An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison

**Simon Willison** (0:00)
A lot of people woke up in January and February and started realizing, oh wow, I can churn out 10,000 lines of code in a day. It used to be, you'd ask ChatGPD for some code, and it would spit out some code, and you have to run it and test it. The coding agents, they take that step for you. And an open question for me is, how many other knowledge work fields are actually prone to these agent loops?

**Lenny Rachitsky** (0:19)
Now that we have this power, people almost underestimate what they do with it.

**Simon Willison** (0:22)
Today, probably 95% of the code that I produce, I didn't type it myself. 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. My New Year's resolution, every previous year, I've always told myself, this year, I'm going to focus more, I'm going to take on less things. This year, my ambition was take on more stuff and be more ambitious.

**Lenny Rachitsky** (0:43)
Such an interesting contradiction. AI is supposed to make us more productive. It feels like the people that are most AI-pilled are working harder than they've ever worked.

**Simon Willison** (0:50)
Using coding agents well is taking every inch of my 25 years of experience as a software engineer. I can fire up four agents in parallel and have them work on four different problems. By 11 a.m., I am wiped out.

**Lenny Rachitsky** (1:02)
You have this prediction that we're going to have a massive disaster at some point. You call it the challenger disaster of AI.

**Simon Willison** (1:07)
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.

**Lenny Rachitsky** (1:27)
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 plus 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. And 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, simonwilson.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. Don't forget to check out lennysproductpass.com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. With that, I bring you Simon Wilson. Simon, thank you so much for being here and welcome to the podcast.

**Simon Willison** (2:47)
Hey Lenny, it's really great to be here.

**Lenny Rachitsky** (2:49)
I am 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. And 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 this, out of your brain from this time that we have together. What I want to start with is essentially an AI state of the union. You've written about this November inflection.

**Simon Willison** (3:22)
Yes.

**Lenny Rachitsky** (3:22)
So what I'm thinking is we start, just give us a brief history lesson of just what happened in November and where are we today? What's possible now?

**Simon Willison** (3:31)
Well, let's talk about all of 2025 very briefly. 2025 was the year that especially Anthropic and opening, I 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 opening, I 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, like 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. It turns out reasoning is great for code. It can reason through code and figure out the root of bugs and all of that. And 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.