This is the first time in computer science I can think of where we've actually abdicated like correctness and logic to us.
Like in the past, it was a resource, right?
So maybe the performance is different.
Maybe the availability is different.
But like, whatever I put in, I'm going to get back out.
But now we're like, figure out this problem for me.
You talk to some devs and they're like.
You know, I've never been more productive, but coding isn't fun anymore.
That's one of the things that we're trying to solve for.
It's like amazing new technology.
It feels like magic.
Never experienced anything like this before in my life.
And the narrative that was spun was like this thing will just, you know, run our lives for us, or it's going to kill us all.
Total annihilation.
Like Terminator.
And there's just like absolutely no danger that, like this thing is going to, you know, acquire a mind of its own and, like try to reach out to the computer and kill you.
Yeah.
If you use this every day, right, this idea that this thing could take over the world.
Yeah, exactly.
That narrative, I think, is is largely been dispelled within our circles.
But I think that it's it's sort of like taking on a life of its own in other circles.
And it's made its way to some of the halls of policymaking in the U.S.
This is the old adage of like, you know, do you blame it on ignorance or or malice?
I honestly don't know, but it is clearly like nonsensical and I think very much in the national interests to be still telling this story.
The United States invented the AI revolution.
We built the chips, trained the frontier models, and created the entire ecosystem.
But right now, if you're a startup building AI products, you're probably writing your code on Chinese models.
Today's guest is Biang Liu, one of the co-founders of Sourcegraph.
Biang is joined by A16Z's Martin Casado and Guido Appenzeller to talk about the shift he's seeing on the front lines of software development today.
Sourcegraph's coding agent, which has hit number one on the benchmark for merged pull requests, runs on open source models.
Many of them are Chinese, not because of ideology, but because they work better for what the company needs.
Here's the tension.
Biang studied machine learning under Daphne Kohler at Stanford.
He spent a decade building developer tools.
He knows the technology cold.
And his view is that we're sleepwalking into a dependency problem, not because Chinese models are dangerous, but because American policy has made it nearly impossible to compete in open source AI.
We dig into why the Terminator narrative around AI safety might be our biggest strategic mistake, whether it's already too late to catch up, and what happens when the atomic unit of software isn't a function anymore but a stochastic subroutine you can't fully control.
Bian, thanks for coming and joining.
So the topic today is AI and coding.
But I mean, I would say you're one of the world's experts on this.
And so we would love to kind of do a deep dive and kind of how you view the problem, how you view the solution.
Of course, you're co-founder and CTO of Sourcegraph.
Yeah.
So we'll talk a bit about that as well.
Of course, we've got Guido.
Thanks for being here.
And so maybe just to start we can do a bit of a background on you, and then we'll just kind of dig into details.
Yeah, so background, I've been working on dev tools for more than the past decade of my life.
I started Sourcegraph about 10 plus years ago, brought the world's first kind of like production legit code search engine to market and pushed that to, I think, a good portion of the Fortune 500.
Prior to that, I was a developer at Palantir and I guess now it's like the early days, right?
That's where I met my co-founder Quinn, and we were working on data analysis software and a lot of large enterprise code bases that were kind of like drop shipped into and realized that there was a big need for better tooling for understanding massive code bases.
And then before that.
I guess relevant now is I actually did machine learning as a concentration when I was doing my studies.
So I did some computer vision research.
I didn't know that.
Yeah, under Daphne Kohler at Stanford.
I didn't know this.
You are actually an AI guy.
Yeah, yeah, OG AI.
I thought you were like Dawson Angler, like Tyler Stoff.
I thought you were a systems guy.
Yeah, yeah.
For me, like this whole phenomenon of like LLMs and coding, it's almost like a homecoming of sorts.
I didn't know that.
That's awesome.
Definitely taught me AI as well.
She's a great teacher.
Yes, that's amazing.
I got to say that was like the one class I was so happy I comped out of because I didn't.
I think I would do well.
If I didn't pass the comp, I thought it would defeat me.
I think I failed the comp too many times.
You had to do it.
There were those two.
I was the TA for that class, actually.
121?
228.
Yeah, that's right.
That's what it was.
Yeah, yeah.
So, cool.
Great.
So Sourcegraph started code search navigation, but now you've been making ways with AMP, which is like an agent, would we call it.
So maybe talk through a little bit about what you've been working on, maybe pre-AI and now just to level set.
Yeah, so kind of like the history of the company is.
We were really built to make coding a lot more efficient inside large organizations and to make the practice of actually building software way more accessible, primarily to professional software engineers.
But I think our eventual vision was always to expand the franchise.
And we started by tackling like, the key problem which is enabling humans to understand code, because if you've ever worked inside a large code base, you know that that probably takes anywhere from 80 to 99 of the time.
And then the remainder is when you actually understand the problem well enough to actually write the code.
So that's where we kind of like built up our domain expertise.
And then when LLM sort of matured, it was something that we were always kind of like monitoring in the back of our minds.
Originally looked at LLMs and embeddings as a way to enhance the ranking signals that we're incorporating into our search engine.
And then, when things really hit their stride with Chachapiti and all that, it was fairly obvious to us that there was a big opportunity to combine LLMs, which are this amazing technology, with a lot of the stuff that we'd built up to that point.
And then I guess to round that out, finally, our latest product is this coding agent called AMP.
What's interesting about AMP is it's kind of viewed as like a very sophisticated kind of opinionated view on agents.
Do you share that or is that just kind of the outside?
I would say there's certain things that we're doing that I think are quite unique.
It was the top recently on one of these benchmarks, right?
Yeah, I think there's like some startup out there that compares pull request merge rates or something.
We managed to claim the top spot.
That's awesome.
Yeah, it was very gratifying to see.
But again, like I would say that I think we're opinionated on some parts of our philosophy of building agents.
And my own take is I think a lot of these opinions will soon become widespread.
But there's other elements of what we're doing, which are like people like to read in a lot to...
AI these days.
And sometimes it's just like look, we actually did something very simple here, yielded good results, and we shipped that and it works very well.
Okay, so I think your focus is really on large code bases.
How is that structurally different from?
You know me coding my little homebrewing tool and Yeah, so it's funny you mentioned that.
Historically, the company's focus has really been on large code bases.
But with AMP, we decided to build it almost completely separate from the existing code.
And the reason for that was, one, we built AMP.
AMP is really, at this point, seven, eight months old.
So we started AMP in around February, March this year.
And that was right.
At the wave of this new type of LLM hitting the world, the like agentic tool use LLM.
Finally worked.
Yeah, finally worked, right?
Like after so many demo videos.
Finally there was a model that could actually do robust tool calling and compose that with reasoning.
And...
Our original tack was like okay, let's build this into some of the existing things that we've created.
But the more we started playing around with the technology, the more we sort of came to the conclusion that this was actually truly disruptive.
And we should actually start from first principles to see, you know, build the agent from the ground up and see what tools we really need.
So what we've arrived at is the coding agent, which works, I think, very well in large code bases.
Because again, we push this to a lot of our customers.
But It's also great for hobby coding.
I spun my dad up recently on it, and he's been using it to create these iPad games for our kid.
Because, you know, typical Asian dad.
Trying to teach him math, right?
I want to teach him arithmetic and whatnot.
That's awesome.
And so my dad, who's never written a single line of code in his life, is able to just like hey, make a simple game that has him count the numbers.
And then if he gets it right, the little rocket ship blasts off.
So it's kind of interesting.
It's a really interesting time to be building, because even if you're building for professional developers, as we are, a lot of the technology ends up being just kind of widely accessible.
This is the new parenting.
You're taught, as a parent, not to write the games for your kids that are age-appropriate and well-integrated with the curriculum that they're supposed to learn.
Yeah, I love it.
Another thing that's been kind of made kind of splash is you've recently decided to go to an advertisement-based model.
So like on one hand, so I've got this dissonance internally which is, on one hand, I'm like This is the boutique.
Yeah.
Sophisticated.
On the other hand, I'm like, and it's also for everybody with ads.
And so like, how do you kind of reconcile?
It's really funny because I think we had this sort of reputation for being like the primo agent.
Yeah, totally.
Like the super intelligent one.
But we never had like a flat rate pricing model.
We did pure usage based pricing.
And that also meant that there was never any incentive to switch to a cheaper model for our users.
So our tack was like the most intelligence and you just pay for the inference cost.
But as we built more and more we kind of realized there's sort of this efficient frontier that you can draw.
This 2x2 grid, and one axis is intelligence, but the other axis is latency.
And there's multiple interest points along this trade-off curve.
It's not... just that having the smartest model makes your experience the best.
The smartest model often tends to be a significant amount slower than other models on the market.
And so we felt that there was like an opportunity for us to create like a faster top level agent that couldn't do as complex of coding tasks but it could do these like targeted edits.
And when we started to play around with these small fast models, we realized that hey, actually the inference costs are significantly lower.
And that got us thinking, like going back to folks like my dad, right?
Like he's just doing this stuff on the side.
He doesn't want to spend hundreds of dollars per month to create these kind of like simple games.
We're like, hmm, maybe there's like a model here.
I think it started as a joke.
Someone was like, we should just do ads and see how that works.
And everyone was like, nah, that'll never work.
Yeah.
But then it just kind of kept coming back up.
And at one point we're like, all right, let's just try it and see how it works.
And we launched it and it's been growing very quickly since then.
Can I dig philosophically into this just a little bit?
So I had a conversation with somebody that works on cloud code, which is a very successful CLI tool.
And this person was like you know, what we've done over time is we've literally just removed, you know, stuff between the user and the model.
Like, that's it.
Like that's like kind of like the way that we improve things.
Are we just like do less and let the model do more?
And so I guess that makes, you know...
It's kind of intellectually or intuitively interesting.
It kind of makes sense.
But on the other hand, it seems expensive.
You're like, here is this state-of-the-art model that costs a billion dollars to train.
And like now it's just a user in the model.
And so it's almost like that statement is almost contrary to to an advertisement based model or like what you're talking about, like you know, like a fast model or smaller models.
So like, are we seeing two parallel paths in the industry?
I, so there's definitely, you can, there's definitely different like working styles, right?
Like, depending on the task or maybe depending on the person you talk to, people using coding agents and some of them are like I just want to write a paragraph long prompt and then have the agent go figure it out.
I want to come back to something that's like mostly working.
Yeah.
And then there's other people who say like, actually I don't want to do that because half the time I myself don't have a clear idea of what I want yet.
The creative process is sort of one where you kind of like figure out what the software looks like as you go along.
Yeah.
And sometimes it's the same person saying both things, right?
Like when I go, there's some features where it's like implement billing, where I'm like okay, I know exactly what protocols we need to support, and the Stripe integration.
I know what feedback loops we need to hit.
Then it's like, okay, big prompt, agent, go at it.
But then there's other types of development where it's like you know, I want to build a brand new feature.
We just shipped this code review panel in our editor extension.
And that was a kind of like situation where I was like I don't actually know what this review experience should look like, because it's not me reviewing other people's code, it's me reviewing agent's code, which is like a new workflow.
And for that I kind of did want like a more interactive back and forth interaction between me and the agent.
So...
I don't think it's necessarily like these.
Two things don't have to be completely separate products, but they are distinct working modalities.
Interesting.
That's a great way to put it.
How do you think about the difference between like using somebody else's model, like one of the Soda Labs, versus building your own model versus, you know, using an open source model?
Yeah.
How does that fit in your philosophy?
Yeah, so I would say our philosophy is not model-centric.
It's more agent-centric.
So we view the model as an implementation detail.
Yeah, I don't know what that means.
Okay, so let me explain.
So, like, when you're interacting with an agent... Yeah.
At the end of the day, you care about how that agent is going to respond to your inputs.
You know what tools it's going to use, what sort of trajectories it's going to take, what sort of thinking it does.
A lot of that goes back to the model, but it's not solely dependent on the model.
There's a lot of other things that can influence how an agent behaves.
There's the system prompt.
There's a set of tools that you give it.
There's a tooling environment.
There's a tool descriptions.
There's the sort of instructions that you give it for connecting to feedback loops.
Let's see.
And with the same model with wildly different like tool descriptions and system prompts, you actually get, like you know, completely different behaviors out of that model.
Is that true in both directions?
Like with the same prompts and two completely different models, we'll get different behaviors?
Oh, for sure.
For sure.
It's like if you have, like an agent harness, like a set of tool descriptions and you swap out the model, then there's no guarantee that that thing is going to work well with the model that you swapped in.
Yeah.
And so what we view as like the kind of atomic composable unit is not the model.
It's this thing called the agent, which is essentially this contract of like.
User puts text in and gets certain behaviors out.
And that agent is really a product of both the model plus all these other things that I just listed.
And so, when it comes to like figuring out what models we want to use, it's not so much like hey, we want to use like the latest quote, unquote frontier model from XYZ lab.
It's really about, hey, what behavior do we want the agent to take or in some cases the subagent?
And how do we find the right model that enables that agent to do its job?
It sounds so hard to me.
Like this is the first time in computer science.
I can think of where we've actually abdicated like correctness and logic to us.
Like, in the past, it was a resource, right?
So, like, whatever.
It's not logic.
It's like, okay, so maybe the performance is different.
Maybe the availability is different.
But like, whatever I put in, I'm going to get back out, whether it's a database or a compute or whatever.
Like, these are, like... You know, but now we're, like... figure out this problem for me, right?
So you're kind of abdicating, like, you know, core logic and correctness.
Your unit test comes back with works 45% of the cases.
Yeah, yeah, yeah.
The non-determinism is something that people struggle with a lot.
So for me I actually do think, you know, like historically pre-AI, like when you think about computer systems, the basic unit of composability is like the function call and programming right.
So it's like when you think about your system, it's like this function calls out to these other functions and those other functions delegate to these other functions.
I do think there's still an analog to that in the agent world.
Like the agent is really the analog of the function, but just updated or generalized to AI.
Can I just push on this?
Sure.
I mean, listen...
Call me a traditionalist.
Yeah.
But for me, like, computer infrastructure is compute network and storage, right?
And, like, databases.
And these are resources that are abstracted.
Sure.
Like, so, give me storage, give me network.
Yep.
But, like, The semantics, like what actually happens, I write, right?
That's like my code.
Where here, we're like, figure it out for me.
It's like we're abdicating actual logic and correctness.
It just feels like, in a way, like a little bit...
You know, like in your case, for example, if you pick up, you know, let's say you're using model v21, and then you go to model v22.
Like, you're going to have wildly different answers, right?
It's almost like a new instruction set or something.
Yeah, you might have different answers, but I think if you construct the agent right, they're not going to be wildly different.
So like, for instance, we have a subagent that's designed to search for things like uncover relevant context.
And... you know, it is a bit of a dice roll every time, right?
Like it takes a slightly different trajectory.
It might search for different things.
But it's to the point now where, if I want to find something in the code base, I have like 99 confidence that this thing will eventually be able to kind of like stochastically iterate to the right answer.
And so in that way of thinking, it's like, yeah, how it gets there might vary.
But if I wanted to do a specific thing, it's reliable enough that I can invoke it.
It feels like there's kind of a backlash right now in the industry to evals.
So do you view like this is an eval problem or like a runtime system problem?
Yeah.
So you know, my take on evals is evals are definitely effective as a sort of like unit test or a smoke test.
Yeah.
Because if you push a change to your agent and it breaks something, you want to know, right?
Like, if there's like an important workflow that you're like hey, this should work reliably well, because if this doesn't work, then probably a lot of other things break.
And that's a great instance where you want an eval that will alert you when it goes from green to red.
I think where it gets hairier is treating evals as a kind of like optimization target, because any eval set like what are you trying to capture?
If you're building an end user product.
At the end of the day, what you care about is the product experience.
And so you construct the eval set to kind of proxy the vibes of the user using the product.
And by definition that means your eval set is always like lagging a little bit from the frontier, because it takes time to like distill what is a good product experience into a set of evals.
And we've had multiple times in our past where we picked a number.
Just to take an example, like with you know, back in the kind of like code completion days of you know 2023 or whatnot, we had a coding tool that would do coding autocomplete and the kind of like banner top line metric.
There was completion acceptance rate.
You know like, given that I suggest this change to the user, what is the likelihood they're going to accept?
That seems like, you know, bulletproof, right?
But actually, like, I think in building that, we ended up over-optimizing that to a certain extent.
Because there's, like, any metric you choose, there's going to be a way to game it.
Well, I mean, even in this one, like okay, so like the developer accepts it, but do they end up committing it?
Oh, they committed it, but, you know, like, whatever.
Did it pass a code review?
Yeah, exactly.
Did the PR guys do that?
There's, like, a subtle bug introduced or whatnot.
Yeah.
You know, did it get merged into main?
Like, I mean, it just feels like, you know.
Yeah, yeah.
You know, like this is kind of an adjacent topic, but something that Guido and I discuss a lot is to what extent the market is Pareto efficient on the Pareto frontier.
Like, if... you can trade off, let's say, performance for cost or intelligence for cost.
Will the market kind of adopt that uniformly, or does it just optimize only for speed or only for correctness?
Being on the front lines, we would love...
Your sense on this.
Here's a simple question.
We ask this question a lot and nobody seems to know.
Like, is the question here, like, what matters more, speed or intelligence?
It's whether the Pareto frontier is what matters or if it's kind of there's points on the Pareto frontier that matter right.
So you can imagine.
So traditional pricing psychology is you're the expensive one or you're the cheap one, right?
And everything in the middle is called the value gap, which people don't use, right?
And so...
Originally, we were like, oh, that happens here.
So either you buy the most expensive one or you buy the cheapest one.
But actually as we kind of look in the market, it actually feels like most of the frontier is pretty full.
Developers are pretty sophisticated.
There's different cost sensitivities, different price sensitivities.
Yeah.
Yeah.
So you know, it's funny that you mentioned this like you know, the cheap option versus like, the premium option.
It just so happens that AMP has two top level agents.
There's a smart agent and there's a fast agent.
Oh, that's interesting.
And the fast agent is the one that's ad supported, like that we can offer for free.
And the smart agent is the one where we're like okay, we're not, we will always only do usage based pricing for that, because we want to keep that at the frontier of smartness.
Yeah.
But, that being said, like I don't know, maybe there's like a third point in there that could make sense.
It really just comes out of the vibes at the end of the day, like as we use this more heavily and see the usage patterns emerge.
The mid-agent.
Yeah, the mid-agent.
Like, I honestly, yeah.
Well, if you put it that way, they're like oh like yeah, the galaxy brain ideas.
You either want you know smart or fat.
Awesome.
Cool.
So I mean, if you're open to it, I'd love to dig into a bit on kind of your view on open source models.
Yeah, sure.
Do you use them?
Yes.
You know, do you think that they are an important part of the ecosystem?
Yeah, so we do use a variety of open source models.
You know, we use both closed source and open source models quite heavily.
But the open source ones, I think, are becoming a bigger theme now for a couple reasons.
One is, you know, with an open source or open weight model, you can post train them.
Right.
Which means like if you have a domain specific task, like AMP has a growing number of subagents that are specialized for a specific task, like contact retrieval or, like extra reasoning, library fetching.
Yeah.
Those are more constrained tasks where you don't necessarily need frontier general intelligence.
If anything, you want faster, right?
And so the benefit of having open weight models is you can look at the thing that you're trying to optimize for, like what that subagent needs, and post-train the model to accomplish that more effectively.
And the other element of open-weight models that's very appealing is just the pricing aspect of it.
Like there's now more and more like effective open-weight models that are emerging on the scene that are actually quite robust at agentic tool use.
You know, the landscape has changed immensely since, like...
June of this year.
We've gone from, like you know, there was really only one really good agentic tool use model, to now there's like, could you name that?
I mean it'd be great to actually.
I mean I open yeah, I mean like, so you know, originally there was Claude, right like Sonnet or Opus.
That was the first agentic tool use model and that sort of you know ushered in in the current agent wave.
But now, you know, there's GPT-5, there's Kimmy K2, there's Quantry Coder, GLM.
Are these open source models like on par or pretty close?
It depends on the workload.
So I would say in our evaluations for kind of like the top level smart coding agent driver, we still tend to prefer Sonnet or GPT-5.
But for kind of like quick targeted edits, Or specific sub-agents.
I think more and more we're preferring smaller models because they have better latency characteristics and because the complexity of the task isn't high, like you reach a ceiling.
It's like once you reach a certain level of quality, there's diminishing returns and then you start optimizing for latency because that gets you more.
You know interactivity.
What's the smallest models you can use for an effective agent?
I mean for an agent right now.
It's probably still fairly large, like talking to probably like hundreds of billions of parameters for kind of like a top level agent.
But for like search agents, you could go smaller than that.
And then we also have a model that does kind of like edit suggestions.
So you know, for those times where you still have to go into the code and manually edit stuff, this thing suggests the next edit that you'll make.
And for that, we use a very small model, like single-digit billions of parameters.
So do you train your own models?
Yeah, we do.
But I would say we don't train them from scratch.
No pre-training?
No pre-training.
That would be dumb.
At this point, it's just like it would be fiscally irresponsible.
Probably pointless.
Yeah.
Are these for special use cases?
Like a lot of the products that we work with, let's say, just outside of coding, just like a lot of products that we work with, you know, and just I mean.
Here's this general view.
Pre-training is done.
Yeah.
Right?
Paying people to create data, we've hit economic equilibrium, right?
It's like you can keep paying people but, like you know, we're hitting diminishing returns there because you need kind of more expensive people.
Yes.
You need 10 times more data.
And so at some point you hit equilibrium.
But, you know, like there's a lot of product data out there and there's a lot of users out there.
And like, you know, the solution domain is enormous.
And so you can start building smaller models.
And, you know, so it's like, you know, like A, is that correct?
And B.
You know, like the models that you train, do they kind of fit in that general pattern of In specific, smaller models?
I think that's spot on, actually.
It's like the very large generalist models were great and they still are great for experimentation, because it's almost like you know, you train this thing on all sorts of data and it's almost like a discovery process where, like the training team themselves don't quite know, you know what behaviors might emerge.
But once you map those to specific workloads, specific agents that you want to build, then you have a much clearer target.
And, you know, it's widely known that like a lot of the model labs do this now behind the scenes.
Like they might expose an API.
That's like you know one model, but behind the scenes they're routing to you know, smaller models.
Yeah.
And you can also do that at the application layer.
Like if you have an agent architecture like we do, there's all sorts of specialized tasks.
Like we've broken down the process of like software creation to various tasks like context fetching or debugging or things like that.
And once you have a specialized agent for each, then you take a look at what the agent needs to succeed and progress.
You try to get the model as small as possible while still maintaining the requisite quality bar.
So it sounds like it's not just a per-rater frontier of quality versus cost, but there's like a use case as well.
There's also multiple graphs, right?
Yeah, exactly.
It's basically per agent.
Like every agent maps to a workflow.
Yeah, it's emulating some workflow that maybe approximately maps to something that a human used to do.
Maybe it doesn't, but it's a subroutine.
This is why I go back to the function analogy.
And so for any given agent... It's a subroutine where you abdicate the logic.
It's a stochastic subroutine.
It's weird.
I mean, now we have parameters like how much reasoning do you want?
So it's a tunable subroutine.
How powerful do you want to make this?
What's your budget?
But there's like a mini Pareto frontier for each of these tasks, right?
And then the optimal point along that frontier is different for each task.
So I actually want to dig into, you know, like the open source models, the implications.
I mean, I know that you've got opinions on that.
We've got opinions.
It's an interesting topic.
But before we do that, So in 10 years, are we using an IDE or are we using agents on a CLI?
What happens to software engineering?
In 10 years?
Simple question.
Okay, so I mean listen, you're like one of the people that has been in the face for quite a while.
I do have a take on this.
You're probably literally like the world expert on this question.
I'm serious.
Yeah.
So here's my take.
Like...
I don't think it's not going to be an ID that looks like any ID that exists today.
And it's not going to be like a terminal that looks like any terminal that exists today.
My view is that and I don't think this is like a particularly unique view.
It's just that you know.
The effect of AI on every single knowledge domain, including coding, is that it's going to enable the human to level up.
So the job that you do already like that, like my job has changed so much in the past year.
Like I think about all the kind of like toilsome, like line by line editing that I did like a year ago today.
It seems like completely foreign.
I like honestly don't think I could go back at this point.
Um now, when i'm doing stuff, it's more at the level of like telling the agent to make the specific edits or execute like a specific plan, and i'm really playing the role more of like an orchestrator now, and then you still have to like pop in and make some manual edits when it gets stuck.
Um, but increasingly like yeah, like i would say like by by sheer, like lines of code volume.
Probably more than 90 of the code that that i write these days is is through through amp um, And I think it's only going to get higher and higher level over time.
And so, when we think about the interface that a human will interact with primarily, I think the future looks like something that allows you to orchestrate the job of multiple agents and crucially, something that allows you you as the human to understand the essentials of what these agents are outputting.
And I actually think that's probably the limiting bottleneck today.
Of course.
Comprehension.
It's like the human comprehension just on like.
Yeah.
Does it map to like my understanding of like.
Exactly.
The problem needs even at like a business size.
Yes.
Because there are fundamental tradeoffs in the system.
Yes.
Yes.
But I think.
You can't wish those away.
You can't wish them away.
And the human is the bottleneck.
But I think the human is still essential and will still remain essential 10 years from now in software engineering, because it's fundamentally a creative process.
No, no, that's what I mean.
Sorry, I just want to make sure we're talking about the same thing.
Oh, yeah.
Like a human has in their head of what they want to accomplish.
Yes.
And only the human has that in their head.
Yeah, yeah, yeah.
And so like often that's going to require choosing a point between two trade-offs.
Yes.
Right?
Like whatever that is.
Yes.
And so like there has to be some way that this articulation happens.
Yes.
Yeah.
And when you talk to like practitioners today, like a lot of them are very... It's like bittersweet.
Because, on the one hand, it's like, oh my God, like agents, they're writing all this code and they're actually pretty good at it.
On the other hand, it's like oh, I'm spending like 90 of my time like essentially doing code review now, which is, you know, there's like the one in 100 dev that you talk to that says like I really love code review.
The rest of us are like, oh, man, it's like such a drag.
While becoming middle managers of coding.
Yeah, yeah, exactly.
I mean, you talk to some devs and they're like you know, I've never been more productive, but coding isn't fun anymore.
And so, you know, that's one of the things that we're trying to solve for.
The beauty, the elegance is gone.
It's now all looking at implementations requirements.
Yeah, it's that, but also it's just, like, the task of, like, reviewing code, I think, is a slog.
And, like, classical code review interfaces are just not that good.
Like, I think they were never that good.
Yeah.
But it wasn't like blindingly obvious, because the rate at which like lines of code were shipping was remarkable.
It's a super simple example, right?
Today, if I review code from pretty much any coding agent out there, typically it's just like file by file by file by file.
Yeah, yeah.
Like grouping this by task or something like that or explaining it.
A couple of arrows with little buckles.
Yeah, exactly.
You are literally like this.
There's so much low-hanging fruit here.
So we launched a review panel in our editor extension last week.
It doesn't get all the way there, but I think it's the first step.
And it's already like it's way better than like an existing like code host review tool.
Like it's mind-boggling to me that, like we live in an age where, like You can literally have a robot, like you know, one shot, a very large change, and then you pop over to, like you know, GitHub PRs and you're clicking, you know expand hunk expand hunk expand, hunk.
No code intelligence, can't edit.
No diagrams.
Yeah, yeah.
Like it just feels like you know, it's like we have.
We have like a Ferrari engine, but then part of our workflow still requires like strapping it to this like horse and buggy style thing.
So...
Anyways.
It's like I create a microchip and then I give you an oscilloscope.
Yeah.
Yeah, exactly.
Exactly.
All right.
So listen, we're moving on on time here.
So I actually want to get more into the policy side because I do think, like listen, a lot of the way this goes is the way the model goes.
Yep.
The open source ecosystem, we see it all over the place.
Not even talking about source draft, but I would say if a company walks in now, that's a product company that's decided that they need to post-train their own models.
It's going to be on an open source model.
And more and more of these are Chinese models.
And so you mentioned that you do use open source models and Chinese models.
So like, how do you think about that?
As far as like A, maybe just like the implications of dependency, and then B, what does this mean?
Like maybe more holistically with the United States and the ecosystem?
Yeah.
Yeah so, first off, in terms of our production setup, every model that we hit is hosted on American servers.
So from an information security point of view, I think this is best practice across the industry.
It's like you don't hit models that are hosted in China or...
Yeah, so like from that part, it's fine.
I would say though, if you take a step back, it is fairly concerning, because my view is that, as the model landscape evolves, you're going to start to see a flattening in terms of model capabilities right,
Like there's going to be a healthy competition at the model layer and there's going to be a number of options for choosing a model at a given point in the Pareto frontier right.
And with that flattening there's a strong incentive for application builders to you know, at a given capability level, use the one that's open.
For the reasons stated before.
And because the most capable open weight models right now are of Chinese origin.
It essentially means that, like application builders around the world are choosing to post-train on top of these models.
Yeah.
And so if the US open-weight ecosystem doesn't catch up, we're kind of in danger of, you know, the world migrating to a world where most systems are heavily dependent on models of Chinese origin.
Do we have competitive...
U.S. open source models right now?
I mean... I think it could be non-Chinese.
If you look at Europe.
You know, we've sampled a good portion of the model landscape.
Because again, we have all these sub-agents and agents.
We want to find the best ones for the job.
And frankly, the ones that we find most effective at agentic workloads.
They're almost all, I would say they are all of Chinese origin right now.
And that's not to say that like there haven't been like good efforts by American companies.
It's just that you know, when you plop those into like an agentic application, you know the tool use isn't quite robust enough.
It's not quite there yet.
Do you think this is a result of policy or funding or like?
I think probably all of the above.
I mean, the easy answer is like, yes, you know, it's a regulatory thing, this and that.
I just don't know how true that is.
I mean, it just turns out there's very sophisticated, like, You know, so it is interesting.
Like it's.
Like you know, the AI revolution was basically like born and created in the West right.
Down the street, I mean.
Yeah, down the street.
And the US still holds a lead in basically like every part of the stack.
You know whether it's like chips or you know frontier intelligence.
Like, basically every place except open-weight models.
And robotics, yeah.
Yeah.
I guess, like, that's the manufacturing aspect of it.
Yeah, yeah, yeah.
But you know from where I stand.
It's like you know, if you go back to the quote-unquote early days of the AI revolution, back to, you know, 2022 or so,
Yeah.
So I feel like the narrative that was told, that was like the dominant narrative, was this one of like AGI at that point where it was kind of like this
It's like amazing new technology.
It feels like magic, right?
Like never experienced anything like this before in my life.
Yeah.
And then the narrative that was spun was like, hey, AGI is nine.
What does AGI mean?
Well, either one, it's like utopia.
All our problems are solved.
This thing will just, you know, run our lives for us.
Or it's going to kill us all.
Total annihilation.
Like Terminator style outcome.
Scan it.
I love the Balaji view of this.
He's like, there's this very Abrahamic view.
It's either like God or the devil, right?
And then he's like...
I'm Hindu.
He's like, we've got a bunch of gods, some are Capricious, some are... Yeah.
I've chosen the Hindu view of this.
Yeah, arguably that view of the model landscape was the right one in retrospect.
And I think, at the time, people using these models directly kind of realize this right uh, it's like you use the models um, they can, um they could, emulate intelligence of of a certain kind, but it's like mostly pattern matching um, and there's just like absolutely no danger that like, this thing's gonna, you know, acquire a mind of its own and like try to reach out the computer and kill you if you use this every day.
Right, this idea that this thing could take over the world yeah exactly, that's right.
Yeah So, like now, if you talk to practitioners, like anyone who's building it and increasingly, anyone who's using it right,
Because, like now you know, Chachabiti has been out for like three some years and everyone, and their mom, has used it.
Like people kind of understand what the limitations are.
So like that narrative, I think, is largely been dispelled within our circles, right?
But I think that it's sort of like taking on a life of its own in other circles, and it's made its way to some of the halls of policymaking in the US.
It's part of the problem here that not every policymaker is using LMS day-to-day, to put it carefully.
Yeah, I don't know.
You know, this is the old adage of like you know, do you blame it on.
You know ignorance or malice.
Yeah, like, I honestly don't know.
Like it's a black box, but it is like.
It is clearly like nonsensical and I think very much in the national interest to be still telling this story because it
One.
It leads to kind of like overemphasis on like the model as the end-all, be-all of AI, where in reality it's like pushing the models into like all these different application areas where like, the rubber meets the road and things become useful.
Yeah.
But then also like when you're, when you think about making laws and regulations for this sort of stuff.
If you've been sold on this sort of like Terminator style narrative, that's going to put you in a very different mindset with respect to how much risk tolerance you're willing to take on, how much innovation you're going to allow in the ecosystem and your tolerance for open sourcing model weights.
Yeah.
So, you know, you use a bunch of open source models.
And there's a question that we actually debate quite a bit, which is assume the policy environment exists as it is, even with like infinite funding and infinite talent, could you still actually build competitive models?
Or like now are we at a place that, like we're just at a disadvantage just because of like is it too late to actually assume that we can do it without actually changing policy?
Like build adequate OpenAway models?
Well, let me just give an example.
I don't know why OpenAI released the open source models the way they did.
But it seems like they were very, very sensitive to what data was in them.
And I presume this is kind of a concern around copyright.
I don't know the answer to this.
I just assume that.
Interesting.
We haven't seen something come out of meta in quite a while.
Like, are there even any open source models?
So, like, it's just very unusual for the United States not to do this.
And, like, the efforts that have done it have seemed to be, like, handicapped in one way.
And so, like, there's one view of the world that, like, this isn't a tech problem.
It isn't a money problem.
We're already in the overhang of policy.
Like, that's one view.
And so, like, I guess my specific question is, do you think that is the case?
Or do you think we've just kind of, you know, haven't kind of gotten to it yet and we're going to come up with open source models?
You know, I honestly don't know.
Like I don't have like inside knowledge of what goes on inside a lot of these research organizations.
But it's super remarkable that, like if you were in the US, you know we were the first with open source models.
We had Lama 3.
And now he's like, like, listen, you're using Chinese models.
Like where are the U.S. models?
And why aren't they there?
And I guess my best guess I mean again, you both can gut check me on this is like, actually there's like all of the rhetoric around like developer liability, even though it didn't happen, but there was rhetoric around it, all the policy stuff, all the copyright stuff, all the lawsuits.
My guess is that, you know, a lot of these folks are gun-shy.
Yeah, I think that could very well be the case.
And I think that the way that the regulatory landscape is evolving doesn't help at all as well.
Because there was an effort earlier this year to have kind of like a federal set of standards for AI model layer regulation.
But that, I think, fell apart.
And so now we're kind of like slow walking, some case fast walking towards this like patchwork quilt of state-by-state regulations.
Some of that state regulation writes in that it applies to anybody making a model available in that particular state.
So in theory...
It's one state.
Every state tries to drive policy for all the United States.
It's very vaguely worded and it leaves a lot of room for interpretation, which is never good.
I think for A lot of that hasn't been litigated either, right.
Yeah.
It massively increases complexity.
I think for a small startup to build an open-weight model at this point is extremely hard.
Who wants to take that risk?
It reminds me like back in the day when we were like looking at GDPR compliance.
You know when that was the first thing.
And like I was talking with, like our legal team and external counsel and trying to like read the text of that regulation and figure out like oh, you know, is this thing, you know, technically in violation?
It seems kind of high level.
And the answer that I got was like, look, honestly, these are underspecified.
And it's really like it's going to come down to some decision maker within you know that bureaucracy and they're going to make a judgment call.
And hopefully they lean towards going after.
You know the bigger fish in the pond before they come after you.
So Paradoxically, this is the greatest case you could have ever given to the large social networking giants.
They're the only ones that actually could have the legal teams and the policy teams to navigate this stuff.
And we saw this up close as investors.
We're like, as soon as these things came up, it basically entrenched the incumbents who could come play.
Last quick topic.
If you did have some recommendation on how we should think about policy going forward to aid in, you know, open source efforts for the United States.
Yeah.
What would you guide?
Do as much as possible to ensure a dynamic and competitive AI ecosystem within the U.S.
I mean, the best thing that we can do, I mean, we're America.
Like, the best thing we can do is to take a step back and let the free market function.
And so to that end, like, ensuring there's kind of like a standard, like, you know, nationwide set of regulations that's, you know, clear and, you know, like well specified to be going after like specific applications and application areas rather than, you know, like general, you know, existential risk at the model layer.
That would be good.
And then two, just ensuring that there's like competition at the model layer, avoiding any sort of like anti-competitive behavior.
Regulatory lock in any way.
Yeah, regulatory lock and that sort of thing.
You know essentially, like you know, don't let the like Internet Explorer versus Netscape thing play out the way it did in like Internet 10 with like the AI ecosystem.
Yeah, we were very, very lucky that actually academia and the broad industry ended up erring on the side of openness.
Let's hope this happens this time too.
Yeah.
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