Les Orchard <me@lmorchard.com>

When Poole Museum reopens visitors will once again be able to see Bethan's Rock - a treasured stone donated by a five-year-old girl - alongside its other artefacts The rock became a popular exhibit after the museum placed it in a glass case, complete with its own "Bethan's Rock – 2019," label. Bethan, now 11, said it was given to her by her late grandmother, "who was the one who found that rock... it's really special to me because of her". The museum's curator Gary Edwards said: "Treasures don't have to be rare, they don't have to be significant to everybody it's about the deeper meaning connected to those objects."

I think about this sometimes when I'm writing in Vim at midnight, just me and a blinking cursor and a plain text file that will still be readable when I'm dead. No filesystem-within-a-filesystem. No sector allocation tables. No 5,039-page specification. Just words, a few hash marks, and never having to think about it again.

But my NetHack experience suggests something different. The skills that matter at scale are somehow still about programming, but they are not coding skills. They are: deciding what to build. Figuring out what to test and how to know if the tests are honest. Building tools that make invisible things visible. Managing coordination across agents. Knowing when a metric is a figleaf. Recognizing where the real complexity lives in a system, and routing attention there instead of everywhere else. These are the conceptual skills that have always been underneath large-scale programming. They are the hard part that the mechanical craft obscured. The reality is that CS has never really been about writing code. Underneath coding is critical thinking about algorithms, engineering principles, analytical methods. It is the science of managing complexity, and code is just the medium we manage it in. In the era of AI code generation, the field does not die. It transforms.

If they can gain parity with hosted alternatives, local open source models have a compelling value proposition: fast, private, and free. This possibility has not gotten much attention: no one stands to get mega-rich from them. But the threat to current leaders is a potent one.

My advice to other coders, or at least the advice I’m taking myself, is that if you’re looking for art in coding: stop looking. If you’ve never taken an interest in poetry, or painting, or dance, or whatever, now would be a good time. In an era where the internet is increasingly full of bots pumping their bland bot ideas into everybody’s brains, seeking out distinctly human forms of expression has become vital.

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There are two big issues currently with LLM writing: it is potentially wrong current LLM writing has a particular style that is itself becoming noticeable The first bullet, you need to review what it writes. It is much easier to have it write on content you are an expert in, so it is easier to review and spot errors. (It is the same current problem with using the tools to help you write computer code – they are boons for seniors but can write a ton of slop that more neophyte coders have a hard time spotting issues.) The second bullet, having the style mimic your own, is what I am going to discuss here. It is worth understanding at a high level how generative AI LLMs work – if you ask “answer question X” vs “here is a book, …., answer question X” the LLM will generate a different response. The first part in the former prompt, “here is a book, …” is what is referred to the context. Current models have context windows (how large of a potential input) at around 500,000 words (technically they are around 1 million tokens, one word is often multiple tokens though).

The organizations that figure this out will develop stronger engineers faster than ever, because AI is a powerful tool for deliberate development when someone is actually steering it. The ones that don’t will produce a generation of engineers who can ship features with AI assistance but can’t independently reason about hard problems. The difference won’t show up on a quarterly roadmap. It will show up the first time something goes seriously wrong and someone needs to think their way out of it without a prompt to lean on.

One caveat matters here. The tension between craft and efficiency doesn't disappear if you remove capitalism from the picture. LLM coding assistants produce faster results whether anyone is being paid or not, and any community, however it's organized, will eventually have to reckon with what to do with that speed difference. Capitalism gives the harshest possible answer to that question: the slower worker loses their livelihood. But the question itself would survive capitalism. Other forms of social organization might answer it more gently, but they'd still have to answer it.

Essays and stories by Paul Ford. Since 1997.

Drugwars for the TI-82/83/83+ Calculators