00:28:23 · Jonathan Siddarth
[00:28:23] S1: Yeah, let me unpack this. I think there are two parts to your question. There's one part which is, are we slowing down? Like, are things going to be more incremental going on? That's one part to your question. And then there's another part which is, how should we think about safety in these AI systems. So let me take that separately. First, I think GPT-five is fucking awesome. I think it's awesome. I think we've just gotten used to magic that maybe we are not fully seeing the impact of what's just out there. And I'm not just saying that because we work with the company that built it. I think GPT-five is awesome. I think one thing is real though, which is the AI doomers talk about this concept of rapid takeoff. It's sort of the I mean, you've seen The Matrix or the Terminator movies, like somehow we'll hit a singularity and there's just this rapid takeoff where humanity gets left behind. I don't think rapid takeoff is how things will unfold. I think it's going to be steady continuous progress. Every step of the way we're going to keep moving forward and it's going to be great. And it's great for a few reasons. The reason I think rapid takeoff will not happen and I know why the doomers believed in rapid takeoff is because GPT-three felt like magic. It was like fucking magic. It was magic because there were a lot of one time jumps in there. Jump number one on the data front. We ate the internet. We ate the internet. There is only one internet and we ate it in GPT-three, right? And GPT-four, again, in all of these and all the models like GPT-four, like Gemini 2.5 Pro, Grok2, Grok3, Claude, it's the same formula. We all ate the internet. And that was a huge one time jump. And we put in all of humanity's knowledge. All the books that you owned the licenses of were fed into the models. So humanity's collective intelligence was eaten in the GPT-three, GPT-four era. And we scaled up compute. Some of the early gains in compute were we also got that, right? And computers takes time to build. You've got to solve energy and you've got to build data centers. And you see what Elon is doing with projects, you see what Sam Altman and OpenAI are doing, you see what Meta is doing. Compute also takes time to build, right? So we got a huge jump by eating the Internet and using all the easily accessible compute.
00:30:58 · Jonathan Siddarth, Molly O'Shea
But the scaling laws, which is an empirical law, is continuing to hold, meaning you take a big model with lots of parameters, you dial up compute, you dial up data, and the models smoothly keep improving. Right? I'm a huge optimist in humans and human potential. I think we humans will solve bigger, more interesting problems instead of the more mundane stuff that we do today. I believe I will be 100x more productive. If I'm 100x more productive, maybe I'll run 100 companies in parallel. Elon might run 600 companies in parallel. I think the solution is simple. Keep hiring the best researchers on the planet. Keep scaling up compute, keep scaling up data, we will get to ASI and it's honestly the most important problem humanity could be working on. Because if we solve intelligence, we can solve diseases, we can solve aging, we can solve perhaps interstellar travel. Travel. Right? It's gonna be it's gonna be awesome. [00:32:01] S0: On the safety side, I think the narrative has changed a little bit. Kalshi actually has a market out on this of will models pause research for safety reasons? And this goes until 2027. And so right now, it's at about 9% chance, but it was trending upwards of, like, 40 not too long ago. It's really shifted and it's taken a nosedive in the last week or so. So why do you think that is? Why do you think the narrative on safety is changing?
00:32:35 · Jonathan Siddarth
[00:32:35] S1: People have realized that the best way to deploy these systems is with humans in the loop. And with humans in the loop, a lot of the safety issues go away. You have to design these systems for partial autonomy. People's mental models when they think about AI is self driving cars. I feel like the self driving car industry did a lot of damage to AI in one particular way. With self driving cars 99% accuracy is not enough. That last 1% really matters. But with a lot of AI systems, if you're automating the job of an investment analyst or if you're automating the job of a CEO or a CFO, you don't have to be 100% accurate as long as there is a human overseeing the results of the model. So I think we know how to build good safety harnesses around these systems with a human verifying the results of the model. So I think a lot of the risks, many of the risks were somewhat exaggerated. And we have good techniques to address safety in the post training phase. You can teach the models to decline to answer questions that could be dangerous in the CBRN side the space, a chemical, biological, radioactive nuclear. So we have some good techniques to contain the negative behaviors of these models. Safety is important but I think it was some of the risks were over exaggerated and they could be solved with really good engineering. I think what's the bigger risk is we don't deploy these systems quickly enough. I mean there was that MIT study that showed how ninety five percent of Gen AI pilots fail. I'm not surprised by that result at all. They fail because oftentimes firstly there is a big data gap. To solve real world enterprise workflows you need real world enterprise data and that data doesn't exist on the internet. To have that data, you have to hire people doing those workflows and train the models the right way, which is what we're doing. So there's an enterprise data gap. And there's also an enterprise expertise gap. There's a lot of first mile schlep and last mile schlep to make these systems actually work well. And there is work to do. And the bigger risk is an insurance company or a company in financial services doesn't adopt or make the best use of these models quickly enough. And if you don't do it quickly, their competitors will and whoever does it really well will win.
00:35:23 · Molly O'Shea, Jonathan Siddarth
[00:35:23] S0: Okay, but how are you helping close the enterprise gap? [00:35:27] S1: With enterprises, the biggest gap is making sure that you are building custom models, which could be an off the shelf model that you take, but you fine tune it on your proprietary data. [00:35:40] S0: This is what you're doing with enterprises?
00:35:42 · Jonathan Siddarth
[00:35:42] S1: That's right. With enterprises, the old way of thinking was you would take this off the shelf model or you give everybody access to ChatGPT or Claude and call it a day. That's the SaaS era thinking, plug and play. But the best enterprises know that it's not about integrating general intelligence, it's about building proprietary intelligence. You'd want to take a model, maybe from built by one of the Frontier Labs, but you'd want to fine tune that model on your proprietary enterprise data. Your data is your moat. And fine tune it on your proprietary human intelligence. You might automate your proprietary internal workflows. It will probably be a custom model that's on prem. It will probably be a smaller model. Oftentimes we see a 500,000,000 a 10,000,000,000 parameter model. That's the regime, not a trillion parameter model. And this will be faster, more accurate. You'll have full control over governance, confidentiality. You don't want your model to help your competitors. So that's what you want to build. But the constraint for these models is: step one, you first have to prepare your data. Enterprises have lots and lots of unstructured data, but you want to convert it into structured data, into a format to fine tune these LLMs. You'd want a platform to make sure you can distill your human expertise into the models and suck up your proprietary data into a way to fine tune these models. And you'd want to build the right workflows designed for partial autonomy. You'd want to have good evals for your specific workflows. You'd want to solve safety. And enterprises need a neutral evaluator to help them pick the right models, pick the right systems to build this, and that's where we come in. The same technology that we built to help the Frontier Labs get to AGI, we are building a version of this for enterprises to build proprietary intelligence to build their own internal ChatGPT or systems built on top of chat GPT. And that's going to transform. And I'm very bullish for Fortune 500 companies that have good data collection practices. As long as you at the very least maintained good unstructured data from your prior business interactions with your customers, you have an opportunity to win because your proprietary data is your moat as as you fine tune LLMs based on that knowledge.
00:38:26 · Jonathan Siddarth, Molly O'Shea
Most companies have really amazing human expertise inside their organizations. They just have to transfer that expertise from those human minds into those machine minds and that will give them an edge. It will actually give them an edge over a startup starting from scratch. If you're GEICO, long as you do a good job of fine tuning your custom models on your underwriting data, on your claims processing data, and building systems around it to automate underwriting and claims processing, you can win. But if you don't do that, there could be an upstart AI company that starts today, that does this from scratch, that has a good data collection feedback loop. That company could jump you. But that company will still need time to acquire that data. If you're GEICO or Progressive, you have access to data. So it's your battle to lose. But to do that, you have to act fast to make sure that you own your proprietary intelligence and you don't outsource it away. [00:39:34] S0: With over $300,000,000 in revenue, I'm sure this number has changed because that was a couple months ago, but who are your customers? You can talk categorically. You don't have to tell me their names. I know some of these things are sensitive, but, like, what ways in which are you helping these different industries deploy their own models?