"Today, we choose to pursue AI superiority and defend Western liberty, not because it is easy, but because it is necessary."
This is straight from my award winning 2024 paper for
@FedSoc on establishing US AI superiority. 🤖🇺🇸
https://t.co/nOhFE8mgwp
@intheworldofai Cool. We did much of that at @DeepSIML back in March , found there was a slightly different way to efficiently handle ideas, and pivoting. Cool to see we had the right path though. Love it!
@GJarrosson Out of the 260 companies in the 26 Spring Batch, 3 had founders over 40.
Just stop with the cope. It’s beyond discrimination at this point.
OpenAI built their in-house data agent for structured warehouse data, where schema, lineage, and queries come for free. Files in S3, GCS, or Azure - videos, sensor logs, image corpora, PDFs - have none of that, and the problems get a lot more interesting. Here is how we built the four foundations that close the gap. https://t.co/L4xWfznBqB via @DVCorg
OpenAI built their in-house data agent for structured warehouse data, where schema, lineage, and queries come for free. Files in S3, GCS, or Azure - videos, sensor logs, image corpora, PDFs - have none of that, and the problems get a lot more interesting. Here is how we built the four foundations that close the gap. https://t.co/L4xWfzo9g9 via @DVCorg
@jondelarroz The Romulan War book series by Michael Martin retconned Trip’s death being part of a larger plan to win the war, and he ends up alive and living with someone (I won’t ruin surprise).
Trip needs to be retconned back in. I tagged @_MichaelSussman about it too…
My biggest takeaways from @simonw:
1. November 2025 was an inflection point for AI coding. GPT 5.1 and Claude Opus 4.5 crossed a threshold where coding agents went from “mostly works” to “almost always does what you want it to do.” Software engineers who tinkered over the holidays realized the technology had become genuinely reliable.
2. Mid-career engineers are the most vulnerable—not juniors, not seniors. AI amplifies experienced engineers by letting them leverage decades of pattern recognition. It also dramatically helps new engineers onboard. Cloudflare and Shopify each hired a thousand interns because AI cut ramp-up time from a month to a week. But mid-career engineers who haven’t accumulated deep expertise and have already captured the beginner boost are in the most precarious position.
3. AI exhaustion is real and underestimated. Simon runs four coding agents in parallel and is mentally wiped out by 11 a.m. He’s getting more time back, but his brain is exhausted from the intensity of directing multiple autonomous workers. Some engineers are losing sleep to keep agents running. This may just be a novelty issue, but the underlying dynamic—that managing AI amplifies cognitive load even as it reduces labor—is a real tension. Good companies will manage expectations rather than expecting 5x output indefinitely.
4. Code is cheap now. This simple idea has profound implications. The thing that used to take most of the time—writing code—now takes the least. The bottleneck has shifted to everything else: deciding what to build, proving ideas work, getting user feedback. Since prototyping is nearly free, Simon often builds three versions of every feature when he’s getting started.
5. The “dark factory” is the most radical experiment in AI-assisted development happening right now. A company called StrongDM established a policy: nobody writes code, nobody reads code. Instead, they run a swarm of AI-simulated end users 24/7—thousands of fake employees making requests like “give me access to Jira”—at $10,000 a day in token costs. They even had coding agents build simulated versions of Slack, Jira, and Okta from API documentation so they could test without rate limits.
6. "Red/green TDD" is the single highest-leverage agentic engineering pattern. Having coding agents write tests first, watch them fail, then write the implementation, then watch them pass produces materially better results. The five-word prompt “use red/green TDD” encodes this entire workflow because the agents recognize the jargon.
7. “Hoarding things you know how to do” is one of Simon's other favorite agentic engineering patterns. Simon maintains a GitHub repo of 193 small HTML/JavaScript tools and a separate research repo of coding-agent experiments. Each one captures a technique, a proof of concept, or a library he’s tested. When a new problem arrives, he can point Claude Code at past projects and say “combine these two approaches.”
8. The "lethal trifecta" makes AI agent security fundamentally unsolved. Whenever an AI agent has access to private data, exposure to untrusted content (like incoming emails), and the ability to send data externally (like replying to email), you have a lethal trifecta. Prompt injection—where malicious instructions in untrusted text override the agent’s intended behavior—cannot be reliably prevented. Simon has predicted a “Challenger disaster” for AI security every six months for three years. It hasn’t happened yet, but he’s pretty sure it will.
9. Start every project from a thin template, not a long instructions file. Coding agents are phenomenally good at matching existing patterns. A single test file with your preferred indentation and style is more effective than paragraphs of written instructions. Simon starts every project with a template containing one test (literally testing that 1 + 1 = 2) laid out in his preferred style. The agent picks it up and follows the convention across the entire codebase. This is cheaper and more reliable than maintaining elaborate prompt files.
10. The pelican-on-a-bicycle benchmark accidentally became a real AI benchmark. Simon created it as a joke to mock numeric benchmarks—get each LLM to generate an SVG of a pelican riding a bicycle, and compare the drawings. Unexpectedly, there’s a strong correlation between how good the drawing is and how good the model is at everything else. Nobody can explain why. It’s become a meme: Gemini 3.1’s launch video featured a pelican riding a bicycle. The AI labs are aware of it and quietly competing on it.
Don't miss our full conversation: https://t.co/ghZZeyvWBZ
"Using coding agents well is taking every inch of my 25 years of experience as a software engineer."
Simon Willison (@simonw) is one of the most prolific independent software engineers and most trusted voices on how AI is changing the craft of building software. He co-created Django, coined the term "prompt injection," and popularized the terms "agentic engineering" and "AI slop."
In our in-depth conversation, we discuss:
🔸 Why November 2025 was an inflection point
🔸 The "dark factory" pattern
🔸 Why mid-career engineers (not juniors) are the most at risk right now
🔸 Three agentic engineering patterns he uses daily: red/green TDD, thin templates, hoarding
🔸 Why he writes 95% of his code from his phone while walking the dog
🔸 Why he thinks we're headed for an AI Challenger disaster
🔸 How a pelican riding a bicycle became the unofficial benchmark for AI model quality
Listen now 👇
https://t.co/wlEIyOehU8
@_MichaelSussman@razorfist So… what could any of us out here do to support this?
Just be sure to retcon Trip’s arc back by using “The Good That Men Do” fantastic book series… 😉
@oldyzach Anybody know where you can get an actual original box with the real 4 3.5” disks, manuals, etc? All you ever see on eBay is fake disks and maybe the manuals and tech tree…