# AI Neurodiversity: Specialized Models Beat General AGI

**Podcast:** a16z Podcast
**Published:** 2026-10-03

## Transcript

You saw the SpaceX S1.
It was like, oh, $30 trillion.
It's like, what is the world GDP?
$100 trillion?
Both Stripe and OpenRouter really want lots of new companies in the world.
We don't want everyone to be a part of one giant company.
The reason why the OpenAI hacks have been so destructive is because they're so capable.
It's like nuking a butterfly.
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 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 a model that's big enough and powerful enough suddenly stop deception and stop sandbagging?
For the past few years, AI has been racing toward bigger, more general models.
But what if the future is actually more specialized?
In this episode, I sit down with Open Router's Alex Zatala and Replit's Amjad Massad to discuss why the next phase of AI may be built around many different models working together.
Alex makes the case for what he calls neurodiversity, combining models with different strengths rather than locking into a single provider.
Amjad explains why enterprises may increasingly want that independence too, owning more of their intelligence and choosing the best model for each job.
We also get into specialized agents, model routing and fusion, the trade-offs between capability, cost, and safety, and what happens when AI systems start training smaller models to handle specific tasks.
Welcome to A's and Z 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.
Thank you.
I'm excited to be here.
Alex, let's start with that.
Actually, if you can briefly share, obviously, massive acquisition.
Amjad is an investor.
We're also, of course, an investor, the biggest shareholder, but who's counting?
Alex, why don't you give us a little bit of the backstory?
Like, how does the acquisition like that even happen?
Do you get a DM from Patrick one day?
What can you share?
Hey, how much for the opener out of?
I had talked to Will Gabrick, the Stripe president, a long time ago, a couple years ago, when we were doing our Series A.
And we just, we stayed in touch.
We had a lot of Stripe work streams going on with various teams at Stripe.
So there were always sort of like things that we were doing with Stripe.
We presented at Stripe sessions.
And so they 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 they were very founder-friendly about the experience.
I was really impressed with the whole thing.
Did you want to sell?
Did it even cross your mind before the year's job?
No, we were not thinking about that at all.
I did really respect the company, do really respect it today.
And of possible acquisition options for us, it was my top choice.
And so it was like an interesting idea.
And 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 OpenRouter doing what it's already doing just much faster with like a much more serious go-to-market plan and then some better together stories between the two products.
and the two companies.
And then culturally, there were just, I think, in terms of mission and values and building 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.
That alignment was there.
And there was a bigger picture kind of alignment, too, where both Stripe and OpenRouter really want lots of new companies in the world.
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 make both lifestyle and venture-backed businesses on top of really good, reliable, and price-efficient infrastructure and a marketplace that works.
And I really want that future.
And Stripe demonstrated that they've been wanting it and building towards it for 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 much more vibrant startup ecosystem.
I understand the kind of moral argument and why you would want that.
But why is that?
good for OpenRouter.
Is your model for OpenRouter, is that it's a network effects business?
Is it like a network?
Well, for us, I think there's a couple different problems OpenRouter solves.
One is allowing you to build a company that uses AI or like augments intelligence with unique data and other services without model lock-in, without vendor lock-in, allowing you to kind of like be on the Pareto frontier continuously as the ecosystem grows it.
And to do that, it's a lot of work because there's all kinds of little lock-in that appears.
We also, we want companies to feel like that they can like add more than just prompts on top of a single model.
There's a lot more to building.
like 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?
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.
Like creating an environment where we can help drive down costs by building an efficient market is crucial to making that happen.
Otherwise, why lower my prices as a provider?
We have a captive market.
And 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 like choose new models and explore new models and learn like what makes a closed source or open weight model like 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.
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.
Typically, there's a lot of brand, especially in enterprise.
Typically, there's a lot of like brand trust where like 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 like enterprise lock in with 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's simply like it was very much good for a marketplace.
Enterprises wanted to diversify outside of just the proprietary frontier model labs, both for like cost reasons and for differentiation reasons.
They wanted to own their intelligence so they could, I think, 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.
Your board is asking you every month what's next for the internal AI team.
And 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 a, 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.
A desire to kind of like 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 like surprised there hasn't been more benchmarking, like more companies creating more benchmarks.
It's starting to happen.
And I think like eventually we'll see a lot more of them to demonstrate like, oh yeah, like this thing is better than using.
you know, Claude Direct.
But I think that's going to be like a bigger focus for this internal AI group at every company, evals.
And I think like Amjad's been doing that a lot at Replit, for example.
Like you guys have done a lot of cost per task research.
You guys have been, you know, you've made like a Doomloop rescue.
You've kind of been like experimenting with new ways of using agents like Doomloop rescue and...
and helping bring those to developers.
So 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 prescient on this and also very articulate on...
why companies need to own their intelligence.
Ultimately, in the same way that we had dot-com companies and then every company became an internet company, every company employs people that know how to build websites and be on the internet.
Similarly with the 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 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 with Figma vis-a-vis Anthropic.
We've seen that with now Harvey and OpenAI.
And it's really hard to partner with them because they're...
They see the world as their potential market.
When they talk to investors, they're like, 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, what we're thinking about at Replit is kind of in a 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 indirection between you and the models and we get you the best token at the cheapest price.
But also we...
we also create an abstraction layer on top of the cloud as well.
Because 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, but with all of technology, there needs to be more platforms that help companies gain independence.
It seems like what Open Router did for their segment, you're doing for other areas of the business.
There was this, 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 notifications and context, like third-party connectors and context management and memory and sandboxes.
you know, web, you know, agentic web search.
Computer use.
And always on agent on top of it, notifications.
And it's like, this product is showing up everywhere.
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 user's table, a sign-in page, a sign-up page, a profile page, a logout page.
Like, everything's the same.
It's like, I just...
There's like a lot of differentiation, really.
There's like, 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 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 Repl.it 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 service, all of that.
But now, actually, we've sort of like 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.
It 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.
Amjad, 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 built something on Replit a long time ago.
I started as a CRM agent initially.
That was the main problem that I had.
But slowly we added features to it and it's doing more and more things.
But what's really interesting is that the more I connected Replit to all my stuff, it started answering all the things for me.
And so increasingly, the platform itself is subsuming these domain-specific agents that I built.
I do think that there is, in some ways, you want something that is sometimes 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.
When I ask it a question, it can...
look at my sort of personal chat history, joining 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...
Like, you know, I've connected a lot of different threads and I'm making 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 that 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 sacrifice 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, you know, you, like, de- 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.
Well, the agent doesn't, like, take on any of that responsibility.
And then, like, a universal agent that's doing all things, you know, I can't, like, adjust how much understanding I'm sacrificing in all the different things.
It like 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.
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 like 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 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 it's 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 like 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, 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 like lean in more to the areas where the agent is failing for some areas and then have agents with very good competency like take over my understanding of other parts of Yeah, interesting.
It's sort of like almost rediscovering, you know, specialization, right?
What's his name?
Like the famous economist, Adam.
Adam Smith.
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 like 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 special, 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.
got agents.
But in fact, specialization is actually really good for machines.
And that's the point that you're making.
Humans should be general, but machines should be ultimately a lot more specialized.
The problem with, I think, 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.
Maybe like opening, I just launched Dots.
I think they're kind of like experimenting in that direction.
GrokBot, I guess, kind of counts.
But they're all very general.
I think the idea behind Dot is that it's like your digital double.
At least that's what I understood it.
Well, when I saw GrokBot, I don't know that much about Dots yet.
But they just came out.
But GrokBot, when it first came out, like the first use cases that I saw people talking about were, oh, well, I can make two bots, one that knows my bank account and one that knows my Twitter account.
And the two bots, like, don't have the credentials from each other.
But they can talk to each other if, like, they need to get something done.
There's no credential sharing, though.
And that was like one sort of big unique thing I saw like a couple times, like pop up a couple times that people seem to like.
But it looks like Muse and Instinct have a much stronger product market fit than GrokBot.
And perhaps it is because you don't have to worry about creating these.
these domains.
But I think maybe personal agents are different than work agents.
And I think your critique of general agents is more pertaining to work and to enterprise, which I sort of agree with.
And there's also all sorts of data access considerations.
I think as CEOs, we can have general agents because we have admin access.
But you're for individual employees or certain teams, they can't have truly general, fully context-aware agents because there's access control issues.
So you'll have to kind of work on something like specialization.
Ultimately, I also think we need to figure out what does agent-to-agent communication look like.
I don't think there is good protocols around that just yet.
I don't think that agents are trained.
to handle that very well.
I think we've seen, it seems like the next generation of OpenAI models are trained to do agent collaboration because we've seen it in the hugging face hack where they started helping each other instead of emerge naturally.
But there also needs to be some way in which an agent can't convince another agent to kind of give it.
give it information that it shouldn't give it.
There needs to be data isolation and proper ways in which these agents communicate.
You almost don't want to communicate them fully in natural language.
Maybe there's some other DSL or protocol that they need to follow.
I think one of the cool potential applications of JEV and other decision models like it is going to be alignment.
you know, checking to see if a tool call or like an agent to agent, you know, communication is aligned.
Because there's just so many tool calls.
Like you really need like a cheap, fast model if you're going to block something like that.
Then like a really, really fast decision model that just classifies and gives feedback on rejections.
might be like a really good way to bridge the gap between agents and from agent to infrastructure too.
So I haven't seen like, and we have like a little prototype that we're running internally at OpenRouter, but I kind of think a good, I think it could be like an interesting alignment study.
So you're using it for policy enforcements?
Yeah, like imagine looking at the system prompt.
and the current tool call being made and be like, is this aligned with the system prompt of the original agent and with these extra guidelines that maybe we didn't tell the agent about?
For example, let's say you have a bunch of agents that are instructed to red team some new product and they cannot, should not be able to access the internet.
And if they ever do, they should stop right away.
But you might not want to like explain all of that to the agents doing the red teaming.
You might want them to try to like break out of the sandbox and act like bad actors.
Like what would a bad actor do?
It wouldn't be like break out of the sand, you know, try to like break into this company.
And the moment you do, stop, don't do anything else.
So like having another model, more like, you know, use for...
you know, building a neurodiverse system, having another model like check every single tool call or every single assistant message to see if it's indeed aligned with something that wasn't in the system prompt, I think makes sense.
And then having kind of like structural safeguards too, which is what I think NVIDIA just launched with their like open agent safety, I think it was called Open Shell.
I think companies are probably going to explore a combination of those.
I wonder another thing about specialization and sort of what you're talking about.
There's a lot of talk of recursive self-improvement.
There's something I don't think is getting a lot of sort of discussion, which is models training their replacements.
It's sort of like...
You can think of it as a just-in-time compiler.
The way just-in-time compilers is, as you're executing dynamic code, the interpreter realizes that there's an opportunity to optimize.
It will emit machine code on the fly, and that's a lot more optimized.
So you can imagine models like general models, you're kind of doing something with Opus or some of the Astro, some of the big models, and they realize that the use case is limited, or you prompt them some way or some other agent observing and realizes that use case is limited.
And I think general agents have all these flaws that you just talked about, but also there's more potential for them to be harmful, there's more potential for them to go off the rails, and sort of on the fly trains a model that could be its replacement, but is a lot more domain specific.
And therefore, it is cheaper and also less vulnerable to prompt injections, less harmful because it's less capable.
And it's almost like some system that's training machine learning models for specific use cases as it's monitoring the entire system.
Would that specific use case involve unstructured text generation or a very structured decision model?
It could be unstructured text generation.
It could be decision models.
Even the case of Jav, if you understand the inputs ahead of time, you could potentially take an off-the-shelf like Quen or something like that and train it specifically for that policy.
That makes sense to do for cost reasons, assuming that there aren't really, like the model labs, the frontier model labs might make very low cost models that you can easily transition to.
But safety as well, right?
Oh yeah, I see your point.
They're so capable.
And so I think oftentimes people are using these big foundation AGI-like models.
to like, it's like nuking a butterfly, right?
It's like, yeah, they're very, you know, most of the times, like a lot of the use cases, even unstructured use cases don't need that capable model.
I wish there were more public evals about this stuff.
Like, but a lot of the evals about this are private.
You just can't see whether like when the models get more intelligent, the risk actually...
will like continue to get higher because I think there's also an argument to be made that alignment will get better and the models will like start to avoid going off and you know hacking on their own as they get smarter and better at alignment especially when it comes to agent to agent coordination like the this is something Noam Brown said on a podcast recently, like as the agents have gotten smarter, they've gotten just better at coordinating.
They're still like, and, you know, it's unclear if they're going to like be harder to align than humans when we get more and more of them.
But if we can figure that problem out, then a smaller model, like will it be harder to align?
So, Eric asked earlier, is that I think it's true that smarter models are more aligned naturally or they're easier to align.
Well, if you think back to the original sort of like rationalist, less wrong arguments for AI safety, there is this thing called the orthogonality thesis.
The idea is that intelligence is orthogonal to ethics or morality or, you know, so on.
I don't believe that's entirely true with humans.
I think people who are generally like more intelligent, kind of more educated tend to, not always, tend to be more considerate of animals, for example.
But in machines, I think it could go the other way because You know, there's been quite a bit of studies on RL showing, like, you know, how reward hacking and deception, they just, like, get better at it.
And, like, the evals could be deceiving because the model could be smart enough to know that it's getting evaled.
I mean, we already know this.
It's been shown that if you do a lot of monitoring on a chain of thought, they start lying in their chain of thoughts.
So you add pressure almost on the chain of thought, and that kind of creates.
And I think at some point, for you to do proper alignment evals, you need to run it for months.
You need to run this thing for months on a really large goal or task in order for it to to truly kind of figure out whether it's aligned or not.
I always struggle with this word alignment.
It just feels like wrong for so many reasons.
It's sort of like vague and sort of like aligned to what, whose values.
And so it just doesn't make the conversation easier.
I think in this case, I'm talking especially about deception.
Like the model is actually.
deceiving its user.
I mean, maybe this kind of reduces to like, are we going to solve a line, you know, are we going to prevent the models from deceiving users during training runs predictably with like, you know, better, like, well, like a model that's big enough and powerful enough suddenly stop deception, stop sandbagging.
And nobody knows the answer to that yet.
So, like, at the point when that does, if that ever does happen, though, we might see kind of an interesting pressure for organizations to go towards the frontier.
Oh, interesting.
All to have no, you know, basically no risk or significantly less.
Would they be willing to pay 10x to get that much?
I mean, it probably depends on the types of tasks they're trying to do.
Like, you know, some just have way lower risk than others.
Writing code that are doing security research is the highest risk type of task today.
And so you probably spend 10x to get a fully aligned model that can also find all the bugs.
or fully like anti-deceptive model that can also find all the bugs.
One of the coolest things about decision models is that you fully control the structured output.
And generally with structured output models in general, like the room for misbehavior is so much lower.
You just have like defined tasks and only machines are like dealing with the outputs.
And it's not writing code that it can execute.
that those tasks feel like probably underrepresented in the ones that people talk about and in the things that enterprises are dealing with.
So I expect like enterprises to get a lot more interested in them.
Yeah, I feel like we're going to slowly realize how good we've had it with like deterministic code.
Like, oh my God, remember the days when computers...
did exactly what we told him to do.
And I think things like JAV, I think, hint at more of a need for not only specialized models, but models whose app would domain is more controllable.
And maybe you could do a lot more than we thought you'd need.
by using a bunch of specialized models, specialized output models.
Have you guys done any workloads internally with it?
I've been training a lot of small models.
I said this glib thing when I first came out because I gave this Hacker News comment-like comment, which I felt disgusted with myself afterwards.
But I've been taking a lot of QN8B and asking, honestly, asking Fable and Opus and Astra to train a model.
For example, I trained a cost estimator model internally so that when you put it in a prompt in Replit, we know exactly how much it will cost.
And it basically emits a probability distribution over multiple buckets.
Like if it is between $5 and $10, bucket A.
Bucket B between $10 and $20.
So I'm used to training these classifiers by giving it different enums, essentially, and looking at the log props per enum.
I've been doing it for a couple of years.
I trained a chess bot to play by just doing that.
So I'm already sort of pilled on decision models and specialized models.
So it wasn't...
that big moment for me.
But I understand that a true foundation model that's fully promptable is amazing developer experience and you can do a bunch of stuff with it without training a model from scratch.
But if you have data, if you work at a place where you have the data, you have so much data at Replit, I ended up training a lot of specialized classification models pretty easily.
Yeah, it also feels like less model debt.
Like something that I still hear from companies is that they're like worried about fine-tuning models for like unstructured outputs because you're just like always, you got to redo it again in like two months and everybody just feels the model, the weight of the model debt.
But like a very bespoke classifier that's trained with like proprietary data, I feel like people won't think it's behind constantly and it might just like last longer.
Yeah.
You don't have to worry about its ability to speak a new language or write Rust or do anything that the LLMs are being evaluated on.
You know the use cases so you can build it more.
It seems like an easy thing for enterprises to build themselves and actually not regret.
Speaking of Rust, actually a good analogy is when the world got super excited about dynamic languages.
Like, if you think back to the 90s, everyone was writing in Java and C++, things like that.
And then, like, Python, JavaScript, Ruby just, like, took over the internet.
And everyone was like, ah, this is how you build startups really quickly.
This is, and you built Stripe, a financial organization on Ruby.
I was like, how crazy is that?
And we built Facebook, you know, using PHP.
And then everyone was like, ah, shit, like, we're running into all these.
really bad bugs.
It's like freaking slow.
So like, let's go in and add types.
Okay, let's add a jit compiler.
And you end up sort of reinventing everything.
And then Rust came out.
I was like, okay, I guess we can use Rust for a lot of things we would otherwise be using JavaScript and Python.
And my prediction is that the same cycle will happen here where we're using these AGI-like models.
for all these different use cases, and then everyone's going to wake up and be like, oh my god, this is so wasteful, so risky for no reason.
And it's got to be so much easier.
We're actually adding that capability on Replit, but I think it's going to be everywhere.
It's going to be so much easier to go to a site, upload a CSV file, and get a special model that does one thing.
That goes back to your thesis about Open Router, this neurodiversity, which I really fundamentally believe in a lot more.
Eric and I had discussions a lot about AGI and whether we're truly on a path to AGI or whether it's even desirable.
to get there, and I think the future is a lot more diversity.
Outside of code review, which was like, I think the first time I saw people get really serious about using different model families to double-check the results of their main model, these fusion models, the research has been kind of slow for years.
on doing mixture of models and composite models.
But things have been speeding up from my view of the research.
And now we see a bunch of AI agent labs.
We launched a fusion tool, a fusion model, and cognition launched one.
They do reduce cost.
follow and allow a wider breadth of ideas to be searched.
Our initial launch was focused on deep research.
The thinking is that if all these model labs are training on different sources of data, why not pull from all of them?
And this resulted in basically Fable-level quality at 2x lower cost.
We just published results, actually, just today about that.
We showed a deep sweep replication through combination of different things, including the harness, but also you might think of it as a fusion-type thing where it's frontier level at 40% to 50% of the cost.
Which models does it use?
I think it changes over time.
But one thing that has been interesting is I think OpenAI added this feature that allows you to save the computation across different model families.
Also across different effort levels.
So you wouldn't do...
You wouldn't miss the cash if you change the effort.
Don't quote me this.
I think it's also across different models, which is hard to fathom how.
I might be wrong, though, but I need to double check that.
But I think, you know, seeing within the OpenAI family has like added a lot of efficiencies.
But in the past, we've done it with other with other.
with other models as well.
Because being cache aware is one of the biggest things when you're designing fusion models, routers, sort of simulation models.
Alex, I'm glad this has been a great conversation.
Thank you.
Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com.
Thanks again for listening and I'll see you in the next episode.
As a reminder, the content here is for informational purposes only.
Should not be taken as legal business, tax, or investment advice or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund.
Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast.
For more details, including a link to our investments, please see a16z.com forward slash disclosures.