Open-source AI is closing the gap with closed systems faster than many people expected. That changes the economics, the security model, and the assumptions behind a lot of Big Tech’s AI spending.

I joined a press conference at Web Summit to talk about what happens next: who benefits, which business models get squeezed, and how regulation should work when the most capable systems aren’t controlled by one company.

If you'd rather read than watch, the full transcript is below.

Transcript

0:01 Hi everybody. Who's going first? How was lunch? Ah, no lunch. So, I do want to say this is a really broad topic and so we are excited to talk about what you are excited to hear about. Uh so, if there's anybody with prompts, uh feel free. Otherwise, we'll do it like short monologues and then they'll go for some questions. Maybe maybe I'll kick things off. Sure.

0:25 Okay. So, um by way of background, I'm I'm Mike Conover, I'm the CEO and co-founder I yeah, sure. You guys can hear me. Project. I'm the CEO and co-founder of Brightwave. We built a a deep research system that is able to perform effectively the functions of an investigative journalist, but um in complex domains like finance. And

0:48 I'll just talk a little bit about some of the trends that we're seeing with respect to open source AI and um agents in particular. So, if you think about Who in the room has used this cloud code? Right? So, typically you're operating one to three agents at a time simultaneously and I think what we're going to see is massive parallelism, which is that

1:11 you're going to like if I want to understand how the war in Iran impacts commodity markets, there many dozens of different sub-themes and within each sub-theme there are sub-research topics and the ability to parallelize and scale um how many topics am I running down, how many analyses, how many code changes am I running at the same time um increases the number of agents that one person is going to control. And if

1:32 you look at OpenAI's symphony, uh this is a system for making that abstraction where I'm less aware of how many different agents are operating on behalf, um you know, clearer where I'm moving tickets through a linear board. Um This on top of sort of the task horizon link, so that's like the depth, how long can these agents function for independently,

1:54 um gives you this increasing breadth and depth surface area of total compute. And so from a like secular standpoint, that the what I expect is a massive increase in token volume owing to this parallelization increasing depth. Um we're going to see open source models become really competitive on a price and speed basis. So the same 64 GPUs that

2:18 you would use to run um a trillion parameter model, you can get 3x more tokens per second out of those same GPUs running isolated models on individual cards. Um and from a uh sort of switching cost standpoint, this is the last thing I'll say about it before we kind of hand it hand it off to you. Um it's not clear that the fungibility of

2:42 these resources is priced in. Like Anthropic had a moment and I I'm a huge fan of Anthropic, but as soon as a new frontier model comes out or as soon as a new open source model comes out that has some advantage, these all fulfill the chat completion API. And the switching costs are very, very low. Brightwave is all hot swappable so that you can use whatever model is best for the job. And I think that that structural force, that price and and

3:05 speed pressure is is not well appreciated with respect to like how much compute is going to increase through agent parallelism. I think we're going to do the three and I I'm here here to talk about it. All right. Um yeah, I'm Avery. I'm CEO and co-founder of Tailscale. Uh we make an AI connectivity and and governance framework called Aperture.

3:29 Um and like the my my picture of the like AI ecosystem right now, I I think it's very interesting how this is it's got to work out cuz that usually these big technology shifts, you'll have you know, the big winner and then the secondary one, right? And right now we're watching the clash of the Titans uh way up over our heads with the trillion-dollar valuations and the giant data centers and stuff. And the thing is they're all building what I would call like the iPhone of of AI, right? I

3:55 Anthropic, OpenAI, and Google are all building this like vertically integrated system where they're providing all the pieces and they want to lock you into their system so that you pay a lot of money for their tokens, right? I think the ecosystem needs the balancing Android of AI, which is like ecosystem-based, open source, everybody can contribute, and you can plug and play all the pieces yourself. And maybe you have to assemble the pieces

4:17 yourself, and maybe each of the pieces is is not not as beautifully machined and perfectly integrated as in your iPhone, but there's a space for that cuz there's always should be a space for like the super high-end premium thing and the like, you know, flexible thing that you can do at volume. And I think we're not seeing the second one as much yet, but there's a lot of pieces out there that can be assembled into that. And so, that's what at Tailscale we're most interested in

4:41 doing. We want to connect all these pieces together. We want to build that ecosystem. And I think that's where, you know, the lower-priced models, the various different kinds of harnesses, the really complicated agentic systems, the connectivity systems, sandboxes, there's a lot of room to experiment and put all those pieces together. Cool. I'm Bailey, co-founder and CEO of cal.com. cal.com is a scheduling

5:03 infrastructure platform. So, we power scheduling from anywhere from individuals to very large businesses. I have a slightly different perspective to to these guys. We were historically very open source. We were like the largest Next.js open source project. And recently we made a move to go close source due to security risks. So, obviously we're all aware that AI can build things even better, but they we

5:26 believe they can also break things even better. They've become really, really good at detecting vulnerabilities and things like that. And the problem is is AI is still somewhat in its infancy in the sense that they sometimes give inconsistent answers. And so, for us, you know, AI is never going to give the same answer to I don't know how many hours are in strawberry

5:49 or you know many things like that which we all know are quirks of AI and that also means that AI can't give you like a single source of truth as to is software secure. So you've all heard probably about Anthropic's Methuselah model and all these sort of things that are able to break things more and more. We don't have Methuselah none of us here but you know somebody does and you know AI isn't gate capped anybody can

6:12 innovate you know we have our US frontier models that that lead the way and then one day Deep Sea comes in and suddenly they can they can match that. What happens when China's now has a model which can rival Methuselah that means that you know are all of us under under attack because as I'm sure you've read Methuselah is breaking you know Firefox FreeBSD all these things

6:36 that we consider to have like a lot of eyes on them as open source and especially like FreeBSD is is an absolute sort of like staple of you know stability and so for us you know while going closed source isn't unto itself like a a solution it is an option we have on the table which we believe can reduce the risk.

7:01 We run six AI code vulnerability scanners all in parallel they all find different things and um you know that's a a scary thing for us. We also spoke to Hex Security one of the the big ones that that were like a YC company and they said uh open source is five to 10 times easier to hack than closed source and so for me

7:25 the reality becomes pretty clear that if I can make cower.com five to 10 times harder to hack um although that is not a complete and holistic solution to this it is definitely an option that I feel like we have to take to protect our customers. So, yeah, slightly different perspective to to these guys, but Can you please state your name and

7:50 affiliation? Jim Harris, Corporate Knights magazine. Uh just like we have hybrid cloud and multi-cloud, I think we'll have hybrid uh models, multi-cloud models. So, some things will use large language models, medium, very small, niche. And similarly, open source, closed source. So,

8:13 uh where we choose to put that load or query will depend on the context or nature of both the data, the security considerations, the cost, the speed, whether we use open or closed source. So, uh this is the vision uh that my clients are are taking, those I talked to, to optimize both cost, speed,

8:39 safety, all these considerations. Uh would you agree with this view that that's where we're going? While the you know, Open AI wants to lock you into their vertical stack, many companies uh don't want that. Just as AWS, Azure, Google wanted to lock you into their cloud. So,

9:03 Yeah. I think I I like my iPhone analogy uh for that one, right? Like every year a new iPhone comes out, they raise the price by a little bit. Uh everybody when I remember, I'm pretty old now, when the first iPhone came out in like 2007, it was like $800 USD, and everyone's like, "Oh my god, who's going to pay $800 for a phone?" Right? A bunch people did, right? And the price has gone up from there. And like, "You know what? That phone

9:25 is a perfectly fine phone even today, right? Nobody wants it because like we're willing to pay the premium for like a slightly better phone, right? And there are going to be people willing to pay a premium for these slightly better tokens because they believe it gives them a competitive advantage. It's going to come up a lot in the security world, right? Where if you have a slightly better model for finding security vulnerabilities, you have an incredibly big advantage over the person with the

9:47 second best model, right? For a lot of stuff though, that's not the case, right? We have a lot of communications technology. I have a watch that is more powerful than the phone from 2008, right? And all it does is tell me the weather really badly. Like it it can't even keep up with the weather updates, right? But it's okay, you know, I wear the watch and you know, it doesn't it doesn't cost me as much for my cellular subscription, right? So absolutely, it's

10:08 going to be a big market. There's like you're just buying these commodities of different values, right? And I think it, you know, there's as the price goes up, I I firmly believe the the cost of the most expensive tokens we have not seen the ceiling and won't be for a while. It's going to it's going to make your eyes bleed how expensive the most expensive tokens get, right? But the cheap tokens are going to get really cheap, right? And that's going to be both of those things are

10:30 going to be really exciting. Especially once like the VC money runs out. Um you know, it's it's like Uber. Uber was dirt cheap in San Francisco when it first came out and then now like the VC money dried up. So I think it's exactly what you say. Like the the expensive tokens will get even more expensive, but because of open source, like if you can, you know, self-host DeepSeek, uh and you can get it through any number of the inference providers, they're all

10:52 competing like on the way to the bottom. Um so yeah, I think you're also going to have that price discrepancy. And then there's also just what's the best fit for the job. Something that's often overlooked is like we look at, you know, benchmarks and overall intelligence scores, but say for instance for working with legal contracts, Claude, even though certain things outrank it on the intelligence index, Claude is better at that like long-form like understanding

11:15 the nuances of every word and and things like that. So I think for like AI to to become truly dominant, it needs to be, uh you know, versatile in terms of what provider you use. And we see that because there's like Vercel's AI SDK, you've got Open Router, and all these different sort of like switching things, where you can have the same, you know, core API function, and it will just

11:38 route it to whatever provider you want. [clears throat] All right. I'll check up there for a bit happier news. According to Forbes magazine back in 2025, 42% startup have failed in the Silicon Valley. And uh That seems low. Yeah. It could be more.

12:02 Maybe they're in the process of filing bankruptcy. Uh and I'm not sure you know about builder.ai. They ran through 450 million dollars, and they had to file for bankruptcy, too. You all of you are in your AIs building your AI companies. What is the moat? What competitive edge do you think companies have these days while they're building their product?

12:24 Because to us, it sounds like everybody's trying to build the next frontier model. But where do you think it's a competitive edge? Is the data set? Is it the privacy, security? We'd love to hear your feedback. I'll weigh in on that. Um My Yeah, thank you. Um So, I do think

12:46 Are you familiar with the bitter lesson? Like the idea of the bitter lesson that like effectively like more data and more total compute subsumes all bespoke like classical natural language processing a good example. There were a lot of methods for like vision models, a lot of methods for like detecting boundaries in images or like, you know, faces, and it's like none of those are relevant anymore. Um

13:07 and so that I think is kind of a a large inertial force, where stronger, more powerful models will subsume many of the things that we used to like wire up harnesses for. I think agent harnesses generally are a good example of something that will not exist in 18 months. Um I do think though that product judgement is like it's hard to describe what you want.

13:33 And I think like if you and I were to vibe code workout app we don't necessarily like know how to articulate like all what are all of the things that a person would need from a tool like that or cal.com. Like I imagine that there are a lot of decisions that you've made that like if I was like I I need calendaring software my ability to articulate that and create something a delightful experience would

13:56 be low and I I do think that you know it's like taste is one of these things which is like how quickly can you gather information and make judgements and articulate that to an AI system. Um I don't know that there's going to be one monolithic interface that subsumes all product. Um and then I think integrations like there's a long tail I

14:18 would say that there's a long tail of integrations and capabilities that are not in the call it the blast path of the meteor um that are really important for things like law or networking that um just will not ever be on the like cut list for the foundation labs. So it sounds like you're saying what customer wants uh validating

14:43 Yeah and just like yeah being like being so tight um like in the meta of like what is actually important um and then just creating a really delightful and easy to use product that reflects deep expertise in the subject matter. Um I don't think the foundation labs I mean they maybe they have like they become the one app and they have

15:06 many many different verticals but uh it's unclear that that will be the the business model. I think I think feedback loops uh is what it comes down to like almost all like everything about startups comes down to feedback loops, right? You know the the famous advice to startups is like get out of the building, go talk to a customer, or you're going to build the wrong thing. Right? And and AIs, when they have really good feedback, can produce really good output. And then,

15:31 the quality of when the quality of the feedback goes down, the quality of the output goes down. Anthropic did a project a few months ago where they implemented a full C++ compiler by providing it with a test suite of like 50,000 tests. And they just said like, go. And they spent like I think a million dollars in tokens over a weekend, and it produced this perfect compiler that passed all the tests. And they're like, that sounds very impressive, but like who wrote 50,000

15:53 tests of a C++ compiler? That is the optimal case for this kind of thing, and almost none of us are starting from that kind of perfect specification, right? Even calen- calendars, right? It sounds so simple. It's like, look, I want to display a list of my appointments. How hard can it be? Right? As soon as you as soon as you put it in front of a person, you'll find out how hard it can be. Right? Networking. I like to brag

16:17 that if you ask Claude, like, hey, can I can you make me a clone of Tailscale? It actually tries to talk you out of it. Cuz it knows that networking is really hard. And it'll give you a list of reasons why you shouldn't try to clone Tailscale. By the way, you should just go open fork their open source repository. I can add a feature to it if you want. Right? But like that's the kind of stuff that is that is a moat, right? It's like it took it takes a long time to test

16:39 networking software cuz you need like 100 different devices that it needs to be compatible with, and you can't just pretend to test against it. You actually really physically need those devices to be there to test against. Claude can't set that up for you, at least not right now. This may all just be wishful thinking. It's possible. Any other questions? I guess if I gave Open Claude credit

17:03 card number, it could like have some devices shipped, and then pay somebody to set them up in a data center. Okay, we can then wrap up. Appreciate your time today, folks. Thank you so much for your time. Thanks for attending. Thanks everybody.