person / recommends
Kevin Roose
“On The New York Times, I, I share your disappointment. I, I recommend Kevin Roose and, and Hard Fork out of The New York Times family of-”
Public evidence record
Host · The Cognitive Revolution
Books, apps, and tools
person / recommends
“On The New York Times, I, I share your disappointment. I, I recommend Kevin Roose and, and Hard Fork out of The New York Times family of-”
tool / uses
“And certainly if you were to triple it from there, you'd be getting into something on the order of magnitude of parity with human headcount. What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
tool / uses
“And certainly if you were to triple it from there, you'd be getting into something on the order of magnitude of parity with human headcount. What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
app / uses
“What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
app / uses
“What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
tool / uses
“I think I used Gemini 3 flash at the time.”
app / uses
“That's probably the biggest reason that I use Claude is that I perceive it to be most robust to that kind of stuff.”
app / uses
“I use Beeper Desktop to try to like aggregate a half dozen or so of them. That's also kind of painful. I feel like Beeper Desktop is, Good idea crashes a lot for me.”
other / likes
“Is there stuff that we can do or is it, you know, is there is it a different organization's job to figure out how to fill that gap. Because I do feel like I want some more, and I love some of the AE Studio stuff, including self-other overlap.”
Claim ledger
258 transcript-backed records
01 / belief
“How what are the kind of design principles that go into this? Because I think listeners are immediately gonna say, woah.”
02 / belief
“I think, like, the the broad synthesis of every bit of analysis I've seen about all these recent incidents basically boils down to, wow.”
03 / belief
“There's also this emerging, like, task completing monster of understanding, which I think sounds like maybe you lean more toward.”
04 / belief
“Are you gonna lie to me right now, or are you gonna come clean? And, again, with a bunch of hemming and hawing, it eventually I think that's where it's headed.”
05 / belief
“OpenAI's retired its or on the verge of they've certainly announced, and I think maybe at this point have pulled the trigger on retiring their fine tuning product.”
06 / uncertainty
“I don't know if you wanna share it, but do you have a ratio of not I don't mean all inference spend, like, including your customers' use cases, but, like, your own internal inference versus your payroll.”
07 / belief
“I found that there were interesting edge cases that I was constantly running into in my own personal export heuristic that I tried to write.”
08 / belief
“We hear we're I think we kinda go through these cycles, right, where it's like, oh, the gap is closed.”
09 / belief
“I think you might be prepared to bite a bullet on your libertarian principles when it comes to the tremendous price discrimination that we see between API prices and first party Quadmax or GPT Pro subscription prices.”
10 / belief
“I guess another simple argument is that I think our frontier companies are doing just fine. That, you know, that could change perhaps at some point in time, and I you know, as the facts change, I I think our response to it, you know, might also ought to change.”
11 / belief
“Maybe I'll let you choose the order of topics when it comes to Chinese models and what if anything should be done about them because you did recently post something, I think, quite counterintuitive given the fact that you're running your company on deep seek.”
12 / uncertainty
“One let's do one more beat on the stuff that's relatively mundane, then we can zoom out to some some real big picture considerations. But you did have, as you alluded to earlier, this highly viral post, I don't know, two months ago maybe, moving a significant part of the workload to open source models for cost saving reasons and for you don't need God to schedule your meetings reasons.”
13 / belief
“I do worry when I see the picture of Sam and Dario not holding hands that if there's a if there's a picture on our tombstone, I think that might be the one.”
14 / preference
“Because now I'll have a more parody kind of choice between I could try to go hire a human or I could plug in an AI, and that's gonna be easier in a lot of ways.”
15 / evaluation
“am a little more sympathetic, I think, off the top to the argument that there was some massive reappropriation of human knowledge that is upstream of all AI.”
16 / belief
“You know? And one example of that in my mind would be, like, don't train agents to maxim you know, with a reward signal that's, how much money they made on the Internet Yeah.”
17 / belief
“One of the challenges obviously with, like, trying to develop techniques that you wanna hopefully will be relevant at the frontier is there's not too many open weights models that you can hack on that have the intensity of RL that is going on at the Frontier Labs, which is leading to these colorful problematic behaviors that we're seeing. But at the same time, it also, like, really amazes me over and over again that astounding work, including the the Cameron Berg paper that I think about all the time about the anti correlation between deception and role playing features and claims of subjective experience.”
18 / belief
“I think we've covered in the past the linear representation hypothesis, which I would summarize super plain spokenly as models basically represent a concept as a direction in their activation space, and the intensity or the sort of salience of that concept is represented by the magnitude of the vector that points in that space.”
19 / uncertainty
“Is are we still in that regime? And if so, is there are there things that you're, like, wanting Opus five one to be able I I don't know if you're even able to use Fable interestingly enough.”
20 / preference
“I'm gonna fork your long running autonomous research project and take it in a little bit different direction on my own. So I think that's pretty cool because honestly, like, I kinda need that to probably get oriented.”
21 / evaluation
“Definitely getting bogged down sometimes in listening to all these Suno song generations and trying to iterate to find something that I actually feel like I really like.”
22 / belief
“How worried should we be about bio risk in the near term? Because I think part of like, all that analysis, to me at least, that there's quite a few iterations of the game to come and reputation long term is gonna matter and all that kind of stuff.”
23 / belief
“I think you said something like the AI knew that this wasn't what the operator would want.”
24 / belief
“There seems to be some pretty strong correlation between the model's self conception as a moral patient entity that has subjective experience and its natural tendency to be aligned in other ways that we care about.”
25 / belief
“Obviously, there's some problems in that analogy. But I think that could be a a I would love to see somebody pursue that kind of rather than scaling up always bigger, better, more.”
26 / uncertainty
“Are there any other I don't know if you share this premise, but one feeling that I have over and over again is I just kinda hate the fact that we are doing such an intense first search in AI space in terms of architecture, in terms of training techniques, in terms of just we're now, like, designing chips to be very highly coupled with the architectures, which is deepening this issue.”
27 / belief
“How how well supported do you think that conclusion is with, like, firm evidence? Because I I think you could also tell a story that, like, it never occurred to it.”
28 / commitment
“Because that's gonna bring about all sorts of deceptive behavior, and it's such such an adversarial environment. How about we agree that for the next six months, we won't do that?”
29 / uncertainty
“I don't know if you have a sociological take on how in the world that's happening, but it's a real puzzle from my perspective.”
30 / belief
“I guess my sense of your overall position is, like, you're fairly optimistic that the and I think I share this for the most part that the irreducible part is, like, relatively small Yep.”
31 / recommendation
“On the topic of whether or not it's OK to put pressure on the chain of thought, the obfuscated reward hacking paper from Open AI is canonical in my mind for why you maybe shouldn't do it.”
32 / prediction
“They are, they are beings that are meant to be helpful, right? So there's, as I'm sure you're well aware, there's this line of thinking that's even if we get the alignment right, we might end up in a spot where we're quite unhappy because we'll hand over more and more responsibility and key decision making and ultimately kind of power to AIS because they're better at a lot of things.”
33 / evaluation
“In today's world, one of the reasons I can't send my AIS out to do all my stuff for me is that humans are pretty clever about tricking and ripping off the AIS, so I'm not sure how we avoid a situation.”
34 / evaluation
“I think if you survey most people that use AIA lot, they would say, Oh yeah, Claude's the most aligned right.”
35 / evaluation
“Forecasting gives us an opportunity to do some world modelling. So Future Search talked about this a little bit at the Manifest conference a couple weeks ago and the feature in the product is rolling out I think literally today.”
36 / recommendation
“We should fund more AI safety research and do more policy because if we have time for the wisdom of having these alien intelligences around helping us, if we can leverage them and actually make better decisions before the critical decisions get made, there's going to be a series of decisions in the 21st century that we're going to look back on, like this decisions made in the 20th century about communism and World War 2 and the atom bomb and all of those things.”
37 / uncertainty
“Is it convergent evolution or is it a reflection of, of how we think somehow encoded in the data that it's then reverse engineering our structure from the shadow of that structure as it's encoded in text? It's a, it's a very interesting question and I don't know that the paper really has anything to say about that yet, but there's certainly going to be a lot of future work, I think, downstream of this one.”
38 / belief
“I mean, it's a big question, but I think people are familiar with things like Cerebrus, which obviously has this giant chip and has like a ton of memory on chip.”
39 / belief
“You usually don't see in interpretability context a training method that leads to better behavior in a way where you can actually see the mechanism. This is pretty notable in in that respect, I think.”
40 / evaluation
“The iteration time from model to model is now potentially shorter than the time horizon that it would take a model to top out in terms of the absolute best performance on a super hard, ambitious, you know, long running task. So I, I had even heard him kind of propose something along the lines of like a claw back or sort of a recall program almost where, and obviously this doesn't work in open source, but it can work in AAPI paradigm where a model might get released, you know, day N after it's been deemed to be ready.”
41 / belief
“I think human experts, doctors, lawyers, engineers, financiers, whoever, who are trying to make evals to try to produce the data for the frontier labs are finding that they are not smarter than the things being trained anymore.”
42 / uncertainty
“We don't know why I don't think at this point it's shaping up this way, but it is the case that if you oblate this J space, then you do lose these advanced reasoning capabilities.”
43 / belief
“I would say if you're curious about this or if you have forecasting needs in your life, you really should try it.”
44 / belief
“I, I, I think that's like not entirely clear, but you know, whether we're putting whether these are like orthogonal swords through the space that, you know, really chop it up well, or they're kind of more aligned and, and leave more space to hide.”
45 / evaluation
“So there's like a really lot of unpack there here. So just like a little bit of the background, the reason that we started to create our own foundation on models like this realisation that what closed model providers are offering does not make sense for us economically.”
46 / evaluation
“They basically think that what they're doing is somewhat near optimal and any sort of accuracy improvements you're going to get over them is going to be tiny and like hard to understand. And I think that's just because we only really understand human intelligence.”
47 / evaluation
“Because the ablation of the J space just leaves causes such a performance degradation on these like hard multi step type of tasks that if you don't see concepts in the J space, you can be, they might be represented elsewhere, but they're seemingly at this point very unlikely to be represented in a way that allows for very advanced planning, reasoning, scheming, deception, etcetera, etcetera.”
48 / prediction
“One unfortunate thing about it is it's hard to test this. So I think the more that AI continues doing strange things to the world and we wake up and see strange things in the news and those strange things are metaculous questions and on forecast bench and see teams like mine are trying to predict them better, we will actually get more evidence.”
49 / evaluation
“Because like you, the sensor dynamic range is limited and then you're losing either some details in highlights or in shadows or for example, let's say you're taking a stream from a camera and want to simulate how it looks like with a different focal length.”
50 / evaluation
“And I tried my best over basically like, you know, 12 to 16 hours of the Fable situation. I think I made a pretty good model.”
51 / prediction
“I think the things that have happened in the years since AI 2027 come out very much indicate the theory that the most important thing going on is how useful is AI and improving the productivity of AI researchers within Frontier Labs.”
52 / prediction
“Because I think once you start to realize that the robot will need to create like the simulation 30 times a second, you just like realize the amount of tokens that is going to be burned for the simulations.”
53 / evaluation
“Overall, Pangram is quite accurate and yet we have at least one example out of 400 or so essays where I think the zero score I would confidently assert is wrong and unfair and should not be the basis for like a pylon.”
54 / evaluation
“This is very useful for us because we can evaluate things immediately. So when Fable came out, the first time the clawed Fable came out, we were able to evaluate it within 24 hours and it was the best single agent forecaster on our leaderboard.”
55 / evaluation
“Now, excuse me, what I What sort of jumped out at me in terms of your approach is that you've developed a architecture search process where the promise to customers is not that, hey, we developed this one paradigm and the old calculus teacher used to say, when all you have is a hammer, everything looks like a nail.”
56 / preference
“I consider myself extremely fortunate to live to a really remarkable degree, a curiosity-driven life these days. And that's one candidate for a positive vision for the future that I think should inspire a lot of people, especially because, as you note, there's an opportunity to move into that phase already with the AI systems that we have.”
57 / uncertainty
“Are all those different domains that you laid out now powered by a domain specialist model that has learned, for example, the intuitive physics of heat dissipation through a ventilation system? And how, if we just take that one example, if the old version was like actually having a simulation down to the level of I don't know if it was all this detailed, but going all the way down to molecules of air blowing through a space and how they bounce off of each other and what ultimately happens.”
58 / belief
“One thing I learned in researching neural concept I thought was super interesting is that you guys are serving, in addition to a bunch of enterprise customers, a number of Formula One teams.”
59 / belief
“The future of physical abundance, I think I'm feeling more than perhaps I ever have.”
60 / belief
“I mean, I think there's a lot of different ways you could imagine unlocking more agility on the manufacturing side.”
61 / belief
“I think that has felt, even though people have talked about that, imagine that for a while when they think about the self-driving car world, it has felt like that is a long way off even once the technology works because We just haven't seen much change.”
62 / commitment
“How big of a deal is that? How soon do you think we will get there? I assume it's got to be inevitable on some level.”
63 / belief
“Could you give us a little bit more intuition for like how, like just how radical these moments are? It may be a little bit hard for somebody not in the domain to really grok it, but I'd love to try, to get a little bit better sense on kind of, are they really good optimizations or are they really like stepping out and exploring different regions of the design space that people, because I think what made Move 37 qualitatively so compelling was like, no human would have made that move.”
64 / belief
“What does that look like? And I think we kind of know what the old school one looks like.”
65 / belief
“I'm talking, that's like a 50 years ago phenomenon. They've come a long way, but they're now going to be faced, I think, with their most profound challenge ever.”
66 / observation
“I'm thinking for whatever reason, the ML researchers are like most keen to automate their own labor. And then we see a lot of artists are very hostile to the technology, certainly not all, but that's like a pretty common point of view, especially if it's like, I love doing this.”
67 / observation
“I think audience is super diverse, but one common profile is the sort of AI engineer, software engineer who's now doing increasingly everything with AI, both in terms of writing the code, but also the products that they're building are increasingly AI-ified.”
68 / evaluation
“Back in 2023, he called generative AI an existential threat, put his entire org on AI one day a week, and when most of his people pushed back, he replaced them, rebuilding around what he calls AI DNA. We started with one of his recent acquisitions, a company called Chorus.”
69 / belief
“I think there's a couple different senses, and I'm not sure I have gripped all the senses that you mean when you use that term.”
70 / belief
“I think we can all kinda feel that that's coming, and I, I definitely wanna get your, um, of course, your perspective on exactly what that is and what we should be doing about it.”
71 / belief
“Then I think there's maybe another level, and I've got a note from the, and I thank Fable for helping me.”
72 / belief
“I think we'll be okay. The book is The God Test: Artificial Intelligence and Our Comic- Our Coming Cosmic Reckoning.”
73 / belief
“You know, they, that may start to change as they hopefully get a little more serious about their policies around internal deployments as well. But even that is, like, I think un- dramatically underappreciated by the public at large that, like, all this kind of, you know, harmless tr- harmlessness training that they have is the product of a lot of hard work and a very intentional design choice, and it definitely doesn't have to be that way, and in fact, it'd probably be simpler to just, like, get it to do, you know, the task.”
74 / belief
“Um, but you, I think, write another, write compellingly about another sense of evolution, which I think is maybe less appreciated.”
75 / belief
“There's like a, you know, certainly some differences between how the, the leading companies kind of think about their safety training, but they're more similar than different in the grand scheme of things. So we're, we're just, like, very, very focused right now in a very small space of AI possibility, and I think that is dramatically underappreciated.”
76 / belief
“There was, I think, a lot of, lot more hope that under a Biden/Harris or, you know, any Democratic administration, that there would be some, um, push for that, and then it, it kind of got backburnered, you know, with the, the Trump win in, in '24.”
77 / belief
“I'll give you what I think is, like, the short steel man, and I do think it's a real thread-the-needle scenario.”
78 / belief
“I think the Kurzweil graphs and just kind of, you know, in the presence of web scale compute and web scale data, somebody's gonna figure out some algorithm to make it work.”
79 / uncertainty
“How do you... I, I don't know who else I would put on that list. Not, not too many.”
80 / belief
“I think the Pope comes to mind, g- going back to, you know, who are, who are my heroes.”
81 / belief
“Again, easier, easy, easy for me to say, I guess I'll say, 'cause I think I've been very fortunate with this podcast and the fact that there's like a ton of money flowing around the AI space means that, you know, we can get sponsors in a way that would probably be very difficult for many other people in many other niches.”
82 / prediction
“" Uh, but I don't think it gets us probably entirely out of the, the woods and, you know, the, you may have seen the old Gwern essay about, like, why oracle AIs want to be agents. And it's, uh, I think he does make a pretty compelling point that there is, like, just a lot of gravity toward this sort of agentic, um, form factor because even just to get the right answer to a lot of questions, you kinda have to go out and find it, you know?”
83 / evaluation
“I think that is really underappreciated by the public at large. It's more appreciated by the people developing the AIs because they've at least had an experience that was formative for me when I was doing the GPT-4 Red Team close to four years ago now.”
84 / recommendation
“On The New York Times, I, I share your disappointment. I, I recommend Kevin Roose and, and Hard Fork out of The New York Times family of-”
85 / observation
“A mandatory jailbreak reporting field, a non-technical reviewer, a panic that climbed all the way to the White House, and a blunt verdict on the one move he thinks Dario got wrong.”
86 / belief
“I think that we should actually sort of sit here and, and frame what is actually happening when we say, like, Fable outperforms on frontier code.”
87 / uncertainty
“I guess turning to the ensuing fiasco, I don't know if you would even agree with the characterization of Friday night's ban, export control, functional ban on Fable as a fiasco, but it's certainly a bit of a left-field curveball mess.”
88 / prediction
“a war with the United States, and the United States not getting what it wants and kind of having to recognize that, like, yeah, we kind of have to fold this hand because we just actually don't really have escalation dominance in the way we might have thought we did.”
89 / evaluation
“If I try to channel Balaji for a second, which I wouldn't pretend to be able to do it an A+ job of, I think he would say something like, "We all have way too much faith in the U- US government.”
90 / evaluation
“You know, the responses have been, "Well, you know, the ultrasound doesn't see this that well, doesn't see that well," or, you know, "We've, we don't actually recommend whole body scans because, you know, there's a lot of false positives," and all this kind of stuff.”
91 / prediction
“An additional wrinkle that I think you often hear from folks at Anthropic is, like, we need a leader that is going to be inclined to burn their lead at a critical time to use the advanced AIs that they and only they will have at that time to, like, solve all these safety and alignment problems in a super compressed, um, timeframe.”
92 / evaluation
“The other thing that's kind of related to this that jumped out at me is a sort of escalation, I guess, of both the difficulty of monitoring and some recent advances in monitoring techniques that I'm not sure exactly where they leave us on net. But we both see in the system card examples of extremely illegible chain of thought, which, you know, is just like this wall of emojis and sort of, you know, non-human language symbols strung together that I think is pretty spooky and, like, definitely, um, you know, don't like to see that, to put it simply and mildly.”
93 / belief
“I believe that was pretty clearly stated as part of the deal that OpenAI had made right in the wake of the supply chain designation.”
94 / belief
“The-- You, you've alluded, I think, a couple different times to, like, your positive vision of the future.”
95 / belief
“I think last time we talked a little bit about the sort of great man of history theory.”
96 / belief
“I, I think of like a Jay Akatra, you know, sort of advising everybody to figure out how to get AI to work in the area that is like your core area. Because you wanna know when it can do that, and you're gonna need the enhancement to be able to keep up with the pace as things get crazier and crazier.”
97 / belief
“I think you were less critical, but still somewhat critical, of the recent EO and the move, as I understand it, to take certain AI testing characterization responsibilities away from Casey, which I think now has kind of an uncertain future.”
98 / belief
“A lot of the proposals that I understand that you have favored or even championed, including mandatory safety plan publication, certain other transparency meas- measures, whistleblower protections, and even a sort of Fathom-style public-private regulatory, you know, hybrid structure, have all happened in different states to, uh, I think a remarkable degree in a pretty short period of time.”
99 / belief
“Generally had a taste of sort of the good life, I would say, of kind of freedom and ability to pursue your curiosity.”
100 / recommendation
“What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
101 / belief
“For the time being, we still have some agency over this process, and I think that's a great reminder to maintain an ownership mindset and hold ourselves accountable to doing our very best work and not getting lazy and letting the AIs lead us around.”
102 / belief
“You could the decomposition process like multiple times and check for consistency, which I think you're suggesting something like that might be going on.”
103 / belief
“I think of that as building your own harness in a way, which is something I'm thinking about for myself too.”
104 / recommendation
“And certainly if you were to triple it from there, you'd be getting into something on the order of magnitude of parity with human headcount. What are you doing with it all too? Because I use my $200 Claude Max and my Codex Pro and I honestly don't even hit my limits that often.”
105 / uncertainty
“Is it esoteric? I don't know. It strikes me as fairly important from the Fable system card that I'd love to get your take on.”
106 / belief
“For context, Frontier code is the new benchmark asking whether an open source maintainer would actually merge the model's pull request. And this leap of roughly 10% for Opus to 25 upwards of 30% for Fable, I think is a a very similar finding to some of the things that I've just personally experienced where it's like, yeah, this is getting me a lot more.”
107 / belief
“We began with timelines. It's a historic day, I think historic circumstances, both because we are living in a fable era now where I think again important thresholds have been crossed and revealed to the public and so many are adjusting to it in real time.”
108 / prediction
“I think Zvi puts it well when he says you only get one significant digit on your P doom number.”
109 / prediction
“Because I think there's still kind of a synthesis there that I don't mean to suggest that machine learning is over, but the analysis I've come to kind of time and again is like people may invent new techniques that are enough to change the field, change what's possible, accelerate things, maybe make dramatic improvements to the safety profiles.”
110 / recommendation
“It can only do probably the frog game at the end of this training. But building out a world where we have these little role-specific AIs doing their jobs, doing it really well, I think that creates a much more buffered environment that's probably a lot more resilient to another generation of AI that's just like amazing at everything coming in and kind of shocking the system in such a profound way.”
111 / prediction
“How, you know, it's it is going to be a whole new space to explore that is going to be very, very interesting, very productive, very exciting, very, very challenging, I think, for a lot of people, but it's It's definitely happening now as far as I can tell.”
112 / prediction
“It strikes me that the importance of mission and mission buy-in might really be at a premium in the near term, because I can just imagine a hugely different reaction if you sort of imagine your scene from The Office where it's kind of generic widget code, paper co, indistinguishable from tons of other competitors, where if you ask, if we don't do it, who will?”
113 / prediction
“Tell me if you think this will play out differently, but I suspect that we're going to see a pretty similar phenomenon extended to the rest of the economy and probably a few successive waves over the not too long of a time horizon.”
114 / commitment
“I will confess though, as an individual operator, I'm obviously not in the sweet spot of the target market.”
115 / belief
“One thing I wanted to follow up on was your comment on meaning. And because I thought that this was a I would say a striking apparent contradiction in the report is the observation that the people who feel most threatened by AI seem to be most eager to adopt it and use it more and more.”
116 / belief
“I think we kind of have the easiest time imagining a version of the world that's pretty similar to the current one, but where like a lot of stuff is automated and so the savings kind of get passed on to the consumer.”
117 / belief
“I guess another way I might interpret it if I think about the person who was in the traditional sort of customer service or customer success role and is now being asked to babysit bots, is maybe some of this turnover could in fact be healthy.”
118 / belief
“I think I've had fortunately very few instances where I've let something get away from me without noticing that the bots have gone haywire.”
119 / recommendation
“Yeah, I used to tell people, When I did any sort of AI advisory consulting, I would say you could do a lot worse than as a leader just watching your token consumption, but definitely don't tell the team that's how you're gonna be measuring them.”
120 / evaluation
“I think people like me sort of run a bit of a risk of getting detached from, especially because I work by myself largely these days, kind of run a risk of getting detached from what's going on in the real world at real companies that are actually driving most of the economy and where not everybody has the luxury or the inclination to be a bleeding edge early adopter with all of the, I'd say more ups than downs, certainly, but certainly a mix of ups and downs that come with that.”
121 / belief
“There was, I would say, a remarkable amount of not just like cross lab camaraderie, because, I would say people are generally friendly to each other always, even if they're competing fiercely.”
122 / commitment
“Now, obviously there's some selection effect there, but you could just go to, the whole event was under Chatham House rule, so I will respect that and not attribute specific statements to specific people or organizations.”
123 / preference
“I mean, the fact that they offer that for free, I mean, one of my favorite strategies in philanthropy or in general, in efforts to make the world a better place is the unilateral provision of public goods.”
124 / evaluation
“A company running thin margins on top of Opus is going to struggle to say, no, don't use that.”
125 / commitment
“The booking, the research, the clipping, those are AI skills we refine as we go, and we plan to publish them in all sorts of artifacts as this matures.”
126 / prediction
“One of the interesting ideas that I heard there that I had not heard before was that the model that you would want to have internally for AI research might have quite a different constitution from the one that you deploy publicly for kind of general purpose AI assistant use cases. And they seem to think that, in fact, you probably would want to have something even more focused on safety and more sort of restricted in some ways, but maybe also less inclined to refuse certain tasks, but basically a different behavioral profile, which I do think is interesting because if you're going to make this sort of chain of thought monitoring plan work, I do think you're probably going to need some meaningful diversity of the AIs.”
127 / evaluation
“If you believe that models have their own deep-seated goals and that those goals might diverge from ours, then this could be very bad, right? It could be like, it could be extremely bad because they would be using this reasoning to figure out how to please us while like still having their own goals.”
128 / recommendation
“This is thought to be dangerous because if you have a disconnect between what you really want and the signal that you are rewarding the AI for, then you can get into bad places. And so the obfuscated reward hacking paper that I think is still one of the most important papers of the last few years from OpenAI showed that if you have a hackable reward signal and your model learns to hack that, you can then put pressure on the chain of thought.”
129 / belief
“I kind of, you know, can I can visualize the forward pass in my mind and then I can visualize the backward pass of back propagation going through and, you know, gradually updating all the weights.”
130 / belief
“The core idea is I see it in the nested learning paradigm. And what I, what I think is like potentially for simple person like myself, like most exciting about it is for quite some time now, right, we have achieved the greater and greater expressivity of models by stacking more and more layers and just making them bigger.”
131 / belief
“On page 39 of this paper, we get to the part where you also have a new optimizer that is outperforming not just your old Atom standard, but also even outperforming Muon. It does come with a little bit of computational overhead, but I think again, the argument is that it more than pays back for itself in terms of faster convergence or just better learning.”
132 / belief
“I mean, I was, I always say the last and least valuable co-author of the emergent misalignment paper that came out about a year ago and there's been a lot of variations on that since. But the kind of big take away the big theme, you know that I think we should all we would all do well to remember is changes to, I don't want to say 1 area, but sort of changes made to a neural network with one particular purpose or one particular data set can have like very strange and surprising knock on effects in behaviors that at first glance would seem like very far afield.”
133 / belief
“I mean, maybe it's been somewhat fruitful, but I think also people would get very confused and they try to interpret dreams or understand, you know, what's going on there.”
134 / belief
“I think I want to spend one more beat on what you mean when you say generating its own value, because I'm kind of like, OK, I, I know the transformer architecture pretty well.”
135 / uncertainty
“Because I might think, jeez, you know, all the stuff I've done with this model in this One Direction, like probably isn't going to help me over here.”
136 / belief
“I think has been realizing that my top level second brain called Code Agent can change all these repos underneath it without needing to be it.”
137 / evaluation
“Do they handle the sort of confirmation step well? Because I one thing that was flagged for me as I was talking to AI, of course, about how to do this is that the, those sort of VoIP numbers sometimes don't work for like, you know, you sign up for a new account, then you get the, the code or whatever.”
138 / belief
“I yeah, I think on that note, this probably a pretty good spot to draw to a close.”
139 / belief
“Whatever the case may be, I think that wiki has maybe 500 articles in and now and.”
140 / uncertainty
“Anyway, the one thing that I had where it actually like did a data delete that it was actually kind of painful was predicated on the fact that Slack's rate limits, if you're just kind of a indie hacker, are insanely low Gmail, you know, you fly through and it took me a, you know, I don't know, a couple hours for the script to run.”
141 / belief
“One thing that Granola doesn't do, which I think in many contexts may be the right choice for them, is they don't record the original audio.”
142 / uncertainty
“I mean, one thing people sort of seem to find surprise and delight in at least, you know, as reported on Twitter often is when, you know, agents do something cool that wasn't expected or they're sort of, you know, some emergent property. I don't know, I guess I don't, I'm not too worried about it.”
143 / belief
“I mean, it's really kind of a 360 view of me with with no, with really no segmentation or separation, which I think is quite interesting.”
144 / belief
“I could go back and, you know, retranscribe, but I think that's usually so far, like really, you know, as of today, I think that's already pretty good and pretty trustworthy.”
145 / uncertainty
“I don't know about you, but I'm the sort of person who floats plans and doesn't always follow through or see those plans to conclusion.”
146 / belief
“I've kind of, I've got enough, but I, how can I bring the cream to the top? So that was one initial use of that, but then also another post processing I think, I'm not sure if this maybe came in like May or something when everybody was all of a sudden doing wikis was to one idea I had over time was OK, Now that I have this thing right and it comes from like 8 different sources.”
147 / commitment
“Time flies in the AI space, as you know better than most. And I'm excited for this conversation because basically what I've been doing since then, taking inspiration from you and others, has been building up my own personal AI infrastructure.”
148 / recommendation
“I think I used Gemini 3 flash at the time.”
149 / recommendation
“Yeah, portfolio approach, I think is kind of always an answer to this. It does seem like there's room for it.”
150 / belief
“I think we've seen a little bit of evidence this last couple weeks that could even have a positive effect as it flows through the training data and gives AIs a conception of themselves based on our imagination of what they maybe should be like in a mature state.”
151 / belief
“Is there, if there's not, we can move on. But if you have any other kind of specific advice, I think one of the things people are going to ask, there was a pretty good post on this just yesterday that we can link to in the show notes.”
152 / belief
“I'm not sure how many entrepreneurs are going to want to go join the government, actually, because I think that personality type is often one that started a company in the 1st place because they didn't want to work at a big company.”
153 / belief
“One other argument that people have made a lot who I think are especially skeptical of AI companies, but I guess just maybe just have a hype doom as you framed it earlier, is that safety work is ultimately still counterproductive.”
154 / belief
“I think maybe, and to the degree you can focus this on the AI angle on pandemics, I think the audience is If there's one, it's a pretty diverse actually, I've learned over time, but if there's one thing that unites us, it is a general obsession with AI.”
155 / belief
“I would say my subjective sense right now of the AI nonprofit space is, and obviously there will be exceptions to this, I don't mean to pay with too broad of a rush, and you can also feel free to disagree.”
156 / evaluation
“I want to get into that and get your take on how people who maybe don't work directly in the space yet or who do what kind of work but are not sure if they're making the biggest impact that they can, how they might think about pivoting their careers to try to have the most positive, try to make the most positive contribution that they can.”
157 / belief
“Shout out also to Halcyon as another network that I've seen with a very focused and not super scaled, but high success rate approach. They've also been pretty good, especially I think for mid-career people making moves into the AI space.”
158 / evaluation
“AI is one of the premises of this show and one of the reasons I enjoy making it so much is that it's obviously a general purpose technology, a horizontal layer, something that kind of intersects with everything.”
159 / recommendation
“I do think one of my own personal cause areas that I recommend all the time is just trying to develop more concrete visions of what a positive future and of rewarding life in the context of AGI abundance could look like.”
160 / belief
“We, I think there's this big dream, which I'm excited about of like a is taking search costs super low, kind of facilitating all these transactions that previously couldn't have happened because the transaction costs were too high to facilitate.”
161 / belief
“Everything's hacked, has been, you know, I think a pretty good baseline, yet I don't have any major problems.”
162 / belief
“I didn't really care about surviving or taking over doesn't really matter, right? And either way, we have, I think, a pretty alarming demonstration there of even when instructed to allow itself to be shut down, the model refuses.”
163 / belief
“One thing I think is worth calling out though, too, is this is not purely theoretical at this point in the sense I believe that in the mythos system card Anthropic had said that you know, the the classic story of Sam Bowman getting a an e-mail while he was eating his sandwich at the park.”
164 / uncertainty
“There's like a very direct incentive to dial that up and I don't know how we're going to figure out how to balance that.”
165 / prediction
“One thing that you hear fairly often and that I definitely have to say I take more seriously now in light of actually seeing the AIS that we have, that I had expected to even just a few years ago, is a sort of maybe not quite to alignment by default, but a sort of like benevolent basin idea.”
166 / evaluation
“I mean, you can maybe interpret inoculation prompting differently than I will, but my general description of inoculation prompting is there's a generalization, a very problematic generalization that happens if you reward the model during reinforcement learning for something you didn't quite intend, especially if it's like a flagrant hack, then the model can sort of start to generalize to I'm the kind of thing that loves to reward hack and I get rewarded for that.”
167 / evaluation
“I think a general sketch would be like 03 might be the most misaligned model that was ever released to the public. It seemed like it was right in that tween zone where RL had really scaled up and some of these problems were starting to show up, and since then there's been a bunch of work to try to reduce them.”
168 / uncertainty
“One thing that I recall, I don't know if it was 2 IOs ago or whatever, right, but there was going to be 3 sizes of Gemini model at one point in time.”
169 / uncertainty
“Is it going to be available via the API and is it going to be I've noticed with, I mean, Gemini's been the only API that's accepted video for a while now, but I don't know exactly how it works under the hood, obviously, but I do feel that it's sort of kind of down-sampled, or maybe there's like frames taken out of it historically.”
170 / belief
“I would say, as I'm sure you're well aware, like commentary on Google's AI integrations across its vast product suite has been that it is characterized by like some bangers and then there have been some which have been characterized as misses.”
171 / belief
“I think the perception from outside is by analogy too, it's hard to make a good flash model if you don't have a good pro model.”
172 / belief
“Other, so you mentioned, I think five providers, Anthropic, OpenAI, Gemini, Deepseek, and Kimmy.”
173 / uncertainty
“I don't know, you probably know what the ratio is of API cost to effective token cost when you buy a Claude Max subscription and max it out.”
174 / belief
“I think you've maybe navigated this about as well as anyone could in the sense that betting on Anthropic and kind of going all in on whatever the best model is, which has been clawed to make it work as well as possible while the capabilities curve was getting to critical thresholds.”
175 / belief
“Can you tell a little bit more about, I mean, I think probably a lot of people are surprised almost an hour into the conversation to hear the company has roots in Europe, is headquartered in Europe.”
176 / belief
“Maybe just one more beat on the regulatory environments. And I would separate here following the rules, which you've, you know, I think clearly stated that you're committed to doing from advising on like what the rules should be.”
177 / uncertainty
“Therefore, my product decision-making is less about who was willing to bid on my time and more about how much time was I willing to have my AI invest on my behalf to go out and figure out what to do. I don't know, that's more of a prompt, I guess, than a question.”
178 / evaluation
“I do know that they have to be a lot faster because the ad's gotta show up really quickly on the page. And then I know also that there's a pretty challenging matching problem in there somewhere because I've got millions of, you've got, we've got, society collectively has got millions of these profiles of individuals.”
179 / observation
“In our experience at Waymark, we've kind of seen often lack of creative is one of the biggest barriers to new advertisers signing up with a platform like Criteo or for that matter, Meta or Google or what have you.”
180 / uncertainty
“I don't know if it was in the same e-mail or another one, but you described adding generative AI models to the descript product as a polarizing topic, and I guess a polarizing product move.”
181 / belief
“Yeah, that might actually tie back into the original slop question in an interesting way. But maybe one more beat on just kind of some practical product stuff, because I did want to ask about something that I think is increasingly common and is like quite prominent actually in the Descript experience today, which is there are individual button clicks and certainly like individual prompts that I can give to Underlord that will spend a few dollars worth of credits for me in kind of 1 go.”
182 / belief
“I think that's a, Because a lot of times when I have this conversation, I'm like, jeez, it seems like this work is going to be pretty highly automated.”
183 / belief
“I look back at a couple of emails that you sent, one that I think you said kind of right after taking over, and you wrote something I think will be a really great jumping off point for us, which was, the script isn't a slot machine and we don't want it to be.”
184 / preference
“was it glitchy in any weird way or whatever? And I think if there was a model that could sort of make those marginal decisions well, where you kind of try this edit, try that edit and see which one looks better, that right now is like the bulk of the time that I spend in Descript that I would love to offload is kind of, I just made that edit, How does it look?”
185 / evaluation
“one thing I will say about AI is it is allowing me to create stuff that I don't think is terrible, at least, and that I enjoy the process of creating in ways that I just never would have had any opportunity to do before.”
186 / prediction
“I think that's really interesting stuff on generative models. And I think the perspective also on like, there won't just be one winner makes a lot of sense too.”
187 / evaluation
“When you describe, you said more specifically, you know, something that not the model can't get right, but that it rarely gets right. That's key because when we do things like GRPO, the you've got to have at least one right answer, right, to be to have any sort of advantage.”
188 / belief
“I would say our typical complaint was probably most often that, and this would definitely vary through different generations, but more recently it was like able to do the job perfectly well, so to speak.”
189 / belief
“How do you relate this to what I think of as metacognitive behaviors. In the original R1 paper, there was this aha moment that they published.”
190 / belief
“If that happens, you know, I mean, I also remember the Anthropic leaked pitch deck from a few years ago where they basically said, we think the people in 26 timeframe that train the best models might create such a big advantage that like nobody will ever catch up.”
191 / belief
“When I think about like how much the weights change with fine tuning, I usually think of that as kind of more a function of like some sort of divergence penalty, some sort of tethering of the, you know, the model as it's evolving to the base, to the starting point.”
192 / belief
“I think that's basically the return of the PPO value model, right? That's I should think about that kind of the same way.”
193 / belief
“I think one of the interesting questions is I think the Uber CTO came out and said that they busted through their clawed budget, you know, for the year in the first four months.”
194 / belief
“Like I think there's a, there's a, there's a piece of us which gets threatened by, you know, having it, being able to talk and also having, you know, assigning it subjective experience.”
195 / uncertainty
“There's never any like really like step change for like now you're in a new environment or anything like that. It's just like a continuous loop, but whenever it hits some kind of token threshold, which will change every day, maybe it's 100K today, I don't know.”
196 / belief
“The, the Exa paradigm is like you can write a whole paragraph and it's all very sort of semantically oriented, very embedding based. And but, but I've heard, I think I even spoke to will about the idea that, you know, nobody's going to type in a paragraph long query, but the, your AI can, you know, it has time to do that.”
197 / evaluation
“I have AI, have my own test. And it's been, you know, every time we try it, it fails and I'm like, OK, another, another one doesn't work right.”
198 / belief
“Given all of that, it's like, really hard to imagine how it's not having some experience. And the big reason, I doubt it for the AI side is that all that stuff that, you know, in some unknown, mysterious way is giving rise to consciousness is like that same stuff isn't there, broadly speaking.”
199 / uncertainty
“Do you think that you can get there with pure search or is there still something to be said for kind of continued pre training or mid training, whatever you want to call it that would try to bake in a sort of corporate world model that presumably would complement a search, but I I don't know if it's necessary.”
200 / belief
“I'm not a bidder, but I didn't have the model way back then. Because I think that, you know, does help us drive really the the fundamental architectural approaches for for programmability and scalability, which will serve us into the future.”
201 / evaluation
“Try doing bunch of these things that like you wouldn't want someone participating in like the water economy to do because and I think quite a lot of these things it's like illegal, like price collusion and stuff like this.”
202 / prediction
“You know, I don't know, maybe I, I wouldn't give it that low of a percentage that they have subjective experience, but I, I think I feel comfortable saying my best guess is like well below half chance that they do so.”
203 / evaluation
“Because I, I often feel like you almost, you're almost kind of trying to prompt inject the LLM which is running the search and you're trying to get in there and hack it so that your, you know, your page goes up.”
204 / observation
“I don't know if you are doing this, but obviously there's a big cottage industry that has sprung up to develop and sell reinforcement learning environments to the Frontier labs and you're sort of simulated ending benches like essentially ARL environment, right?”
205 / evaluation
“It, it strikes me that we haven't really seen the true unleashing of the Internet's adversarial potential. And so, you know, that's one thing that they, I, I would say one of their biggest weaknesses, even Frontier models biggest weaknesses these days is how gullible they remain.”
206 / observation
“I, I, I feel especially because I think Noam Brown and some of the other people from open AI Rune, etcetera said that they are actually using these models in research.”
207 / evaluation
“Now, the problem is today we don't know how to build abstractions in a robust and scalable way that, you know, sort of represent the noise of that underlying substrate.”
208 / evaluation
“It turns out for the kinds of capacitors we use, you see variations that are on the order of, you know, sort of 10 parts per million, right. So giving you levels of precision that are in the neighborhood of 20 bits of precision, which it turns out is well beyond what we need for the quantization kinds of, you know, levels that we care about, which are typically at the level of eight bits and you know, higher than that in some cases.”
209 / evaluation
“Turns out we don't need anywhere near that precision for the capacitors that we use. But, but it's really because of this alignment with this geometric control that this particular approach has that allows it to be brutally accurate in the ways that you need it to be through all of these layers of abstraction to be able to scale up.”
210 / recommendation
“That's probably the biggest reason that I use Claude is that I perceive it to be most robust to that kind of stuff.”
211 / evaluation
“I don't know, maybe simplifying oversimplifying this a bit, but interventions of that sort seem maybe not any or all, but like in general seem to promote affirmative responses from models such that maybe you could say, you could, you know, once you make these kind of interventions, they'll say yes to anything.”
212 / belief
“I think I just have to go back to confused and probably not going to quit using it.”
213 / belief
“I think the the basketball 1 is, is good as well because you have to be like very precise to make the hoop right.”
214 / commitment
“I think we are going to get a artificially inflated rating and a sort of happier than maybe is actually under the hood account out of interviews like that.”
215 / prediction
“I think I predict a lot less wriggling on my part to try to get out of it. And I think you'd see a lot less motivated reasoning in general from people if it was all like that.”
216 / belief
“I think would be really interesting to see maybe we can put together a little a little campaign.”
217 / evaluation
“I, you know, I think we talked with this more last time than this time, but this notion of mutualism as a positive vision for the future, I think is another major strength of just everything that you bring to the table.”
218 / observation
“One of the one that comes to mind you had actually mentioned last time, but I also think is quite compelling is the seemingly quite strong inverse correlation between the intensity of our consciousness or the sort of resolution, you might say, and how much we are learning as we go. And I think that you used the example of driving last time where it was like when you're first learning to drive, you are very conscious of what you're doing.”
219 / evaluation
“It's got like my Claude MD and it's got access to like my, you know, sort of who Nathan is and all the, you know, I'm building up a lot of context that it has consistent access to every time. So I think in that sense, like I sort of see this like whole model versus, you know, single thread thing as kind of being blurred anyway, because I've got the same like rather large prompt that I'm using every time.”
220 / belief
“I think I'm following a fairly similar pattern, although probably in keeping with my generally less structured personality, I kind of start with everything in one repo, but then over time, especially if I want to share something, then I'll split it out into a separate repo.”
221 / belief
“Obviously, I don't want to share my entire e-mail history, not because I don't trust anyone, but just because that's what the people who've sent me those emails I think would want me to do.”
222 / belief
“I think, um, the extension or the kind of corollary of my intro is people should watch other people use computers more.”
223 / belief
“Chrome extension also is another one that jumps out at me as like, mine's probably different than yours, but there's definitely got to be. And I took this note from Zvi, too, but I still haven't acted on it because he's also got a Chrome extension that he uses to, I think, also collect notes and reformat and move things around.”
224 / commitment
“What I do these days often is when I see something that people are excited about or, you know, whatever, that's the viral thing of the moment, I will ask clawed to dig into it and see what about it or what ideas in it might be useful to us.”
225 / belief
“The new Twitter API, I think, is going to be probably a big hit for them, as much as it is a quite painful one to try to make work.”
226 / belief
“I think you put your finger on something there that is like, I very much associate this style of thinking with you of kind of coming at it's and the, you know, it's in the title of the, of the Substack second thoughts as well, coming at these core questions from both perspectives.”
227 / recommendation
“I use Beeper Desktop to try to like aggregate a half dozen or so of them. That's also kind of painful. I feel like Beeper Desktop is, Good idea crashes a lot for me.”
228 / prediction
“e even before ai in 2010 it turns out like big companies didn't need five to ten percent of their people how high does that go to me it seems like it clearly goes to a majority of people that are just going to have a really hard time contributing in the sort of fully realized aiified enterprise of the future and maybe we still have executives because we want judgment or decision making or whatever but there's not a lot of executives so i tend to come to an end state of we're going to need a new social contract we're going to need a ubi and then obviously it becomes a huge question how do we get there and on what timeline and what does that transition look like and i don't have good answers i like often wave my hands and say we'll have to figure that out but it doesn't have a lot of time to figure that out but anyway”
229 / uncertainty
“Obviously, you're betting on this Pi framework as opposed to like these sort of products that kind of exist in like a constellation where they're out there off orbiting around this center thing. I don't know.”
230 / belief
“I'm, you know, I'm creating whole simulations of things that I previously just thought about. And I've got, you know, memory palaces that are now like not just in my mind, but are actually, you know, in durable mode on computers.”
231 / belief
“I think that's probably, I think that's very close to kind of the best ideas that I've come up with so far as well.”
232 / evaluation
“Obviously, people have radically different understandings of what's coming, everything from still outright denialism, which I think is increasingly discredited and can be ignored, but there's still this sort of more credible version of AI as normal technology.”
233 / belief
“I wonder how that, going back to the first question about how AGI pilled various organizations are, I wonder how, how do you see that from a competitive dynamic standpoint? Because I think one of the things that's most interesting where, you know, the dice are in the air, so to speak, it feels to me right now in the software market is like, Sure seems like the pace of software development is increasing dramatically.”
234 / belief
“Going back to the models, though, themselves, what's your read right now on this is another thing where I think people have very different intuitions.”
235 / belief
“Before going into your expectations for the year ahead, what are you seeing in terms of guardrails. And obviously one big pattern that I think is like very natural to you guys is sourcing answers back to the document or the sort of authoritative place from which it came.”
236 / belief
“There's going to be a lot to learn. I think people, we have a very diverse audience.”
237 / belief
“I was very surprised by this because I think, man, if I had a, if I'm GE or if I'm 3M or any number of a hundred year old, millions of employees over the generations, companies that have this incredible history and so much data that's accrued that nobody really understands at the company these days.”
238 / belief
“On the sort of performance reliability side, my experience has often been And sometimes it's for good reason.”
239 / belief
“At the point where the models are better able to predict the future than our best super forecasters or even aggregations of super forecasters, that will feel, I think, like a very meaningful shift in what's going on in the world.”
240 / belief
“I'm no astronomer, but my experience with platforms in the past and definitely experiences with the Meta platform, formerly known as Facebook, where it was like they came on the scene, they opened up a ton of stuff.”
241 / preference
“Because that is a bit of a narrative violation relative to what you typically hear is, We can't use that because we'd have to send the data to them and we're not comfortable with that.”
242 / evaluation
“If I think, though, even just about my own ability to search through my own stuff, my own Gmail, my own Google Docs, One of the intuitions I have pretty strongly is if I were to give you full access to my Gmail and give you full access to my Google Docs, you couldn't search through it nearly as well as I can.”
243 / recommendation
“Is there stuff that we can do or is it, you know, is there is it a different organization's job to figure out how to fill that gap. Because I do feel like I want some more, and I love some of the AE Studio stuff, including self-other overlap.”
244 / uncertainty
“I mean, you mentioned Les Wrong and Eliezer, and there's this sort of, I don't know all the lore of Matt's, but I do understand that a lot of people who have participated in it over time come out of the Eliezer discourse and had a certain set of assumptions that were like, we're not going to be able to teach this thing our values, it's going to be extremely unwieldy from the beginning.”
245 / belief
“I kind of I think of them more as like Obviously they're quite different too across the different companies, but I think of it less as like trying to get rich and more like trying to make a real mark on history is kind of the biggest summary that I would give for a lot of them.”
246 / uncertainty
“I both watched a talk of yours and read a blog post from about 18 months ago where you kind of sketch out the different archetypes of AI researcher that you have seen, and then also kind of map that onto the demands of organizations. And I don't know how much it's changed.”
247 / belief
“One thing that jumps out is like maybe not as emphasized as I would have thought is being in command of current research. That's something I think at this point, like really nobody can keep up with all the current research because, you know, that exponential has gotten away from all feeble human minds, I would say, maybe with a few hyperlexics that can still keep up.”
248 / belief
“I think the MIRI line today would be like, we don't really have time for that much research.”
249 / belief
“I thought we would maybe just start with kind of big picture from your perspective. And I think, you know, having watched some of your previous talks, I know that you play sort of a portfolio strategy where you're not like, I have a very specific, narrow prediction, and I'm trying to maximize the value of this organization, this program for that very hyper-specific prediction.”
250 / uncertainty
“What does the kind of funnel look like in terms of, I don't know if there's intermediate steps that, you know, would make sense to talk about selected. And then I think the good news, though, is if you do get into the program, your success rate on the other end of like getting into the field in a professional W-2 employee status sort of way is really high.”
251 / uncertainty
“Maybe I'm wrong on this, but I feel like this has seesawed back and forth, and I don't know exactly where we are today.”
252 / belief
“I think that sounds like safe super intelligence in a nutshell. Maybe that's the setup that they've got.”
253 / evaluation
“Yeah, so that brings up another, I think, huge question for AI safety research in general, and probably the strongest, maybe not in, I don't know if you would say strongest in the sense of being most compelling to you, but certainly the most hawkish or fiercest criticism that AI safety research gets is that it always ends up being dual use and that it always ends up somehow accelerating the core capabilities track.”
254 / preference
“Because I do feel like I want some more, and I love some of the AE Studio stuff, including self-other overlap.”
255 / evaluation
“Then now obviously we've got pretty amazing language models, I would imagine that like the best language models are maybe an overkill for some of the use cases, if only because of cost and latency.”
256 / belief
“I as a power user of all of these things, I have to say I I do find myself going to different services depending on the query. It seems like I've noticed and I would say I would I think Brave is in the middle, I would say, right now between like, perplexity gives me a lot of times the shortest, most to the point answer.”
257 / belief
“Going back to this plumbing example, and I think trying to generalize from that to what I think a lot of people who listen to this show are working on, I think in a lot of scenarios, there's like a proprietary dataset that is like too big for people to like page through.”
258 / commitment
“We'll always be fully transparent about sponsorships, and we will only produce sponsored episodes with companies that I personally judge to be worthwhile on their merits.”