Conversations with Tyler / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
36 published records
01 / uncertainty
“To go off and do anything you want with literature, with art, with travel, with music, climbing the Alps, I don’t know.”
02 / belief
“I think we’ll have that across every different vertical, with every different tool, with these very long horizons, whether it’s 100 hours or 100 days that we want the model to work on something.”
03 / belief
“I think in some companies and industries, that will happen. I agree with it. My hope is that we have models that are helping to run companies in a very thoughtful, efficient way that are data-driven about it, where the models have an eval set of all the performance reviews of people in that given company, and they’re able to make an accurate prediction over whether this reference, or that piece of nepotism should actually be considered, or maybe as a countersignal.”
04 / belief
“I think that while evals are the thing that everyone’s talking about in Silicon Valley and the AI labs, it feels like most people in the rest of the country couldn’t quite describe exactly why you need an eval.”
05 / belief
“Brendan is the CEO and co-founder. I believe he’s the youngest unicorn founder ever.”
06 / belief
“I think what you’re getting at is there’re two key things the models struggle at that humans tend to be very good at.”
07 / belief
“I think there will be a lot of second chances, and the reason that there will be is that oftentimes they’re effective.”
08 / belief
“I think that there’s so much elasticity with respect to how we build more products, how we distribute those products, and how we allocate resources within companies more effectively.”
09 / belief
“I think the most interesting part of our business is that everyone else in Silicon Valley is talking about how we automate away jobs, versus we’re very focused on how do we build this new job category of people training agents, building RL environments to help teach models.”
10 / belief
“I think, in many ways, the economic incentives and how knowledge work will change has a lot of similarities to software, and that we’ll move towards these fixed-cost investments of teaching an agent how to do something, building an RL environment for something, and then being able to use agents as many times as we want to perform that activity.”
11 / belief
“I think certainly in San Francisco. Not in New York, [laughs] but certainly in San Francisco.”
12 / belief
“I think the largest takeaway is that the rate of model improvement at economically valuable tasks is incredible.”
13 / belief
“I agree with that. I think that’s certainly the case. Imagine if you had a panel of domain experts across every industry that were able to perform these interviews.”
14 / belief
“You’ve had a recent project lately. You hired Larry Summers, I believe, for finance and economics.”
15 / belief
“If he asked it 1,000 questions, I think he could induce an error. Fifty questions — I think, in less than a year, we might be there.”
16 / belief
“I think education is one of the things I’m most excited about, where a good heuristic is if everyone has Sal Khan as their personal tutor, available 24/7 to teach them whatever topic they want to learn.”
17 / belief
“” I think there’s always going to be some level of sensitivity around these topics.”
18 / belief
“Maybe in medicine, it’s a test around how well the model is doing a certain diagnosis in a particularly difficult domain where we think the models can add a ton of impact.”
19 / commitment
“One thing I will say, though, is that there are two kinds of data, is a good way of thinking about it.”
20 / belief
“I believe that, in that case, it’s very much a matching problem — less so a distribution and aggregation problem — to facilitate this effective flow of information and aggregation within knowledge markets.”
21 / uncertainty
“I’m so in the company, I don’t know. I obviously was in college for a couple of years before I dropped out, and so I had some people around me.”
22 / belief
“Maybe we only need so much accounting in the world, or we only need so much customer support, but I think software engineers certainly will be able to do so much more.”
23 / belief
“I think a lot of them obviously have meaningful real-world experience, but also this broad vantage point of the entire industry, of not just someone that specializes in a particular type of law or a particular industry in big law, but rather having this very large perspective in how we should structure the project, how we should think about the rigorous processes associated with curating the datasets, setting up the reviews, et cetera.”
24 / belief
“I think that one of the largest inefficiencies in labor markets is that everything is disaggregated, and that when one of our friends is applying to a job, they would apply to a couple dozen jobs.”
25 / belief
“I think that the largest way that these deep domain experts can help to contribute to the advancement of AI is defining the evals.”
26 / belief
“I think that different AI labs, different researchers will go down different routes, and that will frame the ways that these products feel and the things that they ultimately achieve.”
27 / evaluation
“Going to India then becomes very important because you get a better sense of just where they’re coming from.”
28 / prediction
“I think that high clarity of thought often correlates very strongly with people that speak very well, and so, as you mentioned, intelligence plays another role.”
29 / evaluation
“I’m very familiar with India, and all these different things” is really helpful in building relationships and setting up trust across all the different people that we work with and interact with.”
30 / preference
“For higher end food, I like Cotogna in California, Quince, lots of good restaurants like that.”
31 / preference
“[laughs] We’re quite good at figuring it out and also moving towards assessments where we almost encourage it, and are comfortable with the fact that they’re using these tools because we want to see what they’re able to do with them.”
32 / preference
“I think that I definitely am already at the point where there are certain domains where I trust ChatGPT or whatever model I’m using more than I trust a particular expert in that industry for a very quick medical perspective even in some cases, or whatever it is.”
33 / commitment
“I think that sounds like it would be a lot of fun because, as you can imagine, in running the company, I’ve worked a hundred hours a week for the last three years, and I love doing it, and I’ll continue doing that.”
34 / recommendation
“If we tell them, “Hey, use all of these phenomenal Codegen tools, and record your screen in building a product to see what you’re able to do over the course of an hour,” that’s a far better predictor of this person’s ability to actually deliver impact than it is to say, “Don’t use the tools at all.”
35 / prediction
“One view is no one respects the experts because the machines are better, but I think an alternative possibility is the machine, by not being tied to a personality, is less disliked.”
36 / evaluation
“The reason is that there’s so much taste involved in legal responses that effectively getting all of the taste that Cass has into the model is going to be difficult.”