Invest Like the Best with Patrick O'Shaughnessy
00:14:28 · Patrick O'Shaughnessy, Ben Thompson
[00:14:28] S0: I'm curious, setting aside the commercial and competitive components of this, like you're describing, how AI pilled on the pure technology would you say you are relative to other people thinking about this space? [00:14:42] S2: I have a view that is both super bullish and less bullish in some respects. [00:14:47] S0: Okay.
00:14:48 · Ben Thompson
[00:14:48] S2: So I am not fully convinced about the generalizable argument. AI is clearly incredible at coding. It kind of blows my mind that people were doing this a year ago, like actually writing out code. Is very good at math obviously. But the obvious repost is that these are sort of verifiable domains. What is the evidence or where is the compelling evidence of being very good at verifiable domains cleanly translates to being very good at sort of unverifiable domains or domains that take, have a very long sort of verification loops. I think that's still a little bit to be determined. And it's interesting because I raised this question and there are some people at the labs that were on a panel. And I was kind of annoyed at the answer because the answer took me for an AI bear. Oh, well, people thought we couldn't solve chess or we couldn't solve gold. We solved those easy enough. And I'm like, I thought we could solve chess. I thought we could solve gold because they're knowable domains. Scale was the answer to both of those, but also both of those were bounded. What is the go to example that's not chess, that's not go, that is genuinely in a new space that's sort of an unknowable space where it's doing things that were not possible. That is sort of the, I'm not fully convinced sense. However, AI trained at a rough approximation trained on all the data of the Internet. All the data of the Internet, that's distillation. It distilled all of the end state of human thought. Actual It typing on doesn't have the traces. It doesn't actually have the thought, the emotion, or whatever that went into typing that comment or typing and writing that essay, say Neuralink, whatever. What if the actual payoff from Neuralink is actually capturing the traces
00:16:36 · Patrick O'Shaughnessy, Ben Thompson
[00:16:36] S0: Training data. [00:16:37] S2: Of of human thought that actually dramatically expands the capabilities of these models? In this world, my concerns about verifiability is like, well, we solve verifiability by getting more data. My sense is that a huge number of jobs, a huge amount of economic activity does not exist in these domains that I'm not convinced that AI is good at. Actually, there's a lot of people in the world who are kind of like sentient AIs to a certain extent. They operate very well in verifiable domains. They're given jobs. They do them. And it's almost like a somewhat pessimistic view of humanity to a certain extent. But I think that market is so huge and so large that if the models did not improve at all from where they are right now, the economic opportunity is actually massive. I wrote an article a while ago. There's the whole like accelerationist movement. What I call myself was a reluctant accelerationist. I think we need to push forward because we can't go back. And the worst thing we can do is get stuck where we are. So I'm very AI peeled in terms of its impact on the economy. It's sort of upside in terms of monetization. I'm not sure about the timing. [00:17:49] S0: What would be like the gradient towards it? Imagine law or medicine where I don't know whether or not you would consider those verifiable, like law is like a code of some sort. Medicine, we have a certain state understanding of things.
00:17:49 · Patrick O'Shaughnessy, Ben Thompson
[00:17:49] S0: What would be like the gradient towards it? Imagine law or medicine where I don't know whether or not you would consider those verifiable, like law is like a code of some sort. Medicine, we have a certain state understanding of things. [00:18:01] S2: I mean, I think medicine is by far one of the biggest opportunities. It's both of the biggest opportunities and also one of the most challenging ones because of all the regulations and all the access. Like if you could turn an AI, turn machine learning onto all the medical records, I think the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable. So that is a very optimistic view. On the flip side, like when is that gonna happen, right? I think the optimistic frame I put on humans is our capacity to create needs is sort of unlimited. So I think we'll do a very good job of creating new opportunities and jobs sort of in the fullness of time. The sort of more pessimistic way to put it is our ability to create red tape and muck is also fairly unlimited. How much of our economy is actually we've managed to create more and more jobs that is just make busy and make slow to a certain extent.
00:18:57 · Patrick O'Shaughnessy
[00:18:57] S0: Vanta automates compliance so your team can spend less time on security reviews and more time getting customers. Vanta cuts audit prep by 82% and gives you instant up to date proof of trust without all the manual work, so you can close deals faster and with less friction. Customers report a 526% return on investment, and more than 16,000 companies use Vanta, including Ramp, Harvey, and Snowflake. Get started with $1,000 off at vanta.com/invest. Bridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting, reconciliation, reporting, trading, and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. I've been hearing from a lot of investment managers about AI and they fall roughly into two camps, with some unsure where to even start and others convinced that they can build their own order management system over a weekend. The reality is that running an investment firm will always require governance, controls, and a single source of truth for your data. And no amount of AI enthusiasm changes that requirement. Ridgeline is built on exactly that foundation, which is why I believe that the firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation and you can request a demo at ridgeline.ai. If I go back to the early twenty tens, maybe the aggregation theory was stewing in your brain and then you published it in 2015. I think it's fair to say like that theory, that idea, maybe you can just quickly remind people what it is, defined the winners and losers of that era of technology. I'm really curious how you're thinking about what theory or principles will define this era of winners from like a financial perspective and market cap perspective.
00:20:50 · Ben Thompson, Patrick O'Shaughnessy
[00:20:50] S2: I go back and forth even just on the question of aggregation theory itself. How much does that apply in the The [00:20:57] S0: same thing. Yeah.