If AI Writes All the Code, What Do the Programmers Do?
Eight months ago I was producing roughly 90% human written code and 10% AI written code. This has switched surprisingly rapidly and now my code is probably 90% AI. I’ll go through a change that I made to a matrix-multiply kernel, for which I’ll sadly have to be a bit vague.
- ▪Eight months ago I was producing roughly 90% human written code and 10% AI written code.
- ▪This has switched surprisingly rapidly and now my code is probably 90% AI.
- ▪I’ll go through a change that I made to a matrix-multiply kernel, for which I’ll sadly have to be a bit vague.
2 outlets in our directory ran this story, first to last over 34 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,755 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Probably Dance |
| Canonical URL | https://probablydance.com/2026/07/27/if-ai-writes-all-the-code-what-do-the-programmers-do/ |
| Publication time | Tue, 28 Jul 2026 08:17:58 +0000 |
| Retrieval time | 2026-07-28T08:24:16.757Z |
| Last seen | 2026-07-28T08:24:16.757Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | IOuxwc3w8XZH · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Eight months ago I was producing roughly 90% human written code and 10% AI written code. This has switched surprisingly rapidly and now my code is probably 90% AI. So what do I do all day? I’ll go through a change that I made to a matrix-multiply kernel, for which I’ll sadly have to be a bit vague. Since GPUs are now giant matrix-multiply chips, where north of 96% of the flops are in the tensor cores (latest Nvidia GPUs have 2250 tflops in bfloat16 matmuls, compared to 75 tflops for everything that’s not a matmul), you’d think that they’d make it easy to use all those flops. But no, matrix multiply kernels are giant crazy beasts that are incredibly tricky to get right. Some quick googling finds this explanation on Nvidia hardware and this one on AMD hardware.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Probably Dance.
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