# LangChain Founder on Agent Economics and Infrastructure

**Podcast:** The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
**Published:** 2026-10-17

## Transcript

I think absolutely it's worth taking it to start for Sequoia and Benchmark.
We spend probably roughly 30% of our engineering, sadly, on coding tools.
It's actually gone down the last two months, while tokens, the number of tokens that we've used, has actually gone up.
I think I really underestimated the demand for inference.
Everything just works.
This market is so big.
There's just so much demand for it, and I don't know if it's going to get less anytime soon.
This is 20VC with me, Harry Stebbings.
Now, I remember sitting down with my friend, Miles Grimshaw, when he was at Benchmark, and he said about this incredible founder he made.
met, Harrison, the founder of Langchain.
It's since become a multi-billion dollar company.
It is a leader in the ability to create and monitor your agents.
And today, we welcomed him to the hot seat to discuss why it is so hard to build agents today.
What no one knows that everyone should know about what is coming with agents.
And then, my favorite, some really kooky predictions, which no one else believes will happen, but he knows will definitely happen.
This and so much more in the show today with Harrison.
But before we dive into the show today, what do Uber, Cursor and Harvey have in common?
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While Laredin tracks AI impact, Superhuman helps you move faster.
Can I be honest with you for a second?
Here's what my week actually looks like.
Back-to-back meetings all day, and the real pressure builds up in the gaps between them.
I'll come out of six founder meetings, with a whole stack of follow-ups waiting.
And my inbox, it's hundreds of messages.
And somewhere in there is the one thing that really matters, which I always tell my mother is her.
I've tried AI tools for this, but they always lived in another tab.
I'd have to stop, go find it, paste in the context, write the perfect prompt.
It was just enough friction that it never...
really stuck.
Superhuman Go from the makers of Grammarly, totally different.
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It's already up to speed and I'm always the one in control.
Try Superhuman Go from the makers of Grammarly.
You have now arrived at your destination.
Harrison, dude, I'm so looking forward to this.
I've been looking forward to it for a while because I remember Miles telling me about you when he was at Benchmark and just singing your praises.
So I wanted to make it happen for a while.
So thank you for joining me.
I'm excited to be here.
For those that don't know, 60 seconds, what is Langchain and where do you like sit in the stack?
We build tools to make it easy to build agents.
So we build developer tools primarily.
We have an open source framework, which basically connects the model to everything else that you need to build a useful agent.
So tools, data, computation, things like that.
And then we also have Langsmith.
Langsmith is observability and evals.
Agents go wrong all the time.
How do you know how they go wrong and how do you measure them so that you can increase their performance over time?
That's what Langsmith does.
Why is it difficult to build agents?
The hard part about building agents is they don't behave how you want them to behave.
So the models are these non-deterministic black boxes, right?
And they take natural language as input, and then they output natural language.
And inside the black box, they're also non-deterministic.
And so what that means is that the input you get is infinite in dimension.
It can be anything.
You put a chatbot in front of someone, they can type anything that they want, right?
And so that makes it really hard to know what they're going to type.
And then once it goes into this model...
And by the way, agents are not just one model call.
They're a series of model calls, and they're interacting with other tools.
And so you have the small block...
black boxes that are then put inside this large system, and you get basically these cascading types of errors.
So you have this very big kind of input space.
You have this very big output space because two similar inputs can go in and they can arrive at completely different answers.
And the thing that's making all this hard is the LLM itself.
Yes, they're very, very smart, but you don't know exactly what they're going to do, and they change answers based on small changes to the input.
And so you build this system, but it's really non-robust.
And so getting this system to behave reliably, that is the hardest part.
When we talk about agents three years ago, four years ago to agents today, they've gotten a lot better.
And the main thing that's happened is one, the models have gotten better.
And two, we've figured out how to build systems that basically harness these models better.
And that second part, the harnessing of these models is what we focus on.
How does your business change in a way that people don't see as the models get better?
The main way our business changes as the model gets better is we basically have to erase big parts of our product.
So what I mean by that, and Kursar said this really well as well, I think this was one of their actual operating principles as a company is like erase the product or delete the product, I think is what they called it.
Where basically these models get better and things that you had to do six months ago, you no longer have to do.
And so this is very relevant in our open source.
So our open source is all about basically the harness and the framework for taking these models and helping them do things.
And the right way to take these models and help them do things changes every month, three months, six months, choose your time period, but it changes pretty rapidly.
How do you build for such an uncertain future?
Do you just have to get in bed with the labs to know the roadmap so you don't waste six months building something that then they erase?
There's no easy answer.
On one hand, what are you going to do?
Not build.
That's what I like to say to founders who ask this.
What do I do?
Stuff is changing.
The models are changing.
If I build something now, it might not be relevant in six months.
What's the alternative?
You just don't build anything.
And so I think you have to make educated guesses as to what parts are going to be around and what parts aren't going to be around in the future.
So for example, early on, the way that we would do tool calling in LinkedIn was by having the model generate JSON, DSL, and then parsing it.
And that was something that I think we all thought or knew was like a little bit of a hack at the time.
But we had to do it because that was the only way to get it to actually steer things, because you have to get it to do this type of tool calling.
And then a few months later, OpenAI came out with tool calling.
And I think we recognized pretty early, and this is like a more native version of tool calling that's directly in the model and in the APIs.
And I think we recognized pretty early on that that was the future, and so we invested a lot of efforts and just completely switched from JSON parsing to this tool calling.
And so I think you have to basically recognize what things are short-term hacks and what things are long-term kind of like will always be there.
The models will never know your proprietary data.
They will never be able to kind of like actually get the output of APIs.
You have to take their API call.
pass it to the API, get the output, and then continue in the loop.
And so I think the system around the model connecting it to other places will always be there.
But a lot of the hacks we do around chain of thought or planning or tool calling, things that can just be much more closely integrated into the model or into the model API will go away.
And I think you just have to be quick to recognize that and go on.
Why do we need neutral harnesses?
We see, you know, bluntly, very brilliant agent builders from the models themselves.
Why does your layer not just get eaten by them?
I think the reason we need neutral harnesses is that there is a lot of state that you build up either before you build the agent or as part of the agent running.
And the state can include things like memory, whether that's short-term memory or long-term memory.
Memory also comes in different form factors.
It can be from interacting with the agent versus from the agent learning from the environment.
And the reason that this state gets built up is the agent interacts, it goes out, it learns, it chats, and it learns things to make it better.
I think one of the most interesting things right now is how to make these agents do better over time and learn.
And some of that learning and some of the doing better over time will be in the model weights, but a lot of it is outside the model and a lot of it is in this context.
When you build up this context, you really don't want it to be linked to a single kind of like model provider.
The reason for that is that then it becomes linked there.
You can't go to other models.
Being able to change models is very offensive and defensive.
So it's offensive in the fact that you want to be able to take advantage of the best model when it comes out, no matter who it's coming from.
It's defensive in the fact that you don't want to get locked in, so they can't raise rates, things like that.
So you want to be able to switch models.
The harness can be very tied to the model.
It can also be neutral.
The context right now is really intricately linked to the harness.
The harness's job is to basically take the model and interact with all the other things.
All the other things includes data and APIs, but also includes memory.
And so if you build this memory in a way that is linked to the harness, and the harness is linked to the model, then you're kind of stuck.
So the true value of the harness structure is the portability of data that it allows the user.
Yeah.
You want to basically not be locked in to models.
And if memory, if context starts building up in a way that is specific to how one model wants to interact with memory, wants to interact with context, that's very bad for portability.
And then that puts you at a disadvantage building your business going forward.
We talk with a lot of people.
about owning their intelligence and what that means, and there's a lot of different versions of that.
The version that we think owning your intelligence means, we think there's a few different components of that, but one key component is having this open and controllable agent.
You want to be able to adjust all the different pieces.
You want to be able to adjust the model.
You want to be able to adjust the harness as well, because sometimes these model labs are training harnesses to be really good at the things that are in distribution for them, which oftentimes is coding, and coding is great, and there's a ton of use cases around coding, but there are a ton of use cases that...
that aren't coding.
And maybe they'll write code as part of it, but they're using that as a way to do sales, as a way to do research, as a way to do the law, as a way to do these other vertical specific things.
And so when you get out of distribution of what the model labs are training on, you want to have control over what the harness does.
And then you also want control over the context as well.
So model, the harness itself, and the context make up this open and controllable agent architecture.
And we think that's a big part of what it means to own one intelligence.
I just want to back up a little bit.
When we think about agent value, so to speak.
Some people say that the value is actually in the verification, like whether it did well or not, because that unlocks the next step, which is automation.
If value is derived through verification, who wins?
The harness?
If value is derived through verification, I think the people who win are the domain experts, the people who are able to kind of like quantify what that value means, which is very hard.
Like building evals is a very hard job.
And so I think a lot of the value will...
accrue to whoever can write those verifiers, capture that domain knowledge.
I'm massively bullish on vertical agents for different domains for this reason, because I think the domain experts who are best suited to write those verifiers or build those evals, as we talk about it, they will be able to build harnesses and models.
And it won't be just the harness.
It won't be just the model.
It will be some combination of them.
So very practically, if you're getting started building a vertical agent, I would highly suggest you start.
at the harness layer.
You do context engineering, you tweak the harness, and then when you've got users, then you start tweaking the model in post-training.
I think it's much higher ROI to start at the context or harness layer first.
And so I think we'll have all these different verticals.
They'll be able to quantify what good looks to them in a very domain-specific way, and that'll let them build not only vertical agents, but also vertical agent experiences.
Different agents might have different experiences.
And so I think a lot of the value will be massively bullish on vertical agents.
want to enable a lot of that.
You said, like, if I was building that, I would start at the harness layer.
We're seeing a lot, whether it's Harvey or I think it's Ramp, build their own models.
To what extent are they actually building their own models?
Mike Cannon, Bruce, at Atlassian said on a show we released today that it's largely kind of respectfully BS, and they're fine-tuning open source Chinese models.
Is that true?
And how do you think about that?
I think a lot of it is they are post-training open weight models, absolutely.
I still think that's a valuable thing to do.
I don't think there's anything wrong with that.
I think there's different dimensions that you can go in terms of owning your intelligence.
I think the cheapest way is you just control the context.
You use an off-the-shelf harness, off-the-shelf model.
The next level down is you start tweaking the harness, and that gives you more control.
than just the context, but it's a little bit more expensive to do.
The next level down is you post-train an open weight model.
And so you might take an existing open weight Chinese model and post-train it for your domain.
And then the very bottom layer is they actually train a model from scratch.
I don't think many folks are training a model from scratch.
I think most are post-training.
I think if you're training a model from scratch, you're probably a Neolab.
I think those are one of the few that are doing that.
But yeah, the RAMPs and the Harveys of the world, they have a ton of talent.
They have a ton of data.
They have a bunch of usage and are trying to think about really optimizing it.
And so they've gotten all the juice they can out of the context, all the juice they can out of the harness.
And now they've arrived at basically post-training of these models.
Can I ask you, the jury is kind of out on the efficacy of these specialized models in the post-training realm, like we mentioned there.
Some reports I've seen say they're massively more efficient and better, and some less so.
How do you think this will play out?
I think what we've seen in regards to post-training is that a lot of the post-training that practically makes sense right now is probably for simpler tasks that make up a larger harness.
So what I mean by that is when you have an agent harness that's doing interesting work, it's actually very rarely just one model running the loop.
That might be the core of it, but there's a bunch of other model calls that happen either as guardrails or routing or sub-agents or sub-workflows or things like that.
And so a lot of the places where we see post-training being efficient now are for simpler tasks like classification or extraction or summarization that might be a tool the agent uses or a guardrail or things like that.
I think those types of post-training right now make a ton of sense and will continue going forward.
I think the question that is more unanswered is for this core model that's driving the loop because the main way an agent works is there's an LLM running in a loop calling tools.
And so the question is for this model that's driving that loop.
Will those be kind of like post-trained models or will those be kind of like frontier models?
And I think this is where we see more of like a back and forth where like every time a new frontier model comes out, the better they are at just running this kind of like core loop.
We do see people like Harvey and Ramp and others kind of like experimenting with post-training models for this core loop.
And so I think Harvey recently in the past month or so announced something exactly in that vein.
I think that's an open question.
I think- If you were building your own vertical application, would you- Post-train your own or not?
Not to start.
Absolutely not to start.
But if we're talking five years in the future, assuming that I'm getting a ton of usage, I think it could be a realistic possibility.
One of the things that harnesses also need to do, I imagine, is model routing, depending on request.
How much value is in the model routing layer?
Model routing is so interesting.
I think it's one of the hottest and maybe most divisive topics right now.
So a few thoughts.
First, there's a question of where the model routing should even be.
Should it be at the harness level or should it be in the gateway level?
And this is like getting a little bit into the technical weeds.
What is the difference?
I'm sorry.
The difference is in the harness level, it's in kind of like the code of how it runs.
And in the gateway, it's just an API call.
So the benefits of putting it in the gateway level, because it's just an API call, you can drop it into any harness.
could put this routing that's happening at the gateway level into cloud code, into any harness without having to change the code.
The downside and why I think we're more bullish on routing happening in the harness, as are, I think, Factory and Cognition have also written things about this, is that in the harness, you just have more knowledge of what's going on.
You have more awareness of the state.
When you have an API, it's generally assuming that it's kind of like an open AI chat completions API.
And so it only takes in a certain amount of state.
You have more state in the harness to know, hey, what's going on with the prompt cache?
You want to try to keep the prompt cache warm and available.
You can also change the prompt more easily.
So a lot of these models have different prompting strategies.
And so if you do the routing at the harness layer, you can more easily change the prompt.
And so I think a lot of what we've seen is, one, this debate between gateway and harness and where does routing go.
I think we probably believe it belongs in the harness.
And just so we understand, the open routers and all the other providers, which there are several, they are in the gateway.
They are in the gateway.
A lot of, obviously, OpenRouter, incredible company, incredible usage.
Had them on the show.
Love Alex.
Alex is great.
I think a lot of why people use Open Router and Vercel and other gateways is largely for the model access.
So I think there's also a difference between a router and a gateway.
And so I would probably say most of Open Router's usage, and Alex obviously knows the numbers a lot better than I do, but if I had to guess, I would guess a lot of it just comes from being a gateway, not a router.
So people specify the model they want upfront, and Open Router just provides a single API with a single API key for accessing that.
In terms of smart routing, I think that's actually...
being done way less than people think.
And I think when it's done, it's built into the harnesses by folks like Factory.
I think Factory does a tremendous job about this.
They've written a number of technical blogs that are very well done on terms of how they do routing.
But I think right now, I pretty strongly believe that routing, you have to be harness aware.
You have to know what the task is.
You have to know what the harness looks like.
So I don't think you'll have routing that works for law and works for finance and works for coding and have it all be the same.
It's going to be very domain and very harness specific.
I'm sorry, when I listen to you it seems so unwaveringly obvious that routing should be at the harness layer, given the context and memory that is stored there.
When I have such an obvious response, I like to understand why am I stupid?
What is the bull case for it being at the gateway layer?
I think when people talk about it at the Gateway layer, I think there's maybe two things that they're talking about.
So one, it makes it way easier to port.
So you can take Cloud Code.
People love Cloud Code, right?
If you tell people, hey, you can't use Cloud Code, you have to use this other harness.
There are other good harnesses out there, but let's pretend for a second that you love Cloud Code.
And you want to let them keep on using that, but you want to do some semblance of kind of like routing to maybe like a DeepSeq or an open source model or something like that.
then the only way you can do that is at the gateway layer because you don't control the harness.
Side note, this is another argument for open source harnesses that you can control and modify.
And then the second thing is when people talk about kind of like routing at the gateway layer, I think they're oftentimes implicitly assuming that there's a domain.
So there's kind of like two general domains that you could maybe argue that you can build general routers for.
And I actually think Open Router has done this to some extent.
You could argue coding.
Coding is the most popular domain.
That's where all the usage is going.
Let's build a router that works really good for coding.
Maybe we won't be harness aware, but we'll be use case aware.
So we know different models are good for different things in coding, and we can put it at the gateway.
The other thing you could say is, hey, we've got this general chatbot.
We're not trying to be best in the class at law or best in the class at finance, but we're just going to be a general chatbot across the company.
And we can know well enough that if you say, Hi, how are you doing?
You don't need Opus for that.
You can use a cheap model.
And so I think when people talk about routing at the gateway level, they're implicitly kind of assuming that you're doing either general purpose chat or coding, and you're not doing a domain-specific thing.
As soon as you get into a domain-specific thing, I really think it has to be at the harness layer.
You're not going to use an off-the-shelf gateway router for that.
So in two to three years' time, you think the majority is within the harness, given the...
benefits that come from that context, but for very horizontal tasks, it will sit in gateway.
I think that's exactly right.
If you're building a vertical agent, I think routing needs to be in the harness.
If you're thinking about a general purpose horizontal task, like just general chat or coding, it could maybe be in the gateway.
Is it a commodity?
Does everyone just build it?
I don't think routing is a commodity.
I do think people will build it, but I think building it is quite hard even today.
And I think part of the reason for that is the models just change so rapidly.
So you can argue that certain models are better at different things, but then a new Opus or new OpenAI model comes out and it's just better at everything.
And so then the value of routing kind of goes down.
Do you trust evals?
I think evals are useful, but I wouldn't trust them fully.
And so what I mean by that is I think a lot of the academic evals that are out there that are used when a lot of the models come out.
are not really that useful anymore.
I think all the models that come out are benchmark maxed against those evals, and so you don't learn a lot.
I think internal evals that you have and that you build for your domain absolutely can still be useful.
This is the main reason that building evals is such an important part of building a vertical specific agent.
I think you have to have some way of measuring things.
I think you can generally trust those evals.
But even then, one of the most important things to do is just look at the data.
Just look at the data and see what it's doing.
Don't rely too much on a single number.
I think this is why you want to have traces.
You want to look at what's going on.
Maybe your evals aren't perfect.
Evals are really hard.
You can have some sense, but maybe models are doing better, but in just a different way that you didn't know how to capture before.
And so I do think, yes, I think you can trust vertical specific benchmarks, but you have to evolve those as well.
And that's one of the main things that we've seen because these models just get better in different ways.
Can I ask you, you mentioned traces there.
If traces are exhaustive agent work, where is the value then?
Is it the app, the observability layer, the model layer?
Where is it?
If you're building an agent, I think the value is at what makes that agent good at the domain or vertical that you're working in.
So what I mean by that is, again, I think there's different ways to control what an agent does.
There's the context, there's the harness, and there's the model.
Having specific proprietary things for any of those can be an advantage.
I think controlling the context is the easiest and the most obvious thing to do when starting.
And that would be data.
And that would be data.
That would be memory.
That would be specific APIs, specific tools that you can use.
Controlling the harness is like...
a little bit harder, but you can still put a lot of your domain knowledge around what should do, both routing and things in there.
And then the model is the hardest to control, but potentially the highest ceiling.
The thing that lets you iterate on each of those is evals and traces.
So there is a ton of value in these, but there's only value in that you capture insights that you bring back to the context, or bring back to the harness, or bring back to the model.
And that exact process can look different.
So post-training...
great for bringing these insights from traces back to the model.
For bringing things back to the context or memory, you're probably going to want to run another agent over those traces and then have that agent output learnings from memory.
And then for bringing these learnings in the traces back to the harness, you're probably going to want to run an agent over those traces and update the code of the harness itself.
And we actually have something that does this for people.
It's called Engine.
It's an agent in Langsmith that runs over traces and tries to update code.
And then when you update these things, you're going to need to know that it's actually better.
And this is where evals come in handy.
Can I ask, how does continual learning change agents and how they work?
Continual learning, I don't think so much changes how...
agents work, but I think it changes the systems that you want to have running around the agent.
So what I mean by that is I think the core loop for a continual learning agent, the core inner loop maybe to say, will still largely be the same.
It will be an LLM running a loop, calling tools, interacting with the model, and an environment.
I think what continual learning adds is an outer loop.
an outer loop that looks at the exhaust of agents and then does things with it.
And again, what it does could be different depending on what continual learning means for you.
So I think continual learning is also one of those words that doesn't have a super precise definition or maybe- How would you define it?
I would define it as a system that gets better over time as it runs.
And again, there's different ways it could get better over time.
It could update the context, it could update the code of the harness, or it could update the weights of the model.
And the outer loop will look roughly the same for all of those.
It will look like take traces, get learnings, fix something, measure it to make sure it improved, and then repeat.
But that exact kind of like changing, again, updating weights of model, very different from updating a text file that is like a memory file.
If we look at the context layer, we mentioned data being a part of that.
We're seeing data companies scale to hundreds of millions of revenue in the fastest time ever.
I mean, ever, ever, ever.
And everyone is selling data.
How do you expect this market to play out, given your neutrality on not being involved in it, but being adjacent to it?
Will this be a market of consolidation in years to come?
How do you see it play out?
I actually think the data market is still like...
pretty early in the grand scheme of things in so much as we're talking about kind of like a context layer for agents.
So what I mean by that is there are a bunch of databases that are...
ClickHouse is maybe a really good example of one that's doing incredibly well recently.
I think they're still largely focused on kind of like their database.
They're good old fashioned database.
They're good at storing data and querying data, but it's still in pretty kind of traditional ways.
ClickHouse existed pre-AI.
When we talk about a context layer for agents, and again, there will be a bunch of data produced from software systems and great.
I think existing databases, ClickHouse, Mongo, Elastic, whatever, are great for that.
When we talk about a context layer for agents, I think a key part of what exists there is, yes, there's a database, but I actually think there's a system around the database.
I think a context layer for agents will look more similar to an agent around a database than just a database.
And the reason for this is that taking all of these learnings and ingesting testing them into a database, you need some sort of intelligence to do that.
And I think that intelligence is actually really, really hard.
The hardest part about memory is actually not the database at all.
When we talk to a number of companies, the way that they do memory is they just store it in a single file.
Very kind of simple.
The hard part is taking all of the observations that happen, whether it's from the user interacting with, let's say it's a traditional SaaS interacting with the software, or the user interacting with the agent.
and distilling that into information that's valuable enough to go into this context layer.
That's the hard part in memory right now.
Why is that so hard?
Because it's tricky to get LLMs to do what you want them to do.
And this type of meta-reflection, I think, is something that LLMs actually still struggle at a lot, like reflecting on prompts and improving prompts, reflecting on agent architecture, improving agent architecture.
We should build a benchmark for this internally.
We do have some benchmarks for this in terms of engine, which is our agent that's running on things.
But I think this type of meta-reflection is still really difficult for agents.
I think it's also generally a hard thing for humans to do as well.
I don't know if we actually understand...
Some people probably do, so I shouldn't say this, but I don't know if we understand...
Like what goes on inside our brain?
Like I feel like our brains are still kind of like complex things that we don't fully understand.
And so if we're trying to now replicate this kind of like learning process in a system, I think there's a bunch of different pieces.
And so I think models aren't great at kind of like reflection.
And I don't think we know what this system should look like.
What should the inputs be?
What should the outputs be?
What should the harness for this agent be?
What should the different workflows be?
Will the models be meaningfully better at reflection in 12 months?
Probably.
I mean, it seems like they'll get better at everything.
Do you think the speed of model improvement will continue in the same manner of acceleration that it has done?
Or do you think we are approaching some form of asymptotic plateauing?
I think the speed of model improvement is something that's probably really tough to say without being inside of a lab.
I would say I think there are a lot of tasks.
for which the model improvements will flatline in the sense that it's already good enough.
I think there are a bunch of tasks where the models are just good enough.
Which would be like coding.
Yeah, coding's getting there, like summarization, routing tasks.
There are probably more difficult tasks or tasks that we don't even know to think about, know to throw a model at.
I would probably say this type of meta-learning is one of those tasks that the models aren't good at now.
I would imagine is very important to the labs in terms of kind of like...
cracking RSI or things like that.
And so I do think the models will get better at that over time.
And it's possible that that progress actually looks much faster than previous.
I think it's really tough to have an educated opinion without being inside of a lab.
There are some tasks where it's saturated.
There are other tasks where I think it will probably just increase faster than before.
What about agents today?
Sounds crazy that you think will be incredibly common in two to three years.
I mean, I think this memory layer for agents is still massively unsolved.
I think one thing about agents that is rare today, but will be incredibly common in the next few years is this memory layer.
So what I mean by that is if you look at like instinct or muse, I think one of the things that they've nailed is this kind of like personalization and learning and seeming like it's something for you, whether it's because it's speaking in the tone or because it's just following up like that proactiveness.
And so I think memory is super, super early.
We've been talking with folks about memory for like two years and trying to do things in the open source.
We launched in Alpha and then killed a commercial solution for memory.
It's just so early on in memory.
And it's so domain specific.
What memory looks like for Instinct or Muse is actually pretty different than what it probably looks like for a company or org harness.
And it's pretty different than what it looks like for legal or finance or things like that.
So I think memory is one thing that really makes a difference.
You see this make a difference.
I think Instinct and Muse have done a great job at this, and so you see how it makes a difference there.
But I think it's still very, very long, and I would expect all products in a few years to have some aspect of memory in it.
With respect, is that not quite obvious?
I think the fact that memory will have an impact is quite obvious, and that's why you see so many people interested in it and having it be interested in it for the past few years.
I still think it's not obvious what the answer is.
I still think it's incredibly not obvious what the answer is.
I don't think anyone knows what the answer for memory is.
Why did you kill the commercial solution for it, given the obviousness of it and the clear need for it in almost everything?
We killed it because we didn't find product market fit.
Why didn't we find product market fit?
I think we actually tried to be too general.
I think memory is so early that I think a lot of the solutions that you want to build are actually very domain or agent specific.
And so you would build vertically specific memory solutions.
And what would you do differently?
So again, going back to what the hard part of memory is, I think the hard part of memory is that process for learning from facts about what to remember and that agent that does that remembering.
As I said, I'm massively bullish in kind of like vertical agents.
I think having those agents be good at learning things specific to their domain is a key part of that.
And so I do think these kind of like learning agents, at least to start, probably have to be domain specific.
And I don't know if there's a general...
What does the harness for these learning agents look like?
It probably looks pretty similar to a lot of the harnesses we have now.
It probably looks pretty...
We build deep agents.
Deep agents is an agent harness.
It probably looks similar to that.
And oh, I'm running the loop.
But it's got specific instructions and specific tools.
And maybe there's some specific sub-agents that are really good at navigating existing memory structures or distilling facts.
And so I think you'll have agents for remembering things.
I just think they'll be domain-specific.
Do you think you will have vertical agents for consumer applications?
I understand and I agree with you when you look at finance, accounting, sales, marketing, legal.
You mentioned Muse and Instinct.
They would be horizontal agents.
Do you think we'll have vertical consumer agents?
I think my take on the consumer space is that you'll have both.
You'll have both horizontal and vertical agents, and the horizontal agents will interact with the vertical agents.
Where does it make sense to have a vertical agent versus not?
I think for most apps, it probably makes sense to have a vertical agent.
And I think the reason that having vertical agents make sense at all is probably twofold, maybe more, but two that jump to mind.
One, a lot of these apps have knowledge about you and can learn things about how you interact with that app better than a general horizontal agent could.
So back to the point about domain specificity, how you interact with music for, say, Spotify is very different than how you might interact with Strava.
Those apps are actually probably best at learning things about you.
And I think the best way to expose those learnings is probably through an agent.
The other thing is, a lot of these apps have a ton of APIs.
And so you could say, hey, Spotify, don't build an agent.
Just expose a bunch of APIs.
I actually think it's a better experience if there is an agent that wraps those APIs and exposes a single, or maybe multiple, but probably a single natural language interface for a horizontal agent to interact with that vertical agent.
How does the infrastructure change when agents are the fundamental interface with sites?
And what I mean by that is, you know, I wanted tickets to a specific concert and I said, check every five minutes and buy any seat that comes available.
And for three days, it checked every single five minutes.
The volume of queries was a thousand times what it would have been with humans.
How does the infrastructure require change in a world of agent interaction or human interaction?
When agents are interacting It makes the number of requests go way up.
I think you saw a story about how, I think it was Resi that blocked a user's account because they were using Instinct to pull it, I don't know how frequently, but pretty frequently.
And so I think there's just way more strain on the infrastructure.
How does that percolate down to lower layers?
I don't know if I have an opinion on that yet.
I think you have to build systems that can be much more bursty, that can scale way broader.
I don't know if I have more specifics other than that.
Do you think providers will allow agent usage or not?
I think the future is providers exposing their own agent and having people either interact with that agent or having people's horizontal agents interact with that agent.
So my horizontal agent, sorry, just to understand this, would interact with Rezzy's vertical agent.
I think that's exactly right.
Why would that work?
Because that still doesn't solve the two core problems of advertising for a lot of these businesses.
They lose their ads model and then upsell.
They lose the, like, anything else you might like also on the side.
So you get reduced basket size.
Why would agent-to-agent work?
I think you could include some of those things in the agent response.
So you could do advertising for specific things on Resi or Etsy or whatever and include that in the agent's response.
Like, hey, I found these three things.
You might also want to consider this other thing.
That's now a natural language response that goes back to kind of like the main agent.
I think also to the point around the strain on the systems, if you have a vertical agent, it's going to be better and more efficient at interacting with the underlying systems than a third-party agent will.
So I think it will make it a lot more efficient for them to do that.
When we look at the amount of queries and the volume that changes with agents, it takes a lot of tokens to do all of this work.
How does the cost of tokens and cost of that intelligence change over time?
Because we always thought it would get cheaper.
It hasn't gone much cheaper.
How do we actually think about that?
Cost is one of the big things that we talk with people about right now.
I think there's a few different dimensions to this.
One, I think the frontier models are relatively...
getting cheaper over time.
Astra is price per performance fantastic compared to a lot of things that existed a year ago.
Two, there's just way more demand.
When you start talking about agents talking to agents or multi-agent systems, there are just so many more tokens that are getting burned.
And so the costs are going up.
We see this in coding primarily.
Coding costs have skyrocketed.
And I think a lot of organizations, including ours, are now thinking about ways to pair those costs back.
How does this end up getting resolved?
I mean, if you look at how we're tackling coding agents, I think there's a few hints there.
So when we're tackling coding agents, we think of it in three steps.
One, we think of basically just like visibility, showing kind of like how much things will cost, how much things have cost to admins.
Two is cost control.
So you could say something like, hey, go get me a reservation, but don't spend more than $5 doing it.
And then third is actually impacting the harness itself.
So I think primarily a lot of harness engineering has been done around making the agent more efficient.
because that's the, or sorry, a lot of the harness engineering has been done to make the agent just better, more accurate because that's the biggest blocker.
I think as cost becomes more a concern, we're seeing people care more about things like prompt caching, which are really good for reducing costs.
We're seeing model providers actually make this easier by, and this is getting a little bit in the weeds, but they introduce things where you can change the tools that are available to an agent mid-flight.
Previously, this would have busted the cache and caused a massive increase in cost.
Now you can just add those dynamically and keep the cost.
the same.
So I think we're seeing more attention to both the model APIs and harnesses in terms of controlling costs.
When you talk to large enterprises today, what matters most to them?
Is it accuracy?
Is it cost?
Or is it security?
Another thing would be latency, by the way.
So we run developer surveys every year where we ask developers what they care about.
For the last two years, the number one response, basically double the next two, has been accuracy.
So that's still the primary thing.
The next two are latency and cost, and security is a little bit after that, pretty close.
When we talked to large enterprises recently, I'd say cost has risen in terms of concern.
I'd still probably say it's below accuracy, but it's probably higher than latency.
And then security is, for large enterprises, security is higher, but still probably below accuracy and cost.
What percent of your engineering salary, give or take, not specifically, but do you spend on coding tools?
I think we spend probably roughly 30% of our engineering salary on coding tools.
It's actually gone down the last two months, while tokens, the number of tokens that we've used, has actually gone up.
And so we've done this by three steps.
First is basically just visibility.
We tracked all of our coding agent usage to Langsmith so we could tell who was spending how much, what they were spending it on.
We could do analytics over that.
Second is just basic cost controls.
We set kind of like a budget.
And if you hit the budget, you can talk to our VP of engineering and he'll almost always increase it.
But it's just a safeguard against kind of like agents.
being used incorrectly or going in a bad loop.
And then third would be we control the harness.
So one of the things we've done is we actually built our own in-house cloud coding agent.
So not an IDE, not a terminal-based agent.
We let people use whatever they want for that.
But we built our own cloud-based agent, OpenSwee is what we call it.
It's actually the only way I code anymore because I'm in Slack all the time, and so I just message it from Slack and it kicks off.
But in that harness, we've done a bunch of work to control costs.
So things like prompt caching, but also things like routing, we've built into this.
harness.
And I actually think that's a pretty repeatable three-step process for getting coding agents spend under control.
Just first, visibility.
Second, basic controls.
Third, try to improve the harness itself.
Totally get that.
I do just want to unpack that.
You mentioned that visibility, see who's spending what.
Were the people spending the most, the most effective?
Sometimes the people spending most are the most effective and other times they're not.
So what I mean by that is we have a few senior engineers who spend a lot and I 100% trust that what they're doing is well worth it.
No questions there.
We've also just had some cases do more to like kind of like...
silly mistakes or improper setup than anything.
We've had massive spikes in usage.
So an example of this, we had one or two tasks where some plugin was being used that basically would screenshot or take a snapshot of the DOM of a web page and send that back every turn of the agent because they were doing some front-end engineering and they wanted to take what it looked like and just send it back.
It would just run this massive loop.
And so I think we spent...
$40K in a week on that one thing.
When it gets to that, again, I think we're pretty kind of liberal as a company and trust people, but I know that's probably not worth it.
And so that's just an example of, again, it wasn't malicious.
It wasn't even intended usage, really.
It was kind of just a mistake.
And so I just think just catching that is well worth it.
So it depends a bit on the side of the spectrum.
30% of salary, as you mentioned.
That's a guesstimate, yeah.
A guesstimate, totally fine.
What is that in two years?
I think the hard part about estimating, coding agent spend is that orgs are just changing so rapidly.
So what I mean by that is I think there's a future where orgs are much smaller and coding agent spend is the same as human salary or more or an order of magnitude more.
And you have a small number of people that are driving and executing incredibly rapidly and incredibly performantly.
And I think that's an enticing vision in terms of what you think about.
I don't think many companies, including us, are probably there.
I think we still see that we need a bunch of different people with a bunch of different backgrounds and a bunch of different expertises collaborating on things.
And there's a bunch of communication and overhead and review.
And like, yes, it would be ideal to have one person driving 100 agents and just like driving everything that like makes up.
you know, a large part of the product.
But I think we just see that it's still a very human component to it.
And so I spend a lot of time thinking about kind of like org design.
Is it possible to have like small teams, one person teams, two person teams that are kind of like driving agents?
I think it's very tempting.
And in that situation, I would say like, you know, agent spend maybe.
3x the cost of...
But then also, the engineers who can do that are incredibly valuable.
And so maybe it actually will just stay the same because the people who can actually do that and who can drive all those agents, there's fewer people, but you're paying them a lot more.
And so the spend actually stays the same.
So I think org design is very, very interesting right now.
And so because of that, I think it's actually really hard to answer that question.
To me, that number is so central because it is the fundamental determinant of Anthropics enterprise value.
If that number goes to 5%, then the $2 trillion IPO might be a little bit overpriced.
If that number actually goes to 300% like it is in McCall's case, then it's dramatically underpriced.
And so that's why I focus so much on it.
Do you agree that number underpins Anthropics value?
I think it's probably right that that number underpins Anthropics value.
I think the tricky part is that the only place we've seen agents really have effect is in software engineering so far, though.
And there are a ton of other places within an enterprise that I would argue have yet to kind of be affected by agents.
And so I think it's possible.
Again, software engineering, I think, very difficult to predict.
But I think there's basically two levers here.
One is like, what happens in software engineering?
Is it the same as today?
Is it more than?
Is it less than?
But I think undoubtedly.
For the other domains, it will be a lot more than it is today.
And so I think from that perspective, I think that's very bullish on Anthropic and OpenAI and other model providers.
I think another question that a lot of people are asking is about the proliferation of open models and how that changes the landscape.
You work with some very large enterprises.
One of my dear friends, Jason Lemkin from SASTA, says that I'm not going to do his accent because I'll butcher it.
And I've done it before, and he got it rather.
He said it wasn't very good.
Do you find that large enterprises are fearful of working with open source Chinese models, or are actually they don't mind.
We see that some enterprises are fearful of working with open source Chinese models.
We see others that don't care.
Do you think that fear is justified?
I don't really think that fear is justified.
I think there's different concerns.
I think one concern is that the data that it runs on will somehow go back to the Chinese labs.
And again, if you're using fireworks or hosting it yourself, I think that's just ridiculous.
I think there's other fears that...
the stuff that they've been trained on will cause it to respond in a way that's not consistent with the values of the enterprise or something like that.
I think that's where evals come in handy.
And I think that's largely unfounded.
But sure, maybe for some domains, there's some realness there.
So I think it's probably largely unfounded.
But for some domains, because of that kind of response, I could see there being some nuance there.
Do you think in the next 12 months, we'll have comparable US open source models?
I think in the next 12 months, we will absolutely have comparable US open source models.
I think you look at everything that NVIDIA is doing to kind of work with the ecosystem, I think they care about it tremendously.
You look at Reflection had a model launched recently.
I think it was still a little bit behind some of the Chinese models on some benchmarks, but I think there is a tremendous amount of attention in that domain, and so I think absolutely this will catch up.
When you look at the state of landscape today in terms of open models, What does the world not see that you see very clearly given your position?
I think when it comes to open models, I think there is a lot of usage of them, but I do think they're for more narrow tasks than people think.
So I think we see a lot of people using more open source models and more post-trained models than I would have maybe expected a year or a year and a half ago.
But I still think they're primarily for these simpler tasks like routing or guardrails or extraction or summarization.
I don't think they're really driving the core agentic loops.
for a lot of these main agents.
And so I think open source models are kind of divisive because you have some people on one end who are like, great, they're good enough, let's use them for everything.
And I don't think they're quite there.
And then I think you have other people who are like, you know, the frontier models are just going to keep on getting better and better and cheaper and cheaper.
And like, yes, that's true.
But if you really know what you're going to do, so you have to have a high volume use case.
And if there are a few components of that that are simple enough tasks, I think it's very good ROI to start kind of like post-training models for those use cases.
How do you see the inference model?
playing out.
You mentioned that Fireworks very openly were a big investor in Fireworks and I love Lynn and the team, but everyone's trying to do it.
We had Chase on the show from Crusoe who also have that as part of their business.
Friends with the Nebius guys, they also do it as part of it.
How does this business play out and how do you think about the need to do all of the stack versus inference only providers like a Fireworks or a Base 10?
I think when it comes to how things will play out with respect to inference, there's two things that I think I really underestimated from the start of LangChain to now.
One is the demand for inference, and two is the demand for coding agents.
I think both of those markets are just tremendously bigger than I thought three, four years ago.
I think actually it's Eric Vichry at Benchmark who said this on either a tweet or podcast recently, where I think he basically said, everything just works.
This market is so big.
I think Thomas Tungs as well had some.
around just like the market for inference is just so big that i think eric said it about more broad stuff than just inference but i think applying it to inference i think any inference will work that's like you know there's just so much demand for it and i don't know if it's going to get less anytime soon and so yeah sure fireworks will work aws will work crusoe will work i just think there's so much demand for it where would you not be investing today I can give you some examples.
I think the customer support space is wildly uninteresting.
20 providers, all funded with over 100 million in the last 18 months.
You've got Salesforce and Atlassian and big incumbents there too.
You've got kind of mildly old ones like Intercom and Talkdesk doing it too.
Okay, cool.
Or like robotics, like bold take, but there's so much money going into robotics, but massively far away from having a chat GPT moment.
Humanoids in particular, really struggling form factor.
I just think the funding and the excitement exceeds the capabilities significantly would be my answer.
I like to think I'm somewhat of an optimist.
And so I do think this technology is transformative.
And so I think there will be a ton of opportunity.
And so I might answer that, and then through that we can back out what I wouldn't invest in.
I think inference we already talked about.
I think there's a tremendous...
Let's do this.
You're my nearest partner in the fund.
Welcome.
It's so good to have you on your first day.
Thank you.
Number one, we should have inference.
We should have inference.
There's going to be way more software written, so we should also invest in lower-level infrastructure primitives.
Modal, incredible company.
Temporal, incredible company.
Superbase.
Do they not compete with fireworks?
Modal does on some things, yeah.
Okay.
But they also do more.
They do sandboxes.
So we've got inference layer, one, the coding layer.
I would say inference and then like infrastructure.
So like low-level infrastructure, temporal, modal, super base, convex, vertical agents.
I think vertical agents are going to be a good layer.
Any specific verticals?
I don't know if there's any.
kind of like verticals that I would overweight so much.
I think the important parts are having domain-specific kind of point of view and really diving deep there.
I think Patrick Collison said this on a recent podcast, I think with Amjad from Replit, when he was talking about what the next startup he would build, it probably wouldn't be a sexy area.
It would be some area where technology maybe, or at least AI penetration is low, but there's a lot of domain expertise built up there and kind of going in there and really transforming that.
The key part is...
basically just having some domain expertise that can either be captured in data or logic that goes in the harness and eventually maybe the model weights, but I wouldn't start there.
Having some domain expertise that can be captured there and then putting it to work.
I think, by the way, there are maybe like three...
things that I would look for in all of these, and I think our prereqs, if you look at all of these teams that are succeeding, one, I think the talent that they have is tremendously strong.
Modal has an incredibly strong talent team.
Linn and Fireworks, right?
They wrote PyTorch.
How cool is that, right?
When we met them, you know when you're going to meeting founders, the last time you do the brief intro is like...
I built PyTorch.
That works.
Yeah, yeah.
Tick in that box.
So I think to build, especially this really deep infrastructure, you have to be super technically strong.
And there's just not that many people that can do that.
Two, you have to go really, really fast.
Being able to iterate quickly, I think, is incredibly important.
The market just changes so rapidly.
I think speed and execution speed.
When I think about our advantages at LinkedIn, I think we iterate really, really quickly.
And I think that's one of the advantages we have over other companies.
And then the last thing would be brand.
I think a lot of companies that made it in this AI era basically caught lightning in a bottle and then rode that wave and captured that.
You look at Langchain early on.
We went very viral and have executed against that.
Cursor, a great example.
They went very viral, executed against that.
Jev, even Claude Code, ChatGPT.
It seems like the space is moving so fast.
And because there are so many competitors, and maybe this is even because of it, because there are so many competitors, there were like 100 different frameworks that came out in the two months after LangChain came out.
But I think we had a brand advantage that was built up, and I think that was a durable advantage that we kind of rode.
And so Instinct, another example of this, is probably a bunch of personal assistants.
I think if there is, in terms of like, it would probably be at maybe higher valuations than is rational, but in terms of some of these funding rounds that are getting done, where there's a breakout company with a really...
strong technical roster that has proven that they can execute quickly, I think you just don't look at the price and you just invest.
Is being first more important than ever before?
I think so.
Absolutely.
Yeah.
You look at, I mean, it's possible to catch up.
I think OpenAI is an incredible job of catching up with Codex, but being first, Anthropic and Cloud Code, that, wow.
What an advantage that was.
But to your point, yeah, Langchain comes out and then months after more and more come out, but Instinct, same, same, but actually the durable brand monopoly that comes from being the haloed first is very real.
I think that is super real.
I also think that you have to execute incredibly well after that.
A counterexample to what I just said, just because you're first doesn't mean that you're guaranteed success.
AutoGPT, I don't know if you remember the AutoGPT project, but it came out a few months after LangChain, was the fastest growing GitHub project of all time.
I haven't heard much from them since.
And that's not a knock, because it's very hard to execute with speed.
But I think just because you're first in an area doesn't mean that you'll survive.
I never like to knock people.
So I find it helpful to praise them instead in some respects.
If you were to write a memo joining the firm for why we should invest in $9 billion Jev round, what would your bull case be for Jev?
The bull case for Jev is that there's a new type of inference and new type of model, and the model category is literally named after them.
And so they're going to ride that wave, and they're a very talented team that can execute on that.
I don't know the team super well.
I think that would have to be the assumption for a round at that value.
From everything that I've seen, that seems to be the case, but that would be the bull case.
They basically invented a new model category, are associated with it, and have a talented team that can execute on that.
Does taking money from Benchmark change the trajectory of a company?
100%.
I think Benchmark, when Miles and really the whole Benchmark partnership invested in us, it was me and Ankush.
We were two people.
We really thought of them as kind of like a third co-founder.
When I think about company building from two...
people to where we are now today, which is like 400 people, they have been kind of like our most consistent kind of like partner in that journey for sure.
You know, I am not a VC.
I've never done VC.
There's probably also other market dynamics where great benchmarks involved in that signals thing.
And we're very lucky to have Sequoia join us after like two months, three months after the benchmark round.
And they've been a tremendous partner as well.
Who was it from Sequoia?
Sonia from Sequoia, who is one of the most plugged in people in the spaces.
How do their styles differ?
Benchmark is maybe a little bit more what's the core nucleus of the company.
And like, that's like, we're going to be a sounding board for that.
And we're going to kind of like help with that.
So what I mean by that is I feel like, yeah, a lot of the company building things, positioning things, I would go to them for and they would spend time there.
Sequoia's style, I think, is they help with everything.
They are just a machine.
So they help with recruiting of ICs, of executives.
They help with marketing and launches, and they'll really cooperate.
Does it really work?
I think so, yeah.
I mean, we've gotten a lot of our candidates from Sequoia references.
Really?
Yeah.
They have a talent network, and we've gotten a bunch of really, really strong candidates from there.
Would you say to founders it's worth taking a discount for Sequoia?
I think absolutely it's worth taking it to start for Sequoia and Benchmark and probably other of these top one or tier one VC firms, but those are the ones that we work with.
What do you know now about fundraising that you wish you could tell yourself when it was just the two of you?
I think there is like this game to fundraising almost, and I definitely didn't.
know that going in.
I'm a first-time founder.
Where I spend all my time is on product, basically, and so I'm very focused on that.
I do think there is this game to fundraising where you want to fundraise without fundraising.
You want to get one term sheet and then tell others you have a term sheet.
post a revenue graph on Twitter and then it leads to all these inbound.
Yeah.
Do you fucking like this?
I fucking hate it.
Maybe I'm too old for this game now.
It's just posturing.
It's like, Harrison, do you really like me and want to build a business with me?
I'll give you the same price and the same money than whatever.
Let's just do this.
Stop fucking posturing.
I don't like fundraising at all.
I think, I mean, kind of what you were saying.
I want to build something great with people I enjoy working with.
I think, yeah, especially for the scale of ambitions that we have, we need to fundraise to get that done.
I think that was the approach that Benchmark took when we were fundraising from them as well.
Like, hey, let's figure out if we like each other and want to work together.
By the way, that was very good negotiating by them, and so I think they're very good negotiators as well.
But when I look at our most recent rounds that we've done, we raised from Tom at IVP, our Series B.
I'd been chatting with Tom from like, I think right around the Sequoia round for like over like two and a half years.
And so that was like a relationship that we just built up over time.
And I got to know him and he got to know me.
And so when it came time to fundraising, I feel like there was like less games involved there.
And I really appreciated that.
I would love to do a quick fire round.
So I say a short statement and you give me your immediate thoughts.
Does that sound okay?
Let's do it.
What was your single favorite VC meeting?
First pitch with Benchmark.
So many legends in the room.
Who is the coolest to hang with?
Bill Gurley.
Were you intimidated?
I think I was too naive to be intimidated.
He's also just like a big man.
It's an intimidating figure looming over you.
What is the single most important thing you believe about agents that most of the ecosystem would disagree with?
It's still really hard to build them, like really, really hard.
Maybe people would agree with that, but I think it's just underrated how hard it is.
What have you changed your mind on most in the last 12 months?
Market for inference is massive.
I remember talking with the board about whether we should do a gateway a year and a half ago, and we didn't see what value we could add over something like Open Router.
And I think that was 100% the wrong decision.
What's been the hardest technical thing for you and the team to build?
We built our own database for storing agent traces, SmithDB.
How do you think about competing with ClickHouse and being a customer?
Kind of weird or normal?
I think there are 500 different companies that we are frenemies with.
And so I think I've personally gotten very used to it.
You can invest in one IPO, OpenAI at 1.5 trillion or Anthropik at 2 trillion?
OpenAI.
I think they have more diverse, different paths to really tremendous exits, valuations.
Totally get that.
What do you know now that you wish you'd known when you started in 2022?
I wish I knew what harnesses today looked like so we could have built them earlier.
Final one for you.
What are you most excited for when you look forward five years?
I'm really excited for what creative things we can do with AI.
I think a lot of AI, and a lot of what we work on, is helping enterprises be more efficient and delivering outcomes.
I think creative things like Suno are tremendously interesting, and I wish we'd see more of those.
Totally agree.
I remember having Mikey on the show.
That was a fun one, actually.
And this has been so much fun to do, Harrison.
Thank you so much for putting up with my waywardness and divergent schedules.
But you've been an amazing guest.
No, thank you so much for having me.
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