Sriram Krishnan on Open Source AI's Biggest Week Yet

Summary

Podcast Summary: "Sriram Krishnan on Open Source AI's Biggest Week Yet"

Podcast: The a16z Show
Episode Date: July 24, 2026
Host: Andreessen Horowitz (feat. Theo Jaffe & Sophia Puccini)
Guest: Sriram Krishnan, former White House AI Policy Advisor

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Episode Overview

This episode dives into a seismic week for open source AI, as multiple new open-weight models have been released, challenging the dominance of proprietary "frontier labs." The hosts, alongside Sriram Krishnan—a policy veteran and industry insider—unpack what this momentum means for industry players, AI policy, cybersecurity, pricing, and America's role in a newly global AI race.

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Key Discussion Points & Insights

1. The Explosion of High-Quality Open Source Models

[01:45–05:28]

• Recent Model Releases: In just one week, models like Grok, Thinking Machines, Kimik3, Quen 3.8, and Muse Spark have debuted, with Kimik3’s open-weight release described as a pivotal moment.
• Impact on the Ecosystem: The sudden proliferation of credible open models gives developers and enterprises much greater choice, previously constrained by the technical and licensing moats of "frontier labs."
  • “It’s just great to have choice in the ecosystem and to be able to point your harness of choice, your agent of choice, to multiple models.” — Sriram Krishnan [04:10]
• Security & Safeguards: Some American models feature stronger safeguards (e.g., refuse to generate certain security-related content), which is pushing some cybersecurity workers to opt for less-constrained open models like Kimik3.

2. Pressure on Pricing and Business Models

[05:28–08:20]

• Frontier Labs' Response: Open models close to “frontier” capabilities force labs like OpenAI and Anthropic to reevaluate pricing and their "moat."
  • “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, you’re going to start seeing pricing pressure come on the frontier labs.” — Sriram Krishnan [06:28]
    • Frontier labs will likely emphasize the unique features of their top-tier products, while open models commoditize basic intelligence.
  • “Where is the real moat? If you’re a frontier lab, is it in the intelligence or is it in the harness?” — Sriram Krishnan [07:40]
• Effects on Suppliers: The rise of open models is great for value-add layers in the AI stack (clouds, GPUs, infrastructure providers) as more organizations seek to run their own instances.

3. American versus Chinese Open Source AI

[08:20–15:59]

• National Security Concerns: U.S. government is reportedly concerned about leading Chinese open models, both on national security and cybersecurity grounds.
  • “It is not great that the leading open-weight models...are not American...I would much rather prefer that the leading models are American.” — Sriram Krishnan [09:30]
• Security and Open Source: Sriram champions “Linus’s Law”—given enough eyes, all bugs are shallow—arguing that open models can be more secure than closed ones, if defenders have access.
  • "Open-weight models are inherently secure because...the entire world [can] take it apart, inspect it, fine tune...you absolutely cannot [do that] if they are closed." — Sriram Krishnan [11:05]
• Distillation & Fairness: He notes a key asymmetry: Chinese models can (and do) distill knowledge from U.S. models, but complicated legal/ethical grey zones prevent American startups from reciprocal distillation.
  • “Some of these models from other countries can train off American models. Whereas if you are an American open-weight model...you don’t know what the legal situation is.” — Sriram Krishnan [14:34]
• Policy Recommendation: Suggests the U.S. government should clarify and permit distillation practices so domestic AI has a fair shot.

4. Governing the Next Wave: Automated AI Research

[16:00–18:53]

• Runaway AI Advancement?: The group debates whether automated AI researchers will trigger a rapid leap in capabilities.
  • Krishnan is skeptical of imminent, runaway feedback loops, preferring a focus on competitive ecosystems and targeted policy for “credible threats” (e.g., cybersecurity / biosecurity risks).
  • “When there are credible risks...making sure you tackle them...Very often, the answer is more AI—to go scan your codebase and make your codebase more secure.” — Sriram Krishnan [17:48]

5. Economics of Openness: Can Both Thrive?

[18:53–20:44]

• Healthy Coexistence: Responding to concerns that open models cannibalize capex and revenue for big labs:
  • Krishnan asserts that “capitalism will take care of all the rest” if value is delivered—even open model providers create value for every layer in the supply chain.
  • “If you are providing a product of value, capitalism will find a way to make the supply chain work for you.” — Sriram Krishnan [19:21]
  • He cites the robust growth at “every other part of the stack”—inference clouds, hardware, services—suggesting open source increases the pie.

6. Sriram’s Next Moves

[20:44–21:49]

• Krishnan hints at future involvement in enabling U.S. and allied access to powerful AI, mixing policy, tech, and communication.
  • “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.” — Sriram Krishnan [21:12]

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Highlighted Quotes & Memorable Moments

• On the Open Model Explosion:
  “You had Elon and Michael at Cursor…come out with Grok4.5...Meta come out with Muse Spark…Kimik3 coming out...Quinn coming out...Just a lot of choices and alternatives.”
  — Sriram Krishnan [02:07]

• On Security, Choice, & U.S. AI:
  “It is not great that the leading open-weight models…are not American…I would much rather prefer that the leading models are American.”
  — Sriram Krishnan [09:30]

• On the Business of Open Source AI:
  “If you are providing a product of value, capitalism will take care of all the rest.”
  — Sriram Krishnan [19:21]

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Timestamps for Key Segments

• Recent Open Source AI Releases & Industry Shifts: 01:45–05:28
• Frontier Labs' Pricing & Market Impact: 05:28–08:20
• U.S./China AI, Security, and Distillation Discussion: 08:20–15:59
• Automated AI Research & Policy: 16:00–18:53
• Open Models, Capex, and Ecosystem Economics: 18:53–20:44
• Sriram on Life and Work After Government: 20:44–21:49

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Tone & Style

The conversation is rapid-fire, candid, and insider-heavy—grounded in business realities but openly reflective about technical, geopolitical, and even philosophical implications of open source AI. Krishnan especially blends a practical optimism (“capitalism will find a way”) with a realpolitik concern for U.S. competitiveness and security.

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Summary Takeaway

This was a landmark week for open source AI—one that promises more competition, consumer choice, and global complexity. The rise of open models challenges the dominance and pricing abilities of proprietary “frontier” labs, raises urgent policy and security questions, and suggests that open source will be foundational to the next era of AI infrastructure and governance. Sriram Krishnan’s perspective consistently advocates for enabling U.S. leadership and openness—by ensuring fairer rules, defending open development, and letting the market and innovation cycles do their work.

Transcript

[Sriram Krishnan]

You kind of bring it back to very business first principles. If you are 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 NEO cloud, the chip provider, somebody who provides gas turbines or fire suppression, is going to orient itself to provide value. If you are 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 doing, the growth is pretty strong and spectacular and I think you're going to see that continue.

[Podcast Host (a16z)]

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 Sriram Krishnan to unpack with the latest wave of open models, means for Frontier Labs, AI policy, pricing, cybersecurity and America's position in the global AI race.

[Sophia Puccini]

Race.

[Theo Jaffe]

We are back. We are live with Sriram Krishnan, who just finished his tenure as the Senior White House Policy Advisor on Artificial Intelligence. Previously he was a general partner at Andreessen Horowitz and held senior roles at Microsoft, Meta, Snap and Twitter. So Shriram, we're so glad to have you on. Welcome to mts.

[Sriram Krishnan]

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.

[Sophia Puccini]

Yeah, amazing.

[Theo Jaffe]

So there's so much going on in open source. Last week we just discussed we had Grok Build was open source and then we had Thinking machines and then Kimik3 and then Quen 3.8. So you tweeted the other day, Kimik3 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?

[Sriram Krishnan]

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 4:6 or 4:7 at the time GPT 5:4 or 5 or wherever we were and it felt like there was really no one else and we were on this curve of 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 XAI team come out with Grok4.5, 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 Thinky come out with Inkling. I don't know their version number, but the first version of that model, which I think is nearly SOTA on many, many benchmarks. But I think that the big news over the last three, four days was obviously Kimike three coming out, I think on Thursday or Friday and then I think the last 24 hours, I haven't really played with it yet, but Quinn coming out. So just a lot of choices and alternatives coming out. And what I was referring to is, with Kimiketri is the following. One is that it's just great to have choice in the ecosystem and to be able to point your harness of choice, your agent of choice, to multiple models. Second, 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 Kimik 3 rather than Fable, because with Fable he would run into these refusals and safeguards. So that seems like a very weird spot to be, which we can talk about for a second. I think it's also, 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 Neo clouds and every other layer of the stack because if you are a Neo cloud, like a Bas 10 or a fireworks, or if you just have a bunch of black walls and you can power it in, you can run that and 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 how this is good, but a very, very interesting moment.

[Sophia Puccini]

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 OpenAI?

[Sriram Krishnan]

So a few things. I think it's very clear the Frontier Labs are going to push at the very, very frontier of the jagged performance we get. 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. 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, that the number of tasks that you need absolutely Frontier Intelligence from is, let's call it like one subset. But for a lot of other tasks, for example, I have an agent which checks my email, or I have an agent which quickly scans through my calendar. You may not need Frontier tokens. You may be able to get by with Frontier minus 1 or your OpenVait token of choice, in which case I think you're going to start seeing pricing pressure come on the Frontier Labs. You're probably going to see them try and make sure the absolute Frontier models stay available and accessible. I think we've already seen Anthropic, and I have no inside confirmation about this was a motivation. But you've 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 there'll probably be 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 Claude Cowork and Claude Code and Codex are amazing products and I suspect you'll probably see more effort on making them sticky because it might be that 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 Neo Clouds and anybody else with power and GPUs and I know it's going to be a very, very interesting time.

[Theo Jaffe]

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 agent decoding and it could be a cybersecurity issue. So do you think the US 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?

[Sriram Krishnan]

Well, I have 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 I think from everything I hear, look, if you go back to a year and a half ago, in the first few weeks the Trump administration said, hey, we're going to come out of this AI action plan. And when we came out of the AI action plan, it's right in the first page, if you kind of 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 are not American. I think there's some great innovation happening with Moonshot and with Deepseek 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 Gemma from Google, you have Nemotron from Nvidia, you have obviously Thinky, you have 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 is not great. I would much rather prefer them to be American. I think the second part of it is look, I grew up loving open source. Open source is a big part of how I got into computers, a big part of my career. And I was a big fan of Linus's Law, as in Linus Torwell's 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 when you download a model of hugging face it means you have the entire world being able to take it apart, inspect it, 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 I think there's an active tweet. And what Hugging face seems to have found is that somebody was using essentially definitely an AI LLM agent to hammer them at multiple places and to kind of find ways to break in. 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 just more secure. So, which is why, 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 a weird spot to be, and I think 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, every model we have today distilled off of all human knowledge, right? Like if you go back to the original GPT or the original claude, they all had to derive of crawling off the Internet, crawling off all of our blogs or tweets and content, and 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. Okay, let me ask you this. When I write a tweet these days, I am terrified of accidentally using 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 humanoid, 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. We're like, oh, 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 industrialized ways where you have maybe a bunch of fake accounts or maybe you have people reselling say cloud 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, with the help of everybody from the government should be doing everything they can to make sure whether they're doing kyc, whether they are doing things to check the IP addresses where they're coming from. There are a lot more sophisticated things they do. They are making sure these kind of these things. A heavy industrial extraction of reasoning traces isn't happening. And I'm going to steal this from Dean Mayer of Sequoia who had a fantastic post and please give him credit for this in his 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 don't know what the legal situation is. So one great idea which I think came from him, I think Ben Thompson of Statichery 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 outputs of other models or sometimes it's more subtle. 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 how do we make sure that is protected and enshrined? So if you are an American in a model company, you have the same level playing field as the Chinese models. Now again, I think we have this kind of this very interesting unique moment in time. But overall I think these open bait 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?

[Sophia Puccini]

Totally.

[Theo Jaffe]

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, OpenAI 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?

[Sriram Krishnan]

Well, again, I don't speak for the government anymore. I don't have any inside information. I don't know whether the government has a real role in this, whether RSI is real or not. Where you are on the exponent is a much debated topic. I've heard many, many schools of thought where they believe you're going to have automated AI researchers in a couple of years. But then there are others who 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 steep curve. So I think when I was in government, for me or for the entire focus was how do we have a fantastic ecosystem where there are people competing to build products. You are pushing innovation as fast as possible. And then when there are credible risks, making sure when they appear, making sure you tackle them. So for example, with cyber, 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 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 I think the final part 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 using more AI to go scan your code base and making your code base more secure. So very often I think, how do we then deploy to actually meet these threats as we go along?

[Sophia Puccini]

Totally, totally. So another point that Dean Ball raised actually was sort of this sentiment that 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 promote a very healthy open source ecosystem while ensuring that the Frontier labs are still able to do what they're best at?

[Sriram Krishnan]

Well, I think at the end of the day, if you kind of bring it back to very business first principles, if you are providing a product of value, capitalism will find a way to make the supply chain work for you. So if you have an open weight model that is providing value, that means that every part of the stack underneath, whether it is a new cloud, 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 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 if you are providing a product of value, the capitalism will take care of all the rest. And by the way, I think I'm being born out like if you go look at the traction 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 doing, the growth is pretty strong and spectacular and I think you're going to continue seeing that continue.

[Theo Jaffe]

So to the extent that you can tell us what are you working on now that you're out of government?

[Sriram Krishnan]

Well, I'm going to be doing more live drop ins I think and I'm going to be streamer. 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 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 it in many different ways if you think about it, for many years now. So I want to continue to do just that and write some banger posts and jump in on you know, live streams from time to time.

[Theo Jaffe]

Yeah. Well, we're excited. This has been so great. Thank you so much, Sriram for coming on mts.

[Sophia Puccini]

Yes, I'm sure we'll have you on again.

[Sriram Krishnan]

Thank you. Thanks for having me. Much more to come. Absolutely.

[Podcast Host (a16z)]

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