# Stanford "widening gap" chart — original research located

#query

## Question asked

The user saved a chart screenshot (`raw/assets/G6g3O60bkAE05ZW.png`, filename pattern = X/Twitter image) of a Stanford slide titled *"Teams that master AI are accelerating their productivity gains, widening the gap with laggards"* and asked: **find the original Stanford research behind it.**

## Answer

The slide is from **Stanford's Software Engineering Productivity Research group (SWEPR)**, led by researcher **Yegor Denisov-Blanch**. Since 2022 the group has analyzed private Git repositories from 600+ companies and 120,000+ engineers, scoring every commit with an ML model trained to replicate a panel of human expert reviewers.

The specific chart is their **causal difference-in-differences analysis**: 46 teams that adopted AI, matched against 46 similar non-AI teams, with net productivity gains measured quarterly.

- **April 2023:** 4.8% gap between top-quartile and bottom-quartile AI adopters.
- **July 2025:** 19% gap — a **4× increase** in ~2.25 years.
- Slide footer: Stanford University / SWEPR; the specific slide (page 3) matches Denisov-Blanch's September 2025 AI Conference deck.

**Primary sources:**

- Research group home: <https://softwareengineeringproductivity.stanford.edu/> (AI Impact research, AI Practices Benchmark)
- Talk the slide comes from: *"Will AI Replace Software Engineers?"*, AI Conference, Sept 2025 — [slide deck PDF](https://aiconference.com/wp-content/uploads/2025/09/Yegor-Denisov-Blanch-Will-AI-Replace-Software-Engineers_-.pptx.pdf)
- Video walkthrough: [Can you prove AI ROI in Software Eng? (Stanford 120k Devs Study)](https://www.youtube.com/watch?v=JvosMkuNxF8)
- Researcher site: <https://yegordb.com/>
- Peer-reviewed methodology paper: [Predicting Expert Evaluations in Software Code Reviews](https://arxiv.org/pdf/2409.15152)

**Caveat (Status: tentative):** the 46-vs-46 difference-in-differences result itself has been presented via talks, webinars and decks — not (yet) a peer-reviewed paper. The peer-reviewed publications cover the *measurement methodology*, not this specific analysis.

**Surrounding findings from the same study** (useful nuance): AI raises developer productivity ~15–20% on average, with high variance — largest gains on greenfield/simple tasks in popular languages; AI can *decrease* net productivity in complex legacy codebases (rework eats the gains, ~2.6× increase in rework reported).

## Why this matters to the vault

This is the **first quantitative, external, longitudinal measurement** of a claim the corpus so far held only as practitioner assertion:

- [[2026-07-14-gap-between-ai-users-irreversible]] — [[allie-miller]]'s central prediction ("in 12 months the gap will be irreversible") is the same shape as this curve, asserted from advisory experience. Stanford now supplies measured team-level data pointing the same direction.
- [[2026-07-28-webinar-theses]] — the "stakes" thesis group (irreversible gap) gains a citable number: *4.8% → 19%, 4× in about two years*. A Stanford chart is far stronger webinar ammunition than "an ex-Amazon AI leader predicts…".
- The "AI can decrease productivity in complex legacy codebases" finding is honest-caveat material aligning with the vault's recorded tensions ([[maintenance-is-the-real-cost]], rework costs; [[make-more-cheap-code]]'s verification burden — cf. the study's 91% increase in PR review time).
- The mechanism Stanford implies (teams that *master* AI compound, laggards stall) is the team-level twin of [[levels-of-ai-usage]] — the gap grows between rungs, not between haves and have-nots of licenses.

## Evidence trail

- Screenshot: `raw/assets/G6g3O60bkAE05ZW.png` (raw asset; likely captured from an X/Twitter post sharing the talk)
- Web search + fetch of the SWEPR site and the AI Conference deck (2026-07-30); slide title, footer, chart annotations and page number all match the deck's era (data ends July 2025)

## Follow-up questions

- ~~Ingest-worthy?~~ **Done, same day:** the user authorized a new raw source file; the dossier lives at `raw/sources/Stanford SWEPR - AI and the widening productivity gap.md` and is ingested as [[2026-07-30-stanford-swepr-widening-gap]] — concept pages now cite it directly.
- Does the webinar want the number? One line — "Stanford measured it: the gap 4×'d in two years" — would upgrade the stakes beat from prediction to measurement.
- Watch for a peer-reviewed version of the difference-in-differences analysis; the claim's status upgrades from tentative when it lands.

## Whether this output changed existing pages

- [[2026-07-14-gap-between-ai-users-irreversible]] — added an external-corroboration pointer to this page under Connections.
- `index.md` (Queries section) and `log.md` updated.
- No concept pages changed — deliberately, since the underlying talk is not yet ingested as a source (citation policy: concept evidence should point at `wiki/sources/*`).