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67 turns · 29 min[upbeat music] The AI people are making it impossible for anybody to understand what they've done, and they're using words like, "Well, the AI failed to be aligned." Okay, what does that mean? What it means is there was a bug in the software. When Word ate your file and deleted all your content, we didn't think that demons had taken over Word and made it do things against the will of man. When software doesn't do what it's supposed to do, it's a bug.
When AI does something we didn't expect, is it misaligned or does the software just have a bug? a16z board partner and former Microsoft Windows president Steven Sinofsky joins Theo Jaffee and Sofia Puccini on MTS to argue that the language around AI safety is making the technology harder to understand. Drawing on decades of building software, Steven explains why AI labs need better telemetry, debugging, and incident reporting, and what today's industry can learn from earlier software failures, computer viruses, and Y2K. They also discuss why terms like alignment, goal-seeking, and rogue agents can distort the policy debate, and why treating AI more like software could lead to clearer conversations about both safety and regulation.
We are live with Steven Sinofsky, who is a board partner at Andreessen Horowitz. He's a seed investor, author of the Substack and the book Hardcore Software. Previously, he was the president of Microsoft's Windows division, engineering and marketing across Windows, uh, Windows Live and Internet Explorer, and he posts on Twitter at, @stevesi. Steven, welcome back.
Howdy.
So we were just talking about, uh, we're in D.C. right now at the FAI office and, uh-
Shout out FAI.
Yeah, [chuckles] we're at FAI, and we've been talking about, uh, all of the new regulation and new laws that have been proposed for the AI industry over the last few weeks, uh, including the Stop Rogue AI Act, which was proposed by, uh, Josh Gottheimer of New Jersey, and you're offering some thoughts on this. Uh, what's, what's your take?
Well, sure. Well, right now we're, we're in the maximal phase of like, let's just throw tons of legislation out there. So real quick, I'm just gonna take you back. I can't believe I'm gonna take you this far back. This is gonna seem like infinity years ago in another dimension of time and space. But in nineteen eighty-three, I was a freshman in college, and one day in the fall, like, and there were two people in our hundred-person dorm that had computers, me and another guy, and that was it, and there was nothing to do with them. Nobody knew why we had them, blah, blah, blah. And one day, literally, G-men in suits crashed our dorm, confiscated the other guy's computer and arrested him.
Wow. What?
And it turns out, fast-forward a little bit, blah, blah, blah, he was part of, of a hacker group, one of the very, very, very first hacker groups, um, called the Inner Circle or the Inner Ring, and they were hacking this thing called GTE Telemail. GTE was a big communications company, and it was the one of the very first online mail services that you used a modem and you dialed up and you could just mail other people on Telemail. Who were some of the clients of it? The United States
Episode notes
a16z Board Partner and former Microsoft Windows president Steven Sinofsky joins Theo Jaffee and Sofia Puccini on MTS to argue that the language we use to describe AI failures is making it harder to understand what’s actually going wrong.
Steven takes aim at terms like “alignment,” “goal-seeking,” and “rogue agents,” arguing that they can anthropomorphize problems that software engineers have dealt with for decades. His framing is simpler: when software doesn’t do what it’s supposed to do, it has a bug. And as AI becomes more widely deployed, labs need the same kind of telemetry, debugging, incident reporting, and operational discipline that previous generations of software eventually developed.
Drawing on everything from early computer hacking and Microsoft’s response to major software failures to Y2K and cybersecurity standards, Steven makes the case for treating AI reliability as an engineering problem. They also discuss what AI labs can learn from CVE reporting, why industry has a responsibility to make its systems safer, and how confusing terminology can lead to equally confused regulation.
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
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