Heya and welcome back to Five Things AI!
No, this newsletter doesn’t arrive late in your inbox because of the outages at Anthropic and OpenAI yesterday, this is all hand-researched and hand-written. Good old manual labor.
What I am seeing right now is OpenAI doing smarter things than before and at the same time the open weights models are getting better and better all the time, so I guess that’s why Anthropic and OpenAI are trying to get to their respective IPO as fast as possible, before the market finds out.
Read on my dear, or have your agent recite this for you.
OpenAI Cut Off a Billion-Dollar Customer to Avoid Elon Musk
“We are making this choice because we cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk’s companies violating contracts,” said OpenAI in the blog post.
As OpenAI prepares to go public next year, it’s trying to show investors a more stable business—ideally one that doesn’t rely so heavily on the goodwill of Musk. OpenAI reportedly now generates more than $40 billion in annualized revenue, drawing on several different lines of business, including subscriptions, ads in ChatGPT, and selling access to its AI coding tool, Codex.
I think this is the right thing to do and it is consequential. I always liked Cursos and was amazed by what they were building, but selling to Musk essentially sent them to the dark side. And it feels weird that OpenAI suddenly seems like the adult in the room…
LLMs: Intelligence vs. cost
There is an immense difference in cost between the state-of-the-art models from Anthropic and OpenAI and the much cheaper Chinese models: the former are too expensive even for large corporations, while the latter can be as cheap as a mobile phone subscription.
How much extra intelligence emptying the wallet purchases obeys the law of diminishing returns: while a top-tier engineer or scientist is probably going to be able to appreciate how much better Fable 5.1 (intelligence score 66, $3.69 per task) is compared to GLM-5.3 (intelligence 60, $0.49 — 7.5x cheaper), most people will have a hard time doing so.
And yet I find it fascinating how people on Reddit, X and elsewhere claim that they absolutely must use the just released shiny new LLM, even though they probably won’t recognize the difference in quality. I am certain that most people will actually be content with this year’s open weights models and do not need the latest frontier models for what they are doing.
The Harness Playbook
A while loop around a fetch sounds simple, but there’s a reason OpenCode, Pi, OpenClaw and omp are all concurrently working on a complete refactor: this class of software did not exist before, and only by starting with the simple version, we could see the cracks to work towards a better one.
Unavoidable complexity needs an owner. At the moment, the conservation of complexity tips toward extensions and users, making it impossible to write reliable software on top of omp or Pi. I can already hear the “whaaat, it is so simple and pleasant to extend.” Give me a few chapters to change your mind.
Dijkstra wrote that “simplicity is prerequisite for reliability”, and yet he is known for algorithmically solving pathfinding. Why not just brute force? He was not, at all, making the claim we now repeat as simple good, complex bad. The advice was to help implementers reason. We shamefully use it to excuse the implementer from reasoning.
I openly admit that until recently I thought that only knights were wearing harnesses, but apparently it is a thing now. And when you do any agentic coding, you encounter different harnesses. This is a fascinating breakdown on how they work.
Agency and Agents
Human agency, the willingness to push, experiment and act without waiting for instructions, seems increasingly important to getting value out of AI, and I have a longer post on that coming soon. But this post is about the agency of AI, and how the choices we make about how to use it (or constrain it) will shape all of our futures. For much of the last few years, the AI would sit in a chat window until you asked it for something. Even when it became capable of doing hours of work, you generally had to decide what work to give it. That is no longer always true.
Brave new world.
Inside OpenAI’s Reboot
It’s been a difficult stretch for the company that ushered in the AI boom. “We clearly had some missteps as a company,” Altman told me the following week, sitting in the tastefully appointed MB0 library for more than two hours of interviews. “Both in terms of product direction and specifically on pretraining in research, we fell behind where we wanted to be.” Over the course of the past year, OpenAI lost the lead in the AI race to archrival Anthropic, which spotted the business opportunity in AI coding, built Claude Code into a market-defining product, and surpassed OpenAI in reported annualized revenue and private-market value for the first time. Anthropic, founded by OpenAI defectors, is now expected to be the first of the two companies to go public, two people familiar with its plans say, with the IPO as early as September.
OpenAI really seems to focus more lately, which is a good thing for its customers and also for the development of AI in general.
If you missed last week’s edition of Five Things AI, you can read it here:
That’s it for Five Things AI this week! 🤖
— Nico