Lenny's Podcast: Product | Career | Growth
00:01:08 · Simon Willison, Lenny Rachitsky
[00:01:08] S0: Lots of people knew that those little O-rings were unreliable, but every single time you get away with launching a space shuttle without the O-rings failing, you institutionally feel more confident in what you're doing. We've been using these systems in increasingly unsafe ways, this is going to catch up with us. My prediction is that we're going to see a challenger disaster. [00:01:28] S1: Today, my guest is Simon Willison. Simon, in my opinion, is one of the most important and useful voices right now on how AI is changing the way that we build software and how professional work is changing broadly. What I love about Simon is that he doesn't just pontificate in the clouds, he's been what you'd call a 10X engineer for over 20 years. He co-created Django, the web framework that powers Instagram, Pinterest, Spotify, and thousands of other platforms, he coined the term prompt injection, popularized the ideas of AI slop and agentic engineering, and amongst his 100+ open source projects he created Datasette, a data analysis tool that has become a staple of investigative journalism. What makes Simon rare is that very few engineers have made the leap from the old way of building to the new way as fully and visibly as he has. As he's leaned into this new way of building, he's been sharing everything he's learning in real-time through his incredible blog, simonwillison.net. Simon does not do a lot of podcasts, and this conversation opened my mind up in a bunch of new ways. I am so excited for you to get to learn from Simon.
00:01:28 · Lenny Rachitsky, Simon Willison
[00:01:28] S1: Today, my guest is Simon Willison. Simon, in my opinion, is one of the most important and useful voices right now on how AI is changing the way that we build software and how professional work is changing broadly. What I love about Simon is that he doesn't just pontificate in the clouds, he's been what you'd call a 10X engineer for over 20 years. He co-created Django, the web framework that powers Instagram, Pinterest, Spotify, and thousands of other platforms, he coined the term prompt injection, popularized the ideas of AI slop and agentic engineering, and amongst his 100+ open source projects he created Datasette, a data analysis tool that has become a staple of investigative journalism. What makes Simon rare is that very few engineers have made the leap from the old way of building to the new way as fully and visibly as he has. As he's leaned into this new way of building, he's been sharing everything he's learning in real-time through his incredible blog, simonwillison.net. Simon does not do a lot of podcasts, and this conversation opened my mind up in a bunch of new ways. I am so excited for you to get to learn from Simon. [00:02:47] S0: Hey, Lenny, it's really great to be here.
00:02:47 · Simon Willison, Lenny Rachitsky
[00:02:47] S0: Hey, Lenny, it's really great to be here. [00:02:49] S1: I'm so excited to have you here. I've been such a fan of yours from afar for so long, I've learned so much from your blog. Even though every guest I have in this podcast is my favorite guest, you're my favorite kind of guest, because you're on the ground building with the latest tools, using it for real, you're very good at articulating what you experience, so we're going to get a lot of ROI out of your brain from this time that we have together. [00:02:50] S0: Absolutely. [00:03:15] S1: What I want to start with is essentially an AI State of the Union. You've written about this November inflection. [00:03:22] S0: Yes. [00:03:23] S1: So what I'm thinking as we start, just give us a brief history lesson of just what happened in November and where are we today? What's possible now?
00:03:31 · Simon Willison
[00:03:31] S0: Well, let's talk about all of 2025 very briefly. 2025 was the year that especially Anthropic and OpenAI realized that code is the application, like having these things generate code. I think partly because Anthropic came up with Claude Code back in February of 2025, and it took off like crazy, and a bunch of people started signing up for $200 a month accounts. So, suddenly, wow, it turns out people are willing to pay a lot of money for this stuff for that specific field. Both Anthropic and OpenAI spent the whole of 2025 focusing all of their training efforts on coding. If you look at what they were doing, it was all the reinforcement learning stuff. The reasoning trick, the thing where the models say they're thinking, that was new in late 2024. OpenAI's o1 was the first model to exhibit that, and now all of the models do it. So, that was the other big trend of last year was these reasoning models. Turns out reasoning is great for code. It can reason through code and figure out the root of bugs and all of that. So the end result of this, the end result of these two labs throwing everything they had at making their models better at code is in November we had what I call the inflection point where GPT 5.1 and Claude Opus 4.5 came along. They were incrementally better than the previous models, but in a way that crossed a threshold, where previously if you had these coding agents you could get them to write you some code and most of the time it would mostly work, but you had to pay very close attention to it. Suddenly we went from that to almost all of the time it does what you told it to do, which makes all of the difference in the world. Now, you can spin up a coding agent and say, "Hey, build me a Mac application that does this thing." You'll get something back, which still leads some back and forth, but it won't just be a buggy pile of rubbish that doesn't do anything. That was fascinating, because all of the software engineers who took time off over the holidays and started tinkering with this stuff got this moment of realization where it's like, "Oh, wow, this stuff actually works now. I could tell it to build code, and if I describe that code well enough, it'll follow the instructions and it'll build the thing that I asked it to build." I think the reverberations of that are still shaking us.
00:06:34 · Simon Willison
To software engineering, a lot of people woke up in January and February and started realizing, "Oh wow, this technology, which I'd been kind of paying attention to, suddenly it's got really, really good." And what does that mean? I can churn out 10,000 lines of code in a day and most of it works. Is that good? How do we get from most of it works to all of it works? There are so many new questions that we're facing, which I think makes us a bellwether for other information workers. Code is easier than almost every other problem that you pose these agents, because code is obviously right or wrong. It produces code, you run the code, either it works or it doesn't work. There might be a few subtle hidden bugs, but generally you can tell if the thing actually works. If it writes you an essay or if it prepares a lawsuit for you, it's so much harder to derive if it's actually done a good job to figure out if it got things right or wrong, but it's kind of happening to us. So software engineers, it came for us first and we're figuring out, okay, what do our careers look like? How do we work as teams when part of what we did that used to take look most of the time doesn't take most of the time anymore, what's that look like? It's going to be very interesting seeing how this rolls out to other information work in the future. I write so much of my code on my phone, it's wild. I can get good work done walking the dog along the beach, which is delightful.
00:08:29 · Lenny Rachitsky
[00:08:29] S1: Yeah, I had Boris Cherny on the podcast and he's doing the same thing, and I was just like, "Is that even coding anymore?" He's like, "Yeah, it's just another level of abstraction, just like engineering has always gone." Talk about maybe just what else is there around just what is possible now with AI in terms of building that people may not fully recognize? And what's the next leap? Is there anything beyond this?