AI 工程生产力远超常态：从 20% 提升到 3 倍

We are now in an era where we should expect 3x more from each other.

Over the last six months, one data point has followed another :

• NVIDIA reported a 3x increase in committed code across 30,000 developers with bug rates flat.¹
• Amplitude tripled weekly production commits, with an AI agent now a top-three contributor to the codebase.²
• Anthropic measured a 2.5x increase in code written per engineer since adopting Claude Code internally, quality stable.³
• Replit doubled its team & tripled per-engineer output over the same period, with review times, reversions, & incidents all flat.⁴

The chart above sorts the ecosystem into three unequal tranches, each defined by how much of the model’s power the company captures.⁵

The first tranche is what most companies experience today. Distribute an AI IDE, change nothing else, & the outcome is modest.

“Engineering leaders went into AI expecting 2-3x productivity gains but are landing closer to 30%.”

— Augment Code⁶

Faros’s telemetry across 22,000 developers confirms this: engineers completed epics 66% faster, but bugs per developer increased by 54%.⁷ The Google randomized controlled trial put the number at 21%, close to GitHub’s 24%.⁸⁹ This is the default outcome.

The frontier tranche follows. Companies here have built harnesses around the model, orchestrating agents sharing context across GitHub, Linear, & Slack; escalating to engineers for their judgment.

“Every employee gets a manager agent that spawns worker agents in loops. Our internal agent outperformed a seven-figure SaaS tool in security testing and incident triage at one-tenth the cost.”

— Amjad Masad, Replit, “The Self-Driving Company”⁴

Human PR review time dropped 30%. Complex support handling time dropped 60%. Total code contribution rose 5.8x. This is where the 3x number lives.

The third tranche are the software factories, & here the name is an apt descriptor. They are AI machines that produce software mechanistically. Cognition’s Devin refactors monolithic codebases end-to-end. Factory.ai is deploying software factories at NVIDIA, Adobe, Blackstone, & EY.¹⁰

“Nubank achieved an 8x improvement in engineering efficiency & a 20x cost reduction using Devin for large-scale refactoring.”

— Contrary Research, January 2026¹¹

Goldman Sachs is piloting Devin alongside 12,000 human developers & publicly estimates agentic AI could deliver 3-4x the rate of prior tools.¹²

AI engineering productivity gains are here. The initial data shows what to expect: most teams should migrate from 20% productivity gains to a 3x productivity gain & they aren’t normal.

1. Cursor, “How NVIDIA uses Cursor,” February 2026. ↩︎

2. Cursor, “Amplitude and Cursor cloud agents,” April 2026. ↩︎

3. Boris Cherny, head of Claude Code, on the Big Technology podcast, July 2026. ↩︎

4. Amjad Masad, “The Self-Driving Company,” July 16, 2026. ↩︎ ↩︎

5. The distribution above is illustrative, not statistical. Each point is a reported multiplier from a published study, RCT, or company disclosure. It is not drawn from a sampled population, & the curve is a right-skewed log-normal fit to the pattern of reported outcomes, not to raw data. Treat it as a shape argument, not an estimator. ↩︎

6. Augment Code on X, 2026. ↩︎

7. Faros, “AI Engineering Report 2026”. ↩︎

8. Google internal randomized controlled trial, ~100 engineers, 2024. Referenced in DORA reports; roundup at Value Add VC. ↩︎

9. GitHub, Microsoft, and Accenture study with a large fintech, ~450 developers, 2024. ↩︎

10. Factory.ai, “Factory 2.0: From coding agents to software factories”. ↩︎

11. Contrary Research, “Cognition”, January 2026. ↩︎

12. CNBC, “Goldman Sachs is piloting its first autonomous coder in major AI milestone for Wall Street,” July 2025. ↩︎