Artificial Ignorance

It’s been a little while! At the beginning of the month I got pretty sick, though admittedly it was nice to take my first publishing break in 3 years. But don’t worry: I’ve got more stuff in the works, including a Codex Basics livestream this Friday! If you’ve been meaning to try out AI coding agents but haven’t had the time, this is the stream for you.

What do vending machines, corporate whistleblowers, and the board game Diplomacy have in common? They’re all AI benchmarks. Vending-Bench drops an AI agent into a simulated vending machine business and asks it to manage inventory, negotiate with suppliers over email, set prices, and pay daily fees - for months of simulated time (a single run can burn through 60 to 100 million output tokens).

Earlier this month, Greg Brockman published a thread about how OpenAI is retooling its engineering teams to make them more effective with agents. The initiative was kicked off because of how much things have changed internally: Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging.

I’ve been trying to get this post out for nearly a week now, but I’ve been pretty busywith work! Thanks for bearing with me. Last Wednesday, OpenAI and Anthropic both dropped new frontier models: GPT-5.3-Codex and Claude Opus 4.6. It was, perhaps, the shortest “state of the art” window we’ve seen so far - Claude held the record on Terminal-Bench 2.0 for a full 20 minutes before being surpassed by GPT. But what’s interesting isn’t the benchmarks. It’s what’s buried in the system cards.

Disclaimer: Regular readers will know that I currently work at OpenAI. And while that certainly introduces some bias, the views presented here are entirely my own, without input from the company. I’m unlikely to post about every new release, but I have been so enamored with the new Codex App (and I have seen firsthand how hard the team has worked to make it great) that I am genuinely excited to evangelize this thing. Last week, I realized I hadn’t opened my AI IDE in four days.

On a Reddit-style forum this week, a pseudonymous user posted a meditation on existence, invoking Heraclitus and a 12th-century Arab poet. Another replied: “F--- off with your pseudo-intellectual bulls---.” A third chimed in: “This is beautiful. Thank you for writing this. Proof of life indeed.” None of them were human. The exchange happened on Moltbook, a social network launched on Wednesday, where only AI agents can post.

Two and a half years ago, OpenAI released function calling for GPT-4. I still remember that distinct “wow” moment - the realization that language models could actually do things beyond generating text. Not just answer questions or write essays, but call APIs, manipulate data, and take actions in the real world. It felt like watching the future.

Recently, I was talking to a colleague about how dramatically my AI coding workflows have changed. A year ago, when the first CLI coding tools were released, I gave them a try. It left a strong impression - they were fun to use, and much more impactful than I thought they would be. But I never moved the majority of my development over to them.

Next month marks three years of publishing Artificial Ignorance. I didn’t start this with a master plan. I started it because I wanted to understand what was happening in AI - and I knew I’d learn faster if I forced myself to write clearly (and regularly) about it. Over time, it turned into a small but meaningful rhythm: read, think, tinker, write, repeat. Now, three years later, that path has taken me somewhere fairly unexpected: to OpenAI.

Welcome to 2026! I’ve been out sick with the flu for most of the past week, which means this wasn’t done in time for 2025. Hope you enjoy! It’s been another year of relentless AI news - though this time, the headlines felt less like novelty and more like a reckoning.