You saw the SpaceX S1. It's like, oh, $30 trillion. It's like, what is the world GDP? 100 trillion.
Both Stripe and Open Router really want lots of new companies in the world. We don't want everyone to be a part of one giant company.
When the models get more intelligent, the risk actually will continue to get higher. And yet, no one new is taking responsibility.
We're going to slowly realize how good we've had it with like deterministic code. Remember the days when computers did exactly what we told them to do.
Are we going to prevent the models from deceiving users during training runs predictably? Will like a model that's big enough and powerful enough suddenly stop deception and stop sandbagging?
Welcome to Asz Podcast. We're here with Amjad of Replit and Alex of Open Router. And this is the first podcast that Alex has done since the acquisition. So, we're really excited to have both of you.
Mhm. Thank you. Excited to be here.
Alex, let's start with that actually. If you can briefly share, obviously, you know, massive acquisition. Amjad is an investor. We're also of course an investor, the biggest shareholder, but yeah, who's counting? Alex, why don't you give us a little bit of the backstory, like how does an acquisition like that even happen? Like do you get a DM from Patrick one day? You know, what can you share?
Hey, how much for an OpenRouter?
I had talked to Will Gabbrick, the Stripe president, a long time ago, a couple years ago when we were doing our Series A, and we just stayed in touch. We had like a lot of Stripe work streams going on with various teams at Stripe. So there were always sort of things that we were doing with Stripe. We presented at Stripe Sessions, and so it always kind of felt close. And then yeah, just in July, I believe, they reached out, wanted to chat, met both of them in person, and it kind of just progressed from there fairly quickly. They're very efficient, and you know, they were very founder friendly about the experience. I was really impressed with the whole thing.
Did you want to sell? Like, did it even cross your mind before they reached out?
No, we were not thinking about that at all. I, you know, did really respect the company, do really respect it today, and you know, of possible acquisition options for us, it was I think my top choice. And so it was like an interesting idea. And you know, as we kind of fleshed out the reasons why it would make sense for both companies, it got more and more interesting. It was really clear how aligned they were with us kind of having autonomy over the brand and roadmap and product and keeping Open Router doing what it's already doing, just much faster with a much more serious go-to-market plan, and then you know, some better together stories between the two products and the two companies.
And then culturally, there were just, I think, very, in terms of mission and values and building like a neutral, trusted platform that businesses can depend on and scale on top of, that's also really developer friendly with the best possible developer experience to encourage new companies to emerge. You know, that alignment was there, and there was a bigger picture kind of alignment too, where like, you know, both Stripe and Open Router really want lots of new companies in the world. You know, we don't want everyone to be a part of one giant company. We want to create really good incentives and really sort of easy, streamlined workflows for people to start new companies and grow them successfully, and you know, make both lifestyle and venture-backed businesses on top of really good, reliable and price-efficient infrastructure and a marketplace that works. And you know, I really want that future, and Stripe demonstrated that they've been wanting it and building towards it for like many, many, many years. And in many ways, like payments and inference are going to blend together for companies of the future.
I'm curious. I understand why Stripe's incentive is to have, you know, a much more vibrant startup ecosystem. I understand the kind of moral argument and why you would want that. But why is that good for Open Router? Like, is your model for Open Router that it's like a network effects business? Is it like a network?
Well, for us, I think there's like a couple different problems Open Router solves. One is, you know, allowing you to build a company that uses AI or augments intelligence with unique data and other services without model lock-in, without vendor lock-in, allowing you to kind of be on the Pareto frontier continuously as the ecosystem grows. And to do that, it's a lot of work because there's all kinds of little lock-in that appears. We also, you know, we want companies to feel like the best way that they can add more than just prompts on top of a single model. There's like a lot more to building unique intelligence, and I think a big component of that is neurodiversity. You really need the power of multiple models that are trained in different ways, including some of your own, to do more than ChatGPT or Claude would on the task.
If like someone who is thinking about buying from you as a potential customer is wondering, well, what if I just use the model directly? Like, how do you really show that you are significantly better and able to build a business that matters?
And I think a lot of it will involve neurodiversity and blending like powers and good data from multiple models. Another component is like helping people get really good cost efficiency. Like there are a lot of businesses that just cannot, do not emerge until they become cost effective, and like creating an environment where we can help drive down costs by building an efficient market is like crucial to making that happen. Otherwise, like, you know, why lower my prices as a provider? We have a captive market. And like, you know, I think that's like a key point of marketplaces that was just totally missing from AI before we showed up. There was just one player, OpenAI. It could have been like a very strange world.
I'm not saying that we did all the work. Of course not. But like helping people choose new models and explore new models and learn like what makes, you know, a closed source or open weight model actually good at your task involves like seeing what the whole ecosystem is doing and learning automatically. Like LLMs are not things where you can just enumerate all the features on a web page. It's impossible. You have to see how they're being used to know what they're good at.
So, you started the company three years ago. I'm curious, what has surprised you the most about sort of the evolution of open and closed source models to the present, as it relates to model performance or just people's perspective on the variety of models they have available to them?
People have been more open-minded than I thought they would be towards open weight models. Like typically there's a lot of brand, especially in enterprise, typically there's a lot of brand trust where enterprises in general are like, oh, I don't really know how to tell the difference between these things, so I'm just going to buy the one that all the other credible enterprises are buying. And there's a lot of enterprise lock-in with like a mentality like that. And that just didn't happen that much. It did happen like a bit, but we saw a lot of enterprises want to explore new models. It was very much good for a marketplace. Enterprises wanted to diversify outside of just the proprietary frontier model labs, both for cost reasons and for differentiation reasons. They wanted to own their intelligence so they could, you know, I think like one, keep their talent, have like an internal AI practice. Like AI is just like a huge strategy topic. It's not like you go to your board and you're like, oh yeah, we fixed the AI problem, quarter complete. Like your board is asking you every month like what's next for the internal AI team. And like every single enterprise now has just this internal AI team that they're developing where they need a strategy behind it. It's not just like we check the feature off, we set up the database and we're done. And so I think that dynamic has resulted in just like a desire to explore and diversify, and a desire to figure out how to reduce costs and figure out how to do benchmarks for the first time in the company's life. I have been surprised there hasn't been more benchmarking, like more companies creating more benchmarks. It's starting to happen, and I think eventually we'll see a lot more of them to demonstrate like, oh yeah, this thing is better than using Claude direct. But I think that's going to be a bigger focus for this internal AI group at every company, evals. And I think like Jud's been doing that a lot at Replit, for example. Like you guys have done a lot of cost per task research. And you know, you've made like a doom loop rescue. You've kind of been experimenting with new ways of using agents like doom loop rescue and helping bring those to developers. So like more of that kind of research I think is going to pop up internally everywhere, for all of those reasons.
Yeah, I think Satya, CEO of Microsoft, has been very, very, you know, prescient on this and also very articulate on why companies need to own their intelligence ultimately. In the same way that, you know, we had dot-com companies and then every company became an internet company, like every company employs people that know how to build websites and be on the internet. Similarly with software, every company has software engineers. Every company needs some AI practice, AI capability, and that will compound over time the knowledge, the intelligence inside the company, the use case model fits, like which models actually work for them, how do they save money. They need sort of that independence.
The other thing that I think Alex Karp of Palantir has been talking about is that there's like a risk that when you work closely with the foundation model companies is that they're going to move into your business. And we've seen that, you know, with Figma, we've seen that with like now Harvey and OpenAI. And you know, it's really hard to partner with them because, you know, they see the world as their potential market. Like I mean, when they talk to investors, they're like, you know, you saw the SpaceX S1, it's like, oh, $30 trillion. It's like, what is the world GDP? 100 trillion. And so there is a sense in which these companies are different than other generations of companies. It's harder to partner with them because their ambition is such that they want to subsume a big part of the economy.
And increasingly, like what we're thinking about at Replit is kind of in the similar vein to what Alex has sort of innovated, is Replit is becoming more of an independence layer inside enterprises where we create a layer of interaction between you and the models, and we get you the best token at the cheapest price. But also we create an abstraction layer on top of the cloud as well, because you know, you should be able to deploy to AWS and Azure, and you should be able to use Databricks and Snowflake and so on. And so increasingly, I think there is not just with AI, with all of technology, there needs to be more platforms that help companies gain independence.
It seems like, yeah, what Open Router did for their segment, you're doing for other areas of the business.
Yeah, this kind of reminds me of this tweet I saw the other day when somebody was like, basically all companies are building the same thing now. Everybody's building an agent loop with like notifications and context, like third-party connectors and context management and memory and sandboxes, you know, agentic web search, computer use, and an always-on agent on top of it and notifications. And it's like this product is showing up everywhere.
Um, in a way, yeah, it is like showing up everywhere, but it also kind of feels to me like these are just the new table stakes primitives. It's kind of like a 2005 version of that tweet would be, oh, everybody's building the same thing. It's like a database, a users table, a sign-in page, a signup page, a profile page, a logout page. Like everything's the same. It's like there's like a lot of differentiation really. It's just there's table stakes needs for AI just like there are table stakes needs for the web.
Yeah. And I think as well, like inside the enterprise, making these products actually do real work is still an unsolved problem. Like you can use Muse in your personal life and connect it to your credit card and bank accounts, and no one's connecting Muse to their enterprise data or even Grokbot and things like that. I think there's an even more emphasis on data sovereignty and security. So we spent the past year almost working on making Replit deployable on your own cloud, basically on-prem, like bring your own cloud. Like two years ago, I would have thought I would never do this because, you know, it's just like, yeah, the cloud is the future, like software as a service, all of that. But now actually we've sort of reverted a little bit back to a world where companies are a little bit more protective because there's so many ways in which data can leak. Like all these agents that people are using. I mean, there's all these screenshots on Twitter. I don't know how true, where Instinct is like, or Muse is like mixing people's data, starts to call you by a different name or something like that. And so yes, the kind of consumer stuff is kind of obvious, but on the enterprise, there's still a tremendous amount of work for the entire industry to do in order to actually get these things to be useful and productive at work.
Do you, are you doing any, like do you have like a custom personal agent other than Muse or Instinct that you use for like work stuff that you've been building?
Yeah, I mean, I built something on Replit like a long time ago. I started as like a sort of a CRM agent initially. That was the main problem that I had, but slowly like we added features to it and it's sort of like doing more and more things. But what's really interesting is that the more I connected Replit to all my stuff, it sort of like started answering all the things for me. And so increasingly like the platform itself is like subsuming these sort of domain-specific agents that I built. I do think that there is some, in some ways you want something that is sometimes like focused on one particular thing and you don't want it to be able to do everything. On the other hand, once you have your entire company's context in one place, it's really cool to join across totally different domains. Like when I ask it a question, it can look at my sort of personal chat history, join it across the GitHub repo, across Salesforce. And so it says like, it will link like random things. It's like, oh, you met this guy like a year ago at a conference. I see it on your calendar. And by the way, someone else from their team is in discussion with your sales team. And it creates all these different synergies. And when I go into a meeting, I've connected a lot of different threads and I make much more progress on a deal or something like that. So this is where it's sort of trending now.
Well, I think I'm like a little bit, I'll take the counter on that. I think the worst part about doing cross-domain joins with your personal agent is that the more work you give it to do, the more understanding of what's going on you're sacrificing. And yet no one new is taking responsibility for that sacrificed understanding. Like agents don't have any responsibility. You can't like, if there's a fixed level of cortisol that the whole company can tolerate between everybody, and you know, you like, I want to be like less stressed about some area if I'm going to be like sacrificing my understanding of it, someone else needs to take the cortisol, but the agent doesn't take on any of that responsibility. And like a universal agent that's doing all things, you know, I can't adjust how much I'm sacrificing in all the different things. It points me a little bit towards, you know, maybe like down the road, like the sub-agents that people use will be like very vertically focused. Maybe we have like a chief of staff type agent that, you know, coordinates between them. But I feel like you do need like vertically focused agents where you're like, okay, like this agent is more responsible psychologically for these things. And like I want like quality checks that make sure it's doing those things correctly. And it doesn't need to focus on anything else. It just has one focus area. I wonder if that's going to help people like at least get like a weird loose sense of responsibility on top of agents.
Fascinating. So you're saying general agents create like a tragedy of the commons of sorts?
Kind of like I have a general agent that every day looks for things that need me and tries to figure out what to do. And it's just like impossible to improve this agent. Like every time I try to make an improvement, I end up like ignoring its output about a week later. It just feels like it doesn't really care about any of the specific things it's diving into. And like kind of imagine having a chief of staff where they're very good at, you know, like drafting all of your replies across the whole organization. And then compare that to something where you have like 10 chiefs of staff, each as competent as that one chief of staff, but they're all responsible for like individual sectors of what makes up your life. Like the latter, I feel like gives you a way of tuning how much understanding you sacrifice compared to the gain you get from like basically I can lean in more to the areas where the agent is failing for some areas and then have agents with very good competency take over my understanding of other parts of my life.
Yeah. Interesting. It's sort of like almost rediscovering, you know, specialization, right? What's his name? The famous economist, Adam, like...
Adam Smith, like with a pencil kind of thing, where you know that was like a huge realization for humanity that like specialization is actually good. The problem is like we kind of over-specialized as like a civilization, and I think over-specialization is oppressive in its own ways. I mean, I think we've, you know, sort of like, you know, there's the Marxist theory of alienation, right? The idea is because of over-specialization, you know, people are doing just focus on one thing, they do not see the fruits of their labor, they don't actually know what their impact is on the larger organization or the product they're producing, and therefore they actually kind of feel depressed and detached, and you're kind of acting like a machine and you're not actually fully human. And so maybe there's like a bit of a reaction to that. And I think with our agents we're like, oh, there should be like one god agent, but in fact specialization is actually like really good for machines, and that's like the point that you're making. And like humans should be general, but like machines should be ultimately a lot more specialized.
The problem with what I'm describing is that we don't know what good looks like. There hasn't been a system of specialized agents that feels as elegant as like ChatGPT or Claude or Muse, where you're basically just talking to one thing only. It's yet to be discovered. Like maybe like OpenAI just launched Dots. I think they're kind of like experimenting in that direction. Grokbot, I guess, kind of counts that.
