Q1 2026 Data Insights Report

We scanned 500,000+ PRs across 10,000+ engineers and 500+ organizations (sizes 1 to 4,000+). Engineers are producing dramatically more with AI and the gap between teams that measure this and teams that don't is becoming permanent.

Six biggest findings below!

Grab the full report here:

1. Claude Code pulled ahead

Claude Code output per engineer nearly doubled from 18 to 32 units*/month between October 2025 and March 2026. Through Q4, Claude Code and Cursor tracked almost identically. Then in January, Claude Code broke away and by March it was outproducing Cursor (30) by a meaningful margin. We track Claude code and Cursor data directly via their APIs, as well through Claude telemetry & Git metadata tracking.

2. AI now writes 43% of all code

In October 2025, AI accounted for roughly 16% of code output. Six months later, that number is 43%. The shift accelerated sharply in Q1 2026 as teams moved from experimenting with AI tools to embedding them into daily workflows.

Nearly half of all code being shipped right now was written or substantially generated by AI. This is no longer an edge case or an early-adopter story. Many teams (including ours!) are at 100% AI written code.

The question is no longer "should we adopt AI tools?" It is "how far can we push the AI tools we use?"

3. Staff engineers write the simplest prompts and get the best results

This one surprised us. You would assume junior engineers, having grown up with AI tools, would be the most effective prompters. Our data says the opposite.

Staff engineers scored 64.7 on conversation effectiveness with an average prompt complexity of just 56.5. Juniors scored 54.7 effectiveness while tackling prompts rated 84.4 in complexity. The pattern is consistent across every seniority level: the more senior the engineer, the simpler their prompts and the better their results. Our data set here is a bit smaller (a few hundred engineers), but it’s still interesting to see the trends and will continue to report on this as we get more granular data.

Our guess is that senior engineers know what to ask for. They break problems into smaller, well-scoped requests. Juniors try to solve everything in one complex prompt. This has real implications for training. Teaching juniors to write more focused prompts will likely improve their AI effectiveness m

ore than any prompt engineering course.

4. AI usage Is diversifying fast (and infrastructure is the surprise winner)!

A year ago, AI-assisted coding was mostly about front-end and back-end feature work.

Infrastructure's share of AI-assisted output more than doubled, jumping 8 percentage points to 15.5% of all AI code. Documentation saw the largest relative jump, growing 7x from 0.7% to 5.0%. Meanwhile, backend and frontend shares both declined, not because teams are using AI less for features, but because AI usage is spreading into all categories.

Teams are using it for the unglamorous but high-value work: infrastructure, devops, testing, and documentation. The teams getting the most out of AI are the ones deploying it across the entire engineering workflow, not just the parts that ship user-facing features.

5. What does "normal" AI spend look like?

This is the question every engineering leader is asking and nobody has had good benchmarks for until now.

We are building industry-level spend tiers based on actual usage data across our customer base. This is the first quarter as it’s a new feature, but based on Claude Code we are seeing that light users are about $200/user/month. Average sits at $550/user/month. And heavy usage comes in at $1,400/user/month.

Note: this is only claude code spend, and not aggregate across all tool stack.

6. The top 1% produce 4.3x the output of a P90 engineer

The gap between the P90 and P99 engineer is 248 units/week. The gap between P50 and P90 is just 70. That means the distance between "very good" and "exceptional" is 3.5x larger than the entire distance between "average" and "very good."

A single P99 engineer outproduces a small team of median performers. And this gap is widening as AI amplifies already-strong engineers. The best engineers are the ones adopting AI most effectively, compounding their existing advantage.

This changes how you think about hiring, retention, and compensation. The talent distribution is not a bell curve. It is exponential at the tail, and AI is making it steeper every quarter. Losing a P99 engineer is not losing one headcount. It is losing the equivalent output of 4+ strong engineers. Retaining and enabling your top performers is now the highest-leverage investment an engineering org can make.

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Postscript: how is this collected?

Weave builds ML models to measure engineering productivity and AI usage. Our model scans every PR and answers the question ‘how long would this PR take an expert engineer to complete?’ which produces our ‘code output’ measure. We then integrate the most granular AI usage data for each customer (via API, Git tracking, or telemetry) and determine how much output is coming from AI vs. manually writing code.