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.
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Transcript
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 wanna 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 a advice about everything that you want to do in your life. There was a a a new 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.
Hello, everyone. You're listening to the Blockchain Socials podcast. And I'm Josh, and I'm here today with a, for the second time in my podcast history, Nick Sernick, who you may know potentially as the author of a few different books, including Platform Capitalism, The Accelerationist Manifesto back in 2013, back in the day, 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 for the second time.
Thank you very much for having me back.
I figured it'd be great if 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 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, 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. Yeah. Would you like to lay out that argument for us?
Yeah. So this argument in part comes from, a series of sort of defenders of big tech companies, 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 then, in fact, if you look at the history of technology, whenever there's a sort of major technological shift, then 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 20, and give rise to all these new new competitors. And I 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. OpenAI and Anthropic are the two most notable ones. But as much as powers 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. It's not disrupting them. And, 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. Mhmm. They are, you know, one of the three top companies in the world pushing at the frontier of AI. They are dominating the space in many many ways. So So it hasn't been a disruption which has affected their power in any significant way.
Yeah. I definitely remember, there was a moment maybe a couple of, maybe a year or two two years ago where, I mean, the main two companies we were talking about were OpenAI and Anthropic as having the two, you know, biggest frontier models or the most the the strongest frontier models. But then and everyone was kind of thinking like, yeah, that this is going to replace search. Google is kind of, they're kind of screwed right now. But they caught up extremely fast. I I mean, it was, like, for, you know, within a few months, you know, at least, there was they they had a AI search result 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, 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 a 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. So that has caught up they have caught up incredibly fast. Fast. And I guess, yeah, there's, one of the things, you know, that I think people are talking about a little bit right now, is maybe that big tech is more or less okay, but all the SaaS companies are kind of screwed because now anybody can kinda, like, vibe code their own their own version of, you know, whatever SaaS products that that they're using. Mhmm. 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. But, yeah, I think I think it does drive things cheaper at the very least.
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. And you ended up with extremely bloated pieces of software that you're paying for despite only needing maybe a handful of those features. 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. I wanna touch on that maybe maybe a little bit later, but I'm curious just to to 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 or 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, at least in terms of, like, a general generalized, like, types of tooling. 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?
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. 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 in various services, including AI search, including the Gemini chat bot, 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. You know, incredibly capable piece of hardware that is, at least in terms of cost and performance, is on par, many many accounts say with with NVIDIA stuff. And then you've got the infrastructure, which you know, I was reading 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 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 is really known for is, you know, really fast inference, really cheap inference. You know, things like Gemini Nano are extremely cheap and, very very quick. And then you contrast that with something like Anthropic, where Anthropic at the moment is doesn't have enough compute and doesn't you know, because it's split across TPUs and Trainium 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, you know, cutting people off from some of its services. And, that is the sort of benefits of vertical integration that Google has, and others just don't to the same degree.
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 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, I mean, for the average person, especially just because you get so much just with, like, one thing. 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, largely being pushed by more progressive minded people has been basically we have to ban AI because it's, because it's bad, because of this, power, grab that is effectively happening, 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. I'm curious what your thoughts are at least on, like, these big bigger kind of, propositions that are being made to public.
I think a lot of these sort of banning efforts are political nonstarters. 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. A ban just seems like 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 these 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. Who cares if it has an impact on energy prices or anything like that? You know, just you can have it because we want you to we wanna be able to say we have a billion dollar investment coming in, regardless of all that money just goes to Jensen Huang's pockets. But 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. You have to build, you know, a certain proportion of renewable energy. You have to do all these things that are 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 the protests against data centers. So I don't think, you know, essentially, I don't think you're gonna stop data centers being built. 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 hyperscalers. They'll just go and build in Texas. You know? It doesn't matter to them. Right. Right. So the real benefit of these pushes against data centers is that negotiation, that leverage, that bargaining power that comes with it.
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.
Yeah. I think so. I think that's the the best benefit from them.
Yeah. So in your previous book, Platform Capitalism, you'd talk about, how data extraction is sort of the core business model of these platforms. Platforms being large, like social media platforms, but also just like platforms generally over the Internet. I'm curious, you know, if you could explain a bit how, you know, the this now existing trend of, of generative AI. I don't know if it's a trend, but, AI, I think, is, it's probably here to stay. But how does that, extend, transform, or break with that framework? Like, does it challenge these platforms in some way, or does it kinda just plug into it,
to extend it? I mean, one way I've been joking about Silicon Empires as a book is to say that it's platform capitalism two point o, but there's no platforms and far less capitalism. I think I think a lot of the AI 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. You can see that for instance in the way, in which a company releases a new frontier model and everybody just leaps over to it. You know, there is no sort of lock into a particular model. There is no network effects built around a particular system. 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, you know, particularly social media companies. So social media was all about collecting personal data in order to build these targeted advertising systems. Building incredibly detailed profiles of individuals to be able to target them with these these precise ads. And the role of data in AI systems is has become effectively just whatever data you can find, scrape 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 pretraining 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 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 gonna see very soon, really good at biology and health sciences and financial services. So there's been a real focus not on not on personal data as it was with social media and not on collecting the Internet, which is that pretraining 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. And there's a real pursuit of that right now, which is, far more narrowly targeted than that sort of initial GPT period.
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, services as well as, like, a a part of this? I think one of the thing that, it's, like, at one in one way useful, but also kind of scary how much my Claude model knows about me because of all the questions that I ask. But it gives me better answers because it knows more about me. 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. So, yeah, there's 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? 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, 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, and and and ensuring that it's not just a massive concentration of power.
Right. Yeah. No. The I think this yeah. This question of interoperability, I think, is is is pretty key and is one that, yeah, it's it's hard it's hard to convey, I think, beyond a technical audience sometimes. It's kinda like wrapping their head around that concept and then wrapping around, like, why is that politically important or not. In terms of technical systems, yeah, I think I think I think it was Cory Doctorow that made this term, adversarial interoperability, which I think was a was a fun term. And it's interesting because it's something that is, the it's the norm in, like, the crypto world, for better or worse. And it has, yeah, it does There's a lot of data portability. There's a lot of interoperability. You're never attached to a single service, per se, in terms of, like, data. But, yeah, the it it it's it doesn't really necessarily get that much attention, I guess, in many ways.
I mean, one way I always try and help with my students to understand interoperability is to point to, like, telephone networks. And, know, here in The UK, you've got Vodafone, you've got EE, you've got o two 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 sort of, you know, the benefits of interoperability made made visible.
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 Yeah. Not caring, you know, what provider your friend has. Yep. Although that is that is kind of like the the the tactic of Apple with, like, with with iMessage was definitely trying to to make some sort of separation of that.
Yeah. Yeah. Exactly. I've never had an iPhone, so I'd never quite experienced it. But, yeah, apparently, there's a lot of social stigma around having an Android phone because you you appear differently in iPhones.
Yeah. 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. 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, open source and local models? Sure. You know, two different things, but, I think kind of get bucketed together a little bit sometimes.
Yeah. So I think, you know, there's not one solution various issues posed by AI. I think as a general sort of approach, two of the key issues are, avoiding the massive concentration of power, 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 and 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. 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 think this is gonna become 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 a news story from yesterday about 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 AI agent to replace those people. Mhmm. So workplace struggles over this, I think, are gonna 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 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, that I think is the the way forward for the left.
One of the things that I was, you know, kind of kind of joking, but I think, 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.
Okay. Yeah. I mean, it'd be it'd be nice. Yeah.
If we're getting if we're getting 20%, you know, increases in in, in productivity, then that's that's one day gone from the work week. They Exactly. Maybe maybe we can make it towards it's specific to their company to make it, like, more, more real for them so they can't like, if it's going to be more productive, then they then they lose that. But, something like that, I feel like it 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 clue 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.
Exactly. Exactly. 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?
Right. Yeah. Yeah. Yeah. There's no questioning that we need to have, you know, a forty hour work week and you have to work five days a week for full time. Yeah. So, yeah, one of the things that I've also seen from some people that I thought was interesting, in relation to local models. Right? So you have, by and large, you know, people are using models that are being held in big, clouds. The cloud players are also, like, a big, a big player in in this entire ecosystem, and the ones arguably potentially benefiting the most or making most money, at least, in the in the short term. 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, 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 it's called distilling, where they take a model and they run it in certain ways in order to distill it into a a smaller form so that it's more easily able to be run-in, local hardware or hardware that you're able to purchase, you know, at the market or or whatever. 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, predicting that in the next, you know, maybe couple years or something like that, the VC money is going to run out. That they are going to, no longer have the, it's not it's no longer going to be, subsidized by venture capital money. And then either prices are going to be jacked up by a lot or they are going to, you know, not provide free models for people to to use. 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, you know, crazy potentially illegal things with with these models if if people have that much compute on them.
Yeah. Yeah. So I will say in terms of so I wouldn't say there's VC money drying up because I think 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, revenues. 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, particularly OpenAI's sort of committed spending. 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. There's a lot more rate limiting. There was a bit of a controversy yesterday that they were going to take away cloud code access for, one of the subscription tiers that had did have access before. All of that, I think, is indicative of what's going to happen in the future, 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 OpenAI, it 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. 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, 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, 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 all can always default back to these open source ones and and run it yourself. Mhmm. So it provides a sort of like ceiling for where the prices can go in terms of, the the cost of inference and things like that. In terms of the sort of safety issues, I mean, I 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, it does seem like a real threat that needs to be addressed. The capabilities of these models, being just widely distributed and, you know, easily accessible to anybody and, without some quite significant regulation of them.
I'm 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 kinda found a bit strange. But at the same time, you know, 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, 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. 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. Yeah. 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. Mhmm. 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. Right. 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.
Yeah. Yeah. And, 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, other than that, it's kind of, yeah, been there's been some sort of gap. We're talking about these, like, geopolitical, 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, like, country wise, at least, who are building these a m AI models. It's The US block, which is, you know, for American companies, anthropic, open AI, etcetera, Google. And then you have the Chinese, bubble of AI companies and models that are being produced, 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, as far as what even Alibaba, like all all these big companies largely just produce, if not solely, if I'm not mistaken, open source models. I'm just curious, you know, is it worth having a dog in this fight? There was, like, a a moment there was a moment that maybe one one thing that I've noticed that at least the a lot of the on on Twitter, the online, you know, Marxist Leninists, and Maoists, you know, they love to mention Chinese AI models every once in a while. But, 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 and there's trade offs on both of them, I think. 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. Alibaba, which did have the most popular series of open source models, the QEN models, seems to be definitively moving against open sourcing them now. So their latest model was not open sourced. There's been some people leaving from the company because because of this transformation. And part of the part of the issue here, I think, is that the the training cost 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. So So it might be that the training for these things is becoming too expensive for open source to be a viable financial argument. So there may be a real drawing up of open source models over 2026. And the sort of closing in of capabilities that we've seen between the closed models and the open models, you know, I think open models are around six months behind or something like that. That may start to broaden this year, which I think is to everybody's detriment, you know, unless, of course, you're one of these big companies. 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. But a a 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. 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. 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. Precisely so you don't have to make that choice between America and China. 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, within Asia. And I haven't heard much about Latin America, but I imagine there's some some efforts there as well. But they pitched their models as being, like, cheaper to run, more accessible, you know, maybe not as capable as OpenAI and things like that, but, still highly capable and useful. But, you know, again, I think that that choice is it's a really dangerous choice to have to make, because it fosters these sort of AI race dynamics. It fosters competition and conflict between these two great powers. 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 Mhmm. 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, you know, first time since World War two. Germany is building up its, you know, its its arms for the first time since World War two. Everybody's spending much more on militaries. All these sorts of things which were the lessons learned from World War one and World War two are now sort of disappearing. And that for me is an extremely worrisome set of conditions being built up right now.
Right. Yeah. There's something, at least for me, that felt, kind of, 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, before everything blows up. And it's kind of, I can't tell if it's simply, you know, just the end of a certain or the 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 is the idea of your, you know, enemy or frenemy having this type of technology, has just such huge political implications that you kinda want to, have some sort of buffer against.
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, particularly the last, you know, four months. And you sort of take from that, you know, this extremely rapid chain. You know, the coding industry in particular has entirely transformed its workflow in the past few months. Yeah. 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 wanna 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 Right. I need to have 20 agents going around and I need to, you know, be calmly telling my agents what to do, producing on my behalf and precisely in order to not be that permanent underclass. And I I I think there's, you you know, I think this argument is wrong because I think the diffusion of this 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 these sort of theories of a permanent underclass. 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, and not just a handful of people? Yeah. Totally.
I I feel like part 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 the, 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 of the city or whatever. And then many many layers, 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 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's there's more negative, like, association with with AI and, like, the the thoughts of, 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, difficult to live, you know, without having like, you just don't expect state support for for most people, versus China, where there is a stronger state, where there probably are better public services, where health care 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, state services than in a place like The United States.
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. You can also look at, you know, this open cloth phenomenon in China, this, you know, raise your lobster sort of thing. It's gonna ask you about that as well. Yeah. Yeah. Which is fascinating. Just absolutely fascinating. And did I actually Did you open the glass it up? Up? I did briefly. I did briefly. Yeah. I messed around with it, and then I was like, the security issues, like, I 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. But, yeah, it was fascinating to see that 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, just infatuated with it. Whereas, apparently, in China was, you know, grandmother's interested in how does this work? How do I get it to do things? So, yeah, a different set of approaches to it. Yeah. It's interesting about the underclass versus the overclass discourse. And I wonder how much of it is reflective of Silicon Valley and San Francisco as sort of local milieu that people are interacting with and seeing, you know, a quite clear class divide, and sort of extrapolating from that to say, you know, this is this is the future. 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, decade after decade after decade. I'd be curious to see how long that holds out because I think, you know, China is obviously running into, economic growth is slowing. It's got, you know, a massive gig economy. For older workers, it's a really, really tough labor market. And I don't even mean, like, fifty or sixty year olds. I mean, like, 40 year olds. For a lot of people in China, it is not a system that is continuing to show those rapid improvements. So I 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.
I wanna talk a bit about, maybe to end it off, the kind of third geopolitical, maybe, center, of the world, which is the which is Europe, which, you know, by and large, the past ten, twenty years, perhaps, has not been able to really Europe has not been able to keep up in many ways as far as, like, technological and advancements as, at least ones that are being made in in The United States in and in China. They've taken really, like, a passive role in in in all of this, even though plenty of AI researchers came from came from Europe. But they've there's been, you know, I think just kind of now, like, the past, maybe, couple months or something like that, quite a lot of money that are now earmarked for AI infrastructure, some, like, €150,000,000,000 from different funds. I know Germany is spending a ton. France is spending a ton. I'm curious what your thoughts are on on this, you know, part of, of this, you know, fight, I guess, you'd call it. Like, if if you're European or live in Europe like I do, is it worth supporting something like that? 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. 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, you 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 the leaders of power over American companies are far less. 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. So, you know, I think that needs to be at the heart of what 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, 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 is it called, super gigafactories or something. You know, building massive compute clusters effectively. They want to do that sort of they wanna 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 $200,000,000,000 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? So it is 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, EU also has these sort of applied AI projects and strategies going on. 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? All of this stuff, it seems to me, like, the EU doesn't have a clear focus at the moment, and it's pouring money and strategic initiatives into everything, but without a sort of overarching logic for what it's trying to do. So I think it it will have an impact. You know, this amount of money will do something, but it's not gonna be nearly as successful as it could be if it was a more focused strategy on, you know, various settlements.
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, at 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, undercut a lot of the power that American AI companies were expected to have, 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 if you because it did get a big difference depending on the language that you that you, talk to an AI in is what I've heard. 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 six, 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. So, yeah, I'm I'm not I'm not entirely convinced that that's a a great approach yet. Cool. Was there any were there any last words you would like to leave to leave the audience before we before we end it? 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. The left needs to take it seriously. The left needs to think about how we want to use this technology, and how we want to ensure that it benefits the working classes and doesn't harm workers. All of these things, I think, are areas where there there hasn't been nearly enough thought and consideration given to yet. But it's it's absolutely essential.
Totally. I could not agree more. One last story I'll I'll leave. I think I mean, one, there's a ton of work to be done, on this that just hasn't been. 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, 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. And they had their own, I think it was, like, socialism AI or something like that That was just an AI model that was, I guess, it had post training on, like, Trotskyist ideology and then had, like, a rag, you you know, retrieval augmented something, to pull specifically, 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 sass. You know, pay $10 a month for, you know, your your own Trotskyist AI to, you know, spread the gospel of of the working class, 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. 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. It's a nice little experiment, though.
Yeah. Well, awesome. Thanks so much, Nick Sverdacek. Check it out. His book is Silicon Empires. It's out online. I'll have links to it in the in the description as well. 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/theblockchainsocialist to support the show.
