00:30:59 · Aditya Agarwal
[00:30:59] S1: And one of the things that I've been personally curious about is why are these models, right, all of them so fundamentally inefficient when you compare them to carbon based life forms, right, for relatively low use of memory, low power, and basically small fluctuation in voltage, we are able to do amazing stuff. Right? And we... At least we also seem to be continual learning machines of short sight. One of our founders, Ravi Shu, is building in that problem space. So I'm just curious on what are the technical challenges? What are possibly the research based challenges that you're struggling with.
00:31:30 · Srinivas Narayanan
[00:31:30] S0: Yeah. Biology is awesome, and sometimes you have to pay respect to that. We've had six billion years of evolution, so give a give a little bit more time to AI. We've only had a little bit of time. But, also, I see this trend in every technology. Right? Generally, what happens in technology is that you prove that something is possible. And when something is possible and it becomes valuable, an entire industry collapses around how do we then make it scalable, efficient, fast, cheap, etcetera. And you're seeing that happen as well. Look at the amount of innovation and investment that is going in chips, in energy, and so... And in algorithmic innovation as well. And, you know, the cost per unit of intelligence has continued to come down pretty dramatically over the last few years. Right? I would say it probably comes down 10 x, like, every year or every two years. Right? And so that continues to happen through algorithmic innovation, hardware innovation, and innovation in the infrastructure layer. I think that'll continue to happen. You know, I still think there are there are differences between biology and stuff, but, you know, whether we do continue on learning. But you should continue to expect the cost curve to come down quite a bit.
00:32:37 · Aditya Agarwal
[00:32:37] S1: You know, today, one of the things that most builders struggle with is both memory degradation and context compaction, right, that happens once in a while. And suddenly, it seems that a bunch of things that you thought that can be outsourced, you have to take care of them yourselves, right? We internally have built our own harness for working with agents. So I'm just curious. How are the teams inside the labs thinking about some of these things?
00:33:03 · Srinivas Narayanan
[00:33:03] S0: Yeah. There's quite a bit of innovation happening around the model itself in terms of how do you manage the context, how do you compress context if it gets too big, How do you learn whether it's memory or other things? How do you get them really good at using tools? Mhmm. How do you express them in skills, and what are the primitives? So, yeah, I think it's not just the model, but the entire environment that it happens in. One of the things that all the mall... The the labs also do is that they try to incorporate a lot of that into the training process itself so that, you know, what you encounter in different situations matches the distribution. And so that, you know, the RL training kinda matches what you see, especially in coding products and things like that. But at the same time, I think there is also... You know, if I am starting a company and I want independence from a model, you want your hardness to be able to work with different models. I think that gives you more flexibility. So I think that is also exciting because you can then kind of build your own sort of customization and modes around these things. So I I think there is tremendous innovation left to be happen, not just at the intelligence layer, but things around it as well.
00:34:06 · Aditya Agarwal
[00:34:06] S1: One of the trend that's often being discussed today, and especially with Satya Nadella's post sometime back, right, that a lot of orgs care a lot about keeping their intelligence within their own walls and judgment and a bunch of other things. And they're talking about, at org level, sovereign AI of sorts, right? And especially with the latest controversy that you had with the Navier Stokes Millennium Prize, right? There's almost a bunch of the paranoia seems to be getting fanned that our labs using our judgment, our data to get better. Right? How do you... Since you've been an insider, what's your POV on this?
00:34:48 · Srinivas Narayanan, Aditya Agarwal
[00:34:48] S0: Generally speaking, the labs make certain promises to the customers, especially the enterprise customers, and they keep those promises. Right? In the case of OpenAI, for example, you know, if you were a business and if you're using our APIs or if you're using chat from enterprise, you don't train on those data. So I think there is sometimes unfounded fear that, oh, my my model... The... My data from my models are getting trained. In practice, that doesn't happen. But I also understand the concern from companies because, for example, Anthropic changed their terms from, you know, they used to have zero data retention and then say, no. No. No. We need to keep your data for thirty days to understand our security risks. And, you know, that was kinda like changing the terms. Right? So these sorts of things create more concern for companies, and they're kinda worried that we're at the mercy of these labs and what happens in the future if they start changing the term. So I think their concerns are real, which, you know, will get addressed by a couple of ways. I think the labs will try to have policies that they can give better guarantees to these companies on what they will and will not do. [00:35:51] S1: Mhmm.