This Week in Startups
00:53:49 · Jonathan Siddarth
[00:53:49] S0: Correct. Correct. And the version of this for enterprise is you create an RL gym, a reinforcement learning gym. I think this is one of the coolest things in computer science. Just like a gym where humans go to train, this is like a gym where an agent trains. And in these gyms, you create basically clones of different websites like a DoorDash or an Uber Eats or a NetSuite or a Salesforce. You create this virtual environment with prompts or workflows and ways to verify whether the task is completed. Now the task could be, Hey, salesperson, why don't you research everything you need to about this prospect and then update Salesforce in this way? Long as when you create this virtual environment, you create these prompts and you create these verifiers, now an agent tries out different combinations. The agent is basically trying to use the tools in an appropriate way to actually complete that task. And it's a cool way to learn whether the agent is learning through trial and error, how to execute a complex multi step workflow. You're not teaching the agent, go to LinkedIn first, go to ZoomInfo first, and then check out Salesforce for prior conversations. You're not teaching it that, but it's learning. It's learning just based on feedback on, okay, if I accomplished this, I get a reward. And then it learns how to operate in a way that maximizes future rewards. It's really cool. To your question of what happens if we run out of human intelligence, right? Fortunately, I think we are still a significant distance away from that. We've run out of internet data, but there's lots of intelligence trapped in the minds of humans that is yet to be transferred from human minds to machine minds. I would say, Alex, even the way like, I love your show. I love what you and Jason have done with this show. But when you're thinking about how to interview somebody who's on the show, what type of questions to ask, what type of prep to do, I guarantee that knowledge is not distilled into the models from OpenAI or Anthropic or Meta or Google. The only way that's gonna happen is if we hire somebody like an Alex or a Jason to work on Turing, to use our tools. And we didn't get into this, but we have tools that make it easy to keep the quality of the data high. And we have AIs that will assist you in generating this data so that we can make this scale. Pun pun not intended.
00:56:18 · Alex Wilhelm
[00:56:18] S1: No, I was gonna let that one slide. We don't allow more than two dad jokes per episode. So we have to we have to save them, if that makes sense. So here's my question. I I I understand that scale being subsumed by Meta has been great for the Turing business. You told me before the show you guys are at, 9 figures of revenue and profitable and growing. And honestly, hell, yeah. I love that. But if I'm OpenAI, why wouldn't I try to replicate what Turing has built inside of my domain? Because if there's one thing that large AI model companies have today, it's a lot of access to capital. And so as we've seen from, you know, Meta trying to buy literally every human who's touched an AI before, there's a lot of money to play with here. So are are you guys just so good that OpenAI doesn't wanna try to replicate what you've built? Or is there something else that I'm missing in that it's good to have a third party versus an internal group be kind of telling you where you might wanna work on things?
00:57:10 · Jonathan Siddarth
[00:57:10] S0: Yeah. I mean, that's a great question, Alex. Firstly, to advance towards ASI, you need three things to move in parallel. Actually, you need four things. You need research and algorithms. You need compute. You need data. And you need the application layer. Those are the four things, right? Research, compute, data, and applications. Now, the labs are really good at research, right? And kudos to OpenAI, Anthropic, Meta, Google, Apple, all of these companies for advancing the frontier forward. So they're spending their energy there. We could flip this question to also ask, why don't they build their own compute? And in some areas, the labs are investing in it. I mean, we've heard of Google having TPUs and other companies trying to do that. But NVIDIA clearly has the edge today. And there are some good companies like Grok, Cerebras, etcetera, that are also building custom chips. So compute is also advancing. And the third pillar is the data pillar, right? Now, generating this data is exceedingly complex. Again, three dimensions. It has to be complex. It has to be realistic, meaning it has to mirror how a real human would interact with the model. And third, it has to be diverse. It's very hard for a lab to get all of this in house. I'll give you an example. Today at Turing, this month alone, we are hiring PhDs across the board in physics, chemistry, math, biology at the level of granularity of somebody who's an expert in dark matter, somebody who's an expert in black holes, somebody who's an expert in molecular biology, right? Like these very niche fields. And you ideally need these humans part time because the way these humans are good at the job of training the models is because they are also good at their day job, which keeps their skills sharp. And once you've ingested that knowledge, you might want to move to the next frontier and the next frontier and the next frontier. So you need a platform or a partner that can scale up very quickly to elite talent, manage the talent to make sure the talent is generating data part time, the data is high quality. We have to build a ton of tools. We have this platform called Allen, where AI is used upstream of the human to minimize the work for the human. AI works alongside the human, and AI does quality control. It may sound a little dystopian. It's like AI overseeing humans to improve AI.
00:59:41 · Alex Wilhelm
[00:59:41] S1: I'm not afraid of our AI future, personally. So that doesn't... My P doom, I guess, is very low. But I wanna go back to your black holes dark matter point because I'm a bit of a science fiction nerd, I'm also a bit of a space nerd. So those are topics that are near and dear to my heart. But if I spend a lot of time helping a particular LLM better understand why we think about dark matter, why we came up with the idea, you know, galaxies and spinning and not having enough matter, blah, blah, blah, blah, blah. Will that work to make the LLM smarter in that particular domain have spillover effects to other areas? Does it make it more intelligent writ large or more intelligent only in that specific domain? I'm not sure of the answer here, so I figured I'd ask.
01:00:28 · Jonathan Siddarth
[01:00:28] S0: Yeah. Phenomenal question. What we with relatively high confidence is that when the models get better at coding and math, they seem to get better in a wide variety of other tasks that have nothing to do with coding and math. Something about coding seems to teach the models how to think in a more structured way, how to communicate with less ambiguity, how to think step by step. So coding and math, there have been some experiments in how they have out of domain performance, outside of just pure code generation. And coding and math is also interesting because sometimes when you ask complex questions, Alex, the sub steps involve being able to compute stuff or calculate stuff and pass the results forward. For example, Alex, if you had a question like, hey, what are some interesting themes in investing in AI? The answer to that might involve a model that knows how to write code to query PitchBook, write some Python code to analyze the data and say, you know, AI in health care seems to be spiking. AI in retail seems to be going down. That requires the model knowing how to write some Python code to analyze the data. It knows Matplotlib to plot the results and show you the results. So coding and math have broader applicability. But it's also true that this is one of the mysteries of these language models. The models seem to learn some representations about the world that seem to carry over to other areas. I can totally imagine you understanding human psychology. If the models were really good in human psychology, it could help the model write better website code that designs the website to be more persuasive, maybe more conversion optimized, maybe makes it better for generating copy. So it may be... Who knows? Maybe there is something to learn from studying the universe that helps people in their day to day. It could be some indirect thing, but we don't know for sure.
01:02:26 · Alex Wilhelm, Jonathan Siddarth
[01:02:26] S1: So, Jonathan, here's to a next great couple of years as the world gets bigger and faster and smarter. And just before I let you go, where can people find Turing on the internet? And is there a particular job that you're having a hard time hiring for that you wanted to shout out to the world? [01:02:41] S0: Great. Thank you, Alex. But If you're working on AGI research or in human data, come talk to us. We are hiring across the board. And if you're building a frontier foundation model and you need data to make the model smarter at coding, reasoning, STEM, etcetera, come talk to us. We're at Turing dot com and you can email me at, jonathanturing dot com.