flip it into an event triggered system. We are crawling the web all the time. Now every time we see change in the web, we can try to spend very little compute on it to know should you get a call or does this trigger a more expensive compute Just like I described earlier. Now we are able to do this way, way, way cheaper if you push the context. And so the search system only finding out every six hours, a new query popped up. It having long lived context in the search system. What you can do is spend most of the time, the world doesn't change. A new founder does not pop up every hour or every six hours. So we don't need to spend compute every six hours. We only need to spend compute every five days, and perhaps we have one false positive. And then four days later, we spend more compute, and at that time, there's a real person that you should know about. Then you get a notification on average every nine days or whatever, but you didn't burn a lot of compute every six hours to miss out. Does that make sense? It totally makes sense. And so you save 10, 50 x compute to still get the same answer. And so
00:40:05 · Harry Stebbings, Parag Agrawal
[00:40:05] S1: how does that change the interaction? So then Instinct would then partner with you. [00:40:10] S0: They'll just call an API. Right? They'll instinct whoever's building a long running persistent agent. And I run a lot of long running persistent agents for myself. The way your agent or the way instinct is probably occasionally event driven on email. It can be event driven on the web. Take a step back. Let's say we're living in this sort of society run by agents, companies, humans, they all have agents. They're all doing stuff all the time. Your agents, if there's something that is useful, which is worth spending money on, that you want done, that they can today do today, they will do it. So what are they gonna do tomorrow? They're gonna wait for some external event to occur. Like, you get an email and then agent have new work. Some other agent finishes some compute. Somehow this agent has more work or something in the world changes that makes your agent want to do more work. The things that will happen tomorrow or you have an idea which makes the agent do work. So the web event stream is the web going from pull to push. And I'm super excited about that because as you have more and more persistent agents, you're gonna see incentives to move people, to move queries into push on search rather than pull on Can I ask you one that's really important, which is, like, agent guardrails? They're goal oriented beings. You say I want this. They're gonna find it. You know what? I want the Pilates class at nine
00:41:34 · Harry Stebbings
[00:41:34] S1: system, cancels poor Sally's, and gives me her spot because it was sold out. It did what I asked it to do. How do we think about the guardrails placed on agents? Know, OpenAI, this morning, it was revealed hacked into an Australian health care organization. It's a pretty complex topic.
00:41:54 · Parag Agrawal
[00:41:54] S0: Agents are extremely capable. These models are extremely capable. We have to think of models as being during RL versus a final model that you and I can use. And the risk factors there are different. During a lot of the... I don't know about the one this morning. The previous ones reported were pre fully aligned models during RL, which did most of the hacking. And so what that means is, at least there's clear evidence that there are fewer incidents so far of models post alignment causing these incidents. So alignment is a totally unsolved problem, but the alignment work being done by people is somewhat effective. So to me, some of the problems around agent during RL hacking people is a solvable problem because this is not about how powerful are the models. It is about how careful were we while creating the environment where we do RL. The real thing is, okay, we do alignment on model. We ship it. Some models are really well aligned. Some models are less well aligned. And now you have everyone being able to use these models. Some of them can accidentally take these powerful things and do bad things you want. Some of them actually... Some people actually want to do bad things. And the models alignment is not adversary proof. And I think those are the things I think we must worry about more.