The MAD Podcast: How AI Gets Built — with Matt Turck
00:19:08 · Matt Turck
[00:19:08] S1: Yep. And still in in your predictions, you have some other other good ones, which is slightly scary. A deployed agent copies itself outside its environment and operates after its original instance is shut down.
00:19:23 · Nathan Benaich
[00:19:23] S0: Yep. What is... Yeah. I mean, I'm reading with a sci fi. No. Well, like, you know, even a couple of weeks after that, there was some like, you know, research at Anthropic where, you know, models could like copy each other on an edge device. But yeah, I just think there's there's like a lot of behaviors that we don't that we don't currently understand. For example, models are like generally trained as like individual entities, like not necessarily to collaborate with each other. And there was some work not that long ago that looked at say, hey, can we compare a frontier model like acting by itself or a set of frontier models trying to collaborate to solve a task? And the swarm was like worse than the individual model because it's like kind of not trained for that. But just today there's some, you know, new work from general reasoning where they've trained models to act as a collective through reinforcement learning from collective rewards. So this idea of like, hey guys, you know, work as an organization, figure out your own organizational structure, work together to achieve a goal. And through that, we see like better performance with like lower token budget. The downside so far is that while you you you can put these models that are trained through collective rewards into these like cyber scenarios, like this one that tries to approximate a bit of the Hugging Face incident where they collaborate on a message board to try to do some, you know, guardrailed cyber attack. And, you know, none of the models actually do anything bad, but if they do think about doing something bad over training, they increasingly do not want to tell the human about it. So I think I put that prediction there basically because like I think it's possible because we just don't understand how we don't fully understand like how agents work by themselves and like, you know, as swarms I think it becomes even more complicated.
00:21:16 · Matt Turck, Nathan Benaich
[00:21:16] S1: Okay. All right. So maybe to, close this part of, the conversation on, AGI, RSI, cyber, and basically things getting out of hand. Where are you on the pacing the frontier idea? Is that something that you think makes sense? Something that's realistic or not? [00:21:37] S0: I can see multiple like angles on this one. You know, in in some way if you ask the general public, okay, all the lab leaders are saying we have something that we just like increasingly can't control that's dangerous, that has caused these incidents. Like, do you think it's worth continuing or like just figuring out what's going on before you make it worse? And I think a logical, like, immune reaction is like, yeah, we should probably, like, figure it out before we go much further. I think the practical reality is that there's so much money at stake from all sorts of different parties in the economy that I think it... That kind of level of collaboration and coordination is really difficult and is sort of like peak game theory where maybe some some participants, like, support it because they are behind. Other ones support it because it plays to their general agenda, whether their agenda is like fair or not fair or seeking another goal. I I I don't know. But I think the the practical things are like who decides it? How do you measure it? How do you implement it? When do you know when to stop or start. There's some proposals like sending like external evaluators into labs to sort of measure the stuff. Trying to see [00:22:48] S1: big revenge of KPMG.
00:22:48 · Matt Turck, Nathan Benaich
[00:22:48] S1: big revenge of KPMG. [00:22:51] S0: That's a good one. Or trying to like somehow survey like workloads that are running on data centers, maybe like, you know, put compute thresholds on certain workloads, but like that stuff has been tried in the past and it's like very difficult to monitor. [00:23:04] S1: Yeah, especially when one thinks about the international part of the prisoner's dilemma, right, especially US versus China. [00:23:12] S0: Yeah. So like this part of the report, like I don't have like the definitive answer but I've tried to do my best to present the different perspectives on things and then let people conclude with their with their own data. But I find it's... If we extend this towards like, you know, this nimbyism debate, which we predicted in the last year's report of, you know, most people would not wanna, you you know, live next to a data center and then this would start to become a politically important component to midterms. [00:23:37] S1: Yeah. That was a resoundingly correct prediction. Right? [00:23:42] S0: Yeah. Yeah. Yeah. It seems so Where more and more states have tried to implement a moratorium or have done so or established new criteria by which grid connections can happen. Like, for example, you know, data centers would have to figure out and bring their own power on-site or they would have to make certain investments in the communities that they want to build in.
00:23:42 · Nathan Benaich, Matt Turck
[00:23:42] S0: Yeah. Yeah. Yeah. It seems so Where more and more states have tried to implement a moratorium or have done so or established new criteria by which grid connections can happen. Like, for example, you know, data centers would have to figure out and bring their own power on-site or they would have to make certain investments in the communities that they want to build in. [00:24:03] S1: Yeah. And while you added, so maybe just go into this a bit more, explain what the current state of things. Obviously, we all know that there is opposition to data center construction, but, like, any color, you can add. [00:24:15] S0: Yeah. Well, there's there's... They're like projects, I think, worth about $50,000,000,000 that are currently stalled, through a variety of these these reasons. Like Texas, Pennsylvania have been ones that have generally, like, paused paused new data center connections. There's a there's a stat on polling of, like, American American people where individuals would prefer to live next to a nuclear power plant than live next to a data center, which is quite wild to think of. [00:24:44] S1: And what what do you think that is by the way? Is that lack of understandingeducation? Is that just terrible PR
00:24:44 · Matt Turck, Nathan Benaich
[00:24:44] S1: And what what do you think that is by the way? Is that lack of understandingeducation? Is that just terrible PR [00:24:55] S0: Yeah. I think it's kind like everything because, you know, there... There's like public narratives that data centers consume tons of water and that your water bill will go up. But realistically, like, data center is kind of more like a closed, like, swimming pool. Like, you fill it once and then the water is, like, constantly, like, recycled in there, so it doesn't actually consume that much once it's filled. Then there's, you know, points around, hey, your energy bill will go up. But that's also kind of inconclusive because certain facilities like consume grid, other ones are off grid. So there's around noise. I think that's, like probably uniformly true that these things are really noisy and like droney and like pretty horrible. There's, you know, others around like job creation or job loss. I think that's that's a little bit like tough to read, but in general... Generally speaking, there's been we see a rise in like the need for like HVAC engineers of construction staff, like operating heavy equipment and like actually doing the nuts and bolts of building the building. [00:25:56] S1: You had a number somewhere that was actually very high. It was like 300,000. [00:26:01] S0: New jobs. [00:26:01] S1: New jobs.