Invest Like the Best with Patrick O'Shaughnessy
00:06:44 · Patrick O'Shaughnessy, Neil Movva
[00:06:44] S0: Why are you so confident in that? To me, it seems like I want everything as fast as possible. [00:06:49] S2: When you're waiting on it, you absolutely deserve the fastest answer possible. Yeah. My trick is I don't want you to be waiting on it. I want it to be proactive. I want it be in the background. One way to say it is like the best latency is no latency at all. When you wake up in the morning, the work's already been done overnight. You didn't even have to ask for it. That's the dream. We're not quite there yet. More importantly, I think the more you're in the loop as you prompt agents and wait for a response, in fact, you're the bottleneck in having agent do more or less work. What we'd like is the agent to operate on more human time skills. You don't manage your colleagues every five minutes. You ask them to do a high level task and you come back and maybe every day but more likely once a week. And that to me is the future of human agent collaboration, more like human time skills. [00:07:28] S0: Say more about the early indications that this is happening and therefore you should be building this company.
00:07:28 · Patrick O'Shaughnessy, Neil Movva
[00:07:28] S0: Say more about the early indications that this is happening and therefore you should be building this company. [00:07:33] S2: So the first and most important thing is the idea of test time compute scaling. The idea that you can give an agent more time and it will give you a better answer. So that was theorized about two years ago now. But it wasn't really something that we could actually bet on until, I would say, late last year with OPUS 4.5. OPUS 4.5 was the first agent that was at all suitable for longer horizon tasks. It was pretty mediocre when it first came out, but you look at the more recent models and what we've done on open source as well. And you see that agents are capable of running for an hour at a time. I wouldn't say it's days, but definitely an hour is quite suitable today. Seeing that, like, average task length get longer and longer doesn't take many points to have you draw out the exponential and see that agents are worth running for longer periods. [00:08:14] S0: What do you think will be the market share of long running agents in three years or something like this?
00:08:14 · Patrick O'Shaughnessy, Neil Movva
[00:08:14] S0: What do you think will be the market share of long running agents in three years or something like this? [00:08:19] S2: I love this market because it's unbounded. There's no human in the loop, so you can consume as many tokens as you like in the background. Versus human attention span, if you tell me to consume 10 x as many tokens at codex or at cloud code, I'm actually not sure if I can anymore. I'm already in the loop and locked in coding for most of the day that I'm at the laptop. What is undoubted is how many tokens can be consumed in the background or proactively. Long term, I think, you know, we're gonna end this year at maybe fifty fifty background and real time workloads. But I see this going to ninety ten in favor of background. What are [00:08:49] S0: your favorite examples of something that gets accomplished much better as a background task than as a human in the loop task? Most deep research.
00:08:56 · Neil Movva
[00:08:56] S2: Most questions where you want to have a definitive answer over not 100 sources, not a thousand sources, but 10,000 sources or more. If you want to build an authoritative index of information, like for example, one of our customers parallel web systems seeks to do. They want to build an index over the whole internet. And they want to monitor the Internet in real time for changes. That is the kind of crazy exabyte scale task that you need a very different kind of intelligence or scale of intelligence to achieve. Deep research is a top category for us. And then increasingly, we see cyber security following this direction. If you think about, yes, there's so much code you can generate, but there's exponentially more ways to break that same code than is to generate that code. There are some great customers out there who are working very hard to find agents that can break any piece of software and proactively patch them. When Fable first came out, for example, or Mythos first came out, basically, there was this push in the cybersecurity community to run Fable against every line of code we've ever written and look for bugs in 20 different ways. Meaning, you're looking for both memory errors, you're looking for business logic errors, and looking for like network vulnerabilities. All these things. And these are all actually things that you would write specialized agents for. You wouldn't just have Fable look at the source code once, you'd have it actually set up environments where you can pen test these applications. At some point, people started to make this joke that security has become proof of work. When you want secure software, it's really a question of how many dollars did you spend on Anthropix APIs trying to break into your software. That is the best indication for how secure it is because that's the best tool in the world. And increasingly, we found that open source models, well, the frontier of intelligence here is quite jagged. It's not the case that Fable finds a superset of all bugs in software. You would find some bugs with a very small model that you don't find with the large model. You'd find some bugs with Haiku that you would find with Fable and vice versa. So it encouraged this very diverse approach to sampling and and trying to build cybersecurity agents that break software autonomously such that you can patch them.
00:10:47 · Patrick O'Shaughnessy, Neil Movva
[00:10:47] S0: If you were to get speculative and imaginative about the sorts of things that very cheap, very long running agents can enable, We talked about some very practical examples, deep research, cybersecurity, etcetera. If you get a little bit dreamier about the use cases, new product category that this sort of inference will unlock. I guess the question is just like, so what? If you're maximally successful, dream a little bit about what that might enable. [00:11:13] S2: I think for individual users, what I'm excited about most is this idea of proactive intelligent agents. You can imagine Siri that is running in the background all the time to understand all the emails you received in a day, all the text messages you receive in a day and has a much more encyclopedic view of your life and how to be helpful in that life. Right now, there's still point solutions, you end up doing a lot of prompting. Siri is not very proactive. Something we can fix with abundant inference. If you trust the machine enough that it's reliable and also trustworthy as in private, you might even imagine the machine can understand how you interact with it and proactively surface your next action. Whenever you open your phone, can we build a good model of what you're gonna do next? My estimation is yes, we totally can. [00:11:52] S0: And the key to that is incredibly cheap intelligence.
00:11:52 · Patrick O'Shaughnessy, Neil Movva
[00:11:52] S0: And the key to that is incredibly cheap intelligence. [00:11:54] S2: You have to be willing to spend tokens without any promise of return. That is the unlock. The long lens view to take on this is that we have a form of intelligence that can tackle any verifiable problem. Any verifiable problem means most software. It means a lot of formal math proofs and similar and it could also mean scientific discovery. These are all relatively verifiable problems. And all those things currently have a dollar cost attached to them, essentially, that's a hidden one. It's like how many tokens could you possibly harness to make this work. We have started to bring you within view a dollar cost for these long horizon tasks that is reasonable. It's not millions, it's thousands, and maybe it could be hundreds or even tens of dollars in the near future to have a definitive answer to any scientific question, to any research problem. [00:12:42] S0: So if we dream about that future, we then become limited just by the questions that people can ask basically?