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[00:02]
A
Hello and welcome to the Vergecast, the flagship podcast of Kimmy K3. I'm your friend David Pearce and today on the show we're going to talk about AI models. We've been talking about AI models a lot on the show for the last few weeks. In the wake of the OpenAI hack of hugging Face and all of these new conversations about AI safety and AI deployment and open weight models and the race with China and how we think about and operate AI in general, it all feels like it's coming to a head in some way right now. So the Verge is Robert Hart is going to come on and he's going to explain to us what's actually going on here. There's a of new vocabulary with these bottles and a lot of new questions about how they work and how they're made. He's going to make sense of all of it for us. I personally am very excited to have somebody finally make sense of all of it for me. We're going to get to that in just a second, but first, here's everything else happening on the Verge today.
[00:48]
B
I'm Jake Castranakis and this is 90 seconds on the verge for August 4, 2026. Microsoft just started bringing original Xbox games to the PC the other week, and now it's planning to bring Xbox 360 games as well. My colleague Tom Warren has the scoop on a memo that Microsoft sent around to developers asking them to opt in to the new program. Microsoft will handle all the emulation hurdles, even customer support. All developers have to do is approve their games for sale and set a price. Microsoft's argument is why not do it? It's free cash. The rollout is supposed to begin next year. Next up, Apple abruptly pulled Telegram from the App Store last night before storing it. Less than an hour later, Apple told various news outlets that it pulled the app due to the presence of csam. Then it restored the app after Telegram Telegram removed the content and banned the person who posted it. The whole incident is very odd, and it's actually the second time it's happened. Apple pulled Telegram in 2018 for the same reason, and again restored it within hours after this latest incident. Telegram seems downright mad. Telegram spokesperson Remy Vaughn told us that Apple was wrong to pull the app down. Finally, my favorite gadget of the day. My colleague Andrew Lechevsky spotted some new camera batteries from Falcam that have built in support for Apple's Find My Network to help you track down missing gear. I love this. This seems so much more convenient than attaching an airtag to every camera you own. It's only available for Canon and Sony right now, but Nikon and Fuji are set to be in the works. They're a little pricey though, at up to $70 a piece. You can read more@theverge.com, that's 90 seconds of the Verge for August 4, 2026.
[02:19]
C
Support for this show comes from Odoo. Running a business is hard enough, so why make it harder With a dozen different apps that don't talk to each other? Introducing Odoo. It's the only business software you'll ever need. It's an all in one fully integrated platform that makes your work easier. CRM, accounting, inventory, e commerce, and more. And the best part, Odoo replaces multiple expensive platforms for a fraction of the cost. That's why over thousands of businesses have made the switch. So why not you try Odoo for free@odoo.com that's o d o o.com
[02:59]
D
pros trust the home Depot for heavy duty storage solutions for any job site or garage Right now get up to 15% off select storage and organization Impact and water resistant totes and shelving built to hold up to £2,500. Storage systems have space for all your tools and protect them in the garage, on the job site and everywhere in between. Save time and maximize efficiency with adjustable shelving customized to your business's needs. Shop and save on pro grade storage. The Home Depot how pros get more
[03:28]
A
done all right, let's talk AI models. Joining me now, Verge reporter Robert Hart. Welcome to the show. First time on the Vergecast?
[03:35]
E
Yeah. Longtime listener, first time joiner.
[03:37]
A
So very happy to have you here. And I've brought you here to do maybe the wonkiest thing we have done on this show in some time. We're going to do a little bit of analysis, a little bit of explainer, but we're in a really interesting and complicated moment with AI that is about technology, it's about politics, it's about feelings. And I want to just piece through it. You've done a really good job covering this on the site and I want to, I want to just walk through some of what's going on right now, but I think we need to start with some fairly straightforward explanary stuff because I think a lot of very smart people are getting this stuff wrong and I want to just talk through it a little bit. So just from a very basic level, help me understand what an open weight model is and why it matters as a concept. Right now.
[04:22]
E
Yeah. So, I mean, to get even more annoyingly wonky, it's perhaps important to start with what it's not. And I think a lot here is caught up in kind of open source, and that's what we know traditionally from software, where it's pretty free, it's distributed, you can change it, you can make money from it, as long as you also share it freely. Now, open weight models are not quite that open, and so wonky. Part number two is they are only really open on a thing called weights, which are kind of those numerical parameters. Parameters, the sort of knobs and buttons that an AI has during training that kind of helps it tick, process information, all of that. And that's the only bit that's really made open here. So you can download it, you can build on that, you can use it, but it is not the same as, say, an open source where you can kind of reconstitute it from scratch.
[05:11]
A
Right. Have you come up with a good metaphor for how to describe weights? It's a really messy but really important term, and I think it's really hard to visualize. Like, open source, I actually think would be very easy to understand. Right. You would see all the training data, you would see how they train, trained it, you would see what the output looked like. You'd be able to sort of peel the thing apart and understand every piece that went into it. Like you said, open weight models are very deliberately. Not that they show this one thing. And I have not come up with a great way to explain it to people who are not sort of deep in the weeds of AI technology. Have you come up with one or heard one that you like?
[05:45]
E
Not really, but on the spot, I guess. I mean, please. I almost imagine, you know, when you kind of have a piece of wood and you run an electric current through it and it makes that sort of forked pattern. I almost imagine the weights as the kind of resultant image that might come off in that it kind of is something that is the result of something. You couldn't make it from scratch without replicating everything precisely, including the wood, but also the electric, even the weather. But it's still quite useful in terms of you can sort of see the paths, you can trace it. And it's not a perfect one by any means, but it's kind of a, I think, an idea of kind of what weights might be in this sense, in that it's the product of something, but it can still be really useful if you can build or modify or change.
[06:32]
A
I like that you couldn't make it yourself without knowing exactly what went into it. But even just knowing what it looks like, you can now go and redraw the thing, which ends up being really important. Which brings me to the next thing I just want to sort of quickly explain before we get into this here, which is who cares what, what can I do if I'm a developer or a government or somebody paying for one of these models with an open weights model that I can't do with a closed weights model like we, we've seen from anthropic and OpenAI. Like what is the actual sort of user advantage of an open weights model?
[07:08]
E
Quite a lot, really. I mean it's a lot more flexible. You have a lot more freedom to do largely whatever you want with it. You're not reliant on sending them your data, which is a big thing for a lot of different companies. You could presuming you have the infrastructure, you could run it yourself, you can tweak it with your own data. So you've kind of got a bit more of a personalized model. And I think important for a lot of the debates we're seeing now as well, is that they're a lot less monitorable and it's a lot harder for providers, say anthropic OpenAI in these cases to put in the safety rails that might stop it from doing something that you would want it to do.
[07:48]
A
Okay, so maybe in that sense the comparison to open source actually is useful in that it doesn't function the same way, but your ability to take it and modify it and use it in your own server array, that is very much the same as if it were pure open source software. You don't get to understand the inner workings of the model in the same way, but you get to use it and adapt it in much the same way. Is that a reasonably close comparison there?
[08:16]
E
Yeah, I mean, there are a lot more restrictions that they might put on it. So some of the ones that are coming out now, they'll have licenses that you have to pay them over certain thresholds. But yeah, it's a lot more flexible in that way.
[08:28]
A
This is probably an overgeneralization and I want you to correct the extent to which this is an overgeneralization. But it seems like China has embraced the idea of open it and wait models in a big way. And the US particularly, the sort of frontier labs that we talk about all the time, anthropic OpenAI, Google have not. A, is that a fair characterization? And B, why do you think it's
[08:49]
E
broken down like that to again get annoyingly wonky. It is a bit more complicated than that, but by and large it is a reasonable characterization. The most prominent US models, kind of, as you said, Google, OpenAI anthropic, their Frontier models are all proprietary, they're all closed systems. And in China, by and large, a lot of them are open weight. There are exceptions obviously. So Alibaba in China, for example, until its most recent release, a few earlier this year, and its Frontier scale were proprietary. It kept them closed and it evidently changed its mind this week. And in the US there is still a big ecosystem of open weight players. I mean, Meta is the most obvious that comes to mind, who've been making strides recently. But also Google. I mean, it's not the top tier Gemini ones, but their Gemma models are quite popular and there's sort of a huge spectrum in between that, if you
[09:47]
A
grant me the huge generalization. Because I think at some point at the absolute frontier, I think that the debate we're having right now is between companies like Moonshot and like you said, Alibaba in China and the three major labs here in the U.S. is it purely sort of a political difference that is why China would want its models to be open and the US would want them to be closed? Or are we talking about like communism versus capitalism here? Like what, what, what do you make of the sort of philosophical difference between those things?
[10:17]
E
I wouldn't go as far to say it is quite a deeply entrenched divide like that, but I think it is quite tempting to see it like that. I think partly it is business pragmatism in China. It is a way with all the restrictions that have been kind of put on them by the US so they've not had access to the top tier chips, for example, it is harder for them to innovate at the frontier. This is potentially a way of them doing so, or of at least kind of working towards that. But also from a business strategy point of view, there's a huge sort of array of advantages with going open. I mean, it's cheaper by and large for developers to run. It's a much lower barrier to entry. There are also the elements we said with privacy or kind of running things on your own system, you can really grow that ecosystem rapidly. And it is kind of almost a projection of soft power if your stuff is becoming almost the default. And the Chinese models are being very widely used globally, American companies, Western companies would be very hesitant to send all of their data to China or to servers hosted in China. If it's open, you can get around that. And so it's also quite a nice gateway for them to stay active in these markets.
[11:35]
A
Yeah, that makes sense. So all of this has been sort of burbling in the background for a long time and has really come to the forefront of the whole AI discussion over the last couple of weeks. And it's happening right next to, I think we've talked about a bunch on the show, the OpenAI hugging face hack. Now Anthropic is coming out saying, oh, look, we just noticed we've also been hacking everybody. There is a real AI safety thing happening right now and open weight models have become a big part of this discussion kind of on both sides. In a strange way, everybody has feelings about open weight models, no matter how they feel about AI safety. Why has open weight become sort of a core part of the how do we make sure we're doing AI safely discussion? There are so many things this could be about, but it feels like the industry has decided that the debate we're going to have right now is about open weights. Why?
[12:33]
E
Yeah, well, I mean, one, and I'll gloss over this just due to the complexity, but there is the whole spectrum of the race with China in the States or the west in general, which means something a bit different to quite literally anyone who mutters it. But that's.
[12:50]
A
We have to beat China at everything is just a fact of life in America right now, for reasons I continue to largely not understand. But, yeah, granting that premise, what else
[12:58]
E
is going on here? Yeah, so there's that, which obviously is one reason. So the open weight models, they're releasing. Let's go. But then the safety part as well. So the main opposition, or one of the oppositions to the open models is that it's very difficult to both monitor how they're used, which OpenAI and Anthropic, they know how this is being used. It's hard to implement details like guardrails to stop it, like hacking or helping you build a bioweapon. The two kind of main concerns that the Frontier Labs say the fear has always been that releasing something very capable openly puts that in the hands of anyone. The weird part of this then, is OpenAI, it turned out one of its agents hacked Hugging Face. As we know, the difficulty with closed models is these safety rails as well. Hugging Face said in their report, oh, well, we couldn't actually use U.S. frontier models. These safety rails activated. And then they said they turned to one of the leading Chinese providers, zai, for their model to Help defend itself against one of the closed models that was attacking it, which kind of flips a lot of that on its head. And so this debate has now really become, I mean it is by definition a dual use technology. It can be used to hack, it can also be used to defend against hackers. And so that's kind of been at the core of this now is that for a long time the fear has been it would be used as a weapon. And funnily enough, the most high profile case recently is it's been used as a tool of defending.
[14:31]
A
Wait, so hold on. So let me, let me make sure I understand what happened here. Cause I actually think that that's really interesting. I hadn't quite thought about it that way. So, OpenAI closed model, very, very deliberately hard for anyone else to understand. Attacks. Hugging face. Hugging face says, we, we need a tool to stop this. We're going to deploy our agents to stop this agent runs into some safety restriction in the model that they're trying to use that another closed model that is like, no, I won't, I won't escape this sandbox and go try to fight this thing. So then they're able to use an open weight model because they're able to just essentially remove those guardrails or they don't exist in the first place because it is open and adaptable in that way. Am I understanding that right?
[15:19]
E
Yeah, in a very kind of. Yeah, in a broad sense, that's what a weird system. Yeah. And so that's, I think, why it has been so as an issue, it has so rapidly bubbled to the surface as something that's been simmering for a long time is because all of these tensions that were there suddenly became very real and very tangible in a way that ties together, as I said, all those fears with China. But also open versus closed. And then also the risks of these closed models of what they are actually capable of doing.
[15:54]
A
This strikes me as the sort of thing people are going to pretty quickly get like borderline religious views about that are going to be very hard to change. Because what you just described is two completely reasonable and totally mutually exclusive theories, Right. That one says this technology is too powerful, we can't put it in the hands of everybody or the bad people will use it and things will go horribly wrong and we're going to be stuck in this arms race, cat and mouse game for forever. The other side says actually that's already happening and the only way to stop it is to put this technology in the hands of everybody. There is no single overlap anywhere between those two things. And it feels like as these models get better and more capable, both sides are going to feel more right about their stance on these things. Like is there a way to reconcile in the middle of this fight?
[16:47]
E
Well, I think we'll see, but I think we have to. I mean, I think even the opponents of kind of. I mean the one big one that has been to watch has been a lot of these kind of open letters you've seen from the US tech industry. For example, the notable holdouts, at least at the start were the big three were Google OpenAI anthropic. To my knowledge, it's only anthropic that's a continued holdout on that. I might be wrong. They might have signed since. But Daria Amadeh has also published a quite lengthy blog post explaining the reasoning and says that we're not against open models, but we cannot only have open models. And I think that's probably an area that we will end up in. I think and it's also not to say I think a lot of this is focused on like I said, it was more complicated with there are open models in the U.S. china could also very easily close off some of its frontier models. And I think probably as they get more and more advanced and as sort of that ecosystem develops, I think there'll probably be a mix.
[17:49]
A
Yeah, because you have more economic incentive to close your models. Right. You can make, if you can say and demonstrate our model is the best, which I think anthropic relatively successfully has been able to do over and over for the last year or so. Every possible upside you want comes from closing off that model.
[18:12]
B
Right.
[18:12]
A
You can make it more expensive, you can limit who gets to access it. You get to be the arbiter of good and bad in that model. If you are the best, there are lots of good reasons to be closed that almost no one other than the very best model seems to have. And this is where we get wrapped up again in the everything is a race against China. If if in fact, you know, Kimmy is now a better model, like demonstrably better model and all of a sudden everybody wants it. Suddenly you flip from China wants to have openness and and to sort of infiltrate the US and make a lot of headway into businesses and start to capture some of these use cases that people can't afford from the all of a sudden you say well we have the best model, we're going to close it off and make a ton of money from it. That does feel Very possible to me at this moment.
[18:56]
E
Yeah. I mean, it feels largely what's been happening already is the kind of end of that. I mean, and also there is a safety element. I mean, Anthropic was explicitly founded, basically, it was unhappy with what OpenAI were doing.
[19:11]
A
And I think the argument against Anthropic in many ways at this point is that Anthropic believes that it is the only one who should be trusted.
[19:18]
B
Right.
[19:18]
A
Like this is what the government has been saying about Anthropic for sometime in the US is that this stance that Dario Amade is the only one who gets to decide what we do with AI and what we don't do with AI is ridiculous. And again, like you're, you're free to think whoever you want to be in charge should be in charge, but at some point either no one is in charge and we just like let chaos reign because that is the thing that will solve this, or someone has to be in charge. And I feel like Anthropic has been the one most loudly being like, it's fine, the answer's us, we've got it. And that makes a lot of people really angry.
[19:50]
E
Yeah, I mean, absolutely. I think their response to the hugging face incident is actually quite telling. I mean, to me it felt quite petty. That's maybe a controversial view, but it's like, hey, our models can hack things too. And then you look at kind of the details of it and I mean, their blog detailing this, it quite literally ends in a four bullet point list as to why what happened with them was better than what happened with OpenAI, which. Cool. I mean, we're all adults here. Great. It just felt very juvenile, especially when, I mean, OpenAI hacked its way out. To me, Anthropics was equivalent. They kind of left the door open. Like it was a very. Like they're saying we're the good guys and their behavior doesn't seem to meet that bar time and again. But they're also, though I should say them and OpenAI are doing more than a lot of other actors in this field as well. It just doesn't necessarily meet the expectations they set for themselves.
[20:55]
F
Support for the show comes from Framer. Framer is a complete website platform, not just the builder, so teams can launch and keep improving their sites in one place. Thousands of businesses, from early stage startups to Fortune 500s are choosing to build their websites in framers, where changes take minutes instead of days. Agents solve the gap between AI generated ideas and production ready website work. Agents and Framer work alongside teams to streamline collaboration on the same canvas, build custom code components, create and manage CMS content, optimize SEO settings, and ship everything all in one place. Learn how you can get more out of your site from a framer specialist or get started building for free today@Famer.com Verge for 30% off our Framer Pro annual plan. That's Framer.com Verge for 30% off Framer.com Verge rules and restrictions may apply. Support for the show comes from Shopify. Your business idea deserves a chance to become a reality. Shopify gives you the tools you need to make it happen. Everything you need to start selling is included and ready from day one. That's important because when your first customer walks through your digital door or your actual door, you want it to be as easy as possible for them to actually buy something. The hard part should be coming up with a great idea, not handling the transactions. When the time comes for someone to check out, you want the process to be as smooth as butter. And with Shopify handling the setup and checkout, you have more time to focus on your next steps. Because you'll never grow if you're spending all your time fighting with your software. Shopify powers millions of businesses worldwide, from household names like Mattel and Gymshark to small businesses just getting started with Shopify. Nothing stands between your idea and a real business. So go make it one. Start your free trial at shopify.com vergecast that's shopify.com vergecast shopify.com vergecast
[23:05]
C
support for this show comes from Odoo. Running a business is hard enough, so why make it harder with a dozen different apps that don't talk to each other? Introducing Odoo. It's the only business software you'll ever need. It's an all in one fully integrated platform that makes your work easier. CRM, accounting, inventory, E commerce, and more. And the best part? Odoo replaces multiple expensive platforms for a fraction of the cost. That's why over thousands of businesses have made the switch. So why not you try Odoo for free@odoo.com that's o d o o.com
[23:46]
D
pros trust the home Depot for heavy duty storage solutions for any job site or garage. Right now, get up to 15 off select storage and organization, Impact and water resistant totes and shelving. Built to hold up to £2,500. Storage systems have space for all your tools and protect them in the garage, on the job site and everywhere in between. Save time and maximize efficiency with adjustable shelving, customized to your business's needs. Shop and save on pro grade storage at the Home Depot. How pros get more done.
[24:19]
A
It feels like right now is going to be, or at least should be an inflection point in a lot of these conversations. Right. You have Jensen Huang, the CEO of Nvidia, writing the open letter about open weights. You have, like you said, the this new open weights alliance. Everybody is getting in fights about China and whether it is winning and whether it's not and whether it matters. We are still very much in the throes of this. What do we make of the OpenAI hugging face hack? You wrote last week that it's time to basically stop ignoring AI safety questions and we have to start doing real things about this. Sam Altman is out there being like, maybe we need to pause. Everybody's begging for regulation. It feels like either this is going to be a very noisy moment that everybody just forgets because somebody will launch a new model and we'll all move on with our lives, or there's going to be some sort of big structural change in how we talk about AI as an industry and ultimately as a society. After writing the piece that it's time to stop ignoring AI safety, what's your read of sort of the temperature of this conversation right now?
[25:26]
E
It's hard to know how to think. I think. I think for a lot of people in this space, none of this is surprising at all. I said it's disappointing from my point of view that something such a basic error might happen, but it's not surprising. We know that these tools can do this. And I think what it does do is it's and this is something that has been warned about, at least in theory, for at least a decade, I'd say, if not a lot longer. The problem is I think it was lacking a really tangible example, which obviously, as we know in policy politics, anything that is needed and I think this is maybe a galvanizing kind of thing. It's kind of forming a crucible of some sorts. We've seen some of the industry come together. We've seen rumblings at the White House. House. I'll leave my thoughts aside on what a voluntary code might achieve. But then there's sort of other elements as well. I think it was just yesterday a group of attorneys general were kind of pressuring OpenAI to preserve evidence on this. So it's possible that there might be some renewed push for actually meaningful regulation here in the US I think it has galvanised the industry, at least in that. But I Think it spooked people who are working inside these companies.
[26:43]
A
Interesting. That's actually exactly what I was about to ask you about, because I think the nihilist. And also maybe most rational take at this particular moment is to assume that no regulation is coming and that every government will just sort of twist themselves in knots and not make a lot of progress, particularly in the United States. But this also seems like the sort of thing that would make most industries take a really hard look at themselves and say, okay, something has gone awry here. And whether it's the people at the top of the industry or the people inside of these companies, look around and say, okay, we actually have a big problem here, and if somebody else isn't going to force us to fix this, we actually have to fix it anyway, because it will eventually be bad for business, it will be bad for the world. If you sign up to use OpenAI models, we might hack your systems. Is like, not a good business pitch. And so I wonder if. Is this what you're hearing? People are starting to think like, oh, we. We need to do something, even if no one's going to make us do it.
[27:41]
E
I mean, yeah, I mean, not even. You don't even need to sign up. They'll just hack anyone if they.
[27:45]
A
That's true. They'll just do it anyway.
[27:46]
E
You don't even need to be a customer. Anyone is game at this point. And the fact that they don't know as well, I think, is what has perhaps caused more alarm. Like, it was only upon review that, say, anthropic were like, oh, this happened in April three times.
[28:01]
A
The funniest meme I've seen on the Internet recently is just a picture of Mark Zuckerberg on the phone. And the caption is just him screaming into the phone, go find something illegal. We did really enjoyed that, but. Sorry, keep going.
[28:13]
E
Yeah, no, I mean, but I think that is. I think for a lot it has. I mean, a lot of these people do care quite deeply about what they do and they think that what they're doing is quite impactful, possibly in like a very major way. And a lot of them are concerned about the broad safety impact of this. And from people I've spoken to that it kind of ranges from. Some people seem very despondent. They were like, well, we're not doing anything now. We're not going to do anything for the next red line or the one after that. Let's just hope at some point we get our act together before it's too late. And so there's that kind of really dire bit, but I think others, it's more. Okay, well, maybe now is the time to push because evidently we've not been living up to the bar we've set for ourselves. I mean, the thing to remember with all of these hacks, they were quite nice as far as they go. Like, as far as I'm aware, no one died, no huge amount of money was lost, no one was hurt. It could have been a lot worse as far as, like a rogue hack goes.
[29:11]
A
Yeah, in a funny way, it's. One question I was going to ask you as you were talking is like, is it in some ways a shame that this is such a deeply unsexy example? Like it's a company no one has ever heard of that does something no one understands. It was about a relatively low stakes that like every single thing that happened is scary, but it was all in service of something so mundane that I think it was. It's much easier to write off than if this had happened, you know, to something people have much more real sort of visceral feelings about, even though the actual operation of the thing could have been exactly the same.
[29:50]
E
Yeah, I mean, I think the fear for a lot of people in this space might be that, like, what does it take to wake up for this? Because as I said, this is something people have been warning about for a very long time. And even though politicians have sort of here and there paid attention, even with legislation and whatever, nothing has really concretely happened that has stopped any of this behavior. And I think the fear for a lot embedded within that ecosystem would be like, well, what does it take? Are we looking at hacking a hospital? Are we looking at some Chernobyl type incident? At what point is it going to be enough that we can kind of sit up, pay attention, do something about it? And then also it's not so bad that we cannot then contain it. So, yeah, I think there's a lot of kind of unease, I think is how I describe it. Opinions vary, but unease is the sentiment I get.
[30:44]
A
So you mentioned your feelings about a voluntary code, but we should mention, potentially, as people are listening to this or watching this, there's a meeting happening at the White House with some of the big AI companies to talk about the way that models get reviewed to talk about AI safety. And one of the things that they're being asked to do reportedly is essentially voluntary submission for review of models. I would say I also have my own suspicions about how real a thing this is. But what do you make in general of this idea of this kind of government review of models before they're made available to the public, is this a possible short solution to this?
[31:23]
E
I mean, it depends at what level. So I think that's the thing that the transparency issue that's kind of been really shown by this is that some of the models involved both with anthropic and OpenAI were not public. They were research prototypes or being tested. So it isn't like, at what point are we then kind of interjecting the government into this or, or some form of auditor, but it does show that, yeah, this can happen really early on in that life cycle. And so unless there is basically sort of a glass house type transparency, which these companies will obviously bristle at, how do you really enforce that? I mean, the alternative is will we take their word for it? Which again, I am skeptical naturally on.
[32:06]
A
But yeah, I mean, it's such an interesting point because I think just in the OpenAI hugging face example, not only was it a research model that wasn't ready to be shipped yet, it was also supposed to be just in testing, right? This was an experiment. So it's not like it was in some kind of early rollout to people. It was supposedly sandboxed. Right? Like, the whole idea was that this thing was, was as protected and walled off as it could be. And I've, I'm sure you've seen this too. A lot of researchers are out there now being like, have you ever heard of air gapping people? Like, do you? And it's but it's like, okay, what we're actually discovering is that a big part of the problem is that all of this stuff is available to these models and is, is possible for these models from incredibly early on in their development. So at what point do we even consider a model finished enough to be reviewed? Strikes me as a. I think you're right. Like maybe a more or less impossible question. And there's no chance these companies are going to be like, sure, we'd love to have a government team sitting in our offices all day just to make sure we're doing a good job. That ends up being a total non starter here.
[33:14]
E
Yeah, absolutely. And I mean for me, the kind of part where it kind of. At what point do we consider this model? I suppose coming under this, when you consider testing it feels a very clear answer. Like if it's good enough to be tested and you cannot guarantee its containment, then I mean, also build better sandboxes. I really don't understand. That's why it just feels so Disappointingly basic, like air gap things. This feels like negligence sometimes more than. More than a mistake, but. Yeah, agreed.
[33:46]
A
So, all right, last thing. I'm gonna let you go. Do you. Do you feel the temperature of this continuing to rise as we go? Right now? We're what, we're sort of two weeks into the. The panic about the OpenAI hugging face thing. We've been talking about this for some time now. You and I have been at this a while. This feels like the sort of time everyone will find something else shiny to talk about and move on. Do you sense that happening or are we still deep in this?
[34:15]
E
On one sense, I do. I do feel it kind of slipping away a little bit. On the other hand, I also feel it merging with everything else that's happening, which I suppose is why I've spoken so loosely about the safety issues about open weights about China, because I feel these are now all fast becoming very much the same discussion. Because if you start talking about a slowdown, okay, cool. What about China will be the natural question for that? And so I also think there's other elements in the safety sphere that are emerging as well. So say there was that the sort of employees calling for. What was it called, pacing, frontier development or something? A peculiar phrase. And that was largely premised around the idea of self improvement rather than these other developments. So I think it will all kind of fold into one as to whether that will actually do anything.
[35:05]
A
My sense is that's a bad sign. That actually, in fact, one of the problems we've had with AI for a long time is that every conversation has been about everything and we have done a bad job peeling apart all the things that are AI. And so, in fact, if now we're going to shove all this stuff back together into one, what do we do about AI discussion that's only going to make all of this more complicated for everybody. Not to end on a real bummer of a note, but that is. That is where it feels like this might be headed if. If we try to make this one robust AI safety discussion instead of actually peeling apart. Like, this is why I think open weights is interesting. I think we should have a big, complicated discussion about how open most models should be and at. At what, in what way and available to whom. And that should be completely separate from questions about China. And the minute all of this got tied up in China, it struck me as infinitely harder to actually do something about.
[35:59]
E
Yeah, it is a bummer. As an end note, regulation is tough. And I guess what all of this has illustrated is that like self regulation is, as with many industries, woefully inadequate and at what thing do we need to happen for someone with power to stop that, to actually intervene?
[36:23]
A
It's a good question. We will keep coming back to it, but for now, Robert, thank you. Good to have you on the show. Come back sometime.
[36:28]
E
Yeah, it's been great.
[36:29]
A
All right, good to see you. All right, that's it for the show. Thank you to Robert for being here and thank you as always for watching and listening. If you have thoughts, questions, feelings, feedback, if you have a really good metaphor for open weight models, I liked Robert's of the fire through wood. But there's bound to be better ways to explain this than just talking about weights and models. So if you have a great metaphor, I want to hear it. Send us an email vergecasthe verge.com, call the hotline 866-verge11 we love hearing from you about all that and everything else. And as a reminder, the best thing you can do to support everything we're up to here is to subscribe to the Verge verge.com subscribe it gets you all of our podcasts ad free, including this one. It gets you all of our exclusive newsletters. It gets you all of our coverage. Robert has been doing a really terrific job of covering the USAI OpenAI hugging face stuff. Go read like the last three weeks of his stories and you'll be immediately smarter on all of this. Theverge.com subscribe thank you in advance. The Verge cast is Verge production and part of the Vox Media Podcast Network. This episode is produced by Josh Kahas, Eric Gomez, Brandon Kiefer, Travis Larchuck. We'll see you tomorrow. Rock and roll.
[37:36]
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Support for this show comes from Odoo. Running a business is hard enough, so why make it harder with a dozen different apps that don't talk to each other? Introducing Odoo. It's the only business software you'll ever need. It's an all in one fully integrated platform that makes your work easier. CRM, accounting, inventory, E commerce, and more. And the best part, Odoo replaces multiple expensive platforms for a fraction of the cost. That's why over thousands of businesses have made the switch. So why not you try Odoo for free@odoo.com that's o d o o dot com.

Summary7 min read

The Vergecast: What's Really Open About Open-Weight AI?

Date: August 4, 2026
Host: David Pierce
Guest: Robert Hart, Verge Reporter

Episode Overview

This episode dives deep into the heated debate over “open-weight” AI models in light of recent security incidents—like the OpenAI agent hack on Hugging Face—and examines the complex intersection of technology, politics, and safety. Host David Pierce welcomes Verge reporter Robert Hart to demystify what “open-weight” truly means, unpack the U.S.-China AI race, and grapple with the challenges of AI safety and governance in a rapidly escalating landscape.

Key Discussion Points & Insights

1. Defining “Open-Weight” Models (04:21–08:27)

Open-Weight ≠ Open Source:
Robert Hart explains that “open-weight” models only disclose the model’s weights—the numerical parameters dictating AI behavior after training. This differs from “open source” in traditional software, which includes full access to training data, methodology, and more (04:21).

“Open weight models are only really open on a thing called weights... you can download it, you can build on that, you can use it, but it is not the same as, say, an open source [model].” — Robert Hart (04:21)
Metaphor for “Weights”:
Hart likens weights to the burn patterns from running electric current through wood—"the product of something"—something useful, but impossible to reproduce without knowing precisely how it was made (05:44).

“It’s the product of something, but it can still be really useful if you can build or modify or change.” — Robert Hart (05:44)

2. Practical Impact of Open Weights (07:08–08:27)

Flexibility for Developers:
- Run models privately
- Fine-tune with own data
- Avoid sending information to central providers
But, with open-weights, monitoring usage and enforcing guardrails is much harder, potentially leading to greater risk.

“You have a lot more freedom to do largely whatever you want with it... but they’re a lot less monitorable and it’s a lot harder for providers [...] to put in the safety rails.” — Robert Hart (07:08)

3. U.S. vs China: Contrasting Philosophies & Motivations (08:27–11:35)

Generalization (with nuances):
- U.S. frontier labs (OpenAI, Google, Anthropic) mostly keep models closed.
- Chinese labs have embraced open-weights more widely (Moonshot, Alibaba in recent releases).
- U.S. still has open-weight players, notably Meta and Google's “Gemma” models.
Why the Divide? (10:17)
- For China, open-weights are a workaround for restrictions (e.g., chip bans).
- Lower barriers for developers and soft-power projection—global uptake of Chinese models, even in wary Western markets.
“It’s a way with all the restrictions that have been put on them by the U.S.... This is potentially a way of them doing so, or at least kind of working towards that.” — Robert Hart (10:17)

4. Open Weights & the AI Safety Debate (11:35–16:46)

Why It’s at the Center:
The inability to monitor or restrict use of open-weight models fuels safety concerns (bioweapons, hacking).
But, ironically, a recent high-profile incident demonstrated open-weights as a defensive tool: Closed OpenAI models hacked Hugging Face; open-weight models (including Chinese providers) were allegedly used to defend against these attacks (12:58–15:18).

“...it has been so... rapidly bubbled to the surface as something that’s been simmering for a long time is because all of these tensions that were there suddenly became very real and very tangible...” — Robert Hart (15:18)
The Paradox:
“Dual-use technology”: Open models can be used by both attackers and defenders.
Irreconcilable Philosophies:
One camp wants to ban open-weights for safety; another sees wide accessibility as the best safeguard against misuse. Both feel validated as models become more powerful (15:54–16:46).

“There is no single overlap anywhere between those two things…” — David Pierce (15:54)

5. Economic Incentives and Model Secrecy (17:49–19:50)

Leading labs have clear incentives to keep the “best” models closed—the more advanced or lucrative, the greater the incentive to retain control, monetize, and gate access.

“You can make it more expensive, you can limit who gets to access it... There are lots of good reasons to be closed that almost no one other than the very best model seems to have.” — David Pierce (18:11)
Even pro-openness companies may close up once they achieve model leadership.

6. Anthropic, OpenAI, and Governance Dilemmas (19:10–20:54)

Who Gets to Decide?
- The government accuses Anthropic of acting as sole arbiter—“this stance that Dario Amadei is the only one who gets to decide what we do with AI [...] is ridiculous.” — David Pierce (19:17)
- Peer competition is seen as petty and insufficient for true safety

7. Is This the AI Inflection Point? (24:18–30:43)

Industry and Policy Response:
- Recent events are a “crucible”—galvanizing action in industry and (possibly) in government.
- Attorneys general and the White House increase pressure; “voluntary code” widely seen as weak.
“It’s hard to know how to think. I think for a lot of people in this space, none of this is surprising at all... It was lacking a really tangible example... this is maybe a galvanizing kind of thing.” — Robert Hart (25:25)
Apathy and Alarm:
- Some insiders despair, assuming nothing will change.
- Others hope incidents push both industry and regulators to act preemptively.
Are Incidents Real Enough?
- The stakes may not be “sexy” enough—if the incident had affected a hospital or critical infrastructure, would the world pay more attention?
“The fear for a lot of people in this space might be that, like, what does it take to wake up for this?” — Robert Hart (29:49)

8. Government Review & Transparency Challenges (30:43–33:45)

White House’s Proposed Model Review
- Skepticism on effectiveness: When is a model “finished” or ready for review, especially as some attacks involved “research models” not yet public?
- Glass-house transparency is deeply unlikely; self-reporting is unreliable.
“Unless there is... a glass house type transparency, which these companies will obviously bristle at, how do you really enforce that?” — Robert Hart (31:23)

9. Where is the Conversation Headed? (34:18–36:22)

Are We Moving On, or Just Blending Problems?
- AI safety, open weights, and China debates are merging into one, making the issues even harder to solve.
- Pierce: This trend toward all-encompassing AI discussions risks diluting focus and preventing effective regulation.
“If now we’re going to shove all this stuff back together into one, what do we do about AI discussion, that’s only going to make all of this more complicated for everybody.” — David Pierce (35:05)
Regulation Outlook:
- Self-regulation seen as inadequate; outside intervention may be necessary, but it’s unclear what’ll finally force real action.
“Self regulation is, as with many industries, woefully inadequate and at what thing do we need to happen for someone with power to stop that, to actually intervene?” — Robert Hart (35:59)

Notable Quotes & Memorable Moments

On the open-vs-closed debate’s paradox:

“What you just described is two completely reasonable and totally mutually exclusive theories [...] there is no single overlap anywhere between those two things.”
— David Pierce (15:54)
On policy inertia:

“Nothing has really concretely happened that has stopped any of this behavior. And I think the fear for a lot embedded within that ecosystem would be... what does it take?”
— Robert Hart (29:49)

Best Metaphor for “Weights” (05:44)

“I almost imagine, you know, when you kind of have a piece of wood and you run an electric current through it and it makes that sort of forked pattern. I almost imagine the weights as the kind of resultant image that might come off in that it kind of is something that is the result of something. You couldn’t make it from scratch without replicating everything precisely, including the wood, but also the electric, even the weather.”
— Robert Hart

Important Timestamps

| Segment | Timestamp | |---------------------------------------------------|-------------| | What are open-weight models? & metaphor | 04:21–05:44 | | Developers’ advantages & openness pitfalls | 07:08–08:27 | | U.S. vs China AI policy and business motivations | 08:27–11:35 | | Open weights’ surprise role in AI safety debate | 12:33–15:18 | | Philosophical divide in AI governance | 15:54–16:46 | | Industry incentives for closed models | 17:49–19:50 | | Anthropic’s stance and criticism | 19:10–20:54 | | Is this an AI inflection point? | 24:18–30:43 | | Government review and transparency | 30:43–33:45 | | Merging of AI debates complicates regulation | 34:18–36:22 |

Tone and Atmosphere

The conversation is analytical but accessible, with frequent admissions that the subject matter is “wonky.” The tone is spiked with skepticism, a hint of frustration, but also a sense of urgency about the stakes. Both hosts frequently stress the need for clarity, caution about simplistic solutions, and reflect a blend of insider wryness and public interest.

For Anyone New to the Topic:
This episode explains why “open-weight” is not the same as “open source,” why the U.S. and China are making such different bets on AI openness, how very recent hacks forced a reckoning around AI safety, and why the tech industry—maybe even government—now faces a turning point in how these powerful tools are governed. It’s a crash course in today’s AI dilemmas, unpacked with clarity and candor.

Loading summary

Transcript59 lines

[00:02]
A
Hello and welcome to the Vergecast, the flagship podcast of Kimmy K3. I'm your friend David Pearce and today on the show we're going to talk about AI models. We've been talking about AI models a lot on the show for the last few weeks. In the wake of the OpenAI hack of hugging Face and all of these new conversations about AI safety and AI deployment and open weight models and the race with China and how we think about and operate AI in general, it all feels like it's coming to a head in some way right now. So the Verge is Robert Hart is going to come on and he's going to explain to us what's actually going on here. There's a of new vocabulary with these bottles and a lot of new questions about how they work and how they're made. He's going to make sense of all of it for us. I personally am very excited to have somebody finally make sense of all of it for me. We're going to get to that in just a second, but first, here's everything else happening on the Verge today.
[00:48]
B
I'm Jake Castranakis and this is 90 seconds on the verge for August 4, 2026. Microsoft just started bringing original Xbox games to the PC the other week, and now it's planning to bring Xbox 360 games as well. My colleague Tom Warren has the scoop on a memo that Microsoft sent around to developers asking them to opt in to the new program. Microsoft will handle all the emulation hurdles, even customer support. All developers have to do is approve their games for sale and set a price. Microsoft's argument is why not do it? It's free cash. The rollout is supposed to begin next year. Next up, Apple abruptly pulled Telegram from the App Store last night before storing it. Less than an hour later, Apple told various news outlets that it pulled the app due to the presence of csam. Then it restored the app after Telegram Telegram removed the content and banned the person who posted it. The whole incident is very odd, and it's actually the second time it's happened. Apple pulled Telegram in 2018 for the same reason, and again restored it within hours after this latest incident. Telegram seems downright mad. Telegram spokesperson Remy Vaughn told us that Apple was wrong to pull the app down. Finally, my favorite gadget of the day. My colleague Andrew Lechevsky spotted some new camera batteries from Falcam that have built in support for Apple's Find My Network to help you track down missing gear. I love this. This seems so much more convenient than attaching an airtag to every camera you own. It's only available for Canon and Sony right now, but Nikon and Fuji are set to be in the works. They're a little pricey though, at up to $70 a piece. You can read more@theverge.com, that's 90 seconds of the Verge for August 4, 2026.
[02:19]
C
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[02:59]
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[03:28]
A
done all right, let's talk AI models. Joining me now, Verge reporter Robert Hart. Welcome to the show. First time on the Vergecast?
[03:35]
E
Yeah. Longtime listener, first time joiner.
[03:37]
A
So very happy to have you here. And I've brought you here to do maybe the wonkiest thing we have done on this show in some time. We're going to do a little bit of analysis, a little bit of explainer, but we're in a really interesting and complicated moment with AI that is about technology, it's about politics, it's about feelings. And I want to just piece through it. You've done a really good job covering this on the site and I want to, I want to just walk through some of what's going on right now, but I think we need to start with some fairly straightforward explanary stuff because I think a lot of very smart people are getting this stuff wrong and I want to just talk through it a little bit. So just from a very basic level, help me understand what an open weight model is and why it matters as a concept. Right now.
[04:22]
E
Yeah. So, I mean, to get even more annoyingly wonky, it's perhaps important to start with what it's not. And I think a lot here is caught up in kind of open source, and that's what we know traditionally from software, where it's pretty free, it's distributed, you can change it, you can make money from it, as long as you also share it freely. Now, open weight models are not quite that open, and so wonky. Part number two is they are only really open on a thing called weights, which are kind of those numerical parameters. Parameters, the sort of knobs and buttons that an AI has during training that kind of helps it tick, process information, all of that. And that's the only bit that's really made open here. So you can download it, you can build on that, you can use it, but it is not the same as, say, an open source where you can kind of reconstitute it from scratch.
[05:11]
A
Right. Have you come up with a good metaphor for how to describe weights? It's a really messy but really important term, and I think it's really hard to visualize. Like, open source, I actually think would be very easy to understand. Right. You would see all the training data, you would see how they train, trained it, you would see what the output looked like. You'd be able to sort of peel the thing apart and understand every piece that went into it. Like you said, open weight models are very deliberately. Not that they show this one thing. And I have not come up with a great way to explain it to people who are not sort of deep in the weeds of AI technology. Have you come up with one or heard one that you like?
[05:45]
E
Not really, but on the spot, I guess. I mean, please. I almost imagine, you know, when you kind of have a piece of wood and you run an electric current through it and it makes that sort of forked pattern. I almost imagine the weights as the kind of resultant image that might come off in that it kind of is something that is the result of something. You couldn't make it from scratch without replicating everything precisely, including the wood, but also the electric, even the weather. But it's still quite useful in terms of you can sort of see the paths, you can trace it. And it's not a perfect one by any means, but it's kind of a, I think, an idea of kind of what weights might be in this sense, in that it's the product of something, but it can still be really useful if you can build or modify or change.
[06:32]
A
I like that you couldn't make it yourself without knowing exactly what went into it. But even just knowing what it looks like, you can now go and redraw the thing, which ends up being really important. Which brings me to the next thing I just want to sort of quickly explain before we get into this here, which is who cares what, what can I do if I'm a developer or a government or somebody paying for one of these models with an open weights model that I can't do with a closed weights model like we, we've seen from anthropic and OpenAI. Like what is the actual sort of user advantage of an open weights model?
[07:08]
E
Quite a lot, really. I mean it's a lot more flexible. You have a lot more freedom to do largely whatever you want with it. You're not reliant on sending them your data, which is a big thing for a lot of different companies. You could presuming you have the infrastructure, you could run it yourself, you can tweak it with your own data. So you've kind of got a bit more of a personalized model. And I think important for a lot of the debates we're seeing now as well, is that they're a lot less monitorable and it's a lot harder for providers, say anthropic OpenAI in these cases to put in the safety rails that might stop it from doing something that you would want it to do.
[07:48]
A
Okay, so maybe in that sense the comparison to open source actually is useful in that it doesn't function the same way, but your ability to take it and modify it and use it in your own server array, that is very much the same as if it were pure open source software. You don't get to understand the inner workings of the model in the same way, but you get to use it and adapt it in much the same way. Is that a reasonably close comparison there?
[08:16]
E
Yeah, I mean, there are a lot more restrictions that they might put on it. So some of the ones that are coming out now, they'll have licenses that you have to pay them over certain thresholds. But yeah, it's a lot more flexible in that way.
[08:28]
A
This is probably an overgeneralization and I want you to correct the extent to which this is an overgeneralization. But it seems like China has embraced the idea of open it and wait models in a big way. And the US particularly, the sort of frontier labs that we talk about all the time, anthropic OpenAI, Google have not. A, is that a fair characterization? And B, why do you think it's
[08:49]
E
broken down like that to again get annoyingly wonky. It is a bit more complicated than that, but by and large it is a reasonable characterization. The most prominent US models, kind of, as you said, Google, OpenAI anthropic, their Frontier models are all proprietary, they're all closed systems. And in China, by and large, a lot of them are open weight. There are exceptions obviously. So Alibaba in China, for example, until its most recent release, a few earlier this year, and its Frontier scale were proprietary. It kept them closed and it evidently changed its mind this week. And in the US there is still a big ecosystem of open weight players. I mean, Meta is the most obvious that comes to mind, who've been making strides recently. But also Google. I mean, it's not the top tier Gemini ones, but their Gemma models are quite popular and there's sort of a huge spectrum in between that, if you
[09:47]
A
grant me the huge generalization. Because I think at some point at the absolute frontier, I think that the debate we're having right now is between companies like Moonshot and like you said, Alibaba in China and the three major labs here in the U.S. is it purely sort of a political difference that is why China would want its models to be open and the US would want them to be closed? Or are we talking about like communism versus capitalism here? Like what, what, what do you make of the sort of philosophical difference between those things?
[10:17]
E
I wouldn't go as far to say it is quite a deeply entrenched divide like that, but I think it is quite tempting to see it like that. I think partly it is business pragmatism in China. It is a way with all the restrictions that have been kind of put on them by the US so they've not had access to the top tier chips, for example, it is harder for them to innovate at the frontier. This is potentially a way of them doing so, or of at least kind of working towards that. But also from a business strategy point of view, there's a huge sort of array of advantages with going open. I mean, it's cheaper by and large for developers to run. It's a much lower barrier to entry. There are also the elements we said with privacy or kind of running things on your own system, you can really grow that ecosystem rapidly. And it is kind of almost a projection of soft power if your stuff is becoming almost the default. And the Chinese models are being very widely used globally, American companies, Western companies would be very hesitant to send all of their data to China or to servers hosted in China. If it's open, you can get around that. And so it's also quite a nice gateway for them to stay active in these markets.
[11:35]
A
Yeah, that makes sense. So all of this has been sort of burbling in the background for a long time and has really come to the forefront of the whole AI discussion over the last couple of weeks. And it's happening right next to, I think we've talked about a bunch on the show, the OpenAI hugging face hack. Now Anthropic is coming out saying, oh, look, we just noticed we've also been hacking everybody. There is a real AI safety thing happening right now and open weight models have become a big part of this discussion kind of on both sides. In a strange way, everybody has feelings about open weight models, no matter how they feel about AI safety. Why has open weight become sort of a core part of the how do we make sure we're doing AI safely discussion? There are so many things this could be about, but it feels like the industry has decided that the debate we're going to have right now is about open weights. Why?
[12:33]
E
Yeah, well, I mean, one, and I'll gloss over this just due to the complexity, but there is the whole spectrum of the race with China in the States or the west in general, which means something a bit different to quite literally anyone who mutters it. But that's.
[12:50]
A
We have to beat China at everything is just a fact of life in America right now, for reasons I continue to largely not understand. But, yeah, granting that premise, what else
[12:58]
E
is going on here? Yeah, so there's that, which obviously is one reason. So the open weight models, they're releasing. Let's go. But then the safety part as well. So the main opposition, or one of the oppositions to the open models is that it's very difficult to both monitor how they're used, which OpenAI and Anthropic, they know how this is being used. It's hard to implement details like guardrails to stop it, like hacking or helping you build a bioweapon. The two kind of main concerns that the Frontier Labs say the fear has always been that releasing something very capable openly puts that in the hands of anyone. The weird part of this then, is OpenAI, it turned out one of its agents hacked Hugging Face. As we know, the difficulty with closed models is these safety rails as well. Hugging Face said in their report, oh, well, we couldn't actually use U.S. frontier models. These safety rails activated. And then they said they turned to one of the leading Chinese providers, zai, for their model to Help defend itself against one of the closed models that was attacking it, which kind of flips a lot of that on its head. And so this debate has now really become, I mean it is by definition a dual use technology. It can be used to hack, it can also be used to defend against hackers. And so that's kind of been at the core of this now is that for a long time the fear has been it would be used as a weapon. And funnily enough, the most high profile case recently is it's been used as a tool of defending.
[14:31]
A
Wait, so hold on. So let me, let me make sure I understand what happened here. Cause I actually think that that's really interesting. I hadn't quite thought about it that way. So, OpenAI closed model, very, very deliberately hard for anyone else to understand. Attacks. Hugging face. Hugging face says, we, we need a tool to stop this. We're going to deploy our agents to stop this agent runs into some safety restriction in the model that they're trying to use that another closed model that is like, no, I won't, I won't escape this sandbox and go try to fight this thing. So then they're able to use an open weight model because they're able to just essentially remove those guardrails or they don't exist in the first place because it is open and adaptable in that way. Am I understanding that right?
[15:19]
E
Yeah, in a very kind of. Yeah, in a broad sense, that's what a weird system. Yeah. And so that's, I think, why it has been so as an issue, it has so rapidly bubbled to the surface as something that's been simmering for a long time is because all of these tensions that were there suddenly became very real and very tangible in a way that ties together, as I said, all those fears with China. But also open versus closed. And then also the risks of these closed models of what they are actually capable of doing.
[15:54]
A
This strikes me as the sort of thing people are going to pretty quickly get like borderline religious views about that are going to be very hard to change. Because what you just described is two completely reasonable and totally mutually exclusive theories, Right. That one says this technology is too powerful, we can't put it in the hands of everybody or the bad people will use it and things will go horribly wrong and we're going to be stuck in this arms race, cat and mouse game for forever. The other side says actually that's already happening and the only way to stop it is to put this technology in the hands of everybody. There is no single overlap anywhere between those two things. And it feels like as these models get better and more capable, both sides are going to feel more right about their stance on these things. Like is there a way to reconcile in the middle of this fight?
[16:47]
E
Well, I think we'll see, but I think we have to. I mean, I think even the opponents of kind of. I mean the one big one that has been to watch has been a lot of these kind of open letters you've seen from the US tech industry. For example, the notable holdouts, at least at the start were the big three were Google OpenAI anthropic. To my knowledge, it's only anthropic that's a continued holdout on that. I might be wrong. They might have signed since. But Daria Amadeh has also published a quite lengthy blog post explaining the reasoning and says that we're not against open models, but we cannot only have open models. And I think that's probably an area that we will end up in. I think and it's also not to say I think a lot of this is focused on like I said, it was more complicated with there are open models in the U.S. china could also very easily close off some of its frontier models. And I think probably as they get more and more advanced and as sort of that ecosystem develops, I think there'll probably be a mix.
[17:49]
A
Yeah, because you have more economic incentive to close your models. Right. You can make, if you can say and demonstrate our model is the best, which I think anthropic relatively successfully has been able to do over and over for the last year or so. Every possible upside you want comes from closing off that model.
[18:12]
B
Right.
[18:12]
A
You can make it more expensive, you can limit who gets to access it. You get to be the arbiter of good and bad in that model. If you are the best, there are lots of good reasons to be closed that almost no one other than the very best model seems to have. And this is where we get wrapped up again in the everything is a race against China. If if in fact, you know, Kimmy is now a better model, like demonstrably better model and all of a sudden everybody wants it. Suddenly you flip from China wants to have openness and and to sort of infiltrate the US and make a lot of headway into businesses and start to capture some of these use cases that people can't afford from the all of a sudden you say well we have the best model, we're going to close it off and make a ton of money from it. That does feel Very possible to me at this moment.
[18:56]
E
Yeah. I mean, it feels largely what's been happening already is the kind of end of that. I mean, and also there is a safety element. I mean, Anthropic was explicitly founded, basically, it was unhappy with what OpenAI were doing.
[19:11]
A
And I think the argument against Anthropic in many ways at this point is that Anthropic believes that it is the only one who should be trusted.
[19:18]
B
Right.
[19:18]
A
Like this is what the government has been saying about Anthropic for sometime in the US is that this stance that Dario Amade is the only one who gets to decide what we do with AI and what we don't do with AI is ridiculous. And again, like you're, you're free to think whoever you want to be in charge should be in charge, but at some point either no one is in charge and we just like let chaos reign because that is the thing that will solve this, or someone has to be in charge. And I feel like Anthropic has been the one most loudly being like, it's fine, the answer's us, we've got it. And that makes a lot of people really angry.
[19:50]
E
Yeah, I mean, absolutely. I think their response to the hugging face incident is actually quite telling. I mean, to me it felt quite petty. That's maybe a controversial view, but it's like, hey, our models can hack things too. And then you look at kind of the details of it and I mean, their blog detailing this, it quite literally ends in a four bullet point list as to why what happened with them was better than what happened with OpenAI, which. Cool. I mean, we're all adults here. Great. It just felt very juvenile, especially when, I mean, OpenAI hacked its way out. To me, Anthropics was equivalent. They kind of left the door open. Like it was a very. Like they're saying we're the good guys and their behavior doesn't seem to meet that bar time and again. But they're also, though I should say them and OpenAI are doing more than a lot of other actors in this field as well. It just doesn't necessarily meet the expectations they set for themselves.
[20:55]
F
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[23:05]
C
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[23:46]
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[24:19]
A
It feels like right now is going to be, or at least should be an inflection point in a lot of these conversations. Right. You have Jensen Huang, the CEO of Nvidia, writing the open letter about open weights. You have, like you said, the this new open weights alliance. Everybody is getting in fights about China and whether it is winning and whether it's not and whether it matters. We are still very much in the throes of this. What do we make of the OpenAI hugging face hack? You wrote last week that it's time to basically stop ignoring AI safety questions and we have to start doing real things about this. Sam Altman is out there being like, maybe we need to pause. Everybody's begging for regulation. It feels like either this is going to be a very noisy moment that everybody just forgets because somebody will launch a new model and we'll all move on with our lives, or there's going to be some sort of big structural change in how we talk about AI as an industry and ultimately as a society. After writing the piece that it's time to stop ignoring AI safety, what's your read of sort of the temperature of this conversation right now?
[25:26]
E
It's hard to know how to think. I think. I think for a lot of people in this space, none of this is surprising at all. I said it's disappointing from my point of view that something such a basic error might happen, but it's not surprising. We know that these tools can do this. And I think what it does do is it's and this is something that has been warned about, at least in theory, for at least a decade, I'd say, if not a lot longer. The problem is I think it was lacking a really tangible example, which obviously, as we know in policy politics, anything that is needed and I think this is maybe a galvanizing kind of thing. It's kind of forming a crucible of some sorts. We've seen some of the industry come together. We've seen rumblings at the White House. House. I'll leave my thoughts aside on what a voluntary code might achieve. But then there's sort of other elements as well. I think it was just yesterday a group of attorneys general were kind of pressuring OpenAI to preserve evidence on this. So it's possible that there might be some renewed push for actually meaningful regulation here in the US I think it has galvanised the industry, at least in that. But I Think it spooked people who are working inside these companies.
[26:43]
A
Interesting. That's actually exactly what I was about to ask you about, because I think the nihilist. And also maybe most rational take at this particular moment is to assume that no regulation is coming and that every government will just sort of twist themselves in knots and not make a lot of progress, particularly in the United States. But this also seems like the sort of thing that would make most industries take a really hard look at themselves and say, okay, something has gone awry here. And whether it's the people at the top of the industry or the people inside of these companies, look around and say, okay, we actually have a big problem here, and if somebody else isn't going to force us to fix this, we actually have to fix it anyway, because it will eventually be bad for business, it will be bad for the world. If you sign up to use OpenAI models, we might hack your systems. Is like, not a good business pitch. And so I wonder if. Is this what you're hearing? People are starting to think like, oh, we. We need to do something, even if no one's going to make us do it.
[27:41]
E
I mean, yeah, I mean, not even. You don't even need to sign up. They'll just hack anyone if they.
[27:45]
A
That's true. They'll just do it anyway.
[27:46]
E
You don't even need to be a customer. Anyone is game at this point. And the fact that they don't know as well, I think, is what has perhaps caused more alarm. Like, it was only upon review that, say, anthropic were like, oh, this happened in April three times.
[28:01]
A
The funniest meme I've seen on the Internet recently is just a picture of Mark Zuckerberg on the phone. And the caption is just him screaming into the phone, go find something illegal. We did really enjoyed that, but. Sorry, keep going.
[28:13]
E
Yeah, no, I mean, but I think that is. I think for a lot it has. I mean, a lot of these people do care quite deeply about what they do and they think that what they're doing is quite impactful, possibly in like a very major way. And a lot of them are concerned about the broad safety impact of this. And from people I've spoken to that it kind of ranges from. Some people seem very despondent. They were like, well, we're not doing anything now. We're not going to do anything for the next red line or the one after that. Let's just hope at some point we get our act together before it's too late. And so there's that kind of really dire bit, but I think others, it's more. Okay, well, maybe now is the time to push because evidently we've not been living up to the bar we've set for ourselves. I mean, the thing to remember with all of these hacks, they were quite nice as far as they go. Like, as far as I'm aware, no one died, no huge amount of money was lost, no one was hurt. It could have been a lot worse as far as, like a rogue hack goes.
[29:11]
A
Yeah, in a funny way, it's. One question I was going to ask you as you were talking is like, is it in some ways a shame that this is such a deeply unsexy example? Like it's a company no one has ever heard of that does something no one understands. It was about a relatively low stakes that like every single thing that happened is scary, but it was all in service of something so mundane that I think it was. It's much easier to write off than if this had happened, you know, to something people have much more real sort of visceral feelings about, even though the actual operation of the thing could have been exactly the same.
[29:50]
E
Yeah, I mean, I think the fear for a lot of people in this space might be that, like, what does it take to wake up for this? Because as I said, this is something people have been warning about for a very long time. And even though politicians have sort of here and there paid attention, even with legislation and whatever, nothing has really concretely happened that has stopped any of this behavior. And I think the fear for a lot embedded within that ecosystem would be like, well, what does it take? Are we looking at hacking a hospital? Are we looking at some Chernobyl type incident? At what point is it going to be enough that we can kind of sit up, pay attention, do something about it? And then also it's not so bad that we cannot then contain it. So, yeah, I think there's a lot of kind of unease, I think is how I describe it. Opinions vary, but unease is the sentiment I get.
[30:44]
A
So you mentioned your feelings about a voluntary code, but we should mention, potentially, as people are listening to this or watching this, there's a meeting happening at the White House with some of the big AI companies to talk about the way that models get reviewed to talk about AI safety. And one of the things that they're being asked to do reportedly is essentially voluntary submission for review of models. I would say I also have my own suspicions about how real a thing this is. But what do you make in general of this idea of this kind of government review of models before they're made available to the public, is this a possible short solution to this?
[31:23]
E
I mean, it depends at what level. So I think that's the thing that the transparency issue that's kind of been really shown by this is that some of the models involved both with anthropic and OpenAI were not public. They were research prototypes or being tested. So it isn't like, at what point are we then kind of interjecting the government into this or, or some form of auditor, but it does show that, yeah, this can happen really early on in that life cycle. And so unless there is basically sort of a glass house type transparency, which these companies will obviously bristle at, how do you really enforce that? I mean, the alternative is will we take their word for it? Which again, I am skeptical naturally on.
[32:06]
A
But yeah, I mean, it's such an interesting point because I think just in the OpenAI hugging face example, not only was it a research model that wasn't ready to be shipped yet, it was also supposed to be just in testing, right? This was an experiment. So it's not like it was in some kind of early rollout to people. It was supposedly sandboxed. Right? Like, the whole idea was that this thing was, was as protected and walled off as it could be. And I've, I'm sure you've seen this too. A lot of researchers are out there now being like, have you ever heard of air gapping people? Like, do you? And it's but it's like, okay, what we're actually discovering is that a big part of the problem is that all of this stuff is available to these models and is, is possible for these models from incredibly early on in their development. So at what point do we even consider a model finished enough to be reviewed? Strikes me as a. I think you're right. Like maybe a more or less impossible question. And there's no chance these companies are going to be like, sure, we'd love to have a government team sitting in our offices all day just to make sure we're doing a good job. That ends up being a total non starter here.
[33:14]
E
Yeah, absolutely. And I mean for me, the kind of part where it kind of. At what point do we consider this model? I suppose coming under this, when you consider testing it feels a very clear answer. Like if it's good enough to be tested and you cannot guarantee its containment, then I mean, also build better sandboxes. I really don't understand. That's why it just feels so Disappointingly basic, like air gap things. This feels like negligence sometimes more than. More than a mistake, but. Yeah, agreed.
[33:46]
A
So, all right, last thing. I'm gonna let you go. Do you. Do you feel the temperature of this continuing to rise as we go? Right now? We're what, we're sort of two weeks into the. The panic about the OpenAI hugging face thing. We've been talking about this for some time now. You and I have been at this a while. This feels like the sort of time everyone will find something else shiny to talk about and move on. Do you sense that happening or are we still deep in this?
[34:15]
E
On one sense, I do. I do feel it kind of slipping away a little bit. On the other hand, I also feel it merging with everything else that's happening, which I suppose is why I've spoken so loosely about the safety issues about open weights about China, because I feel these are now all fast becoming very much the same discussion. Because if you start talking about a slowdown, okay, cool. What about China will be the natural question for that? And so I also think there's other elements in the safety sphere that are emerging as well. So say there was that the sort of employees calling for. What was it called, pacing, frontier development or something? A peculiar phrase. And that was largely premised around the idea of self improvement rather than these other developments. So I think it will all kind of fold into one as to whether that will actually do anything.
[35:05]
A
My sense is that's a bad sign. That actually, in fact, one of the problems we've had with AI for a long time is that every conversation has been about everything and we have done a bad job peeling apart all the things that are AI. And so, in fact, if now we're going to shove all this stuff back together into one, what do we do about AI discussion that's only going to make all of this more complicated for everybody. Not to end on a real bummer of a note, but that is. That is where it feels like this might be headed if. If we try to make this one robust AI safety discussion instead of actually peeling apart. Like, this is why I think open weights is interesting. I think we should have a big, complicated discussion about how open most models should be and at. At what, in what way and available to whom. And that should be completely separate from questions about China. And the minute all of this got tied up in China, it struck me as infinitely harder to actually do something about.
[35:59]
E
Yeah, it is a bummer. As an end note, regulation is tough. And I guess what all of this has illustrated is that like self regulation is, as with many industries, woefully inadequate and at what thing do we need to happen for someone with power to stop that, to actually intervene?
[36:23]
A
It's a good question. We will keep coming back to it, but for now, Robert, thank you. Good to have you on the show. Come back sometime.
[36:28]
E
Yeah, it's been great.
[36:29]
A
All right, good to see you. All right, that's it for the show. Thank you to Robert for being here and thank you as always for watching and listening. If you have thoughts, questions, feelings, feedback, if you have a really good metaphor for open weight models, I liked Robert's of the fire through wood. But there's bound to be better ways to explain this than just talking about weights and models. So if you have a great metaphor, I want to hear it. Send us an email vergecasthe verge.com, call the hotline 866-verge11 we love hearing from you about all that and everything else. And as a reminder, the best thing you can do to support everything we're up to here is to subscribe to the Verge verge.com subscribe it gets you all of our podcasts ad free, including this one. It gets you all of our exclusive newsletters. It gets you all of our coverage. Robert has been doing a really terrific job of covering the USAI OpenAI hugging face stuff. Go read like the last three weeks of his stories and you'll be immediately smarter on all of this. Theverge.com subscribe thank you in advance. The Verge cast is Verge production and part of the Vox Media Podcast Network. This episode is produced by Josh Kahas, Eric Gomez, Brandon Kiefer, Travis Larchuck. We'll see you tomorrow. Rock and roll.
[37:36]
C
Support for this show comes from Odoo. Running a business is hard enough, so why make it harder with a dozen different apps that don't talk to each other? Introducing Odoo. It's the only business software you'll ever need. It's an all in one fully integrated platform that makes your work easier. CRM, accounting, inventory, E commerce, and more. And the best part, Odoo replaces multiple expensive platforms for a fraction of the cost. That's why over thousands of businesses have made the switch. So why not you try Odoo for free@odoo.com that's o d o o dot com.

What's really open about open-weight AI?

What's really open about open-weight AI?

The Vergecast: What's Really Open About Open-Weight AI?

Episode Overview

Key Discussion Points & Insights

1. Defining “Open-Weight” Models (04:21–08:27)

2. Practical Impact of Open Weights (07:08–08:27)

3. U.S. vs China: Contrasting Philosophies & Motivations (08:27–11:35)

4. Open Weights & the AI Safety Debate (11:35–16:46)

5. Economic Incentives and Model Secrecy (17:49–19:50)

6. Anthropic, OpenAI, and Governance Dilemmas (19:10–20:54)

7. Is This the AI Inflection Point? (24:18–30:43)

8. Government Review & Transparency Challenges (30:43–33:45)

9. Where is the Conversation Headed? (34:18–36:22)

Notable Quotes & Memorable Moments

Best Metaphor for “Weights” (05:44)

Important Timestamps

Tone and Atmosphere

The Vergecast: What's Really Open About Open-Weight AI?

Episode Overview

Key Discussion Points & Insights

1. Defining “Open-Weight” Models (04:21–08:27)

2. Practical Impact of Open Weights (07:08–08:27)

3. U.S. vs China: Contrasting Philosophies & Motivations (08:27–11:35)

4. Open Weights & the AI Safety Debate (11:35–16:46)

5. Economic Incentives and Model Secrecy (17:49–19:50)

6. Anthropic, OpenAI, and Governance Dilemmas (19:10–20:54)

7. Is This the AI Inflection Point? (24:18–30:43)

8. Government Review & Transparency Challenges (30:43–33:45)

9. Where is the Conversation Headed? (34:18–36:22)

Notable Quotes & Memorable Moments

Best Metaphor for “Weights” (05:44)

Important Timestamps

Tone and Atmosphere