The a16z Show - Steven Sinofsky: AI Doesn't Need New Rules Yet

Episode Date: July 27, 2026

Steven Sinofsky joins Theo Jaffee and Sofia Puccini for a conversation on AI regulation, open-source models, and what history can teach us about technological revolutions. Drawing on decades of experience leading products at Microsoft, Sinofsky argues that governments are rushing to regulate AI before they fully understand the technology, risking innovation in the process. They discuss the "precautionary principle," why open source has historically accelerated innovation, the role of regulation in emerging technologies, the AI competition between the U.S. and China, and why existing laws may already address many of the risks people attribute to AI.   Resources: Follow Steven Sinofsky on X: https://x.com/stevesi 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.

The whole topic of regulation for me just seems completely backwards, because it's starting before we even know what we're regulating. There's no reason why the AI company should be against open source other than we just don't want our competition to exist, and we don't want to bother to compete. We'd rather just compete with each other and not worry about that crazy open source competitor. Truth is, no one knows the future.

What those assumptions mean are, We should regulate this based on our own personal predictions of the future. But the history of being right about those predictions is pretty limited. And so we should be really careful about that, because those are all self-serving. How should governments regulate AI when the technology is still evolving? Theo Jaffe and Sophia Puccini are joined by Steven Sinovsky to discuss why he believes AI regulation is moving too fast, the case for open source models

and what previous waves of technological innovation can teach us about building policy without stifling progress. We are live with Steven Sinovsky, amazing tech analyst, author of hardcore software, longtime Microsoft leader, and we're so glad to have you back on, Stephen, welcome back to us. Hey there, super fun to be here.

Yeah, so much in AI, like where do we even start? Well, our goal is to say stuff that we haven't said yet, so let's see if we can do that. We will try. Novel insights time, yeah. Novel insights. So open source, there's been much discussion of regulation of open source, Chinese open source models in particular, on both sides of the pond. You know, China has considered export controls, but also Xi Jinping has said we're encouraging open source. And then in America, we've had Scott Besson says we're going to be looking into IP theft and some people have considered import controls or requiring a license. So there's a lot

going on right now. It's very fluid. What are your takes on regulation of open source models? Well, it just seems the whole topic of regulation for me just seems like completely backwards because it's starting before we even know what we're regulating. And if you go back in history, one of the best things about the 20th century in America was that so much experimentation was happening, so much innovation. And that does have this potential downstream of like, oh gosh, this thing happened that we have to roll back.

Like a super famous example was safety in cars and that car companies spent the first 50 years making cars, making cars. And then in the 1960s, it sort of became clear that, wow, we could actually make cars much, much safer. Now, it took 60 years to understand that cars were super dangerous, and it was all that evolution.

And in all fairness, like it wasn't a design criteria. You know, seatbelts and airbags, and these things became a very long process to get them put into the upstream of making cars. And you could go through every sort of dimension. There's antitrust law and the whole reason why, you know, oh my God, you can't be an oil company where you sell gasoline, produce gasoline, and also pull it out of the ground, or you can't make movies, have actors and actresses under contract, and own the movie theaters and distribution.

And maybe that shouldn't be right. And there are millions of examples. The challenge is the story of regulation is always told as though if they would have gotten in early, they would have prevented this stuff from happening. And, you know, you look back and you're like, oh, what if they would have shown up at Henry Ford and said, we need airbags? And Henry Ford is like, okay, we can't figure out

how to make engines work. Like, let's get that done. And also these cars go 15 miles an hour and, you know, we're just trying to make them go. And, you know, of course, air travel is like that. Every technology innovation, it took a long time for two things to happen, one for it to just work at all. And, you know, today we're talking about AI, and it can't be that AI is both the most

intelligent thing in the world and it generates gibberish hallucinations and the answers are wrong and you can't rely on it. Like those both can't be true at the same time. You can look at social networking have the same effect where like, oh, it's a toy, it's a giant waste of time. you know, it's either that or it can topple governments. You know, it couldn't be, and they were at the same time, people were saying both of those things about social networks. And so it can't be, and now they've added this dimension

that they are mind control devices on our nation's use. And at the same time, they're a huge waste of time, and the content is all slop. Like, all of these things can't be true. And so a lot of times it just, this whole motion of regulating is this hindsight. and you assume that only good would have happened if regulation happens. But that's just, you can't prove that.

And so you're in this very tricky situation. And so today you have this world, oops, my, I just got lost on video. Hey, give me just a second and figure out what just happened. Oh, I just crashed. No worries. You're still here in voice form. So, yeah, I got you in voice.

And, and I just, I like literally crashed. Hang on. The spirit of Steven Sinovsky still wins. It's a beautiful, like, gear icon. Yeah, is that, I don't know. Oh, is it? Wow, that was super interesting. Yeah, we can pause the stream for a second while we figure it out.

No, I like, I officially grew, and I am, I'm working. I am closer. Well, let's, can we do with this? Is that working? It works. It works with audio. You just become an anonymous guest. Oh, hang on.

Your newest Anon guest is like suspiciously knowledgeable about the Microsoft Surface program. How about that? Okay. You're back. You're so back. I'm gag. It's not the right video, but it is video. And so, so we have this thing where everybody, that was just a total crash, by the way, of my like AV stack.

And so we had this thing where people are just trying to rush to regulate. And that it's, it actually came up, it happened so often that his guy came up with a term. called the precautionary principle. And the idea is regulators who, you know, look, you don't get a job in government to regulate and then say, and then don't regulate. Like you come to work to craft regulations and to come up with what you want to do. And so the precautionary principle was like, well, let's just get ahead.

And let's regulate before anything bad happens. But when you do that, what you really do is you constrain the solution set. And I think back to the internet and what would have happened if we would have precautionarily regulated the internet? Where would it have started? Like, would we all just be using AOL Instant Messenger right now? Because that was like the internet that everybody knew. And it had all these attributes that regulators liked. Like, it was one company and it was in Virginia.

Like, literally, that's even the best case because it was practically in the government already. And, you know, you could go to them and you could tell them to do stuff. and it was a walled garden. If you didn't like something, you could turn it off. And it was perfect. Or would it have been in like Web 1.0 where everybody is using six tags in HTML.

There was not yet really commerce. And we all would have just looked things up on Yahoo and Excite. And they would have said, okay, these are the two companies we're blessing. Yahoo and Excite. And there's no video. There's no audio. You know, we're just, you know, and you just,

you kind of go and you pick your time of when it was we're all comfortable with, like, whatever it is that that's going on. And so that's, of course, what happens is at that moment, the companies that are really hot are the ones that the government seeks input from. And some people think that that's bad. I actually think it's good because they know presumably more than anybody else, but what they also know is what they want to do. And so those companies then, they sort of push government to go in a certain

direction. Historically, technology companies have said, whoa, how about this government? Hands off. We don't like regulation at all. And, you know, that's a lot different than say what happens, you know, if the government's already regulating like safety of chemicals, it's very hard for the pharmaceutical companies to say, leave us alone and don't regulate us at all. And so that was one of the first early regulatory bodies. But the tech companies, this time around in AI, have completely just blown my mind. Now, you have to understand. I spent, like, I was a Microsoft's employee

for like two years, and then the government started investigating Microsoft in like 1992. Told you for what, you wouldn't even understand what I was talking about. But now we had, you know, two years ago. Like two years ago,

what were people doing with AI? Like chatbots were new. And you had the leaders of the AI companies shung up to Congress literally, please regulate us. Oh, we're begging you to regulate us. And that is just this crazy notion. And so what you had was they just played right into the government's view that they missed regulating technology. They missed regulating the internet. They missed regulating the PC. They missed regulating the mainframe. And so this was their chance. And they were being asked. And so they like rolled out the

red carpet. And the government knew what was going on, but because it solved a problem for them, which was how to really get into regulating big technology companies, they sort of went with it. And so the government normally, like, they kind of go, oh, they're going to try to control us. They're going to lobby us and tell us what to do. And in fact, everybody knew that was what's happening. And that's called regulatory capture. And this is a long history. And most of the people in government that are savvy with government know that's what's happening, that they know the companies are saying, do this thing because it's good for everybody.

And they always have good reasons. They always say, oh, the products will be better or the price will be lower or they'll be much clear lines of service and the marketplace will be more fair. Like, for example, AT&T, which is a fascinating example, because they were actually a government-created monopoly. Like literally the government created the phone company

and they owned it essentially. But then when the government was saying, you know, we think we need to control your prices and do stuff, AT&T went to them and they said, okay, here's the deal. We can guarantee that every single address in America will have a telephone. And it's called universal access. And that's why you can only let us do this. And so basically, they made this deal that the AT&T was going to be the national phone company, even though it was a private company. If They promised to deliver phones everywhere. And it got really tricky because with that came like a bunch of rules about using the telephone lines that today led to surveillance.

You know, and all of the stuff. And because so what happened was basically the AT&T, a private company became really the United States phone company. And it was just a branch of the government. And this has happened time and again. J.P. Morgan became the National Bank of America. You know, the Standard Oil became the National Oil Company of America. Even at some crazy extreme, Warner Brothers became the movie theater of America. Yeah, and every interesting, and it turns out radio, television, and telephone all sort of followed the same path, where they were, there were private companies that were basically beholden to the federal government.

You saw a lot of this when the New Deal was created. And FDR basically used the Federal Communications Commission to control the messaging of the New Deal by basically threatening the licenses of the radio broadcasters. And there's a lot of controversy of just how much they did and did it really cross some line? But the truth is they were just in line. Whether they wanted to be or not,

they were going to be in line. And the same thing happened with television. And so this whole idea of being against open source, it's really rooted in, like, no one, the government can't be against open source because if you get a government grant, you're required to release all your software as open source. Like that's the government way of doing computer science research. So how can all of a sudden the government say, we don't want open source? It doesn't even make sense because nobody, none of the leading academic research,

that will be government funded. And so it just makes no sense at all to me. And Microsoft, where we were a closed source, Bill Gates invented the closed source business model. And so we live through the rise of HTTP and Apache and web and Linux and all of his other stuff. But there is a role for open source. And most importantly, the open source existed

even when Microsoft existed, even when IBM existed. And so there's no reason why the AI company should be against open source other than we just don't want our competition to exist and we don't want to bother to compete. We'd rather just compete with each other and not worry about that crazy open source competitor, which I can't even put words into how obnoxious that is.

It's just not true. And they benefited enormously from the, academic research community, all of which was open published in open source, you know, it's just crazy. And that's how we got here. And that's why we're even having this debate. Yeah, well, I think that, you know, most AI labs, AI companies would be like totally on board with this idea of like, yeah, of course we don't want, you know, government centralization and regulation. But they think, you know, AI is not like the other technologies. It's not like the internet. It's not like the PC. they're building towards artificial super intelligence, and this will be like a totally different thing

and will require like a novel approach. So I think maybe different people in this debate are working on different assumptions of like where the technology is headed. Well, those are, let's, that's the most generous way to express it is different assumptions. The truth is no one knows the future. So what those assumptions mean are we should regulate this

based on our own personal predictions of the future. But the history of being right about those predictions is history of being right about those predictions is pretty limited. And so we should be really careful about that because those are all self-serving. There's no reason. And to say it's different.

I genuinely believe there are people who work on AI who really believe in this ASI, AGI, arc of super intelligence. I mean, they went to Congress two years ago and they said literally there was fear in their eyes for the economy and for people dying everywhere. At the same time, of course,

it didn't work and it was hallucinating and it was silly and it was making things up. So again, it's not, they're drawing some predictive arc. And even today, people are fighting over is AI good enough to do stuff? What is it good at? So you can't have all of these things being true at the same time.

So what it really says is not now is not the time to regulate because we don't know where it's heading. So all you're going to do is kneecap innovation. And there are laws in place for a zillion, almost any scenario. In fact, I would say 100% of the scenarios that people say are problematic, there are already laws against them. Like, there are already laws against, you know, putting bad drugs on the market. They're already laws against not giving people bank loans for their gender or their race or their other attributes. And there's already, like, Senator Warner today came out with a big four-point plan about AI. And he's like, well, we have to address the issue of non-consensual nudity.

And it's like, okay, but there are laws against this. Like, you can't be showing nude pictures to children. like that's already law. And you can't be showing nude pictures of children. It's already a law. And so, like, every one of these evil scenarios that they worry about, it's already illegal. Like, I saw one state, like, you can't be registered as an attorney if you're an AI. Well, of course you can't be registered as an attorney.

You can't even sign your name on the license. Like, you know, like you have to have a license to be a lawyer. you have to take a test, you have to do all of this stuff. And the AI doesn't do any of these things, nor can it. Like, it can't register for college. Like, people aren't making, they aren't being coherent because they're just so enamored with, like, getting their point of view and leaving off competition in some way or decapping big tech in some way. Yeah. So if we have, like, the precautionary principle in mind, what would be the opposite of it?

like what would be a very like responsible, like, iterative way to start regulating AI, knowing that like, your viewpoint is like we don't even know exactly what it's capable of. It's not even like, you know, like hashed out enough as a technology or as a sector to even know what to regulate. Well, the first thing that's super easy is you can go through all the laws and say like, hey, in this state with this law for this thing, you know, Did we make it clear that you can't be an optician if you're not an AI? Like, should we just make sure that AI can't just go give eye tests? Like, you can't set up an iPhone in the mall with, like, a camera and write lens prescriptions for people. Like, okay, maybe you can do that because Ohio didn't word their law correctly.

And states are responsible for licensing opticians. So there is this step one. okay, we have a whole new technology. Should we, like, this happened with EVs. Like, EVs weren't gas combustion engines, and they had to adhere to all the safety rules of cars, except they had a whole bunch of different components in them. And all the rules for cars sort of got based on the gas combustion enzyme.

So they need to go back and make sure that all of the laws applied if you had a battery operated car. And so there's, like, how many cars have, had front trunks, not very many. So were there laws about safety and crash zones and all of this that accounted for front trunks? Let's go check it out and make sure we're okay. And so there's a first step, which is there are a lot of laws.

There's two million laws on the books about everything. So do the right ones apply to AI? Like do a bunch of places need to go add and AI to their laws? And maybe that's CSAM. Maybe that's spam. Maybe that surveillance. Like, there's a bunch of stuff to just go do. And maybe today those are wrong.

That's like, that should keep everybody busy while we go figure out AI. And of course, we have to do this with computers. Like, you had to make sure that if you used a computer to do something, that like you couldn't, like, you know, that all the anti-forgery laws applied if you happen to do output in a computer. That, you know, I went through getting the legal system to use Microsoft Word. And it turned out there were a whole bunch of features we had to go change. in word just to work correctly in the legal system as it stood. Legal system didn't have to do anything.

We had to go and just make sure we worked within the constraints of, like, here, a phrase the example. Like in the legal system, having footnotes that are longer than a page, so footnotes have like a limit. They can only take up like a third of the page in a legal brief. But if your footnote is longer than that, you have to have like a feature in a word processor that continues the footnote on the next page.

like we were like wow who thought of doing that what a and it's no good deal for using a typewriter because you haven't even gotten to typing that page yet so it worked fine so we're fine so we had to go do that so that's like a great step and that's an idea like then we'll know what's missing and also while the technology evolves right yeah i'm a big believer in like domain experts will sort of know the like in the case of the like opticians while it should be like registered opticians and people who are like have expertise that are sort of like working with the states to um along with like some AI experts and then it becomes like a perfect union and that's how we like figure out where to go next so I totally agree yeah there was an episode there was an

episode there was an episode of the new TV show the pit where one of the residents is using the um an AI system to sort of make it easier to do the patient notes and then she makes an AI mistake like AI makes a mistake. And there's no ambiguity. It's her no. Like she's in trouble. She can like go, oh my God, it's AI. And there's just nothing you could do.

It's her medical license. It's not the AI's medical license. Completely unambiguous. Now, if she went and hurt the patient, it's super tragic. But she still hurt the patient. And it's her accountability, no matter who wrote the note. Yeah. Going back to open source.

So something interesting of like the last week or so has been that, like, on one hand, you have, like, China considering export controls on, like, chips and, like, open weight models. And then you also have the U.S. government, like, considering maybe, like, some action against, like, Chinese, regulating Chinese open source models. So it seems like on both sides, like, there is an incentive to do that. Like, how do you see this playing out? Well, in this case, this is, this, there is an actual innovation war with China happening right now. There's not a cold war. There's sort of a trade war, but we're very mutually reliant at each other for trade, so it's hard to call it a trade war. But there's absolutely an

innovation leadership war. And if you're the government and you're participating in innovation leadership war, there are two tools that you have. One of them is you bludgeon your international competitors with the regulations that are already in place. And then the other is you fund or nationalize companies to go compete. And those are the tools that you're seeing put in place now. China is very pro-pouring money into the companies at a national level. This is something that we saw with Japan in the 1980s. In the 1980s, Japan was going to take over the technology world.

And they set up a huge ministry that sort of tried to shepherd the memory industry to dominate it. And if you look, they don't dominate the memory industry anymore. so it didn't really work. But they did for a long time dominate other aspects of tech, and they're very, very good at what they do. But this central planning,

it's not, there's no track record where it really succeeds. These trade wars, this is where you get into what I think of as bank shots. The government's not going to go after AI directly. It's going to, like, do these things on the side. Like you see, we're going to,

oh, we're going to ban the chips. Okay, well, that doesn't, that, that may or may not slow them down, but it doesn't have any to do with the model in the end. Like, okay, so it'll train longer or it'll, you know, there's no shortage of power in China, so they're not going to run out of power running the models longer or something like that. And so these bank shots are very popular with, with the government, because also it doesn't look so rude.

Like there's one going on with China or with Canada now over, over Canadian lumber and we put a tariff on. I have no idea what Canadian lumber has done to us. But it's probably nothing. And this is over something that they did to us that is of lumber. Because often you can't do the same thing back. Like it happened, you've seen this in the brutality of war where like once I bombs a school by accident, you don't bomb a school of theirs on purpose.

You go and you bomb, you know, a factory that's important to the military or something. And so you get these in directions and signals. These are the tools of diplomacy. And you can fight all you want about how crazy they sound. But this is what they do. And they're going to come to work every day and participate in the innovation war with the tools that they have. And it's unfortunate for us that there are people helping them with their own bank shots. So like when Anthropics says, hey, the best way to hurt China is to curtail open source.

What that means is the best way to help Anthropic is to curtail our open source competitor, whether it's from China or the United States. And that's just that is kind of un-American. And so, and it's definitely un-tech. Like we've just, the tech industry is just never wanted this. It's very different than, say, the auto industry. In the 1970s, when I was a kid, that, you know, Detroit was just getting crushed by small Japanese cars

because Detroit just couldn't make fuel-efficient cars. And they also couldn't make quality cars. Japan had a reputation for low-quality, and through the 1960s really perfected making super high-quality cars. They invented quality engineering. And then they are a very compressed city with a zillion people, So they made really small commerce that became immensely popular in the United States. And Detroit just convinced the administration that they were selling these cars below their cost, that they were dumping them.

Does this sound familiar? This got used this week about, about, about, uh, candy. Yeah. And so the idea is that, oh, they're dumping it. Well, dumping, it turns out to be it has to have a very specific definition about how much does it cost you and are you really offering it at less price. And even then, this is a bigger international trade diplomacy dialogue. It's not so cut and dry. You can't just say, because this product exists, it's being dumped.

And yet the Detroit was like, dumping, dumping, dumping, you've got to stop it. So what did Japan do? They actually built all their factories. And so you might remember or might know that there's a bunch of Japanese carfent manufacturing plants in the U.S. in the Carolinas and Alabama and Mississippi. And so that's what they got. of this. They still lost.

Detroit doesn't even make cars anymore. So they still lost. Yeah. And so it didn't really help. Right. Yeah. Well, it's been super interesting. Stephen, thank you so much for coming on MTS. It's been great having you on. Thanks for listening to this episode of the A16Z

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