a16z Podcast - The Engine Powering Open-Source AI

Episode Date: August 6, 2026

Elena Burger and Matt Bornstein are joined by Simon Mo, co-founder and CEO of Inferact, the open-source inference engine powering many of today's most advanced AI applications. Together, they explore how open-source AI evolved from a research project into critical infrastructure, why inference has become one of the most important layers of the AI stack, and what it takes to bring frontier intelligence to developers around the world. The conversation covers vLLM's origins, the rise of open-weight models, why companies increasingly want control over their AI infrastructure, and how open-source inference enables the next generation of AI applications. They also discuss model licensing, the economics of open-weight AI, Kimi K3, distillation, AI infrastructure, and why Simon believes the gap between open and closed models is rapidly disappearing.   Resources: Follow Simon Mo on X: https://x.com/simon_mo_ Follow Matt Bornstein on X: https://x.com/BornsteinMatt Follow Elena Burger on X: https://x.com/VirtualElena Follow Inferact: https://x.com/inferact Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The fun thought experiment is if GPUs dropped in price by 99%. Then do we get back to a real open source world? If moderation is never solved in the future, people will go to Open Way by default. Because that is where you know for sure you can control your guardrail for trusted use cases. And can you talk about where VLLM sits in that stack? VOLM is a inference engine. It is kind of like databases and operating a system and other critical software to power AGI. Nvidia and Google, their newest ship will make sure V-ROM can run on them.

And in a lot of cases, they use V-ROM as a benchmark. We're bridging almost a 10-X gap. For proprietary model, there is regular mode and fast mode. But for OpenWaid, every provider can offer potentially even 10 different levels of speed. Five years from now, open-source AI models, have they closed the gap with frontier models? Capability-wise, I don't really see a big gap. Not even today, because open source AI has become one of the most important forces shaping the industry. But making frontier models available to the world requires far more than releasing model weights.

It requires an entirely new infrastructure layer. In this episode, Elena Berger and Matt Borenstein are joined by Simon Moe, co-founder and CEO of Infraq, to discuss how open source inference became critical infrastructure for AI, why enterprises are increasingly turning to open weight models. and what the next generation of AI systems will require. They also explore model licensing, inference economics, and the future of open AI. Today we're here with Simon Moe, co-founder of Infraact, and a lead maintainer of VLM, the open source inference engine,

now running on half a million GPUs at any moment. We're also joined by Matt Borenstein, an A16C general partner. Simon Matt, thank you so much for joining us. I think first we should start with open source AI and kind of the more recent history of open source AI. So VLLM actually has its origins kind of back in 2022 pre-chat GPT. And your team set out to make a slow open source demo faster and instead just found this pile of unsolved problem. So can you talk about what made serving an LLM so fundamentally different from the ML workload? everyone already knew how to run.

Yep, good to be here. So serving large language model is a fundamentally different problem because serving it requires to run it on accelerators like GPUs or TPUs, and it is a computationally intensive process that will require a lot of engineering and ensuring that for each request, user can see the LOM's response quickly and efficiently. So this typically means we need to handle differences in input distribution, how long each request is, output distribution, which is non-deterministic, and batching and scheduling a lot more at the core of the inference engine.

Yeah. And so the team, the project, has been around for about four years now, but the company is a little bit more recent. So I want to throw this out to either of you, Matt, I know you've known the team and observed the team for a very very important. very long time. So at what point did you see this sort of transitioning from being a much beloved open source project to critical infrastructure and then a company? Yeah, I mean, I think you have to go back a little bit, right? Open source was the norm for AI models early on, right? I mean, we literally have this company called Open AI, which, you know, it's become a little bit of a joke. It's not as open as it once was or not nearly as open as

it once was. But early on, all the frontier AI work was being open source or at least released into open weights, which is a little bit different than true open source. And people could mostly run these models sort of on hardware or computers that they already had. I'm curious, I'm curious, I cited, like, maybe you remember, like, what was, like, the first model that, like, you actually needed to go out and get special software and, like, a special set of computers to be able to run? Probably Bert. And before that, it was, like, Resnet for computation, like, images, computer vision, classification. So Resnet already need to run on

Nvidia K80, which is kind of one of the first SKU on AWS and other places. And but way over, but even at this point, Resnet you can still kind of run on a commodity, even CPU devices. It's just very slow. But for Bert, we're running at it is like, wow, you have to run it on GPU to make it anything faster and efficient for anything translation or any task. So that

was like before 2020 even. It's so funny thinking about this. I'm like all the memories are flooding. Hugging Face had a thousand Burt variants on it. You have to go find like the right Burt variant for your particular task. And yeah, and you're right, you had to, some people, I guess, had their own GPUs and can run it. But yeah, a lot of people had to go provision in Cloud. Exactly.

Stuff like that. That's really funny. And so yeah, so look, I mean, Burt was an early language model that newer models were much bigger, much more sophisticated, take up a lot more memory, a lot more compute. And so BLLM, really from the early days, right, was about running these more powerful models that you couldn't just sort of do it, figure it out on your own. Yeah, yeah. And I think to get us to this present day,

I think it would be good to talk about kind of at what point it really became critical infrastructure for these even larger open source models. And when did we even start to see these larger open source models kind of come into the field? Well, we really see the critical account. of the software stack. It's always about bringing open frontier intelligence.

This is open frontier intelligence is a cashphrase for the K3 model release this time. And it's also about VLM's mission about influencing infrastructure. So if we look at it, when do they start being critical is when people are relying on it for their application,

for their day-to-day productivity. So maybe 2023, when GitHub co-pilot and chaty, GBT are something that people just cannot live out with anymore. And at that point, open weight model is already backing and become a cornerstone of how people are living every day. Yeah. And at that point, we will need the accelerator running open weight model and open weight software

to open source software to make sure it works well. Yeah, and have all flexibility and control of it. Yeah, it's sort of interesting from a startup standpoint. Like you mentioned sort of GPT3 or like early 10th. CatchyPT, those closed source solutions were starting to become critical to like a small group of people around that time. And open source existed, but it was a little bit of a curiosity or sort of an enthusiast thing.

As the frontier has expanded, particularly with closed source models, more and more open source has been dragged in as kind of like critical behind it, like if that makes sense. Like at any given point in time, including now, I think models from Open AI and Anthropic are kind of more widely used and more critical kind of in general than open source models. But I do think we passed a threshold. And I want to say about a year ago, where a bunch of smaller companies or new application companies,

as they were trying to figure out, how do I really build an AI without just being a wrapper on top of open AI? The answer to that question turned out to be open source. I mean, this is what Cursor did. This is what sort of Decagon and Harvey are in the process of doing now.

And a bunch of other, like, really, really strong application-level startups sort of made the determination. We can't build just on closed source. We need to do our own, mid-training, our own post-training, our own sort of inference and deployment tricks. And all of that means, it must be built on top of open source. The closed-source vendors won't give you the access to do this.

So my read is like kind of a year-ish ago. Open source became really central in a way that's not always visible because it's deeply embedded in some of these products. But some of the most innovative products and applications now really depend on this very deeply. Yeah. Yeah. And can you talk about where VLLM sits in that stack where we do have these larger enterprise

companies that are choosing to use open source. models, like, where does VLLM sit in the stack for them? Yeah, I mean, you should, like, just about everybody uses VLLM. Yeah, you should describe it. Just about everybody uses VLM. VLM is a inference engine. That means its job is to turn available GPUs into a running M-Point for intelligence. So that means it is kind of like databases and operating a system and other critical

software to power this economy or power of the AGI that everybody really uses today. to ensure they can have cost-effectiveness, efficiency, reliability, and also always staying on the frontier. Because for VOLM, we support more than a thousand model architecture of today, and a lot of those are proprietary, but also a lot of those are open weight, right? And a lot of those model architecture, when they're becoming transitioning from a research prototype to world-accessible open-weight model architecture, they are live on VLM immediately.

So that's a process way called Day Zero model release. And additionally, VOM also work closely with all the hardware vendors. So that means across like Nvidia, AMD, Google, and Amazon, Intel, and a lot more, their newest chip will make sure VLM can run on them. And in a lot of cases, they use VARM as a benchmark to make sure it runs well on them. So this kind of fusion of where models run and where it gets to meet the hardware is where the magic happened. and this is where VL is. And you've told me some of the behind-the-scenes stories.

Like, it's actually not easy. These days are a model releases. It's like a lot of human drama in addition to like technical work. I guess are there any stories there that you think are okay to share? Oh, it's actually a very fun co-design process because from model last point of view, right, these are brilliant researchers who have built this model. Now their biggest question becomes, how do we get this out of the world and make sure everybody's able to use it and run it well?

and we have worked with model labs that are very, just because they just use VOLM already in production or in their research process, they will just done everything for you. Because this is a moment when we go to them, it's like, hi, where's the VOMT and we would like to support your open source model? We would like to offer in a way this kind of open source,

but why gloss service to get your model running well on architecture? And then you were sharing, we get the model labs, like, oh, we're going to working already because we're running it for the RO process. Here you go, just review our code and merge our full request. And on the other end, we really have model lab that just don't know how this can work. Because systems is not like their core. Yeah, because system is not their core, and they have been training,

or maybe they have their internal inference engine that just don't know how we will adapt to the open way ecosystem. And by the way, this is also a very much a multi-party kind of involvement process. Every model is typically involves the model lab, involves primary or secondary hardware vendors, involves us, involves Hugg and Face, who are the model format and like model hub vendors. And then depending on the appetite, the model lab involves 10 or 20 different kinds of release partners.

These could be inference clouds, this could be public hypers, whoever is going to run this model and you want them to ensure the model are running successfully. right? So even up to today, if you look at the K3 model release, is a whole partnership and a drive to make sure that the model is, once the model is released, because it's just a few terabytes of files sitting on the internet, that people are actually going to be used it really, really well.

Even from the beginning of 2020-20204, if you remember, when Mistraw dropped their first model, they just drop a torrent link for PTP, and then everybody's like struggling and trying to get. get up and running and then we're working behind the scene with the Mistral team trying to get the inference engine support working in VR. This one of the most probably early on exciting weekend that we are able to spend on this. And then after the weekend, when everybody's trying to really analyze what's going on, and Monday, Tuesday, well, Mistroa and us just announced, here, you can run it on VLM successfully

here, and everybody will able to easily reuse a lot of the work and start building on top That was sort of a fun time where, like, enthusiasts like me could just, like, scramble to, like, download the model and, like, they're running somewhere. Yeah, yeah. I'm glad the professionals were taken over because it never worked, like, very well. It was, like, a fun moment in time. So to bring things, you know, forward to the present, I think open source models and also, you know, distillation have been in the news recently.

infaract signed the NVIDIA open weights an American AI leadership letter that was signed by also A16C, meta, Amazon, dozens of other companies. Can you just talk about, you know, your decision to sign that and sort of what you were really kind of responding to in the market and kind of in the news?

Yeah, so for us, what really want to stand behind is open weight absolutely matters. in the ecosystem. The world cannot just be controlled by proprietary APIs and where open way, open development, and research of these models are blocked or banned, right? The pledge that infrared is we want to help

and foster this ecosystem. Where we are typically in a little bit downstream of this ecosystem, right? Influence engine are not part of the pre-training process nor the RL process, but where the model actually needs the world. And from what we're seeing, people are just really using their imagination and ability to materialize this imagination of open way model. They're able to leverage this open way model so

much effectively. There's almost two pieces to this, right? There's like the cost thing where it's like the closed models are too expensive. And then there's sort of the control thing where I want to sort of be in control my infrastructure and and control the model, right, if I need to extend it or or put on my own guardrails or anything. I'm just curious, have you heard from customers, like, are both those things important to them? Or, like, are they kind of willing to pay as long as they have the control?

Or maybe there are different use cases. I think, like, you fluctuate over time. So control matters a lot over the last few years, and then costs just start to matter over the last few months. So cost really matters starting from people trying to migrate off, right, their expensive coding plan and like every skyrocketing token maxing spend. But control has always been in the backbone of this. They want to even in a way to control the cost, right?

But also it's about controlling the system performance against what they're paying for. So for example, for a voice agent company, they want to control their own model so that they can make sure the model actually respond by their required time. So the customer, when they're on the phone, they can ensure the agent is responding according to a SLA. And this sometimes is only you can do with your controlled intelligence because you know the whole hardware you're running

and the whole system you're monitoring versus signing up for relying on your critical infrastructure with the proprietary API where they might go down anytime or have violation of the contract anytime. Simon, you also, to go back to the cost point, You actually make the point in an essay you recently wrote about the release of Kimmy K3 that actually the economics is besides the point. And it's actually, you know, in the case of these, you know, just really, really great open weight models that are on the frontier that were designed by really brilliant researchers. Like these models are in some cases just as expensive as, you know, the close source models. So in those cases, kind of what is the point of running them and kind of what do we learn architecturally in the course of running them?

Yeah. So first, on cost, it's not necessarily as expensive as a proprietary model. But rather, first, the cost discourse has been discussed over and over again with even GOM 5.2 a few months back. So opening a model are sometimes definitely a lot cheaper. But for this model, there's a big sort of step change where we're bridging almost a 10x gap, but strike somewhere in the middle, where Kimi K-3 is not as expensive as Claude or GPD Soul, but it is a lot more expensive than G1 5.2. Why is that? And I do believe this is the point of where pricing intelligence with the market correctly and understanding where it is.

And but then the majority part of the discourse should be focusing on, wow, this model is bringing an Opus 4.8 level model to our own infrastructure that I can use, I can run, I can fine tune, I can be able to understand exactly how many tokens do I need, understand the exact performance profile. The reason here, for example, is for proprietary model, there is regular mode and fast mode, and that's only the two switch here. But for Openweight, when you are running it,

every provider can offer potentially even 10 different levels of speed going from the slowest mode, which can be a lot cheaper, to 400 tokens per second, almost up to 500 in many cases for some workloads. And this is typically 2x or 3x faster than the fast mode out there today. So this kind of level of control even in terms of performance, and then let alone control over how customer interacting

with the model control over data retention. Keeping in mind, Fable doesn't have zero data retention policy and at least a lot of the data need to be staying there and let it control security and compliance a lot more. Yeah, this is why I'm particularly excited about

case three, not just from the cost perspective, but a lot more on brain this level of intelligence to something people can own. In terms of, you know, calibrating things like speed, calibrating other things, just sort of on the back end,

what needs to happen? And kind of what are you seeing your users do? And who is being really clever about this? So we do see users are able to get the maximum benefit out of this model when they enable fast mode. Like what I'm talking about here, of course, is V-O-N's own fast mode, getting up to 400 and 500 tokens per second.

Because it is really a big step change. from like especially when developer interacting with the model, they can see, oh, I can really just get my task down faster here. And the model are now no longer stuck in thinking, rather it is just executing, executing, interacting with the environment. So for premium developer blocking focus task, we're seeing it's very benefiting. But also case three are just be able to have the ability for people to modify it

and fine tune on top of it, allow them to make it better for their own workload. And this is definitely happening today as well. Can you just explain what the licensing term is for the most recent open source models compared to the past and why you think they're doing that? Oh, yeah. So historically, the open-way model are just like Apache to our software, which is like take it, modify it, do it whatever you want with it.

Here is a gift to the world. And then recently the model lab are trying to understand a way to economically fund their own model development, after all, model training and researcher, and the data are very, very expensive. So we have been starting to see terms, even to the Lama days. When META was releasing Lama, they do have a term of if you're a daily active user or like annual recurring revenue exceed some threshold, please enter into a commercial agreement with META specifically, right?

I do remember that the numbers were like specifically chosen at that time that you could go find it was like two companies in the world that like fit the definition that they had excluded from their license. Yeah, exactly. But like people have taken a hint from that, especially now the labs are trying to figure out a way to economically fund it, especially when they're open source a model. Everybody can just take it and run it themselves, whereas nobody will use their API anymore in many cases while their API currently still taking up shape, right?

And now we're seeing a very healthy ecosystem development, starting from even Minimax recently, when they're already seen the M2.7 model, they have a term specifically focusing on usage. And Kemi initially also has, like, if you have derivative works, like this kind of big news back then with fireworks and cursor about how they built on top of Kemi model.

Yeah. And it's, if I could just expand on that a little, like I don't think it's greed, at least what I've seen from open source model labs, right? Yeah. Open source models, really what we're talking about are open weights, right? And it's just not software, right? Like an AI model is not software at the end of the day.

And so open source software used to be supported by people donating their time or big companies kind of authorizing their employees to donate their time. So it was sort of like a bulk, in-kind donation of people's time. That really doesn't work at AI, right? Like I can't just, like, go home at night. and train a frontier open source model with friends for fun. Like we need millions or billions of dollars of computing resources in order to do it.

So I think it does support your point that, like, obviously there need to be economic incentives and there need to be funding mechanisms in place. Frankly, I think even more so with Chinese models than with domestically produced models, right? If there's no source of funding for Moonshot to continue to train models. Like, we know where the funding will come from instead,

and it's not like something we write, you know, it's government and things that like are actually worse for us, I think. So like, I think you raise sort of an interesting point that this is an important economic structure. And like, I think this means we'll see more of this in the future. Would you agree with that? Yeah, it's really about sustainability in then. It's about how do you make sure that all this initial cap-ex almost to train the model

fail again and again and train the model again? like how do you really pay it back and how to make sure that there's enough confidence and funding from everybody involved to go to do the next one, right? And I recently heard someone I recently hear someone making analogy to this to the pharmaceutical industry

is almost like how do you make sure that the R&D process of new drugs are properly funded and this proper sustainable method to making sure that people are willing to take big risk, big bat to go to do research for new drugs and then later, because they know there's a economic incentive in the end when the new drug released to the market,

a portion of those, of course, like besides the just distribution channels, right, a portion of those revenue will flow back to continue to fund the next R&D effort. And this is where we're kind of seeing similar to the model development now. That's a really interesting analogy because it's like once a drug, a molecule is released, you have the strongest possible control, nobody else can manufacture it at all. It's like the most closed possible source, right? It's like a secret.

But, yeah, like in the case of models, especially open source models, once it's out there, anybody can take it, use it, extend it, et cetera. So having some economics attached to it probably does make sense. Well, actually, that raises a question for me too, which Matt, you were alluding to this earlier about how different open source models are from the data. dynamics of open source software maintenance. When it comes to open source AI, what actually needs to be maintained?

Is it the infrastructure around it? Do the models themselves need maintenance at all? Just kind of what are those dynamics? Because I think even the developer behavior around it is pretty different. Yeah, I mean, Simon sort of said this already, but maybe I'll just expand a bit, which is, you know, you see the results of a big training run. you know, where training now means it's pre-training and then, you know, RL kind of, you know, mid-training

or sort of post-training on these things, or, you know, pre-training SFTRL, right? Like, it's sort of complicated pipeline. We see only the result of this at the end. And the numbers are big. You're like, oh, you know, this was a $100 million training run. But what you often forget is like

there may have been five failed training run, you know, large-scale failed training runs before you even gets an outcome. Yeah, the blood slantiers. Yeah, exactly. Yeah, exactly. One of my favorite artifacts, maybe we could even track down the link, is one of the early Lama models.

They published the whole conversation log between the people who are babysitting the training clusters while the models were training. And it's so funny. It's just like, oh, no, everything's gone wrong. Like, chaos, like panic. And then the next comment's like, okay, we solved it. Everything's okay. You know, clusters up, losses going down.

So there's a lot, a lot that goes in behind the scenes before these models get released. You know, once they're out there, I guess it's a little bit, more in your zone, you know, to kind of make sure it's like operationalized. Oh, yeah. But this is also a very interesting point. Once it's out there is a whole community effort trying to opt-in this model because the model is trained on a given type of hardware and given type of architecture. And but when it's out in a while, everybody has different cluster topology and use cases.

And it's about how to turn like a use case of one now to a use case of almost infinity. Now you have people trying to adapt it to the edge devices and people trying to run. at the largest scale ever, adapted, making sure it runs for voice agent, but also for coding agent, which are entirely different kind of use cases. So this is a whole community effort trying to further optimize, specialize,

and making sure the running of it is reliable and continue to be able to optimize against it. So that's a whole village later throughout the open stores to make sure it's improved. And that's cool, because that really is like open source software. I mean, and this is what you do, but, you know, like this is like anybody can contribute and like make these better.

The fun thought experiment is if GPUs, you know, dropped in price by 99%, right? Like if GPU-based compute actually became, you know, kind of cheap and widely available, like then do we get back to a kind of a real open source world where, you know, one person sitting in their basement or 100 people working in their free time can like come up with something new,

try many of these sort of model training paths that, you know, that like are in the queue somewhere one of the big companies been, and, you know, kind of see, you know, really expand and advance the field collectively. Yeah. Yeah. Well, I mean, this relates to what you were talking about at the beginning. It's like, you know, at the beginning, the amount of compute you needed to be at the quote-unquote frontier was negligible and now it's just like it's enormous and how do you, how do you get that, you know, back to consumer parity again? I've said this on the podcast before, but I'll keep saying it. AlexNet, first, you know, kind of like neural network to run on

on GPUs that we care about ran on two GPUs. And that's not like there are no missing decimal points or commas in there, literally two. Yeah. Now that would get you literally nowhere. Yeah. Yeah. So I guess this relates to another question that we've had, which is inference has gotten harder over the past 18 months because of this combination of scale and diversity and the

kinds of models that we have. And of course, like agents that are doing increasingly long-running tasks. So what makes open source in this world and in this scenario not just like nice and nice thing to have, but absolutely necessary? Yeah, so scale comes from a few points. It comes from whether or not you can run this gigantic model on a data center rack. But also it comes from can you optimize it to the extreme point, to the speed of light, so that you are getting the most value out of it. But also you need a whole community and collaboration and partners of effort to validate it and making sure that there's no that last remaining bugs that appear like 0.001% of the time.

So this is where scaling up the deployment and making sure more people are running it as like largest footprint possible will ensure everybody's experience of running this model is better. And this is where kind of, this is why open source. inference is the current leading way right now instead of closed source inference engine. And frankly, right, all the, a lot of the open, a lot of the open, sorry, a lot of the influence cloud and API as a service today do use and leverage open source inference engine under the hood. And the reason to do that is there's just so much battle tested and learned recipes and things that they can build on top of. And this is where open source is the current leading way to run models. Something that I find kind of funny in looking back at not just the history of YLM and Infrax,

but also a company like OpenRouter or even Olamma, all of these different teams kind of got started around 2022 and 2023. Some of them even before ChatGPT in your case built around open models. And what kind of special thing do you think was happening at that time, just like in the world of research and in the world of AI, where we see these companies now and we look at them and you think of VLM is mission critical or open router is mission critical? And what do you think was going on at that time where even before we had, you know, a big consumer use case? We had teams like yours that were building out these things and kind of how did you guys know, I guess this is the question. I think there's two parts to this. Our team always have an open source kind of angle

where from UC Berkeley, a long tradition of open source software and our system research principles. But also just curiosity overall, everybody was so curious about how are these AI models going to benefit the humanity and how can we use it better? And this is where open source is where you meet the most mission aligned people together. Right, open router, Olamas, you mentioned, they're all great partners and friends in the ecosystem because we're there to understand how everybody else can leverage AI model better and making sure they have a good use of it.

Speaking of, you know, leveraging open-weed, open-source models really well. So I think another thing that was in the news really recently was, was Hugging Face using a Chinese open source model to help contain a cyber attack carried out by a rogue, un-sandboxed open-AI model that was being tested. So, like, what can we take away from something like that? And can you just first, I mean, walk us through

your impressions and understanding of what happened and then kind of what you think the takeaways there are? Yeah, like, this kind of goes back to our previous point about control. So for the Huggieface incident, they break it down pretty well on their website and blogs. So really thanks for their transparency there. In the end, it's about all the closed proprietary model API, their guard rails are a little bit arbitrary, but also very difficult to enforce. That means they have so much false positive in the guardrails that will have legitimate use cases just being blocked. And this is like every green problem, even in the social media days.

How do you design content filters correctly and be able to do the moderation correctly? So if moderation is never solved, which is going to be very, very hard, then there's always a place where you have a model where you know and trust that you are publishing to and be able to use from. So HuggingFace, they have to use Open Way model.

But in the future, we'll also see for the trusted use case, people will go to Open Way by default, because that is where you know for sure. sure that the guardrail is lessened or you can control your guardrail for trusted use cases. Even for us, like this is actually applied to us today where a lot of the anthropic model are banning frontier AI research. And then when we're studying GPU kernels, even as a invalid memory access error, we are triggering the red line. And so a lot of our developer within Infrax and for VOL and are like retreating from using Fable 5

because you have a two-hour job and you trigger the red line, which is false positive, and then you have to lose all of your work. And so a lot of our developer are using like Kimi K3 today even, just making sure, because it's similar quality and it has a garbriol that makes sense to us. There goes while using it. I think your social media analogy is a really apt one. Because, like, in both cases, what's kind of happened

is you've taken, like, distributed human activity and kind of centralized it in one place. Like, before social media, people talked to each other, and published articles that, like, messaged each other on a message boards. But then you centralize it all under one kind of, like, profit-seeking enterprise. And all of a sudden, the incentives change, like, a lot, right? And we have a specific carve-out for social media,

which is you're not responsible for what people say on your platform. And that allowed the moderation problem to be tractable, I think. Right? It's like, okay, we're going to do our best to eliminate obviously illegal things, you know, like things that are just going to degrade experience on the site, you know, in really bad ways. But like, you know, if it's on the edge, you know, it's something that's maybe out of fashion to say but not illegal. Yeah. Like we just can't police everything. We can't be the police of like all human communications. I think something similar is sort of happening in AI, right, where a lot of work.

not just talking about work, is kind of being consolidated in one place. If I need to write code or create a spreadsheet or get advice, you know, health advice or like anything, it's all happening in like one or two sites. And they don't have that carve out, right? Like, they don't have that exemption of like, hey, we're not responsible for what actually happens on the site. And like some of them, especially, you know, anthropic, like is kind of going further than even what would be sort of like legally required and they're sort of taking ethical stances on these things, which Maybe right or maybe right. That's sort of their decisions. How do you actually do this, right? The problem's actually bigger than just communication because it's like actual work and action.

And you don't have this exemption and you have this sort of layer of technical stuff on top. So I'm just totally. I'm just totally with you. It's like and it seems like some of these companies have gone like aired on the side of caution, which is probably a reasonable thing to do. But very, very frustrating as a user, right? Like I, you know, I saw in mind some translation. attempts are blocked because they think the particular combination of language and content is somehow like triggering like politically sensitive topics and things like this. Yeah. Simon, we're nearing the close of the conversation and I just wanted to take a step back a bit

and ask just about Infraact and, you know, running the company. And I know that Ian Stoic of Databricks is an advisor and a co-founder of Infraact. And I'm just curious what you've learned from him in terms of. of taking open source project like VLM and building a company. Yeah, Yang, as a co-founder, he has always been thinking about open source and how do you support open source better. And then now was experienced from Databics and any scale at an even arena, which is a public platform.

And now at Infraq, his focus really stands from how do we build such a, In a world where this software is being used by so, so many people on such a critical project, how do you make sure they're getting the best quality and where the value come from, right? If the open source movement and open source inference engine is a starting point, then what are the gaps that we can feel as infract? And then what are the gaps that we should absolutely put in? Yon has always been open source first. So for whatever we need to build,

we would like to build them in open source. And then for whatever value we can deliver, we want to really close the last mile and making sure that customer and partners are getting the best out of it. Just, I guess, to close us out, five years from now, do you think open-way, open-source AI models,

have they closed the gap with frontier models completely? Our frontier models always one step ahead. Kind of how do you see that shaking out? Five years. Five years is a lot. All right. All right.

All right, one year, one year. Five years, we're all. Who knows? We're all going to be like just floating around in our Wally Pots on a stage ship. Exactly. Progress, right? Yeah, yeah. For me, really, at this point, there's kind of a point we haven't talked too much about is what really differentiated open way model from close way model, right?

In the end, there's not much differentiation. It's more about the distribution strategy and go-to-market strategy. And the capability-wise, I don't really see a big gap, not even today, because for how these models are coming to being, they're really starting from the first principle, right? You have a computer cluster, you have training data, and you have brilliant researchers that grouped together and really to build this amazing artifact that is this pretrend model and then later our old post-treand model, and that the world can use.

But if you look at the ingredients, right, one of the most important part is just the data is about who gets what data, and then what are the environment you are building to that's a model improve on itself and make better. One of the very useful benchmark that we have on Arena for K-3 has been front-end coding, right? That means for moonshine coding.

they have built some of the best environment for front encoding, right? They have published amazing demo on the ability for this model to code and then see what the rendered is and then continue looping and this iterative process. Now, this is about their environment to improve the model. It's not about just source data. It's not about where they get the data from. Rather, it's who can build the best environment and who can make the most sort of optimization and algorithmic choices to leverage all those learning from this environment.

So the next year is all going to be about that. It's about how open-way model labs are differentiating and really getting the model to meet the real world and have this kind of what people are popular today, like recursive self-improvement almost, to really improve the model overall. And so really project out in your year, there's not going to be any difference. Yeah. And you've used this term brilliant researchers a few times. There are brilliant researchers everywhere in the world, clearly.

Why do you think, you know, in the U.S., all the smart researchers are working on closed models, and in China all the smart researchers are working on open models? I mean, from my point of view, they are attracted to interesting problems, not necessarily on the open or closed stands. But however, open way we've been. model does give people a really, really good boost on the impact of such models. So that is like a plus. And I think all the brilliant researchers are attracted to how to improve the model overall. Actually, one interesting point about this maybe fairly technical for this Kimi K3

model is they removed a rotary position on embedding. So rope has always been there for a lot of the Transformers model. And guess who removed it is the inventor of Rope? Like, yeah, like, Jenning, he wrote the first paper introducing Rope as a concept. And then he now also wrote the explanation of why you don't need it as part of the technical report in this case rule model. So like when we read it, it like really kind of full circle is you have all these brilliant, humble researchers that are able to really study how this work. and really study the secret of training and pre-training and share it across the whole world

and recognizing and iterating on their past. Right? So really a miracle, I would say, for this model to come alive. It's so interesting, like, you know, AI is in this funny zone where empirically it works incredibly well. But then you'd go ask the theorists and have like no idea.

Right. And so like you have these kind of iterative things where when you go read, you know, primer on transformers, you read about positional embeddings. or positional encodings and why it's so important because otherwise you can't sort of like understand meaning. And then it turns out once you understand one level deeper

because we've been doing this for a few years and you have all these smart people like, oh, actually you don't need. Exactly, exactly. You know, simpler actually is better. We didn't talk about distillation much so far on this conversation, but I think it's very relevant to this. I have just one question, which is like,

I'm not going to ask, like, is distillation happening? I think this is kind of speculation on the part of everybody, you know, in the world. But like, you work a lot with these Chinese labs. Do you think distillation, like, is a critical component of what they do? Or, like, are they kind of just doing good work and, you know, distillation if it's done is sort of an incidental part of it? I will lean to the latter part specifically, as I mentioned previously, environment matters so much today. So these are our environments, right?

These cannot be distilled. Like, you don't have other people's environment. to really distill a copy from. It's about constructing it, also understanding the learning process. You cannot distill how the model learns with the environment. A lot of these are just not doable today.

There are things potentially you can do with rewriting the data sets, making better pre-training data. But again, you can do it with any models. Any models that are going to follow instruction are going to be useful in terms of utility there. So I really don't think from currently what we're seeing, this is a big cornerstone

of what's powering the progress today. In the end, what's powering the progress is still just really smart people with very interesting algorithms, data, environment, and they will produce, of course, compute. They will produce the models. I think it has really interesting policy implications. I tend to agree with you, by the way,

that, you know, we have smart people everywhere working on a bunch of smart things, and it's not about, you know, disilling data from any one place. It was a really interesting policy. policy implications, right? Because it doesn't, you know, it's almost tempting if you're, if you're sort of, you know, in the White House to say, oh, sure, we'll just, we'll just turn off distillation. All our problems will be solved. But like, you know, I think it's more the case that they're

just, you know, smart people doing interesting things. And so it's like, how do we, how do we kind of like adapt to that? I think it's. Yeah, and creative innovations, right? Like, one part in my essay will kind of mention that open source and open way really helps innovation because it set out this race track where everybody can learn for each other and see what each person like every each player is in this race track and then you're able to improve and stay on the shoulder of each other kind of to improve yourself so uh that is where everybody can move forward faster yeah one thing we're looking for a lot from an investment standpoint is um is people doing more open source model training all over the world not just in China you know not just in the United States but all you right because

you know you get that sort of magic of collaboration with everybody's doing it and achieve global harmony and all well I think that's a good no tend on Matt Simon thank you so much for joining us thanks for tuning in thanks so much for cool thanks champ thanks for listening to this episode of the A16Z podcast if you like this episode

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