app / likes
ChatGPT
“Yeah, I like ChatGPT voice mode a lot. There's definitely room for improvement, but I am very excited about the future of voice.”
Public evidence record
Published podcast speaker
Books, apps, and tools
app / likes
“Yeah, I like ChatGPT voice mode a lot. There's definitely room for improvement, but I am very excited about the future of voice.”
book / recommends
“Let's see. I would say In Order, High Output Management is a phenomenal book on running companies. Second is Zero to One, which of course is a classic. And then third is Shoe Dog, where I just find it to be a really inspirational story. What is a recent movie or TV show I've really enjoyed? I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not not recent, but if I had to choose a recent one, probably Oppenheimer.”
book / recommends
“Let's see. I would say In Order, High Output Management is a phenomenal book on running companies. Second is Zero to One, which of course is a classic. And then third is Shoe Dog, where I just find it to be a really inspirational story. What is a recent movie or TV show I've really enjoyed? I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not not recent, but if I had to choose a recent one, probably Oppenheimer.”
book / recommends
“Let's see. I would say In Order, High Output Management is a phenomenal book on running companies. Second is Zero to One, which of course is a classic. And then third is Shoe Dog, where I just find it to be a really inspirational story. What is a recent movie or TV show I've really enjoyed? I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not not recent, but if I had to choose a recent one, probably Oppenheimer.”
Claim ledger
46 transcript-backed records
01 / 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.”
02 / 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.”
03 / 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.”
04 / 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.”
05 / belief
“I think there will be a lot of second chances, and the reason that there will be is that oftentimes they’re effective.”
06 / 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.”
07 / 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.”
08 / 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.”
09 / belief
“I think certainly in San Francisco. Not in New York, [laughs] but certainly in San Francisco.”
10 / belief
“I think the largest takeaway is that the rate of model improvement at economically valuable tasks is incredible.”
11 / 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.”
12 / 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.”
13 / belief
“” I think there’s always going to be some level of sensitivity around these topics.”
14 / 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.”
15 / commitment
“One thing I will say, though, is that there are two kinds of data, is a good way of thinking about it.”
16 / 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.”
17 / 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.”
18 / 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.”
19 / 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.”
20 / 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.”
21 / 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.”
22 / 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.”
23 / 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.”
24 / 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.”
25 / preference
“For higher end food, I like Cotogna in California, Quince, lots of good restaurants like that.”
26 / 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.”
27 / 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.”
28 / 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.”
29 / 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.”
30 / 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.”
31 / recommendation
“Let's see. I would say In Order, High Output Management is a phenomenal book on running companies. Second is Zero to One, which of course is a classic. And then third is Shoe Dog, where I just find it to be a really inspirational story. What is a recent movie or TV show I've really enjoyed? I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not not recent, but if I had to choose a recent one, probably Oppenheimer.”
32 / recommendation
“Yeah, I like ChatGPT voice mode a lot. There's definitely room for improvement, but I am very excited about the future of voice.”
33 / belief
“I would say most often it's part time where it's like, you know, someone might work at a fan company where they're underemployed.”
34 / belief
“Daniel, who joined us, was previously scaled two consumer apps to over 100,000 users and all sorts of just like extraordinary backgrounds of our first 10 hires. And I think that that initial talent density shaped so much of what the rest of the org looks like as you scale it up.”
35 / belief
“There's just it feels like there's unlimited demand for the industry. And I think Marc in recent read about this recently, that software is the most elastic industry of all, where when we increase productivity, there's so much more that will be built.”
36 / belief
“Like, I think we'll be able to automate a majority of knowledge work tasks in the next 10 years for sure.”
37 / belief
“I think that previously it might have been more softer skills, but now a lot of the labs are focused on their business models of what are the economically valuable capabilities that drive revenue and leaning a lot into these professional domains.”
38 / prediction
“We started working with all of the top AI labs. What the labs need is a labor marketplace.”
39 / recommendation
“I would say In Order, High Output Management is a phenomenal book on running companies.”
40 / recommendation
“I think like if I had to distill it into advice for founders, one thing I've realized is that I spent a lot of time trying to force product market fit.”
41 / recommendation
“Tying to the point around initiative and that you can just do things, I encourage everyone, especially with AI and it being so much easier to build, just take the initiative to go out and build products and talk with customers and take that leap of faith because I think that that is in so many ways the largest barrier to more innovation in the economy in any way that we can support that.”
42 / recommendation
“um you know just getting word of mouth that the people that have worked with us at other businesses want to keep working with us um and leaning into creating those great experiences and so that's where i spend all my time and i think that some founders can get caught up and like how do they get really good at marketing before they've figured out the thing that really drives um a lot of customer love um and creates the six star experiences that um you're used to building”
43 / prediction
“We need evals for that to effectively reward it. And I think that That road to improving models will last for as long as there is anything in the economy that humans can do, which models can't, and be a huge portion of what the future of work looks like.”
44 / evaluation
“We just knew from that point forward before we even dropped out that the market was about to change radically and we needed to be at the frontier of that. And so then fast forward a few months, one of the crowdsourcing players came to us and actually used our platform to hire over a thousand people where this is very interesting experience because we started getting flooded with support tickets about how those people weren't getting paid.”
45 / evaluation
“And then I could talk more about my high school business as well, which was a more significant scale. But I think the takeaway from that was just like, you can just do things like so many people have ideas, but the barrier to more companies being built, I think is just initiative and taking the steps to build the product or experience that customers want and investing the time and the ambition to scale that up.”
46 / commitment
“Because when we started the company, I met my co-founders when we were 14 years old.”