The a16z Show - Sriram Krishnan on Open Source AI's Biggest Week Yet

Episode Date: July 24, 2026

Sriram Krishnan joins Theo Jaffee and Sofia Puccini just after concluding his tenure as Senior White House Policy Advisor on AI to discuss one of the biggest weeks yet for open-source AI. They unpack the rapid release of models including Kimi K3 and Qwen, why open models are putting pressure on frontier labs, and what it means for pricing, competition, and the future of AI infrastructure. They also discuss AI policy, distillation, cybersecurity, the role of open-weight models, whether the U.S. should respond to China's growing AI capabilities, and how governments and frontier labs should navigate the next phase of AI development.   Resources: Follow Sriram Krishnan on X: https://x.com/sriramk Follow Theo Jaffee on X: https://x.com/theojaffee Follow Sofia Puccini on X: https://x.com/schisofrenia 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.

You kind of bring it back to very business first principles. If you're providing a product of value, capitalism will find a way to make the supply chain work for you. So if you have an open-made model that is providing value, that means that every part of the stack underneath, whether it is a neocloud, the chip provider, somebody who provides gas turbines or fire suppression is going to orient itself to provide value.

If you're providing a product of value, capitalism will take care of all the rest. If you go look at how the rest of the ecosystem is going, the growth is pretty strong and spectacular, and I think you're going to seeing that continue. Open source AI is moving faster than ever, and the balance of power in the industry may be shifting. In this episode, Theo Jaffe and Sophia Puccini

are joined by former White House AI policy advisor, Sri Ram Krishna, to unpack with the latest wave of open models means for frontier labs, AI policy, price, cybersecurity and America's position in the global AI race. We are back. We are live with Shriam Krishnan, who just finished his tenure at the Senior White House Policy Advisor on Artificial Intelligence. Previously, he was a general partner at Andresen Horowitz and held senior roles at Microsoft, Meta, Snap, and Twitter. So Shuram, we're so glad to have you on. Welcome to MTS. Thank you. I've been a fan of everything you folks have been doing for the last few months and excited to be here.

I think this is the first time in about two years I've been able to do a video appearance without a suit and tie on. So I am so excited to be out of that. Yeah, yeah. So there's so much going on in open source last week. We just discussed we had Grock Build was open source and then we had thinking machines

and then Kimi K3 and then Quen 3.8. So you tweeted the other day, Kimi K3 is a big moment with multiple implications for the entire industry. Could you go into a little more detail on that? What are these implications? Yeah, so if you go back maybe four or five months, I think there was a moment in time

when the only leading models were, I think Opus 46 or 47 at the time, GPD 54 or 55 or wherever be aware, and it felt like there was really no one else. And we were on this curve or some self-improvement where the frontier labs were really going to draw really far away from everyone else.

I think the last few weeks, if you are in the token consumption business, which I am, and I think many of you and your viewers are, it's been a great time. Because let's see, you had Elon and Michael at Cursor, you know, the SpaceX X-A-I team, come out with Glock 4-5,

which I've been using. It's a fantastic model. I think we forgot to mention this. We had Alex Wang and META come out with Muse Spark, which is also awesome. Last week, we had Mira,

think he come out with Inkling. I don't know what they have version number, but the first version of that model, which I think is nearly Sota on many, many benchmarks. But I think the big news of the last three, four days, was obviously Kimi K3

coming out, I think, on Thursday or Friday. And then I think the last 24 hours, I haven't really played with, yet yet but quen coming out so there's a lot of just a lot of choices and alternatives coming out and you know what i was referring to is with kmi k3 is the following um one is that it's just great to have choice in the ecosystem um and to be able to point your uh harness of choice or you know your agent of choice to multiple models um second uh i think we are in this really weird moment now where some of the American frontier models are constrained.

For example, on cyber and on security, and I was talking to a friend of mine where this person was actually starting to do security work using Kimmy K3 rather than Fable, because with Fable, he would run into these refusals and safeguard. So that seems like a very weird spot to be, which we can talk about for a second. I think it's probably inevitable that if you are having choices from where you get your intelligence tokens from, that's going to put pricing pressure on the frontier models, which means I think you'll probably see the frontier labs have to drop token prices or find ways to, you know, find ways to match pricing, which means it's probably going to erode into their gross margin.

it's probably great for the neoclouds and every other layer of the stack because if you're a neocloud like a baston or a fireworks or if you just have a bunch of black walls and you can power it and you can run that and you know you can capture some of the economics which is probably going to go to the frontier lab so I think it's great as a consumer I think it's great for the ecosystem lots of obviously questions on security on distillation on you know how this is good but a very, very interesting moment. Totally.

So I guess, like, the first question here would be, where do you predict the frontier labs? Like, how do you predict the frontier labs are going to react to this? Like, now, you know, the ecosystem has been sort of changed. Like, there's no going back. Like, Kimi has been released, and it's very close to the capabilities of Fable.

So what do you think is next for, like, an anthropic or an open AI? So a few things. I think it's very clear the frontier labs are going to push at the very, very, very, frontier of, you know, the jagged performance we get. You know, as somebody who spent close to the last 18, 19 months trying to make sure America wins, I really want to see the American models, whether it's closed or open weight at the

frontier, which, and so I think they'll continue to be that. What I suspect these open models could really start putting pressure on them is one on pricing, because it may turn out that the number of... tasks that you need absolutely frontier intelligence from is, you know, let's call it like one subset, but for a lot of other tasks, for example, like

I have an agent which checks my email or I have an agent which quickly scan through my calendar. You may not need frontier tokens. You may be able to get by with frontier minus one or your open weight token of choice. In which case, I think you're going to start seeing pricing pressure

come on the frontier labs. You're probably probably going to see them and try and make sure the absolute frontier models stay available and accessible. I think we have already seen Anthropic and I have no insight confirmation about this was a motivation, but you already seen them extend Fable. I think Fable is originally supposed to be available until, I don't know, like a week ago. It's already been extended. I predict that probably more extensions just because otherwise you have an open model which is very much near the frontier. I think the other interesting question is where is the real moat?

if you're a frontier lab, is it in the intelligence or is it in the harness? And I think, you know, Clod, you know, Co-Work and, you know, Cloud Code and Codex are amazing products. And I suspect you'll probably see more effort on making them sticky because it might be that, you know, the actual intelligence itself, or at least the levels behind the actual frontier are much more of a commodity. So what does this mean for the ecosystem? One, this could put some revenue pressure on the frontier labs because I suspect that people might start pointing their harnesses at some of these other open weight models for any number of reasons

which we can get into. I think it's probably great, like I said, for the neoclouds and anybody else with power and GPUs. And I know, it's going to be a very, very interesting time. Right. So there's been, speaking of open source, there's been some chatter. Axios just reported this morning that the Trump administration is considering restricting Chinese open source models, either because they are Chinese and might be a national security issue

or because they have reached like fable level capabilities and agentic coding and it could be a cybersecurity issue. So do you think the U.S. government will do things that will crack down on open models? and do you think that there's anything that they should be doing in this respect? Well, I have like no inside information. As I like to tell people, I am no longer living off your American taxpayer

money, but I have a lot of friends there and, you know, I think and I think from everything I hear, look, if you go back to a year and a half ago, in the first few weeks, you know, the Trump administration said hey, you know, we're going to come out of the AI action plan, and when we came out of the AI action

plan, you know, it's right in the first page, if you can't go to the first section of it, you're going to see the action plan talk about how important open source is. Now, I think the thing to think about is there are multiple different issues. Number one, I don't think it is great that the leading open weight models or open source models, you know, are not American. I think, you know, I think there's some great innovation happening with Moonshot and with Deep Seek and with Quinn, but I would much rather prefer that the leading models are American. And by the way, there's lots of great efforts happening.

I expect them to improve. You have, you know, Gemma from Google. You have Nemotron from Nvidia. You have obviously Thinky. You have, you know, newer startups like reflection coming out. I think they will continue to be better. But we are in a moment of time when the leading models are Chinese, which I think, you know, which I think is not great.

I would much rather prefer them to be American. Yeah. I think the second part of it is I. Look, I grew up loving open source. Open source is a big part of, you know, how I got into computers, a big part of my career. And I was a big fan of Linus's law, as in Linus Starvels of Linux fame's law.

And his law was that given enough eyes, all bugs are shallow. And what I believe with that is that open weight models are inherently secure because, you know, when you download a model of hugging face, it means you have the entire world being able to take it apart, inspect it, you know, fine-tune it, modify it, look at it in ways that you absolutely cannot if they are closed. So I kind of believe that they bring a very, very different,

positive angle to security. The weird, I think, or the moment we are in which I think is not great is, I think there was an incident with Hugging Face that got reported on earlier today, which, you know, I think there's an active tweet. And what Hugging Face seems to have found is that somebody was using, you know, essentially, definitely an AI, LLM agent to hammer them at multiple places and to kind of like, you know, find ways to break in. And, you know, I think the way to counter that

is to make sure American or Western or allies defenders have access to the best models to make one our software like just more secure. So which is why, you know, like I said, I don't love that we are in a moment where it may be harder to look at your own code for exploits with, say, Fable, than it is when you use a Chinese model.

That's just like a weird spot to be, and I think, you know, we should fix that. Now, one very interesting discussion, which has been happening a lot on X, is about distillation and what that means. So it's kind of a complex topic because there's a few things in there. So, first of all, you know, like every model we have today distilled off of all of all human knowledge, right?

Like, you know, if you go back to the original GPT or the original clod, you know, they all had to derive of crawling off the internet, you know, crawling off all of our, you know, blogs or tweets and content and, you know, they kind of consume human knowledge and to bootstrap that. So, distillation has always been a core part of how these models have been trained. Second, if you, you know, okay, let me ask you this. When I write a tweet these days, I am terrified of accidentally

using like, you know, multiple hyphens or accidentally saying something which will cause Pangram to say this is AI generated. I once wrote a tweet recently, which I put it through Pangram to make sure it felt human even though I knew I had written every human generated token. And so what we've all seen is, I would say, the rise of AI slop on the internet.

So if you think about that, the internet has grown a lot over the last couple of years. A lot of that has been AI generated. You and I see content every single day. There's definitely some tokens in there. And that also goes into the training of these models. So distillation of AI models is a core part of how these models are trained today. There's just no escaping that. That's number one.

Number two, I think the issue I think people are talking about is that some of these models may be distilling at scale in kind of these industrial ways where you have maybe a bunch of fake accounts or maybe you have people reselling, say, clot subscriptions to somebody else and routing it, any number of ways which are breaking terms of service and which are bad. So here's what I think. Number one is I think all the labs, you know, with the help of, you know, everybody, you know, from the government should be doing everything they can to make sure, you know, whether they're doing KYC, whether they are doing things to check the IP addresses

where they're coming from. They're a lot more sophisticated things they do. They are making sure like, you know, this heavy industrial extraction of reasoning traces isn't happening. But, and I'm going to steal this from Dean Mayor of Sequoia, who had a fantastic post and please give him credit for this

in this tweet. He wrote this yesterday. I think the situation which is bad today is that some of these models from other countries can train off American models. Whereas if you are an American open weight model, it may be really confusing or challenging on whether you can distill off of other American models, right? So we kind of have a really uneven ecosystem here where if you're a Chinese model, you could probably get a bunch of reasoning traces, but if you are, say, a new Valley startup and you want to use some reasoning traces, you know, you don't know what the legal situation is. So one great idea, which I think came from him. I think

Ben Thompson of strategy had a similar idea today was to basically say, how do we find a way to make distillation acceptable in any number of ways, whether you are getting, you know, outputs of other models, or sometimes it's more subtle. You know, if you look at any American open source model today, they are using Chinese models as a teacher or in a way as a part of the fine-tuning process and, you know, how do we make sure that is protected and enshrined? So if you're an American, you know, model company, you have the same level playing field as the Chinese models. Now, again, I think we have this very interesting, unique moment in time. But overall, I think these open-bate models are great for the ecosystem. It's more choice. It's people

competing on price. It's all the great things that innovation is supposed to bring. Right. Totally. So another consideration that is, going on right now is the idea of AI being able to automate to improve first and then automate AI research. So, you know, Anthropic has written about this. Open AI has talked about, you know, building in first an automated research intern and then working towards automating AI research. And many people believe that if that were to happen, the pace of AI capabilities progress would go very, very, very fast. So what kind of policies do you think the government, if any, should have around this,

what do you think the government is going to do around a potential very rapid increase in capabilities of AI if that were to happen in the future? Well, again, I don't speak for the government anymore.

You know, I don't have any inside information. I don't know whether the government has a real role in this. Like, look, whether RSI is real or not, where you are on the exponent is a much, debated topic. You know, I've heard many, many schools of thought where, you know, they believe you're going to have automated AI researchers in a couple of years. But then there are others you

think, well, there are fundamental improvements that you cannot crack. And yes, you're going to see improvements from models being able to train other models. But the exponent is going to be a lot more gradual rather than a very, very, you know, a steep curve. So I think when I was in government, for me or for David, the entire focus was how do we have a fantastic ecosystem where there are people competing to build products, you know, you are pushing innovation as fast as possible. And then when there are credible risks, you know, making sure when they appear, making sure you tackle them. So for example, with cyber, you know, when you have a credible threat, when you know these models are capable of, for example, generating exploits in the latest

firmware or the latest operating system, how do you then go tackle it? So when somebody gives me this question, I sometimes find it very theoretical and I often find it a lot more useful to break it down into, okay, one, how do we make sure we have a bunch of competing choices? Two, is what do we know to be credible? And, you know, how do we actually particularly tackle that? I think there are very credible threats on cyber, on biological advances, a couple of other topics. And I think there are definitely efforts to try and tackle just those. And then, you know, and I think the final part I would say is that in a lot of ways, I'm a big believer in using AI to help.

So often, for example, the answer to helping with cyber is if you have, you know, using more AI to go scan your code base and making your code base more secure. So very often I think, like, how do we then deploy to actually meet these threats as we go along? Totally, totally. So another point that Dean Ball raised actually was sort of this sentiment that like open weight models deter CAPEX and they kind of like destroy the ability for frontier labs to monetize their capability leads. So how do we like promote a very healthy like open source ecosystem while, you know, ensuring that the frontier labs are still able to like do what they're best at? Well, I think at the end of the day, if you kind of bring it back to a very business first principle, principles, if you're providing a product of value, capitalism will find a way to make the supply chain work for you.

So if you have an open-made model that is providing value, that means that every part of the stack underneath, whether it is a neocloud, whether it is a data center, whether it is a chip provider, whether it is somebody who provides gas turbines or fire suppression to a data center, every other part of the stack is going to. to orient itself to provide value because you as an open weight model provider, you might be working with a bank because they don't want to work with a frontier model. They want to work with somebody that they can run in-house. Or if you go look at what somebody like Thinky is doing, they are fine-tuning models for specific clients using internal data that they don't expose. So I kind of think of like, you know, if you're providing a product of value, the capitalism will take care of all the rest. And by the way, I think I'm being borne out.

Like if you go look at the fraction of every other part of the stack, if you go look at how the inference clouds are doing, if you go look at how the rest of the ecosystem is going, the growth is pretty strong and spectacular. And I think you're going to continue seeing that continue. So to the extent that you can tell us, what are you working on now that you're out of government? Well, I'm going to be doing more, you know, live drop-ins, I think.

Streamer. No, look, I think, well, look, I had this amazing life experience, which is unparalleled, and it was such a unique honor. And it's kind of really, you know, given me a sense of what countries and companies need to do to work with each other and to make sure everybody gets intelligence, whether it's from the frontier labs and open weight models or how that happens. and I want to try and make that happen in some shape or form. So I'm going to be annoyingly elusive,

but I think that mission of making sure America, our allies, get access to AI at scale with having governments and these companies work together is a very important one. I've been working on that in many different ways, if you think about it for many years now. So I want to continue to do just that. And, you know, write some banger posts and, you know, jump in on

live streams from time to time. Yeah, well, we're excited. This has been so great. Thank you so much for coming on MTS. Yes, I'm sure we'll have you on again. Thank you. Thanks for having me. Much more to come. Absolutely.

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