00:16:23 · Ruby Justice Thelot, Ruby Justice Thelot
Ad[00:16:23] S1: We'll continue monitoring right after this message from our sponsors. ElevenLabs, AI that communicates at human level across every channel and modality. ElevenLabs dot I o slash m t s. Scale your startup on Neon. Millions of developers and startups have already chosen Neon for their back end. Start on the free plan or get up to a 100,000 in credits for your startup at neon.com/mts. Special thanks to our sponsor, Kong, the AI connectivity platform. Connect APIs, LLMs, agents, and systems with serious security and governance. Konghq.com. Ad[00:17:00] S2: Finally, prove what your AI investment is worth with Larridin. Head to lairdon.com today to start measuring your AI's bottom line. MongoDB, trusted to run critical applications by 75% of the Fortune one hundred, the intelligent data platform for the era. Apply today for access to up to $15,000 in Atlas credits, dedicated technical support, and exclusive events and go to market opportunities. Mongodb.com. [00:17:33] S1: Forgetting about aggregate economic productivity for a second, going back to what you said about employees in white collar businesses having and using these AI subscriptions, Why haven't we seen power law distributions start to form within companies or within sectors where some employees or some firms who are effectively leveraging AI are becoming massively more productive than others that are not? I think maybe we're seeing this a little bit in software, but not really in any other industries, from what I can tell. Even in software, it's not as pronounced as what I would expect.
00:17:33 · Ruby Justice Thelot, Herbie Bradley
[00:17:33] S1: Forgetting about aggregate economic productivity for a second, going back to what you said about employees in white collar businesses having and using these AI subscriptions, Why haven't we seen power law distributions start to form within companies or within sectors where some employees or some firms who are effectively leveraging AI are becoming massively more productive than others that are not? I think maybe we're seeing this a little bit in software, but not really in any other industries, from what I can tell. Even in software, it's not as pronounced as what I would expect. [00:18:11] S0: Yeah, I think it's two things. Partly, we are sort of starting to see some of this power law at the individual level within companies. It's just that that doesn't necessarily translate into a huge aggregate advantage, I say, for that employee. Another thing is I'm starting to get suspicious that many employees are just simply absorbing the productivity improvements themselves and just not passing that on, so to speak, to that company itself.: [00:18:33] S1: Just working less?: [00:18:34] S0: Yes, basically. I think that's definitely the case in some companies.: [00:18:37] S1: I think Nathan Calvin had a tweet on this. He was like, form video... Like, time spent watching short form video has increased. Yeah. Yeah. Because, like, Americans, when they have to work less, they're just, like, spending more time doing talk.
00:18:37 · Ruby Justice Thelot, Herbie Bradley
[00:18:37] S1: I think Nathan Calvin had a tweet on this. He was like, form video... Like, time spent watching short form video has increased. Yeah. Yeah. Because, like, Americans, when they have to work less, they're just, like, spending more time doing talk. [00:18:53] S0: Yes. Yeah. And then the final reason, I think, is just I think Shyam Shankar had a recent tweet where he put it quite well that the atomic unit of productivity in a large enterprise is not really the individual. It's sort the workflow level. And so if you improve one individual on a team's productivity by 10x, then that doesn't necessarily mean that the overall output of that team improves a ton. Even if you have somebody at the head of the power law in your team, that doesn't necessarily improve things, unless you can do some amount of reconfiguration of the work that you're doing or some change management within the enterprise. And so I speak to a lot of people who are doing forward deployed engineering, doing AI transmission projects in enterprise, and they all basically give me the take that diffusion is kind of bottleneck on change management and getting people to accept different ways of doing organizational processes and work processes.
00:19:41 · Ruby Justice Thelot
[00:19:41] S1: And to me, like, this is an area where I guess I am more of a capabilities maxi because it just seems so easy and so intuitive to me. Like, you set the thing up literally by opening a new tab in your browser, and you use the thing literally by telling it what to do. And it's so simple. It's not like you have to learn how to use, you know, some complicated new piece of software like Photoshop or even an Excel. It's literally just you have a little virtual coworker that you can talk to and get to do things for you. And anybody with a credit card can have access to frontier intelligence that all but a few thousand people at the labs have access to. Like, it's just so crazy to me that there isn't much more and much faster diffusion. Like, it's not like you have to build out a whole new network of, like, fiber optics or something.
00:20:29 · Herbie Bradley
[00:20:29] S0: Yeah. Yeah. I mean, I think it is still in some ways overrated, like, that you don't need to do much education or training or whatever to get the average white collar worker to use AI effectively. It sort of reminds me of kind of teaching my grandma to use Google back in the day. It's like, here is this search box where you should, in favor, be able to just type in anything you want to know and get a link to the answer. And yet, it requires some familiarity and some reps and some training of some form in many cases, or it did in certain of the 2000s when enterprise were adopting the internet technologies for the first time in order to really get large productivity increases from that. And so I think this is partly what the job of the FTE is, is firstly helping people inside the company to figure out how to use the thing, and then secondly, figure out what are the organisational blockers to doing that and figuring out ways to route around those. And often that may involve kind of strange things like, oh, some middle manager has some internal politics reason to keep this data siloed, so the FTE needs to go around him to the skip level manager and persuade him to do it. There's just like a ton of internal politics which blocks diffusion in that way, at least.
00:21:38 · Ruby Justice Thelot, Herbie Bradley
[00:21:38] S1: Right. So I guess we should talk about the topic of the day, which is Anthropic S-1. They just submitted an amendment. They added a new line about RSI. They said their internal estimates indicate that they may imminently meet their responsible scaling policy's definition of dramatically accelerated progress attributable to AI. Obviously this is a big deal because anything that you put in an S-1 or any official filing with the SEC has to be completely true. You're not allowed to lie in these filings. So Anthropic clearly believes that they are on the cusp of RSI or that they may be on the cusp of RSI. So what do you think they're seeing internally? [00:22:19] S0: Yeah. They have their latest generation of models, which is not yet released, and I expect they're seeing much greater aggregate research and engineer productivity uplifts. Overall, do think we would benefit the public and analysts outside and the market, in fact, from much more high resolution reporting [00:22:37] S1: Right.