Many would say, well, that's a good thing, being able to control technology that some are fearful of. Why is this a threat to your academic research? Well, good morning, Caroline, and thank you for having me. I think what's really important about good policy is it should solve for the harm that it's trying to solve for. In the case of SB 1047. And unfortunately, it does not do that. However, what is most concerning from my perspective is it harms academia and harms academia because it hurts the open source community and makes it very difficult for the open source community startups or other industry or large players like Mata, who puts out the llama model for us to be able to dissect, understand how that works, what its impact will be for society. And we need to be able to train the next generation to be AI leaders. And if we can't be able to have access to this because this could hurt this

entire community, then I think SB 1047 is actually counterproductive. Now, what those who have written SB 1047 would come back with, and indeed we did have Scott Wiener on the show previously saying, look, we're not taxing, we're not asking those that are running smaller models who are spending less than 10 million or so. We are only after the big players. Why is it such a crucial impact, for example, when Llamas seems to be going great, we just had numbers out, a matter that there's 20 million times downloads in the past month alone. Right. And that's precisely the point. Mita alone right now is looking at maybe restricting llamas use in the EU because of the overly stringent areas. SB 1047 is by far more far-reaching than this when it comes to the liability issues related to that. And the problem with that is, is companies like MARTA won't be releasing

this if there are such strong liability that can come against them for this. And without that, we won't be able to understand and interrogate these models and see how they work, the way academia does and how we do that very well. So that will cut off an entire choke point to us. The second I would also add that academia itself, well-resourced universities, do want to look into how to build these. And so there's consortiums of universities that might want to spend more money in building these so we can train the next generation on how this works. And we won't be able to do that because we won't be able to afford all of the lawyers and the legal fees that will be required to be able to stay in compliance. Something that you just said, Russell, about sort of academic access to technology. You know, we had Nvidia's earnings last night in a discussion with Jensen Huang. I had was about the broadening out of

the use of AI accelerators beyond the Hyperscalers can Stanford Institute for Human-centered I get access to a cluster like is this a situation that works for you. It's it's actually a significant challenge for us. We actually just for able to secure 30/100 and that sounds wonderful, but it's unfortunate because the reality is is if you look at what Mehta has tried to secure, they're trying to secure 300,000. So the reality is, is academia is constrained as to what it can do in this particular space. And we are very limited. And what we need to do is have Congress pass the Create A.I. Act that is a government subsidizing compute and data for academic research, and it's desperately needed. And academia also needs to consider how to reform here and put a greater degree of investment in this so that we can be able to train students so that they can be the next day.

AI leaders. SB 1047. Sorry to go back to it. Is this the end of the story, or do you think you'll continue to be consulted as this moves forward? We are being continually consulted on this, but the reality is, is there is a chorus of people who are deeply concerned about this. It's not just academia. It is indeed small businesses that are startups that are afraid of what this will mean for them. And there's this ironic side of this. It bizarrely will entrench or make the most powerful companies even more secure in this space because they can afford the legal fees, they can afford all of the compliance fees, and things that come from this. Startups in academia cannot.

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