An AI state of the union: We’ve passed the inflection point & dark factories are coming

[00:00] 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 ChatGPT 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? Now that we have this power, people almost underestimate what they do with it.

Today, probably 95% of the code that I produce, I didn't type it myself. I write so much of my [00:30] 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. Such an interesting contradiction. AI is supposed to make us more productive. It feels like the people that are most AI built are working harder than they've ever worked.

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. [01:00] 11am, I am wiped out. You have this prediction that we're going to have a massive disaster at some point. You call it the Challenger disaster of AI. 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. [01:27] 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. [01:40] 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, [01:48] He co-created Django, the web framework that powers Instagram, Pinterest, Spotify, and thousands of other platforms.

[01:53] He coined the term prompt injection, popularized the ideas of AI slop, [01:58] and agentic engineering, [01:59] And amongst his 100+ open source projects, he created Dataset, a data analysis tool that has become a staple of investigative journalism. [02:07] 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, 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. [02:32] Don't forget to check out Lenny's Product Pass dot com for an incredible set of deals available exclusively to Lenny's newsletter subscribers. [02:38] With that, I bring you Simon Wilson. [02:40] Simon, thank you so much for being here and welcome to the podcast. [02:47] Hey Eleni, it's really great to be here. [02:49] I am so excited to have you here.

I've been such a fan of yours from afar for so long. [02:54] 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. Excellent. What I want to start with is essentially an AI state of the union.

[03:19] You've written about this November inflection. Yes. So what I'm thinking is we start. Just give us a brief history lesson of just what happened in November. [03:28] and where are we today? What's possible now? Well, let's talk about all of 2025 very briefly. 2025 was the year that [03:37] especially Anthropic and OpenAI realized that code is the application. Like having these things generate code, I think partly because Anthropic came out with Claude Code back in sort of February of 2025. [03:49] and it took off like crazy, and a bunch of people started signing up for $200 a month accounts.

[03:54] And so suddenly, wow, it turns out people are willing to pay a lot of money for this stuff for that specific field. Both Anthropik 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. 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.

Turns out reasoning is great for code. [04:24] and 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. [04:41] And they were both just... [04:43] they were incrementally better than the previous models, but in a way that crossed the threshold, where previously, if you had these coding agents, you could get them to write you some code.

[04:52] And, [04:53] Most of the time it would mostly work, but you had to pay very close attention to it. And suddenly we went from that to [04:59] Almost all of the time it does what you told it to do. [05:02] 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." [05:09] And 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.

[05:14] 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. [05:25] This actually works now. I can tell it to build code. 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. The software engineering, a lot of people woke up in January and February and started realizing, "Oh, wow, this technology which I've been kind of paying attention to, suddenly it's got really, really good."

[05:47] And what does that mean? Like, what is the fact, like, I can churn out 10,000 lines of code in a day, [05:53] And most of it works. Is that good? Like, 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. Like code is easier than almost every other problem that you pose these agents because... [06:09] Code C4. [06:10] is obviously right or wrong. Like 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, if it writes you a law, like, [06:23] prepares a lawsuit for you. [06:25] 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. [06:31] But, [06:32] It's kind of happening to us as 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 most of the time doesn't take most of the time anymore?

What does that look like? And it's going to be very interesting seeing how this rolls out to other information work in the future. [07:02] companies all have in common? They are all powered by WorkOS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand upmarket ends up working with WorkOS.

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So just to give us a little context, it's like insane how far we've come. I don't know, like a couple of years ago, [08:07] All code was human-ridden. Then it's like tap complete. Then it's like, okay, now the best engineers are 100% AI code. Now it's like I'm coding for my phone. I'm not even looking at my code anymore. That's like where I am. 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 incredible. [08:27] delightful you know [08:29] Yeah, I had Boris Churny 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. [08:36] just like engineering has always gone. Talk about maybe just like, what else is there around? Just like what is possible now? [08:43] with AI in terms of building that people may not fully recognize? And what's the next leap? Is there anything beyond this? Let's talk about the vibe coding side of things. And then there's the, and I like Andre Carpity's original definition of vibe coding, which is when you don't even look at code and you basically just go on the vibes.

You say, build me something that does X and it builds it and you play with it. And if it looks good, then great. And if it doesn't quite do it, you [09:13] code. [09:13] So he originally said, this is great for having fun and prototyping, and it then [09:18] exploded way out of that. And I think today... [09:22] Vibe coding is effectively, it's [09:25] The definition I use is it's when you're not looking at the code, you don't care about code, and maybe you don't understand the code. Like non-programmers can now...

[09:33] tell claude what to build and it can build them a little app and i love that i absolutely love that we're sort of democratizing [09:40] the art of getting a computer to do stuff for you, of automating tedious things in your life by [09:45] knocking out these little tools, [09:47] Of course, the problem is that there is a limit on how much you can do that responsibly [09:52] And like, [09:53] I like to tell people if you're vibe coding something for yourself, where the only person who gets hurt if it has bugs is you, [10:00] Go wild, that's completely fine.

The moment you're VibeCoding code for other people to use, where your bugs might actually harm somebody else, that's when you need to take a step back and say, hang on a second, [10:10] This is not [10:12] a responsible way of using these tools. The challenge is that understanding what's responsible and what isn't is in itself a sort of expert level skill. [10:21] So, [10:21] knowing that once you start dealing with like scraping of people's websites, maybe you'll damage their websites by hitting them too hard. There are so many ways that you can cause damage if you don't know what you're doing.

[10:32] But I love that liberation, and I love that people can come to meetings with a prototype that they knocked up of that idea that illustrates the idea. I think those things are wonderful. [10:42] The big debate, the ongoing debate has been, what do we call it when a professional software engineer uses these tools to write real code that's production ready that they've reviewed and they've checked all of the details of, [10:53] A lot of people call that vibe coding as well. I think that devalues vibe coding as a term, [10:59] Because it's useful to say, I vibe-coded this, as in...

[11:03] I haven't even looked at how it works. It's not production ready, but it's kind of a cool prototype. The moment Vyde coding means [11:08] everything that touches AI, it effectively ends up needing programming, because we're all moving in the direction where [11:13] our code is mediated through AI at some point. So what do we call it for professionals? I've gone with agentic engineering. [11:21] because I think the thing to emphasize is these coding agents. If you're asking ChatGPT to knock out some code, [11:27] That's a different thing from if you're running codecs and having it write the code, debug the code, test the code, all of that.

[11:35] And I think that agentic engineering is such a deep and fascinating discipline, because the art of getting really good results out of this, like the art of having them help you build software you could deploy to a million people. [11:46] That's never going to be easy. That's never going to be trivial. That's always going to require a great deal of depth of experience in everything. [11:53] how software works and how these agents work, [11:57] And I love that. I'm kind of writing a book about it now that I'm publishing a chapter at a time on my blog, [12:03] The best form of writing, because I don't have an editor or any pressure from a publisher, is just when I feel like writing another chapter, I can do that.

[12:11] But there's so much to discuss. But yeah, so I think right now the frontier is [12:16] How do we build professional software using coding agents? How do we build software that is [12:21] I don't just want to build software that's good. I want us to build software that is better than we were building before. [12:27] Like if the agents let us move a bit faster, but we're still churning out the same quality of software, that's less interesting to me than if the software we're producing has less bugs, more features, it's higher quality, it's better software.

[12:38] because we're harnessing these tools. [12:40] The really interesting future is something which some people have been calling the dark factory pattern or software factories. This is the idea where [12:51] Right now, [12:52] If you're a professional using these tools, the way you do it is you tell them what to build and then you look at the code and you review that code really carefully and make sure it's doing the right thing. [13:00] What does it look like if you're not reviewing the code, if you're not looking at that code, but you're also not vibe coding?

You're not throwing everything to the wind and seeing what happened. You're applying professional knowledge. [13:10] practices and quality expectations to code that you're not directly reviewing. The reason it's called the dark factory is there's this idea in factory automation that [13:20] If your factory is so [13:22] automated that you don't need any people there, you can turn the lights off. Like the machines can operate in complete darkness if you don't need people on the factory floor. [13:30] What does that look like for software? And there's some very... [13:34] This company called StrongDM has been pushing this and doing some really interesting experiments around this.

That, I think, is the next. That's futuristic. [13:42] Like that's... [13:43] We're trying to figure out what that looks like and how we can responsibly build software in that way right now. [13:48] and making some quite interesting... [13:51] like discoveries about things that work and things that don't work. But that to me is the next sort of [13:56] barrier. Let's follow that thread. So what is this factory doing? So there's an element of no one's looking at the code really. [14:02] But what... [14:02] How does that change how software is built?

Are they... [14:05] Are people still coming up with the ideas and telling you this factory build this thing for me? Oh, exactly. This is the fascinating thing. There's a policy of nobody writes any code, and quite a few companies are beginning to introduce that now. Just to be clear, the policy is you cannot write code. It has to be written by AI. You cannot type code into a computer. Exactly. Six months ago, I thought that was crazy. [14:28] And today, [14:29] probably 95% of the code that I produce, I didn't type it myself.

[14:33] So that world is practical already because the latest models are... [14:38] good enough that you can tell them, "Oh, no, rename that variable and refactor that and add this line there." And they'll just do it. And it's faster than you typing on the keyboard yourself. [14:46] The next rule, though, is nobody reads the code. And this is the thing which StrongDM started doing back in, I think it was August last year. [14:55] They said, OK, we're not going to read the code. So what does that mean? How do you produce software that works and is good if you're not reading the code?

[15:02] And they've come up with a whole bunch of answers. Um, [15:05] One of the most interesting things [15:07] was the way they did testing, where... [15:10] In traditional software, some companies will have a QA department. Like the engineers write a bunch of software and then you throw it over the wall to the QA department and they sort of test it furiously to figure out if it's working or not. That, I think, went out of fashion a bit over the past sort of five to ten years from what I've seen in Silicon Valley.

[15:26] Because... [15:28] you kind of want your engineers to take responsibility for the code they're writing being good. [15:32] But what if you can simulate that QA department? So what StrongDM we're doing is [15:37] They had a swarm of... [15:39] agent testers who were actually simulating custom simulating end users so the software that they were building this is crazy the software is security software for access management so [15:51] When you start as a company and somebody needs to assign you access to Jira and then give you access to Slack and all of that kind of thing, they were building software for that.

[15:59] That's very security-driven. [16:01] like adjacent, that's not the kind of thing that you should be vibe coding at all based on most people's understanding of how the world works. But that's, and they're a legitimate security company who've been doing this stuff without AI for years, so it's not like they didn't understand the risks. [16:15] So the way they did their testing is they had this swarm of simulated employees all in a simulated Slack channel saying things like, hey, could somebody give me access to JIRA? [16:23] The Slack channel itself is simulated.

We'll talk about that in a moment. And 24 hours a day, they're making requests and saying, hey, I need access to JIRA and all of those kinds of things at enormous cost. Like they were spending $10,000 a day on tokens, I think, simulating these end users. I believe so. [16:39] But it meant that their software was being very robustly tested in all of these different ways. And yeah, it's kind of similar to having a manual QA team, except one that never sleeps. And I thought that was fascinating as a sort of [16:52] example of thinking outside of the box, taking this question [16:56] How do we tell our software is good if we're not reviewing the code?

[16:59] and trying to find creative answers to it. [17:01] The other thing that was interesting is that the Slack channel itself wasn't actually Slack. Because it turns out if you test against real software like Slack and so forth, they all have rate limits. And they won't let you just run 10,000 simulated people at a time. So what they did is they built their own simulation of Slack and Jira and Okta and all of this software they were integrating with. And the way they did that is they basically took the API documentation for the public APIs for Slack.

[17:31] and they told their coding agents, [17:34] build this, build me a simulation of this API. And they did. [17:38] So this company is, and this is one of the things, I went to a demo that they gave back in October. One of the things that really sat with me is that they had their own simulated version of Slack and Jira and all of these different systems that they could then build their software against, which cost them nothing because once they spun it up, it was a little Go binary that sat there.

And they even had interfaces. They had like a fake version of the Slack interface that they'd [18:02] like vibe coded up. [18:04] that let them see what was going on. Absolutely fascinating. [18:07] That is such a cool story. And I love these stories of just companies at the bleeding edge trying to see what's possible and have an advantage, essentially. So what I'm hearing here is the QA piece is like the new piece in this factory. Yeah. [18:19] So we already have codecs called code, they can go off and build stuff.

[18:24] Is the innovation here, okay, now you've built all this stuff, is it actually any good? Is there a reason Codex and CloudCo couldn't do this themselves? Why do you need kind of this factory concept? [18:32] I think they can. Like you can tell Claude Code, fire up a sub-agent that uses Playwright to simulate a browser and all of that kind of thing. [18:40] you'd have trouble getting it to run 24 hours a day. I mean, maybe it would work. But certainly I think that what's interesting to me isn't so much the software you're using.

It is these big IIDs, these techniques that you're using to try and answer these questions. Because even if your QA team, your virtual QA team says, this is good, it doesn't mean it's secure, right? It doesn't mean that you've got all of those other [19:01] characteristics you care about. [19:04] At the same time, the agents are getting really good at security penetration testing now. [19:09] And this is a new thing. [19:11] I think in the past three to six months, they've started being credible as security researchers, which is sending shockwaves through the security research industry.

They were like, "Wow, we didn't think that they'd get to this point." What's interesting there is both OpenAI and Anthropic have specialist security models [19:29] that they will not release to the general public, [19:32] because they can be used to break into websites so they have like invite only [19:37] like registered security researchers can apply for access and they've been [19:42] producing... [19:44] vulnerability reports against popular open source software. I think Firefox just a few days ago, maybe last week, [19:50] said that they'd done a release which was assisted by Anthropik.

Anthropik had [19:55] discovered 100 potential vulnerabilities in Firefox and responsibly reported them to Mozilla, who then fixed them. That's an interesting one as well, because we're seeing a lot of this in the wild, and it's... [20:08] It's just... [20:09] incredibly frustrating for maintainers because there are these people who don't know what they're doing who are [20:14] asking ChatGPT to find a security hole and then reporting it to the maintainer. And the report looks good. ChatGPT can produce a very well-formatted database. [20:23] reportable vulnerability, [20:25] it's a total waste of time.

It's not actually verified as being a real problem. The difference with Anthropic and Firefox is that Anthropic security team actually did do the work. They didn't report whatever the agent said. They actually verified that it was a good quality report before they handed it over. [20:41] There's going to be a lot to talk about on the security side. You've done a lot of thinking and writing about the dangers there, but I want to follow this thread. [20:48] So in terms of what AI has been doing for teams, if you think about it, it's like it's kind of going on the middle and expanding.

So it's like writing, you know, it's. [20:56] It's taking on more and more of the building. [20:58] components. It's doing code reviews now, QA as you've been describing constantly. [21:03] building, and it feels like [21:05] The front of that is the big now gap in opportunity, which is coming up with the idea. What the heck should we build? [21:11] Because then once you tell the AI, build this thing, [21:14] as you were describing, it's getting better and better at building something great. Have you had any luck yet with [21:20] using AI there and do you think it starts to eat that and just becomes the strategy, you know, PM basically?

[21:27] - So this is one of the most interesting problems we're having with all of this is we've taken the writing code bit and we've massively accelerated that. Now the bottlenecks are everywhere else. Like how do we redesign our processes now that the bit that used to take the longest, right? It used to be, you've come up with a spec and you hand it to your engineering team and three weeks later, if you're lucky, they'd come back with an implementation for you to then start. And now that maybe that takes three hours, depending on how well established the coding agents are for that kind of thing.

[21:55] So now what? Right now, where else are the bottlenecks? I don't think it's, I mean, it's coming with the initial ideas. Anyone who's done any product work knows that your initial ideas are always wrong. What matters is proving them, right? It's testing them. [22:08] We can test things so much faster now because we can build workable prototypes so much quicker. So there's an interesting thing I've been doing in my own work where [22:17] any sort of feature that I want to design, I'll often prototype three different ways it could work.

[22:22] because that takes very little time. And then I can start experimenting and trying them and seeing which ones I like. [22:27] And that... [22:28] That feels to me like the really transformational step here is that [22:32] When you get AI involved in your ideation phase, it's much more about the prototypes. It's about, okay, [22:39] We can see, like, a UI prototype is free now. ChatGPT and Claude will just build you a very convincing UI for anything that you describe, [22:49] And that's how you should be working. I think anyone who's doing product design isn't vibe coding little prototypes is missing out on the latest.

[22:58] but the most powerful boost that we get in that step. [23:01] But then what do you do? How do you [23:03] given your three options now that you have instead of one option, how do you prove to yourself which one of those is the best? [23:09] I don't have a confident answer to that. [23:12] I expect [23:13] This is where the good old fashioned usability testing comes in. Get somebody on Zoom, [23:18] screen shared, using your software to see what happens. That's, you can, [23:23] tell the AI to do it, and you can simulate your users with the AI, I don't think that's credible.

I don't think you're going to get as good results from chatGPT pretending to click around on your prototype than you would from an actual human being. [23:36] This is so interesting. A question I've been tackling is just where are human brains going to continue to be valuable? And what I'm hearing here is, [23:44] There's like the initial idea, you made such a good point here. It's like the initial idea is often not the actual winning idea. It's just the beginning of an idea. So there's like the idea for the feature.

[23:52] Then there's the try it out prototype. It helps you narrow on the direction. [23:57] Build it, make it awesome. [23:58] get it out into the world [24:00] And it feels to me like AI is going to be really good at suggesting ideas and coming up with initial ideas. [24:06] And I wonder if the human brain, like, it's not like maybe someday we don't need human brains at all. And that's all of the discussion. But maybe the next phase is AI will help us come up with great ideas. I mean, that's been the case for...

[24:18] probably a couple of years now, they've been strong enough to do really good brainstorming. And I like to compare it to the thing where when you've got a group brainstorming exercise, you book a meeting room for an hour, you've got a whiteboard, you get a dozen people in, [24:30] And the first... [24:31] two-thirds of that brainstorming session [24:34] Honestly, it's kind of just everyone going through the most obvious basic ideas, right? And you get them all out on the whiteboard, you get them all up, and then things get interesting when you start saying, okay, well, let's talk about these, let's start combining them.

The AI is so good at that first two thirds of the ideas. [24:49] I brainstorm with them all the time where I just get them to spit out all of the obvious stuff [24:53] And they're coming up with 20 things. [24:55] and they'll all be kind of done. They just won't be very interesting. What gets interesting is when [25:01] If you ask them for 20 more and now... [25:04] By the end of that list, you're beginning to get things which are not good ideas, but they point you in interesting directions.

[25:10] There are so many other tricks like this, like you can tell [25:14] You can tell AI to combine weird fields. You can say, okay, I want ideas for marketing my new SaaS platform inspired by marine biology, and you see what happens. Yeah. [25:25] Most of it will be complete junk, but there might be a spark that gets you to the good idea. [25:29] So I love them as brainstorming companions on that front. That reminds me of a chat I had with David Plasek. He's an expert naming. [25:37] He helps companies come up with names for products.

And one of the things that he does at his company is... [25:42] He creates three teams to brainstorm names. So, for example, let's say Windsurf was a product they named. [25:49] So the first team is, okay, this is an AI/IDE thing. That's exactly what it is. [25:54] Second team is, okay, this is a boat. [25:57] You're naming a boat and here's constraints. And then here, this is a spaceship. So name it from that perspective. And he finds the best names come from those other directions where it's a different metaphor with the same sort of benefits.

[26:09] Okay, so what I'm hearing here is this is good. This is good for humans right now, that there's still opportunity for us to contribute to the process. And actually, I want to... [26:19] stand in defense of software engineers for a bit because on the one hand these things can write code that used to be our thing right i'm finding that [26:28] Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. [26:36] Like this is something which people are talking a lot more about now.

[26:39] I can fire up like four agents in parallel and have them work on four different problems. And by like 11:00 AM, I am wiped out for the day. Like I have, because there is a limit on human cognition in how much, even if you're not reviewing everything they're doing, just how much you can hold in your head at one time. And it's very easy to pop that stack at the moment. [27:00] There's a sort of personal skill that we have to learn, which is [27:03] finding our new limits, like what is a responsible way for us to not burn out and for us to use the time that we have.

And I've talked to a lot of people who are losing sleep because they're like, [27:14] My coding agents could be doing work for me, [27:17] I'm just going to stay up an extra half hour and set off a bunch of extra things. And they're waking up in four in the morning. That's obviously unsustainable. I hope that that's a novelty thing. The agents only really got good in the past sort of four to five months. We're all learning what that looks like and what that lets us do. [27:32] But it's concerning.

There's an element of sort of gambling and addiction to how we're using some of these tools. But to stand in defense of software engineers, [27:41] I get great results out of these things because they are amplifiers of existing skills and experience. And I have 25 years of existing like pre-AI experience, which I can now amplify because I can... [27:53] talk to the agent at a very high level. I can use very [27:57] I can use sophisticated engineering language that I've [28:01] mastered over the years, which they appear to know as well, and we can collaborate incredibly effectively.

And it means I can look at a problem and I can say this problem is a one-sentence prompt, and I know it'll find that bug and fix that bug. [28:13] As opposed to this other problem, which is... [28:16] Who knows how big a problem. [28:18] There is a flip side to this, which is that I've got 25 years of experience in how long it takes to build something, and that's all completely gone. Like, that doesn't work anymore, because I can look at a problem and say, "Okay, well, [28:29] This is going to take two weeks.

It's not worth it. [28:32] And now it's like, yeah, but maybe it's going to take 20 minutes because the reason it was taking two weeks was all of the sort of crufty coding things that the AI is now covering for us. And that I've been finding really interesting and challenging. I constantly throw tasks at AI that I don't think it'll be able to do. [28:48] Because every now and then it does it. [28:50] And when it doesn't do it, you learn, right? You learn, okay, Opus 4.6 still can't do this particular thing.

But when it does do something, especially something the previous models couldn't do, [29:00] That's actually cutting edge AI research. You can be the first person in the world to spot that AI can now do X just because you were the person you found it couldn't do it. And you've been keeping that sort of backlog of interesting tasks for it. [29:12] This is such an interesting line of discussion, this idea that [29:16] let's say 10x engineers, to use that phrase, are going to be more valuable, is what you're describing here, because you can work with these tools much more effectively.

[29:24] What do you think of junior engineers? Just like what's happening there? What's their future? So there's an interest. So ThoughtWorks, the big IT consultancy, did an off-site about a month ago. And they got a whole bunch of engineering VPs in from different companies to talk about this stuff. [29:42] And one of the interesting theories they came up with is they think this stuff is really good for experienced engineers, like it amplifies their skills, that's great. [29:50] it's really good for new engineers because it solves so many of those onboarding problems.

Like if you talk to Cloudflare and Shopify, both said they were hiring a thousand interns over the course of 2025 because the intern onboarding costs, it used to be takes a month before you enter, you can do anything useful. Now they're doing something useful within like a week because the [30:10] that AI assistance helps them get up and running faster, [30:14] The problem is the people in the middle. Like if you're mid-career, if you haven't made it to sort of super senior engineer yet, but you're not sort of new either.

[30:23] That's the group which ThoughtWorks Resolved work probably in the most trouble right now. That's the open question because they don't have that expertise to amplify and use with these tools. [30:36] They've got all of the boosts that the beginners were getting, they've got already. So that's an interesting open question right now for me. It's more the sort of mid-level as opposed to the beginners or the advanced people. [30:48] It's so interesting how AI is coming at the middle of so many things. It's coming at the middle of the product development process, [30:53] It's coming in the middle of [30:54] seniority [30:55] There's probably other examples.

[30:57] And I'm guessing this is true for all functions, like PM's designers too, just [31:01] new PMs designers, [31:03] Maybe because being AI native basically is what you're describing and ramping up much more quickly. [31:09] I guess while we're on this topic, say you are a lot of listeners here just like those people in the middle. [31:15] What would your advice be to them to help them avoid becoming a part of the permanent underclass? [31:20] That's a big responsibility you're putting on me there. [31:25] I think... [31:27] I think the way forward is to lean into this stuff and figure out [31:31] How do I help this make me better?

[31:35] A lot of people worry about skill atrophy. If the AI is doing it for you, you're not learning anything. I think if you're worried about that, you push back at it. [31:45] Be mindful about how you're applying the technology and think, OK, I've been given this thing that can answer any question and often gets it right. Doesn't always get it gets it right. How can I use this to amplify my own skills, to to learn new things, to take on much more ambitious projects? Something I've been enjoying. I think the thing I've enjoyed most about this as a software engineer is that my level of ambition has shot right up.

[32:08] because now I used to like never I never used AppleScript because AppleScript is a whole programming language you have to learn. And I've been using AppleScript for like two and a half years now because ChatGPT knows AppleScript and [32:19] I don't have to, and so now I can automate things on my Mac. [32:22] And that's great. [32:23] And previously, [32:25] The fact that it would have taken me like two or three months to learn basic Apple script was enough for me never to use it. [32:30] and now I've got all of these technologies that I'm using because that two to three month initial learning curve has been shaved right down.

I think that applies to everything else. [32:39] I'm getting much better at cooking. I've been using Claude, it turns out. Excellent chef, which doesn't make sense because it doesn't have taste buds. [32:47] But it can give you the... [32:49] global average of the world's guacamole recipes, which turns out is good guacamole. So that's been really interesting, like trying to apply this stuff [32:57] just for sort of self-improvement. I think that's a really useful skill to have. Because honestly, everything is changing so fast right now. [33:05] The only universal skill is being able to roll with the changes.

That's the thing that we all need. [33:11] weirdly and [33:12] The term that comes up most in these conversations about how you can be great with AI is agency. [33:19] Human beings have agency and we use that agency to decide what problems to take on and where to go. I think agents have no agency at all. [33:28] I would argue that the one thing AI can never have is agency, because it doesn't have human motivations. [33:34] Sure, you can tell it, make more money or whatever, but it's never going to be able to decide [33:40] on what makes sense for it to act on next.

So I'd say that's the thing is to invest in your own agency and invest in how do I use this technology to get better at what I do and to do new things. [33:52] And also, to your point, be ambitious. Think big. There's an interview with Jensen that just came out yesterday where people asked him about... [33:59] layoffs, there's all these layoffs happening. Is AI actually taking jobs? And he's like, [34:05] The reason a lot of these companies are not [34:07] are letting people go is they don't have enough creativity or ambition for what they can do with all of these resources there.

Because they're not letting people go. They have so much they want to do. You know, obviously, easier said than done, and it's not always the case. [34:19] But I think that's an interesting way of approaching it now that we have this power [34:22] people almost underestimate what they can do with it and don't fully lean into it. So I love this advice of just try to be a little more ambitious. Try the stuff that you think is impossible. [34:30] and see if it might be actually possible. My New Year's resolution this year [34:34] was the opposite 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. Like we've got these tools. [34:46] bring it all in let's try and do everything i don't know if that was a good new year's resolution but that's what i went with how's it going so far how do you feel about this decision fun i'm enjoying myself i think i'll probably get to the end of the year i'll be like wow [34:59] The most important things I should have been focusing on did not get done.

[35:02] But that's the case when it is my ambition to do them. It's a converge-diverge sort of situation. Next year could be refocus. Absolutely, yeah. Oh, man. Kind of along those lines, I want to come back to this point you made about how you're working harder and you're fried early in the day. This is such an interesting... [35:20] I don't know, contradiction almost. [35:23] People, you know, AI is supposed to make us more productive. It's supposed to give us more time off. It's supposed to let us sit around and watch Netflix and do all the great wealth and productivity in the world.

It feels like the people that are most AI-pilled are working harder than they've ever worked. There's this anxiety described of my agents aren't running, I got to stay on top of them. [35:41] What do you think is going on there? Is this just, like you said, maybe it's like a temporary novelty thing and then we'll be like, "All right, I don't need to be this productive." [35:47] Is there anything else there? I think, I really hope it's a novelty thing. And I am actually getting much more, I'm getting more time [35:54] But I'm exhausted.

[35:55] Like your brain is exhausted. My brain is exhausted. I've got more time to go and do things, and I do things, and it's great, but it is... [36:02] that the exhaustion from that sort of intensity of work has been a really big surprise for me. [36:07] Like that's been... [36:08] something which I've been observing especially [36:11] since November, as all of this stuff started ramping up. [36:15] And yeah, I think that's... [36:17] The concern there comes down, it's always expectations from other people. If you work for a company that's expecting you to get five times more done, [36:26] that's going to be exhausting.

And maybe... [36:29] I think the good companies with good management are paying attention to this. They don't want to burn out their best employees for the short-term gain, but lose people over it. [36:40] But yeah, it's a big tension. I think we're... [36:43] Those of us on the sort of leading edge of the AI boom are feeling it first. [36:47] I imagine it's going to come for everyone else as well. The other element of this that we haven't mentioned and you've mentioned a couple of times, it's actually really fun. The drive here is not...

I'm enjoying myself so much. Absolutely. It's so fun. It's... [36:59] A lot of my friends have been talking about how they have this backlog of side projects, right? For the last 10, 15 years, they've got projects they never quite finished and ideas they thought would be cool. And some of them are like, well, I've done them all now. Like last couple of months, I just went through and every evening I'm like, let's take that project and finish it. And that one and that one and that one and that one. Nuts.

[37:17] They almost feel a sort of sense of loss at the end where they're like, well, okay. [37:21] My backlog's gone. Now what am I going to build? Yeah, it comes back to that factory. I was talking to the founder of Linear the other day and this idea of the factory and [37:29] And we're just like, a factory doesn't sound like a place that'll [37:32] create amazing products. It feels like, you know, like what are the chances that it'll create something beautiful and innovative? So either that's the wrong word or it's just this will lead to bad stuff, probably.

I feel like the word artisanal does like like artisanal to handcrafted software, I think is going to be valued more. Something I've noticed in my own work is [37:51] Sometimes I'll have an idea through a piece of software Python library or whatever, and I can knock it out in like an hour. [37:57] and get to a point where it's got documentation and tests and all of those things, and it looks like the kind of software the previous I just spent several weeks on, [38:05] and I can stick it up on GitHub and everything, and yet I don't believe in it.

[38:10] And the reason I don't believe in it is that [38:13] I got to rush through all of those things. I think the quality is probably good, but I haven't spent enough time with it to, [38:18] Tip. [38:19] feel confident in that quality. Most importantly, I haven't used it yet. [38:23] Like it turns out when I'm using somebody else's software, the thing I care most about is I want them to have used it for months. I want other people to have put that software into practice. So I've got some very cool software that I built that I've never used.

Like it was so it was quick to build it than to actually try and use it. And so the way I've been dealing with that, I've always put alpha on it. Like if you see my software and it says it's an alpha. [38:45] That probably means... [38:46] I haven't actually used it yet for most of my projects, which is a bit of a cheat code, you know, Alphadist. But isn't that interesting? Like, it used to be, if you looked at software and it had high quality tests and documentation and everything, it meant it was good.

[38:59] And now, [39:00] that signal is gone. It's almost like we need a proof of work [39:04] for this versus the blockchain. A proof of usage. Yes, exactly. Oh man. On this note of handcrafted code, I don't know if you know this, this is so interesting. [39:12] data labeling companies are buying [39:15] old GitHub repos of handwritten code. [39:20] to train their models on and they're paying a lot of money for like artisanal human written code. Oh, that's fascinating. That's the pre-World War II, the metal that you can dig up from old shipwrecks, which is before the first nuclear explosions.

And so it's... [39:37] not got like the radiation baked into the metal. It's that whole thing. Wow. That's fascinating. So they're looking for code pre-2022 I think whenever ChatGPT kind of emerged. [39:48] Wow. So if you've got some, you can make a fortune. [39:53] I promise I open source all my stuff. So it's already out there. It's in the training. It's been used to train the models already. Yep. Oh, man. Okay, let me ask you this question. I'm just curious about this prediction. I know you're not like a prediction person, although you do make predictions and you seem to be right often.

When do you think 50% of engineers in the world will be? [40:13] AI will be writing 100% of their code. How close to that do you think we are? [40:18] I'm going to refact that to 95% of their code. I don't think we'll get to that. [40:24] It's very difficult to say worldwide, partly because there are cultural differences. [40:31] I spend way too much time on Hacker News, and something I've noticed about Hacker News is a conversation that starts at midnight Pacific time and goes until 8am, very different. [40:41] because it's the Europeans.

[40:44] And the Europeans are a lot more AI skeptic than the Americans are generally. [40:48] So, [40:50] I think different countries are going to have different cultures around this. At the same time, I think it's become undeniable [40:57] this year that this stuff produces good code. It used to be that you could say, "I don't use this stuff because the code is bad." And that was a justifiable position. That's not justifiable anymore. The code is now good. It's good code for [41:11] for my definition of good code at least. [41:13] So, [41:15] So we're saying 50% of engineers, let's say 50% of engineers, majority of their code

[41:20] It could happen by the end of this year. [41:22] It could, because the technology is good enough now. And I feel like the... [41:28] The challenge now is getting people to learn how to use this stuff, which is difficult because using the stuff everyone's like, oh, it must be easy. It's just a chatbot. It's not easy. Like that's one of the great misconceptions in AI is that using these tools effectively is easy. It takes a lot of practice and it takes a lot of. [41:45] trying things that didn't work and trying things that did work.

[41:48] Yeah, I expect by the end of this year, it will not be uncommon to have an engineer say that almost all of their code is written by AI. [41:55] That was the same rough idea I had. And how crazy is that? How quickly this job has changed and what is possible? [42:03] And I think people, this is a good example of people underestimate how quickly things can change. [42:07] We would not have like, [42:09] I think Daria was predicting this a year or two ago, just 100% of code is going to be written by AI.

And we're just like... We will laugh to Tim. Yeah. Right? Exactly. Yep. [42:16] What are you talking about? So bad. So bad at writing code. And this might come for other jobs that people don't see coming, which is scary and interesting and exciting. [42:33] Are we really going to wipe out like a tenth of white collar knowledge work jobs in the next few years? I really hope not, because I don't know how the economy adapts that. [42:43] So yeah, that's [42:44] complicated. Yeah, I'm actually doing a report that's coming out.

It'll come out ahead of this episode looking at the job market in tech. [42:52] And surprisingly, just at tech companies, [42:55] Where... [42:56] at the highest number of open engineering roles, open PM roles, [43:00] Interesting. Except for during the crazy peak during COVID. So it's kind of like coming back to that. Basically, it's the highest number of open roles. [43:08] in three and a half-ish years. [43:11] for engineers and PMs at tech companies globally. [43:15] That's very interesting. It's funny, isn't it? Because you get all of these headline grabbing, like, [43:21] We are.

Yeah. And. [43:23] Was it Block that laid off 4,000 people recently? [43:28] The question there is always how much of that is AI and how much of it is overhiring during COVID and recorrections and all that kind of thing. It's always very difficult to tell. [43:37] so that the number of open jobs [43:39] On the one hand, maybe that's a better signal. But on the other hand, the recruitment market has been driven completely crazy by all of this stuff. All of the job ads are written by AI, the resumes of AI.

People in recruitment are saying that it's never been this hard to filter through and hire people. And people who are hiring jobs say they applied to 200 things and got nobody hearing back. [44:03] It's hard, right? The macroeconomic indicators for this stuff are lagging. [44:08] And at some point, we should start getting more confident numbers about what the impact actually is. Yeah. Interestingly, the number of recruiter open roles is also approaching like record numbers. Hilarious. Which is an interesting leading indicator of demand for hiring. So there's interesting trends in spite of the layoffs.

[44:25] So, yeah, what a wild world. So you've mentioned this book you're working on. This is the agentic engineering pattern stuff, right? Yes. Okay, cool. So I want to talk about this. So you... [44:36] point it out people think it's easy to build with ai it's like i was going to do all these things for us what are we going to do all day [44:41] to your point, it's actually not. There's a lot of very specific skills you need to do this well. And you're putting them together on your blog.

[44:48] We'll point to it. I want to talk through a few of them. [44:50] to help people do this better. So one is this idea of just writing code is cheap now. You've touched on this a bit. Maybe just share why this is such an important thing. [44:59] thing to know and keep in mind. So I think this is the single biggest shock in all of this. The reason that we have to rethink how we build, how we work as software engineers, is that the thing that used to take the time [45:10] takes way less time.

It's never been the case that programmers spend 90% of their typing code into a computer. [45:16] There's always, there's so much additional work around that. But it still used to be [45:20] Like, [45:21] people talk about how important it is not to interrupt your coders, right? Your coders need to have [45:26] like solid two to four hour blocks of uninterrupted work so they can spin up their mental model and tune out the code. [45:32] that's changed completely. Like, my... [45:36] My programming work... [45:37] I need... [45:38] two minutes every now and then to prompt my agent about what to do next and then i can do the other stuff and i can go back i'm much more interruptible than i used to be but yeah so [45:46] And the thing that used to take the time [45:48] is now the thing that takes way, way less time.

What does that mean for everything else that we do? [45:54] And that doesn't just affect programmers, it affects entire teams around software development. But as an individual programmer, you have to start thinking, okay, [46:04] I can churn out 10,000 lines of code now in the time that it would take me to write 100, [46:08] How do I make that code good? How do I make sure that I'm not just turning out total slop, that [46:14] adds up to technical debt that slows me down. How do I take the fact that code is now cheap and use that to produce better code?

Because I don't just want cheap code, I want really good code that does what I need to do, that I can extend in the future, that's got all of those those characteristics of code that's useful and can be used in production. [46:34] The point you made earlier, I think, is a really important one along these lines, which is when you start a project, you fire off three different versions of it. [46:40] and that helps you pick a direction. And that's only possible because code is so cheap now, right? [46:45] Right.

Prototyping is almost free, I think. And that really impacts me because throughout my entire career, my superpower has been prototyping. Like I am very I've been very quick at knocking out working prototypes of things. [46:57] I'm the person who can show up at a meeting and say, look, here's how it could work. [47:01] And that's that was kind of my unique selling point. And that's gone. Anyone can do what I could do. You know, it's like. [47:08] But you still have to learn when it's appropriate to prototype, how to think about prototyping, how to get the tools to build useful prototypes that you can use to explore things.

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So right now, I'm mostly clawed. I do a huge amount of work using clawed code. Well, I'm mainly still a clawed code person, but there are two sides of clawed code that I use. There's the clawed code that runs on your computer, and then there's clawed code for web, which is their hosted version of clawed code. [48:54] more than the one on my own computer, partly because that's the one you can access through your phone. If you've got the Anthropic Claude app installed on the iPhone, there's a code tab and you can go in there and you can tell it to write you things.

And that is running [49:07] On their servers, you need to give it a GitHub repository of yours that it can work within. [49:13] But it's also great from a security point of view, because if you're running Claude Code on your laptop, [49:17] There's risks that bad things can happen. It might accidentally delete things. If I've only got an Anthropics service, I couldn't care less. Like it's their computer, it's not my computer. [49:27] Go wild. So this means that you can run these things in YOLO mode. Claude calls it [49:33] dangerously skip permissions, [49:35] OpenAI actually do call it YOLO.

They've got an option for that. And that's the mode where the agent doesn't ask you if it should do something all the time. [49:43] And that is a different product. I think a lot of people who haven't [49:47] got on board with coding agents yet, haven't tried them in the unsafe mode. They're using coding agent where it's like, oh, can I run this piece of code? Can I edit this file? That means you have to pay complete attention to it the whole time. [49:58] And it's like working with a really frustrating toddler.

[50:01] that's constantly nagging you about what it wants to do. The moment you take the safeties off, [50:07] Now I can run four of them and go and have like... [50:10] go and have a cup of tea and come back and they've achieved something useful for me. But it's inherently unsafe. If it's running in Claude Code for Web, [50:17] The only bad thing that could happen [50:20] is maybe it accidentally leaks your private source code. And my code is all open source, so I don't care. That's a useful trick there.

But yeah, so I use that on my phone [50:30] I often have two or three of those running. A lot of my major projects are done mostly prompting on my phone. [50:37] Security adjacent or super important, I might pull it down to my laptop to do a thorough review later on. [50:42] But most of the review you can do through GitHub. Like these things will file pull requests, and then you use the same tools you'd use to review code from other people to review the code from the agents.