AI, Empire, and the Left's Missing Voice w/ Nick Srnicek

I spoke to Nick Srnicek, author of Platform Capitalism, co-author of Inventing the Future, and most recently Silicon Empires, about why AI is consolidating Big Tech's power rather than disrupting it.

 We dig into why the AI upstarts remain dependent on the infrastructure of the incumbents, why the gap between open and closed models may widen as frontier training costs spiral upward, and the real biosecurity risks buried under Silicon Valley's safety discourse. Nick also breaks down what AI sovereignty could mean for Europe, and why racing to hoard AI productivity gains for yourself misses the actual task: preventing a permanent underclass from forming at all. 

If you liked the podcast be sure to give it a review on your preferred podcast platform. If you find content like this important consider donating to my Patreon starting at just $3 per month. It takes quite a lot of my time and resources so any amount helps. Follow me on Twitter (@TBSocialist) or Mastodon (@theblockchainsocialist@social.coop) and join the r/CryptoLeftists subreddit. 

Transcript

SPEAKER_01 0:00

The nature of contemporary AI is such that basically the advantages that the big tech companies have in terms of access to data, in terms of access to compute, in terms of access to financial resources, all of that is exactly what you need in order to dominate within AI. And in fact, it's just table stakes for being able to be in the AI world at all. The big shift over the past year or two in terms of data center construction was that municipalities and regions were going around to the hyperscalers and basically saying, we'll give you loads of tax breaks, we'll give you all this access to energy, who cares if it has an impact on energy prices or anything like that. We want to be able to say we have a billion-dollar investment coming in. The impact of collecting all this personal data in an AI system is to provide you with very targeted advice about everything that you want to do in your life. There was a news story from yesterday about Meta doing this effectively, monitoring everybody's keystrokes, mouse movements, where their eyes are going and everything. And all of that data is exactly the sort of data that you need to train an AI agent to replace those people.

SPEAKER_00 1:13

Hello, everyone. And also most recently, Silicon Empires, which is his book on the current state of AI and the kind of power grabs that are happening amongst the various AI entities. How do they actually make money? Will they be able to make money? The different strategies that they're taking around capturing the value that they're supposedly producing. Yeah, Nick, great to have you on for the second time.

SPEAKER_01 2:00

Thank you very much for having me back.

SPEAKER_00 2:03

I figure it'd be great. We can just go from the start, just go right into the main argument of the book and then maybe kind of branch out from there. But in your book, you argue that rather than being disruptive, AI is consolidating big tech's existing power. So could you walk through that argument a little bit since some of you know some of the um AI companies are they were big tech companies, or like big tech has got played into AI, but also there are now some new players like Anthropic, like all the various Chinese companies as well. Um yeah, would you like to lay out that argument for us?

SPEAKER_01 2:43

Yeah, so this argument in part comes from um a series of sort of defenders of big tech companies uh who have argued that big tech may be extremely large at the moment, and there may be you know concentration concerns and antitrust concerns there. But that in fact, if you look at the history of technology, whenever there's a sort of major technological shift, that actually those incumbents, those dominant powers, tend to get disrupted, they tend to lose their strength, and you get a new series of companies which emerge in their place. And so the argument from these people is basically that we don't need to worry about big tech because AI is this new disruptive technology which is going to undermine the big tech of you know the 2010s and give rise to all these new new competitors. Um and I just think that's plain wrong. I think you know the the nature of contemporary AI is such that basically the advantages that the big tech companies have in terms of access to data, in terms of access to compute, in terms of access to financial resources, all of that is exactly what you need in order to dominate within AI. And in fact, it's just table stakes for being able to be in the AI world at all. So the issue here is that, yeah, there are a handful of competitors that have emerged. Open AI and Anthropic are the two most notable ones. But as much as power as they may have, they're also heavily dependent on these big tech companies. And the big tech companies are working, you know, overtime to ensure that their power is consolidated through this technological transformation. Um, it's not disrupting them. You know, Google, Google was a primary example of this. Google, the idea was that AI was going to replace search, and so much of Google's money comes from search advertising, and that therefore, if everybody was using chatbots instead of search, Google would just be, you know, their core business would be undermined. But actually, what's happened is that Google has now adopted this technology into their search engine. They are, you know, one of the three top companies in the world pushing at the frontier of AI. They are dominating this space in many, many ways. So it hasn't been a disruption which has affected their power in any significant way.

SPEAKER_00 5:11

Yeah, I definitely remember um there was a moment maybe a couple of maybe a year or two or two years ago, where um I mean the main two companies we were talking about were OpenAI and Anthropic as having the two you know uh biggest frontier models or the most the strongest frontier models. Um, but then and everyone was kind of thinking like, yeah, that this is going to replace search, Google is kind of uh they're kind of screwed right now, but they caught up extremely fast. I I mean it was like uh for you know within a few months, you know, at least there was they they had AI search results or AI was being done every time you do a search result. They were they I mean I think also just like maybe scooting like um looking far farther back, Google has so much data. It was like they're the ones that are able to make if like if anyone can make an incredible AI model, it would be them because the amount of data, like very structured data that they're able to crunch into a model. Um, so that's that has caught up, they have caught up incredibly fast. And I guess um, yeah, there's uh one of the things, you know, that I think people are talking about a little bit right now, um, is maybe that big tech is more or less okay, but all the SaaS companies are kind of screwed because now anybody can kind of like vibecode their own their own version of you know whatever SaaS product that that they're using. I don't know how much I believe it. I think there's like some truth to it, but also I think the the the amount of work it takes to maintain software is like maybe more than people think. Um but yeah, I think I think it does drive things cheaper at the very least.

SPEAKER_01 6:55

Yeah, yeah. I mean the the SaaS company stuff is interesting, and I I don't have any strong opinions on where it might go. I think I think probably the strongest argument is that so much of what the SaaS companies provide to enterprises is a lot of features that those companies don't actually need. Um and you ended up with extremely bloated pieces of software that you're paying for, despite only needing maybe a handful of those features. Uh, and with vibe coding and all of that, it seems quite practical now to be able to just vibe code, you know, some some business software that does only what you need to do and doesn't have to worry about all these other features in terms of security, reliability, and things like that.

SPEAKER_00 7:41

I want to touch on that maybe, maybe a little bit later, but I'm curious just to continue on the you know the arguments in the book and like some of the fears that have popped up I think amongst a lot of people that I've spoken to, or fears are also or also some people just like embracing it, honestly, is like the idea of vertical integration, like full vertical integration by I mean it's it's brought up a lot, I think, with Google or Anthropic in particular. I think Anthropic just has been really good at making AI products that people want to use, um, at least in terms of like a general, generalized like uh uh types of tooling. Um I'm just curious, what do you think in this world of AI in particular, you know, what does vertical integration really look like for these companies and what are its political implications?

SPEAKER_01 8:32

So I think Google's probably the clearest example of what vertical integration looks like at its you know most extreme form. Because you've got Google with you know the incredibly capable AI lab in DeepMind. Um you've got these models which are being produced there. Gemini is the most obvious one, but there's a whole slew of other models being produced by Google. You've got you know various apps on top of that, which are being produced and various services, including AI Search, including the Gemini chatbot, and including all these more sort of industry-specific things that Google's been involved in. And then as you go down the stack, you also get Google with its TPUs building its own hardware, and it's been doing that for a decade now. Um, you know, incredibly capable piece of hardware that is, at least in terms of cost and performance, is on par, uh many, many accounts say, with NVIDIA stuff. And then you've got the infrastructure, which um, you know, I was reading uh uh an estimate the other day, which you know, these aren't public figures, but the estimates are that Google has the most compute access in the world, given how many TPUs they have access to, and along with all the NVIDIA GPUs they've bought and things like that. So, you know, at every sort of stack of the AI, you know, every sort of layer of the AI stack, you've got Google participating in that. And it means that they can then really tightly integrate all these things and get benefits from it as a result. You know, much more, you know, one of the things Google's really known for is, you know, really fast inference, really cheap inference. Um, you know, things like Gemini Nano are extremely cheap and um very, very quick. Um, and then you contrast that with something like Enthropic, where Enthropic at the moment is doesn't have enough compute and doesn't, you know, because it's split across TPUs and training chips from Amazon and GPUs from Nvidia, it hasn't been able to tailor its training and its models and its inference to that specific hardware. So it doesn't gain all those sorts of benefits. And it's as a result, it's now having to rate limit lots of people, um, you know, cut it peep cutting people off from some of its services. And um, that is the sort of benefits of vertical integration that Google has, and others just don't to the same degree.

SPEAKER_00 10:52

Right. In many ways, you know, in the the Google like the there's already intense vertical integration within Google. Like once you're in, like you have a Google workspace, you have access to like hundreds of different other types of services within just within one little thing, and just like the cognitive load of having to try and move from Google to something else and to de-google yourself is like quite a lot for um I mean for the average person, uh especially just because you get so much just with like one thing. Um but I'm wondering like with all these types of you know risks, you know, the the things that or the proposals that I'm seeing um largely being pushed by more progressive-minded people has been basically we have to ban AI because it's uh because it's bad, because of this um power uh grab that is effectively happening. Um or we need to slow down the the the creation of data centers in large part because of maybe like environmental concerns or or or whatnot. Um I'm curious what your thoughts are, at least on like these big bigger kind of um propositions that are being made to public.

SPEAKER_01 12:10

I think a lot of these sort of banning efforts are political non-starters. Um I just don't see it happening. The sort of agreement that would need to be, you know, achieved between different companies that are highly competitive with each other and between different countries that are competing with each other, um uh a ban just seems like uh impossible in any sort of meaningful sense. And then in terms of slowing down sort of data center building, I think, you know, I don't so I think I think on on a practical level, it's not gonna happen that much. What I think the virtue of this these movements are is that by pushing back against data center construction, there's much more leverage on the part of the local communities to be able to say that okay, we'll allow you to have a data center here, to build a data center here, but you need to do all these other things to ensure that we benefit as a local community. And for me, this is the the big shift over the past year or two in terms of data center construction, was that you know, municipalities and regions were going around to the hyperscalers and basically saying, we'll give you loads of tax breaks, we'll give you all this access to energy, uh, who cares if it has an impact on energy prices or anything like that. Um, you know, just you can have it because we want you to, we want to be able to say we have a billion-dollar investment coming in, um, regardless of all that money just goes to Jensen Wong's pockets. Um but uh what's changed in the past year or two is that because of this pressure and because of this focus on data center building, now local communities are able to say, okay, you can you can build a data center, but you have to guarantee that electricity prices will not rise. You have to upgrade our electrical grid in the area to be able to support all this stuff. Uh you have to build, you know, a certain proportion of renewable energy. Uh, you have to do all these things that are actually quite beneficial to the local community. And in the best case scenario, these things, the data centers, can be pretty big sources of tax revenues, but that requires not giving them tax breaks in the first place. And so that, you know, again is sort of a benefit of the shift in power and the protests against data centers. So I don't think, you know, the essentially, I don't think you're going to stop data centers being built. Um, I think Maine just last week or two weeks ago passed a law saying no data centers will be built there. It doesn't matter to the hyperscalers, they'll just go and build in Texas, you know, it doesn't matter to them. Right. Um so the real benefit of these pushes against data centers is that negotiation, that leverage, that bargaining power that comes with it.

SPEAKER_00 15:08

Right, right. So it's less about achieving necessarily the exact goal of banning data centers, but about in that process creating a space where negotiation can happen.

SPEAKER_01 15:20

Yeah, I think so. I think that's the the best benefit from them.

SPEAKER_00 15:24

Yeah. Um so in your previous book, Platform Capitalism, you talk about um how data extraction is sort of like the core business model of these platforms. Um platforms being large like social media platforms, but also just like platforms generally over the internet. Um, I'm curious, you know, if you could explain a bit how, you know, the this now existing trend of uh of generative AI, I don't know if it's a trend, but um AI I think is uh it's probably here to stay. But how does that uh extend, transform, or break with that framework? Like does it challenge these platforms in some way, or does it kind of just plug into it um to extend it?

SPEAKER_01 16:13

I mean, one way I've been joking about Silicon Empires as a book is to say that it's platform capitalism 2.0, but there's no platforms and far less capitalism. Um I think I think a lot of the AI uh companies that we're looking at are not platforms in the sense of like a social media company or a gig economy company. You know, these companies where there was multi-sided markets, where there was network effects which emerged, these sorts of things don't really exist yet in the AI world. Um you can see that, for instance, in the way uh in which a company releases a new frontier model and everybody just leaps over to it. You know, there is no sort of um lock-in to a particular model, there is no network effects built around a particular system. Um the companies are trying to change that to become more platform-like, but it hasn't happened yet. I think data is really central though, but I think in a sort of interesting and slightly different way from what we saw in the sort of uh, you know, particularly social media companies. So social media was all about collecting personal data in order to build these targeted advertising systems, uh, building incredibly detailed profiles of individuals to be able to target them with these precise ads. And the role of data in AI systems is has become effectively just whatever data you can find, scraped from the internet, usually against copyright laws, toss it into these systems and train them on it. And that's sort of that's what's called pre-training with the AI systems. The thing which has happened over the past year, though, and has been leading to a lot of capability improvements in the AI systems recently, is the sort of post-training process, though, where you've got these models that have been pre-trained on internet data are now being trained using more specific data and usually around coding and mathematics and things like this. So using a lot of very sector-specific data to train these models to be really good at coding or really good at mathematics, or as I think we're going to see very soon, really good at biology and health sciences and financial services. So there's been a real focus not on personal data, as it was with social media, and not on collecting the internet, which is that pre-training sort of idea, because everybody's got that now. Instead, the real competitive advantage is coming from which companies have access to which proprietary data, sector-specific data. Uh, and there's a real pursuit of that right now, which is um far more narrowly targeted than that sort of initial GPT period.

SPEAKER_00 19:05

Do you think there's anything to say as well for maybe like the the profiles that AI model or like AI companies are able to make of users who are using their AI uh services as well as like a part of this? I think one of the things that um it's like at one in one way useful, but also kind of scary how much my cloud model knows about me because of all the questions that I ask, but it gives me better answers because it knows more about me. Um, but it's a it's a you know, it's a giant enterprise that I don't who who knows, you know, what happens in the future.

SPEAKER_01 19:42

So yeah, this this personal data that they can collect is I think one of the more worrying aspects, and it's more worrying to me than social media, because the effect of social media was to collect all this data to just serve you targeted advertising. The impact of collecting all this personal data in an AI system is to provide you with very targeted advice about everything that you want to do in your life, you know. Um, you know, if you're planning a trip, if you're asking about a health condition, if you're asking it to build an app, if you're asking it to do all sorts of different things, it's being shaped by these this personal profile of you. And it's a much more highly detailed profile than what social media companies ever had. Um, because it can infer and it can do all these sorts of things, it can combine all this knowledge and questions together in a way that just wasn't really possible before. So all of this I think poses really significant risks for the future. And I think politically, and maybe we'll we can talk about this a bit more later on, but politically, one of the things I think is really important to push right now is to ensure data portability in the future, which is like this sort of you know, seemingly insular technical topic that nobody would really care about. But actually, I think it's going to be central to ensuring that people aren't just captured by a particular ecosystem and that they continue to have options in the future, um, and and ensuring that it's not just a massive concentration of power.

SPEAKER_00 21:17

Right. Yeah, no, the I think this yeah, this question of interoperability, I think, is is is pretty key and is one that uh yeah, it's is hard, it's hard to convey, I think, beyond a technical audience sometimes, of kind of like wrapping their head around that concept and then wrapping around like why is that politically important or not. Um in terms of technical systems, yeah, I think I think I think it was Corey Doctor that made this term um adversarial interoperability, which I think was a was a fun term. And it's interesting because it's something that is uh the it's the norm in like the crypto world, for better or worse. Um and it has uh yeah, it does. it there's a lot of data portability, there's a lot of interoperability. You're never attached to a single service um per se uh in terms of like data.

SPEAKER_01 22:09

But um yeah the uh it it it's it doesn't really necessarily get that much attention I guess um in many ways um I mean one way I always try and help with my students to understand interoperability is to point to like telephone networks and you know here in the UK you've got Vodafone you've got EE you've got O2 as providers and they all work together you know you don't have to pay extra to call a friend that's on a different provider. That's sort of you know the benefits of interoperability made made visible.

SPEAKER_00 22:43

Right. Imagine if you had to have a phone number that was different based on the provider you got and then you can only call within that same provider versus you know not caring you know what provider your friend has.

SPEAKER_01 22:57

Yeah.

SPEAKER_00 22:58

Although that is that is kind of like the the that the tactic of Apple with like um with with iMessage is definitely trying to to make some sort of separation with that yeah yeah exactly I've never had an iPhone so I'd never quite experienced it but um yeah apparently there's a lot of social stigma around having an Android phone because you're you appear differently in iPhones yeah yeah um so what do you think is a position for the left to take when it comes to AI in terms of of tactics I know this is probably like a very big question and I think there's a lot of like many facets to it but I'm curious you know high level what do you think you know we have issues I mean in particular the left cares about around labor displacement I think there's some disagreements on whether AI is actually how much labor is it actually displacing or not um and then I think in particular the thing that I'm interested in is whether or not it's a worthy use of time to be advocating for uh open source and local models which are you know two different things but uh I think kind of get bucketed together a little bit sometimes.

SPEAKER_01 24:14

Yeah so I think um you know there's not one solution to the various issues posed by AI. I think as a general sort of approach two of the key issues are uh avoiding the massive concentration of power and profit and resources and another one is making sure that the deployment of AI doesn't negatively harm workers and in an ideal world benefits workers. These are two sort of core principles I think that you know the left should be fighting for on the first one I think there's a lot of room for open source models for smaller models and also for pushing for interoperable standards and things like that. And I think the ecosystem so far has been quite interoperable and model context protocol or MCP is a sort of a good example of this. And the fact that Anthropic gave up ownership of it to the Linux Foundation I think is you know it's it's a good sign because it doesn't mean that it it won't be closed down in the future. But there's no guarantee that the rest of the sort of protocols and scaffolding being built around AI models today, there's no guarantee that that stuff is going to be remain interoperable in the future that it will you know not become heavily tied to a particular model particular provider and that sort of thing. So I think there's a real necessity here to to build up not just open source models but also continuing to build open source harnesses you know interoperable protocols that anybody can play with you know MCP is the most popular one at the moment but there's lots of people critiquing it for you know a variety of reasons and it may not end up winning out in the end. But we need to ensure that any replacement continues to be open and interoperable. And all of that I think will be really have a real significant it'll be a constraint on the concentration of power and and wealth within the hands of just a few companies. So I think that's a really important gesture. And then on the side of you know making sure that AI is being used to not harm workers and ideally benefit workers this is where struggles over you know workplace struggles over how AI is being deployed is really important ensuring that it is not just becoming which I I think this is going to become a a battle over the next year being implemented as effectively a surveillance mechanism to train AI agents to effectively replace you which is there was a a news story from yesterday about meta doing this effectively you know monitoring everybody's keystrokes mouse movements where their eyes are going and everything. And all of that data is exactly the sort of data that you need to train an AI agent to replace those people so workplace struggles over this I think are going to become really prominent and really important to ensuring that you know workers aren't just cut out of the loop here. Alongside that building up a proper social system to address the fact that you know as I've argued for in the past ideally we have less work to do in the future we have more free time but we also need the social system that's there to provide people with the basic means of existence to be able to have a meaningful and substantial life and not just be sort of you know living in an unemployment line or a job center or something like that. So all of this, you know, there's a lot to do a lot of it is different you know different institutions necessary for it, different tactics, different strategies, but with those two sort of guiding principles of like avoid the concentration of power and benefit workers um that I think is the the way forward for the left.

SPEAKER_00 28:24

One of the things that I was you know kind of kind of joking but I think uh I gotta take it seriously more than I think about it is to like take any time an AI CEO says like how much more productive workers will be by adopting their AI system and then cutting the work week by that percentage.

unknown 28:46

Okay.

SPEAKER_00 28:46

Yeah I mean it'd be it would be nice yeah if we're getting if we're getting 20% you know increases in in uh in productivity then that's that's one day gone from the work week. They exactly maybe maybe we can make it to where it's specific to their company to make it like more uh more real for them so they can't like if it's going to be more productive then they then they lose that but um something like that I feel like needs to be it has to be done like to me it's like we should take what they're saying at face value and just take the logical conclusion of what that means and then see how they just call them out on their bluff or if it's not a bluff then great but if it's a bluff then you know that's on them.

SPEAKER_01 29:36

Exactly exactly um I think it's important to have these ideas in the conversation so it's not just a matter of like yeah so much of the conversation right now around AI and automation for instance is just like what jobs will remain how do we get people into those jobs without any questioning of the necessity of work and wage labor.

SPEAKER_00 29:56

Right yeah yeah yeah there's no questioning that we need to have you know a 40 hour work week and you have to work five days a week for full time yeah yeah um so yeah one of the things that I've also seen from some people that I thought was interesting um in relation to local models right so you have um by and large you know people are using models that are being held in big uh clouds the cloud players are also like a big um a big player in in this entire ecosystem and the ones arguably potentially benefiting the most or making the most money at least in the in the short term um because they're already big established businesses and they make it all happen but one of the things that I've seen people do is uh I mean one you have companies who are making open source models which means that anybody can take that model and they can try to run it either in their own cloud instance that maybe they pay for or also at home if they have the equipment to do so. And like the I've seen people dis it's called distilling where they take a model and they run it in certain ways in order to distill it into a smaller form so that it's more easily able to be run in uh local hardware or hardware that you're able to purchase you know uh at the market or or whatever. Um and it's interesting when I talk to those people when I've listened to them the thing that they're really worried about from their mind is they're they they really like AI they really like using it they really like the models that are being provided by these big companies but they are uh predicting that in the next you know maybe couple years or something like that the VC money is going to run out that they're going to uh no longer have the uh it's not it's no longer going to be um subsidized by venture capital money and then either prices are going to be jacked up by a lot or they're going to you know not provide free models for people to to use um and they find it like important I'm curious what your thoughts on like this particular part like I would say these are people who are not necessarily left wing or anything like that. These are just kind of like hackers who feel very strongly about like access to AI models. I don't know how to feel about it sometimes like is it okay for everyone to have a model in their pocket an AI model in their pocket? I don't know. Maybe it is you know other people will probably say maybe not because you can do all these like uh you know uh crazy potentially illegal things with with these models if if people have that much compute on them.

SPEAKER_01 32:49

Yeah yeah so I will say in terms of I so I wouldn't say there's VC money drying up because I think um the scale of AI is such that it's beyond actually what venture capital can can finance. So a lot of it's coming from the private capital markets a lot of it's coming from sovereign wealth funds and a lot of it's coming from big tech um revenues um I'm still somewhat skeptical that all this compute which is supposed to be built up will in fact be built out and that it won't be a bubble um particularly open AI's sort of committed spending um but at some point you know that that subsidization of AI costs will go away and I think we're already starting to see that right now particularly with Anthropic who's sort of having a shortage of compute at the moment so the most expensive models by far. Yeah yeah so prices are going up for them um there's a lot more rate limiting um there was a bit of a controversy yesterday that they were going to take away clawed code access for um one of the subscription tiers that had did have access before um all of that I think is indicative of what's going to happen in the future um even if you know these ideas sort of get rolled back in the moment I think there will continue to be a free tier but it will be particularly for open AI will be ad-supported so it'll be more like the Spotify sort of business model of a free access but you you get ads all the time and then the premium tiers that people pay for um that's where it all seems likely to go in terms of pricing. And then in that context I think open source models do have a really significant role to play um both as a potentially cheaper option but they can still be quite expensive because if you are you know to run these models is typically very expensive to do um because the most advanced open models you you need to have you know a compute cluster to run it all. And if you're running it at scale of course you need to be able to have access to data center levels of of compute. So it's still expensive but it does provide I think really importantly this sort of option that if the other ones become too expensive you can always default back to these open source ones and and run it yourself. So it provides a sort of like ceiling for where the prices can go in terms of um the the cost of inference and things like that. In terms of the sort of safety issues I mean I on one level I find it hard to imagine that like Claude Mythos for instance will ever be on my phone. But the progression of the technology is such that it does seem like maybe it could happen at some point. And I think you know once you get to that sort of level the cybersecurity issues of everybody having mythos on their phone is would be wild. And then of course you've got like the the sort of biosecurity issues that are going to become very prominent I think in the next year or two if just anybody can access these open models with the capabilities of creating novel viruses and things like that. That is all extremely worrisome. And I I don't have an easy answer to that. It's not it's not my focus of research. I I know there are people doing great work on this sort of topic but um it does seem like a real threat that needs to be addressed the capabilities of these models uh being just widely distributed and you know easily accessible to anybody and um without come some quite significant regulation of them.

SPEAKER_00 37:01

I mean I'm curious what your thoughts are on the whole like AI safety world and and discourse I mean the thing that I I had been kind of it it always a little bit weirded me out just because it was so tightly associated with effective altruism and like this rationalist type of philosophical sphere that I kind of found a bit strange. But at the same time you know uh in many ways they were asking themselves the questions that are now increasingly more relevant. I don't necessarily like the way that they answer those questions but they're asking they were asking the interesting questions before you know now a whole lot more people are are coming into that into that discussion. Yeah it's something that uh uh it's it I I have not been able to broach this topic with people on the left so much because I think there's like such a there's a bit of a gap of separation between people who are really thinking about AI and people who are involved in in left-wing politics.

SPEAKER_01 38:10

I think that's a real problem. I think yeah people on the left need to be more engaged with AI because it is it is a transformative technology and I think it's here to stay and I think it's gonna have pretty massive implications and if left voices are just not in that conversation then there's there's no real chance of you know changing the direction of any of these things. I I I so on AI safety specifically though I will say you know my my intuitions are very similar to yours like the associations with a particular crowd of people that you know on on political and aesthetic levels just didn't vibe with. It turned me off from AI safety for a long time as well but I think you can sort of separate out for instance these fears about superhuman AIs taking over the world you know paperclip maximizers and things I think a lot of this stuff relies on a certain set of assumptions about AGI that just are not likely to hold in practice. So those sorts of concerns I'm not as worried about as other people but the risk of creating you know cybersecurity weapons that can take down critical infrastructure or bioweapons which can knock out you know entire groups of people and potentially threaten humanity all of this seems incredibly possible to me and very worrisome. Like these are risks to humanity and risks to large groups of people that need to be addressed and that can't just be left up to you know the sort of benevolent whims of Dario Amade and things like that. It needs to have proper proper supervision of all of these things. And you know at an international level which seems so far off the table from where we are right now but it's absolutely essential to be able to guarantee that these things aren't being used in in ways which threaten large groups of people or even all of humanity.

SPEAKER_00 40:28

Yeah yeah and I you know extended beyond just like the the the labor issues because I think that's kind of like you know the that's more of the maybe that that particular problem set is maybe the one that I've seen the most engagement on the left but um other than that it's kind of um yeah been there's been some sort of gap um we're talking about these like geopolitical uh risks these larger risks and like kind of the context in which these risks are coming in is you have you know two dominant players uh like country wise at least who are building these a i models it's the US block which is you know uh for American companies anthropic openai etc uh google and then you have the Chinese uh bubble of AI companies and models that are being produced um from from there in the American model it's generally dominated by more closed source you have facebook or meta which has the some of the open source models and recently google released the the Gemma one I think openai has their their their nano one but the Chinese models are I think if I'm if I'm if I'm not wrong have been almost all entirely exclusively open source models um as far as what even Alibaba like all all these big companies largely just produce um if not solely if I'm not mistaken open source models um I'm just curious you know is it worth having a dog in this fight um there was like a a moment there's a moment that maybe the one one thing that I've noticed that at least the a lot of the on on Twitter the online you know Marxist Leninists uh and Maoists you know they love to mention Chinese AI uh models every once in a while but um yeah I'm curious what your what your thought is it worth you know is it better to to be in the Chinese bubble or the American bubble of of these AI models yeah I think better or worse I I there's trade-offs on both of them I think um I will say just on the open source issue there's a real notable shift in 2026 away from open source models so Meta's newest model is not open source and reportedly they they might open source it at some point but that's unclear um Alibaba which did have the most popular series of open source models the Quen models um seems to be definitively moving against open sourcing them now so their latest model was not open sourced um there's been some people leaving from the company because because of this transformation um and part of the part of the issue here I think is that the the training costs for these models are you know going up by orders of magnitude and open source might have made financial sense at the previous order of magnitude but perhaps perhaps not at the you know mythos level of magnitude um so it might be that the training for these things is becoming too expensive for open source to be a a viable financial argument.

SPEAKER_01 44:01

So there may be a real drawing up of open source models over 2026. And the sort of closing in of the capabilities that we've seen between the closed models and the open models, you know, I think open models around six months behind or something like that, that may start to broaden this year. Um, which I think is to everybody's detriment, you know, unless, of course, you're one of these big companies. Um my argument in sort of the the the the choice between China and the US is that we shouldn't have to make that choice. That there should be a third option of open source approaches. And they could be produced by Chinese companies, they could be produced by American companies, or they could be produced by Mistral and other companies as well. Um But a a sort of repository of open models, which any company or country or user could pick up and play with, along with a repository of all the sort of scaffolding that's being built up around these things. So like OpenClaw is a good example of it. Um, ensuring that all of the sort of components that you need to build an AI system today, you can go to an American provider if you want, you can go to a Chinese provider if you want, or you could build your own out of this collection of open material. Um, and I think that's what you know digital sovereignty projects should be working on right now is contributing collectively to this repository of open materials, um, precisely so that you don't have to make that choice between America and China. Um I think you know the way it's being divided up in the world right now is that for the most part, American Allies going with American AI systems, and China's been pushing their AI models, particularly within Africa, uh within Asia. Um, and I haven't heard much about Latin America, but I imagine there's some efforts there as well. Um, but they've pitched their models as being like cheaper to run, um, more accessible, um, you know, maybe not as capable as OpenAI and things like that, but um still highly capable and useful. Um, but you know, again, I think that that choice is it's a really dangerous choice to have to make. Uh, because it fosters these sort of AI race dynamics, it fosters competition and conflict between these two great powers. Um, and that is like one of the major things that I'm worried about is that, you know, we're not on the imminent steps of outright conflict between these two powers at the moment. But if you look at all the conditions of major wars in the past, we're sort of building up a lot of these conditions. You know, just the other day, Japan has decided that they're going to be able to export weapons again, uh, you know, first time since World War II. Uh Germany's building up its, you know, its its arms for the first time since World War II. Um, everybody's spending much more on militaries. Um, all these sorts of things, which were the lessons learned from World War I and World War II, are now sort of disappearing. And that for me is an extremely worrisome set of conditions being built up right now.

SPEAKER_00 47:24

Right. Yeah. There's something, at least for me, that felt um kind of uh intertwined with the rise of AI is increasing feeling of like, oh shit, I need to get my bag, or I I I need to accumulate right now before it's the end. Like, like before the you know the permanent underclass. You know, if I if I want me and my kids to not be part of the permanent underclass, I need to make my bag right now, make as much money as I can uh before everything blows up. And it's kind of um I can't tell if it's simply you know just the end of a certain or just contradictions just finally getting to to a certain point to where they crack and it just so happens to coincide with AI, or whether AI is kind of part of that because of the potential geopolitical risk of you know, this the idea of your you know enemy or frenemy having this type of technology um has just such huge political implications that you kind of want to um have some sort of buffer against.

SPEAKER_01 48:36

Yeah, yeah, I find this discourse really fascinating too, and I think I can understand sort of the intuitions behind it because I think it stems from seeing how rapidly the capabilities of this stuff have been growing, um, particularly the last you know, four months. Um, and you sort of take from that, you know, this extremely rapid change. You know, the coding industry in particular has entirely transformed its workflow in the past few months. Um you take those rapid changes and you sort of extrapolate from that and you say, well, that's gonna happen to every knowledge work job. And what are the impacts of all of this? And there's lots of plausible arguments that most people will not benefit from this. A handful of people will, and if you if if that is your thinking, then you know it's not it's understandable to want to be one of the few that benefits from it. So you get this like real, you know, all these people talking online about how these AI systems are making them more and more productive, but they find themselves busier than ever before because they're like, I need to have 20 agents going around and I need to, you know, be constantly telling my agents what to do, producing on my behalf, and precisely in order to not be that permanent underclass. Um, and I I I I think there's you know, I think this argument is wrong because I think the diffusion of the stuff and the transformation of the economy will be slower than what these visions sort of imply. But I think there's an understandable set of assumptions going into these sort of theories of a permanent underclass. Um, and I think for the left, the question has to be not how do you get your bag now, but how do we ensure that the permanent underclass doesn't arise? You know, how do we ensure that um and not just a handful of people?

SPEAKER_00 50:41

Yeah, totally. Um I I feel like part of it is I mean, we've just had so many. I mean, I I find it my impression is that it's mainly an American thing, maybe, like particular American type of cultural product, that so much of the the sci-fi from the United States has been kind of illustrating this, you know, like cyberpunk dystopias where there is just so many people living in, you know, usually the ground floor level of uh of the city or whatever, and then many, many layers uh levels higher. You have you know the business elites or whatever where they where they live and and work and play, where it's much nicer and and whatever else, and not full of trash. So I guess I think that's part of it. I've I've heard, I can't really like confirm for sure that in China it's quite a bit different. That there is, I think I've I've read that generally in the US there is there's more negative um uh like association with with AI and like the the thoughts of um it it you know proliferating, which I think makes a lot of sense in the context of the United States because there is like hardly a safety net because it's like so much more um difficult to live uh you know without having like you just don't expect state support for for most people, um, versus China where there is a stronger state, where there probably are better public services, where healthcare isn't like you know, you're paying out your ass in order to like get your finger checked or whatever. In China it seems to be a lot different, at least as far as what I can tell than what I've read, and I can imagine that just has a lot to do with the socioeconomic context of being in China where there's a stronger state and perhaps better uh state services than in a place like the United States.

SPEAKER_01 52:31

Yeah, yeah. I think there is a lot more optimism in China, and there seems to be, you know, surveys seem to bear this out. Um you can also look at you know this open claw phenomenon in China, this you know, raise your lobster sort of thing. I was gonna ask you about that as well, yeah. Yeah, which is fascinating, just absolutely fascinating.

SPEAKER_00 52:50

Um and you have your open claw set up?

SPEAKER_01 52:54

I did briefly. I did briefly. Um yeah, I messed around with it, and then I was like, ah, the security issues, like I would just have to spend so much time on setting it up properly in terms of security. I was like, this isn't worth my time. I'll I'll wait till something safer comes out. Um but yeah, it was fascinating to see it become a cultural phenomenon in China, and you know, it was a phenomenon in America as well, but highly niche, like very technical people um just infatuated with it. Whereas apparently in China it was, you know, grandmothers interested in how does this work? How do I get it to do things? Um so yeah, a different set of approaches to it. Um yeah, it's interesting about the underclass versus the overclass discourse. And I wonder how much of it is uh reflective of Silicon Valley in San Francisco as sort of local milieu that people are interacting with and seeing, you know, a quite clear class divide, uh, and sort of extrapolating from that to say, you know, this is this is the future. Um and in China, I mean, at least until the last, you know, the most recent generation, there's been a lot of social mobility, you know, people within a single generation moving up that socioeconomic ladder quite significantly. And so it's understandable that there's optimism about the future if that has been your experience of the past, that things get significantly better um decade after decade after decade. I'd be curious to see how long that holds out, because I think you know China's obviously running into um economic growth is slowing, it's got you know a massive gig economy. Um for older workers, it's a really, really tough labor market. And I don't even mean like 50 or 60 year olds, I mean like 40-year-olds. Um for a lot of people in China, it is not a system that is continuing to show those rapid improvements. So I I wonder how long that sense of optimism about AI will continue and whether or not that might change in say five years' time or something.

SPEAKER_00 55:12

I want to talk a bit about maybe to end it off the kind of third geopolitical maybe center uh of the world, which is the which is Europe, um, which, you know, by and large, the past 10, 20 years perhaps, has not been able to really Europe has not been able to like keep up in many ways as far as like technological advanced advancements as uh at least ones that are being made in in the United States in and in China. Um they've taken really like a passive role in in all of this, even though plenty of AI researchers came from came from Europe. Um but they've there's been you know I think just kind of now, like the past maybe a couple months or something like that, uh quite a lot of money that are now earmarked for AI infrastructure, some like 150 billion euros from different funds. I know Germany is spending a ton, France is spending a ton. Um I'm curious what your thoughts are on this you know part of uh of this you know fight, I guess we can call it. Um like is if you're European or live in Europe like I do, um is it worth supporting something like that? Uh or is it just like I think the the fear is just kind of like, you know, well, we'll just create the same types of companies that are in the United States but in Europe, and that's not really much much of a difference.

SPEAKER_01 56:37

So I think there's three sort of key reasons for something like AI sovereignty, which is what Europe wants and what a lot of countries want. One is to reduce your dependency on a foreign supplier, so to not be at the whims of Donald Trump or anybody else who might cut off your access someday. The other reason is to be able to have more control over these companies, to regulate them, to make sure that they act in the way that you want them to act. And, you know, it's very it's much more difficult to get American companies to act according to EU rules than it is for European companies. You know, the the the sort of the the levers of power over American companies are far less. Um, and then the third reason why you want AI sovereignty is to be able to capture some of this value being produced by AI to ensure that it's not just flowing to America and Silicon Valley and Shenzhen and everywhere. Um, you know, I think that needs to be at the heart of what uh Europe is trying to do. And I think my sense at the moment is that they they're aware of these issues, they want to do something about these issues, and in the wake of Trump's rhetoric and actions, um, they're far more willing to put the money into it than they have been over the past five, ten years. So that's a real significant shift. But the strategy to me seems to be a bit everywhere at the moment. So there's a desire to sort of follow in America's footsteps by building AI, what was it called? Super gigafactories or something. Um, you know, building massive compute clusters effectively. Uh they want to do that sort of thing, they want to be as part of that frontier game with OpenAI and with Google and with Anthropic. But that's an extremely expensive project to try and do. And it's one where, you know, you can imagine a situation where Europe spends $200 billion training a frontier model, and it's a great model, and it leads on all the benchmarks, and then three months later, something else better comes along, and everybody's like, well, who cares about that model? Um so it's an extremely expensive game, and it is a game which doesn't just involve a one-time investment, it's a long-term sort of thing, which requires huge amounts of money. And then, you know, the EU also has these sort of applied AI projects and strategies going on. Um but you know, again, if if the strategy is to be part of the frontier game and innovation, or is the game to be applications and diffusion? Is the game to be about like building fully sovereign systems? Which part of the AI stack are you most interested in? Um all of this stuff, it seems to me like the EU doesn't have a clear focus at the moment. Uh, and it's pouring money and strategic initiatives into everything, but without a sort of overarching logic for what it's trying to do. Um so I think it will have an impact. You know, this amount of money will do something, but it's not going to be nearly as successful as it could be if it was a more focused strategy on you know various elements.

SPEAKER_00 1:00:02

Right. I feel like part of the issue is like knowing where to play rather than just kind of like trying to compete at the the frontier. I mean, I think that's part of why China was so successful. They took this taking this more open source approach whenever they had you know the risk that or like the the issues with like getting NVIDIA chips, for example, that the e that that the US tried to stop, like finding ways to get to have AI without having as needing as much resources as the US has access to turned out to be a great, I mean, a great plan and strategy, and I think probably uh undercut a lot of the power that American AI companies were expected to have um over time. Yeah, with the EU, I'm not really sure where necessarily they should play in that in that mix. Maybe it's maybe it's AI models that are better for different languages in Europe. I don't know. Because it did get a big difference depending on the language that you that you uh talk to an AI in, is what I've heard.

SPEAKER_01 1:01:07

Well, my my one issue with that approach is I wonder how long that sort of difference will last. You know, a generation from now, GPT-6, for instance, might just be amazing at every language. And so all these specific language models, you know, devoted to a particular dialect and things, might just be obsolete at that point. Um so yeah, I'm not I'm not entirely convinced that that's a a great approach yet.

SPEAKER_00 1:01:37

Cool. Was there any were there any last words you would like to leave to leave the audience before we before we end it?

SPEAKER_01 1:01:43

I mean the one thing I've always been pushing, I think, in in in my writing and in interviews and things is just that like AI, contemporary AI is a transformative technology. Um the left needs to take it seriously. The left needs to think about how we want to use this technology um and how we want to ensure that it benefits the working classes and doesn't harm workers. Um, all of these things I think are areas where there hasn't been nearly enough thought and consideration given to yet. Um, but it's it's absolutely essential.

SPEAKER_00 1:02:20

Totally. I could not agree more. One last story I'll leave. I think, I mean, one, there's a ton of work to be done uh on this that just hasn't been. Um, but the one place I the other day I was I just Googled socialist AI just to see, you know, what was there, and I found a uh, I think it was some Trotskyist online sites, you know, kind of very classic, you know exactly what they're going to say in every article, and that Trotsky said this, you know, a hundred years ago or something like that. Um and they had their own uh, I think it was like socialism AI or something like that, that was just an AI model that was, I guess, uh it had post-training on like Trotskyist ideology, and then had like a rag, you know, retrieval augmented something, um, to pull specifically uh articles from the website to show us proof for like whatever query you gave. And then they were kind of like charging it like a SAS, you know, pay ten dollars a month for you know your Trotsky, your own Trotskyist AI to you know spread the gospel of of the working class. Um, which I thought I thought was like on one hand, it's hilarious. I and I think it's like not the right approach to go about AI, um, but at least they tried. At least they tried something, and now we can say at least I feel pretty definitively like there are probably better ways we can do this at this point.

SPEAKER_01 1:03:59

It's a nice little experiment though.

unknown 1:04:02

Yeah.

SPEAKER_00 1:04:03

Well, awesome. Thanks so much, um, Nick Sernicek. Uh check it out. His book is Silicon Empires, it's out online. Um, I'll have links to it in the in the description as well. Um, yeah, appreciate you and having having your voice in the in the mix. If you like what I'm doing here, consider supporting the show on Patreon. Your contributions help me keep doing this work and dive deeper into the politics of decentralized technologies. I promise you absolutely zero financial returns, no airdrops, and your investment may go to zero. But you will get good content. Check out patreon.com slash the blockchain socialist to support the show.