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How software work changed in three years [Linear's first full data analysis]

Linear's first full data analysis of AI usage in software teams found that between January and June 2026, the share of users active on AI features more than doubled in every function, with product teams climbing from 12% to 34% and go-to-market from 5% to 18%. CEOs at companies with 201 or more employees saw the largest jump, from 9% to 36%, and adoption roughly tripled across all company sizes, from startups to enterprises. The findings are based on data from tens of thousands of teams building software inside Linear, though the analysis excludes AI usage outside the platform.

read25 min views1 publishedAug 18, 2026
How software work changed in three years [Linear's first full data analysis]
Image: source

Tens of thousands of teams build software inside Linear every day. Over six years that’s given us a detailed picture of how product development happens, from before AI was widely adopted to now.

Model companies and coding tools have published plenty on token usage and code volume, but that captures only one layer of the work. We’re unusually well placed to see the entire workflow behind building a product, from the first issue to the pull request that closes it. What we can’t see is AI usage that happens outside Linear, so this is a picture of adoption inside our own customer base, not the market at large.

We look at three things across that transition. Who is using AI, how it reshapes where teams spend their time across Linear, and whether it changes how much they ship. Together they make a fixed point for where AI-assisted product development stands in 2026, and something to measure the next edition against.

AI adoption has spread to every function #

Between January and June 2026 the share of users active on AI features more than doubled in every function. Product climbed fastest, from 12% to 34%, and even go-to-market, the function furthest from the codebase, went from 5% to 18%. We classify roles by normalizing job titles, which carries some error at the edges, but the pattern is too broad to be an artifact of labeling.

Percentage of users active on Linear AI features (Last 30 days) by function

Segment Jan 2026 Jun 2026 Change
Founder 14% 30% +16 percentage points
Engineering 12% 30% +18 percentage points
Product 12% 34% +22 percentage points
Design 6% 22% +16 percentage points
GTM 5% 18% +13 percentage points

Adoption goes all the way to the top #

Executives are personally active on AI at rates that match or beat their teams. CEOs at companies of 201 or more people went from 9% to 36% in six months, the largest jump of any cut in this report, suggesting the most senior leaders are learning the technology by using it rather than reading about it. Company size comes from third-party enrichment, so this cut covers fewer workspaces than the rest of the report.

Percentage of users active on Linear AI features (Last 30 days) by executive team

Segment Jan 2026 Jun 2026 Change
Founder, 201+ 10% 26% +16 percentage points
Founder, 51-200 15% 27% +12 percentage points
Founder, 1-50 15% 31% +16 percentage points
CEO, 201+ 9% 36% +27 percentage points
CEO, 51-200 15% 25% +11 percentage points
CEO, 1-50 7% 21% +14 percentage points
CPO, 201+ 3% 24% +21 percentage points
CPO, 51-200 10% 26% +15 percentage points
CPO, 1-50 11% 36% +25 percentage points
CTO, 201+ 11% 35% +24 percentage points
CTO, 51-200 12% 28% +16 percentage points
CTO, 1-50 16% 33% +17 percentage points

Adoption is consistent at every size #

AI adoption roughly tripled everywhere, from startups to enterprises. Company size, usually a good predictor of how fast an organization moves on new technology, barely registers here.

Percentage of users active on Linear AI features (Last 30 days) by company size (employees)

Segment Jan 2026 Jun 2026 Change
1001+ FTE 8% 25% +17 percentage points
201-1000 FTE 9% 27% +19 percentage points
51-200 FTE 9% 25% +16 percentage points
1-50 FTE 8% 23% +14 percentage points

Teams are putting more into the system #

Between June 2025 and June 2026, time spent creating, triaging, and commenting rose in nearly every function, with engineering up roughly 17% on create and triage alone. Founders show much larger swings, up 17 minutes on creation and 26 on commenting, though they’re a smaller cohort and noisier for it. More work seems to need more coordination, and that coordination increasingly sets the context agents act on.

Average minutes per user per month, June 2025 vs June 2026

Segment Jun 2025 Jun 2026 Change
Create & triage, Eng 24m 28m +5 minutes
Create & triage, Product 38m 37m -1 minutes
Create & triage, Design 22m 25m +3 minutes
Create & triage, GTM 27m 31m +4 minutes
Create & triage, Founder 40m 57m +17 minutes
Assign & update, Eng 16m 19m +3 minutes
Assign & update, Product 26m 26m 0 minutes
Assign & update, Design 12m 15m +3 minutes
Assign & update, GTM 12m 15m +3 minutes
Assign & update, Founder 22m 29m +7 minutes
Comment, Eng 35m 40m +5 minutes
Comment, Product 48m 49m +1 minutes
Comment, Design 32m 34m +2 minutes
Comment, GTM 49m 55m +6 minutes
Comment, Founder 39m 64m +26 minutes

AI authors nearly half of all issues #

Two years ago, fewer than one issue in a thousand was created by AI. Teams now use AI to write just under half of everything created in Linear, and at the current pace it will soon author more than people and integrations combined.

Issues created per week (thousands) by source

Week of Agents & MCP People & integrations
Jun 3, 2024 0 605
Jun 10, 2024 0 599
Jun 17, 2024 0 582
Jun 24, 2024 0 689
Jul 1, 2024 0 602
Jul 8, 2024 0 628
Jul 15, 2024 1 621
Jul 22, 2024 0 627
Jul 29, 2024 1 650
Aug 5, 2024 0 654
Aug 12, 2024 1 624
Aug 19, 2024 0 660
Aug 26, 2024 1 650
Sep 2, 2024 1 670
Sep 9, 2024 1 690
Sep 16, 2024 1 692
Sep 23, 2024 1 725
Sep 30, 2024 0 696
Oct 7, 2024 1 726
Oct 14, 2024 1 724
Oct 21, 2024 1 741
Oct 28, 2024 1 721
Nov 4, 2024 1 760
Nov 11, 2024 0 760
Nov 18, 2024 1 795
Nov 25, 2024 1 677
Dec 2, 2024 1 765
Dec 9, 2024 1 800
Dec 16, 2024 1 770
Dec 23, 2024 0 373
Dec 30, 2024 0 460
Jan 6, 2025 1 825
Jan 13, 2025 1 878
Jan 20, 2025 1 869
Jan 27, 2025 1 920
Feb 3, 2025 1 930
Feb 10, 2025 1 924
Feb 17, 2025 1 890
Feb 24, 2025 1 934
Mar 3, 2025 1 942
Mar 10, 2025 1 974
Mar 17, 2025 3 971
Mar 24, 2025 3 984
Mar 31, 2025 1 985
Apr 7, 2025 1 999
Apr 14, 2025 1 974
Apr 21, 2025 1 994
Apr 28, 2025 3 1029
May 5, 2025 5 1037
May 12, 2025 7 1063
May 19, 2025 9 1042
May 26, 2025 11 992
Jun 2, 2025 18 1095
Jun 9, 2025 18 1074
Jun 16, 2025 28 1064
Jun 23, 2025 34 1148
Jun 30, 2025 35 1092
Jul 7, 2025 44 1166
Jul 14, 2025 40 1137
Jul 21, 2025 41 1164
Jul 28, 2025 45 1177
Aug 4, 2025 52 1171
Aug 11, 2025 50 1206
Aug 18, 2025 55 1177
Aug 25, 2025 47 1225
Sep 1, 2025 48 1200
Sep 8, 2025 46 1299
Sep 15, 2025 45 1272
Sep 22, 2025 45 1294
Sep 29, 2025 58 1338
Oct 6, 2025 62 1352
Oct 13, 2025 68 1350
Oct 20, 2025 65 1382
Oct 27, 2025 74 1414
Nov 3, 2025 85 1461
Nov 10, 2025 85 1457
Nov 17, 2025 91 1436
Nov 24, 2025 93 1299
Dec 1, 2025 122 1473
Dec 8, 2025 142 1487
Dec 15, 2025 147 1526
Dec 22, 2025 110 795
Dec 29, 2025 139 808
Jan 5, 2026 206 1601
Jan 12, 2026 273 1725
Jan 19, 2026 291 1721
Jan 26, 2026 323 1806
Feb 2, 2026 401 1897
Feb 9, 2026 451 1901
Feb 16, 2026 516 1875
Feb 23, 2026 599 2048
Mar 2, 2026 707 2123
Mar 9, 2026 794 2170
Mar 16, 2026 837 2106
Mar 23, 2026 916 2297
Mar 30, 2026 935 2104
Apr 6, 2026 1038 2063
Apr 13, 2026 1128 2297
Apr 20, 2026 1209 2173
Apr 27, 2026 1275 2185
May 4, 2026 1382 2238
May 11, 2026 1506 2278
May 18, 2026 1597 2271
May 25, 2026 1472 2132
Jun 1, 2026 1542 2270
Jun 8, 2026 1766 2371
Jun 15, 2026 1652 2256
Jun 22, 2026 1728 2372
Jun 29, 2026 1799 2265
Jul 6, 2026 2078 2532
Jul 13, 2026 2143 2465
Jul 20, 2026 2195 2396
Jul 27, 2026 2348 2357
Aug 3, 2026 2435 2481

Planning time didn’t move inside Linear #

Time spent on customer requests, docs, and projects held steady in a year when nearly everything else in this report moved up. Planning practice varies widely from team to team, and plenty of it happens in conversation before it lands anywhere, so the average blends heavy planners with light ones. What the steadiness suggests is that AI has so far changed how teams execute far more than how they decide what to build.

Average minutes per user per month, June 2025 vs June 2026

Segment Jun 2025 Jun 2026 Change
Customer requests, Eng 1m 1m 0 minutes
Customer requests, Product 3m 4m 0 minutes
Customer requests, Design 1m 1m 0 minutes
Customer requests, GTM 4m 4m +1 minutes
Customer requests, Founder 2m 3m +1 minutes
Docs & projects, Eng 3m 3m +1 minutes
Docs & projects, Product 13m 14m +1 minutes
Docs & projects, Design 4m 5m +1 minutes
Docs & projects, GTM 3m 3m +1 minutes
Docs & projects, Founder 7m 8m 0 minutes

A new layer of work appeared #

Chatting with AI and delegating issues to agents are categories of work that didn’t exist a year ago, and they now show up in every function’s week, with product leaning in hardest. Nothing else shrank to make room, which suggests AI has landed on top of existing work rather than replacing any of it, at least so far.

Average minutes per user per month, June 2025 vs June 2026

Segment Jun 2025 Jun 2026 Change
Agent issues, Eng 0m 1m +1 minutes
Agent issues, Product 0m 1m +1 minutes
Agent issues, Design 0m 0m 0 minutes
Agent issues, GTM 0m 0m 0 minutes
Agent issues, Founder 0m 2m +2 minutes
Chat with AI, Eng 0m 2m +2 minutes
Chat with AI, Product 0m 5m +5 minutes
Chat with AI, Design 0m 3m +3 minutes
Chat with AI, GTM 0m 3m +3 minutes
Chat with AI, Founder 0m 4m +4 minutes

Non-engineers are shipping more code #

The share of product managers attaching pull requests rose from 3% to 10% in two years, and designers from 1% to 8%. We only count pull requests in repositories connected to Linear, so anyone shipping outside that loop is invisible here, which makes these numbers floors rather than ceilings. The people who used to describe a change increasingly ship it themselves.

Percentage of users who attached a pull request (Last 30 days)

Segment Jun 2024 Jun 2025 Jun 2026 Change
Founder 11% 12% 23% +12 percentage points
Engineering 20% 22% 34% +14 percentage points
Product 3% 3% 10% +7 percentage points
Design 1% 2% 8% +7 percentage points
GTM 1% 1% 3% +2 percentage points

Pull requests are up 111% in two years #

Pull requests opened per workspace are up 111% on a June 2024 baseline. Output held roughly level for the first year, then bent upward through 2026 as model quality and adoption climbed together. We count PRs opened rather than merged, and an opened PR says nothing about the value of the change, but the inflection is hard to miss.

Percentage change in pull requests per team per week since June 2024 - All paid workspaces

Week of Change
Jun 2, 2024 0%
Jun 9, 2024 +9%
Jun 16, 2024 +10%
Jun 23, 2024 +3%
Jun 30, 2024 +8%
Jul 7, 2024 -4%
Jul 14, 2024 +8%
Jul 21, 2024 +7%
Jul 28, 2024 +8%
Aug 4, 2024 +7%
Aug 11, 2024 +6%
Aug 18, 2024 +3%
Aug 25, 2024 +10%
Sep 1, 2024 +10%
Sep 8, 2024 +5%
Sep 15, 2024 +12%
Sep 22, 2024 +10%
Sep 29, 2024 +14%
Oct 6, 2024 +8%
Oct 13, 2024 +11%
Oct 20, 2024 +9%
Oct 27, 2024 +18%
Nov 3, 2024 +8%
Nov 10, 2024 +15%
Nov 17, 2024 +11%
Nov 24, 2024 +17%
Dec 1, 2024 0%
Dec 8, 2024 +16%
Dec 15, 2024 +17%
Dec 22, 2024 +10%
Dec 29, 2024 -58%
Jan 5, 2025 -50%
Jan 12, 2025 +6%
Jan 19, 2025 +15%
Jan 26, 2025 +13%
Feb 2, 2025 +15%
Feb 9, 2025 +19%
Feb 16, 2025 +21%
Feb 23, 2025 +17%
Mar 2, 2025 +21%
Mar 9, 2025 +19%
Mar 16, 2025 +26%
Mar 23, 2025 +26%
Mar 30, 2025 +23%
Apr 6, 2025 +17%
Apr 13, 2025 +24%
Apr 20, 2025 +11%
Apr 27, 2025 +10%
May 4, 2025 +7%
May 11, 2025 +14%
May 18, 2025 +21%
May 25, 2025 +22%
Jun 1, 2025 +9%
Jun 8, 2025 +22%
Jun 15, 2025 +16%
Jun 22, 2025 +12%
Jun 29, 2025 +22%
Jul 6, 2025 +8%
Jul 13, 2025 +16%
Jul 20, 2025 +16%
Jul 27, 2025 +16%
Aug 3, 2025 +13%
Aug 10, 2025 +9%
Aug 17, 2025 +5%
Aug 24, 2025 +10%
Aug 31, 2025 +8%
Sep 7, 2025 +4%
Sep 14, 2025 +11%
Sep 21, 2025 +10%
Sep 28, 2025 +8%
Oct 5, 2025 +9%
Oct 12, 2025 +9%
Oct 19, 2025 +9%
Oct 26, 2025 +9%
Nov 2, 2025 +14%
Nov 9, 2025 +15%
Nov 16, 2025 +13%
Nov 23, 2025 +16%
Nov 30, 2025 +1%
Dec 7, 2025 +17%
Dec 14, 2025 +17%
Dec 21, 2025 +14%
Dec 28, 2025 -48%
Jan 4, 2026 -54%
Jan 11, 2026 +10%
Jan 18, 2026 +22%
Jan 25, 2026 +22%
Feb 1, 2026 +27%
Feb 8, 2026 +32%
Feb 15, 2026 +36%
Feb 22, 2026 +33%
Mar 1, 2026 +50%
Mar 8, 2026 +49%
Mar 15, 2026 +54%
Mar 22, 2026 +55%
Mar 29, 2026 +58%
Apr 5, 2026 +41%
Apr 12, 2026 +46%
Apr 19, 2026 +60%
Apr 26, 2026 +66%
May 3, 2026 +67%
May 10, 2026 +80%
May 17, 2026 +91%
May 24, 2026 +95%
May 31, 2026 +85%
Jun 7, 2026 +106%
Jun 14, 2026 +113%
Jun 21, 2026 +111%

Coding agents account for most of the acceleration #

Teams that connected a coding agent roughly tripled their weekly pull requests over two years, from 21 to 65, while teams without one went from 8 to 10. These teams were already higher-output before coding agents existed, so the levels aren’t directly comparable, but each cohort against its own baseline tells a clean story, and nearly all the growth sits on the agent side.

Pull requests per team per week - Fixed cohort (paid workspaces)

Week of Coding-agent teams Traditional teams
Jun 2, 2024 21 8
Jun 9, 2024 24 8
Jun 16, 2024 24 9
Jun 23, 2024 22 8
Jun 30, 2024 24 9
Jul 7, 2024 21 8
Jul 14, 2024 24 8
Jul 21, 2024 24 8
Jul 28, 2024 24 9
Aug 4, 2024 24 8
Aug 11, 2024 24 8
Aug 18, 2024 23 8
Aug 25, 2024 25 8
Sep 1, 2024 24 8
Sep 8, 2024 24 8
Sep 15, 2024 25 9
Sep 22, 2024 25 8
Sep 29, 2024 26 9
Oct 6, 2024 25 9
Oct 13, 2024 26 8
Oct 20, 2024 25 8
Oct 27, 2024 26 9
Nov 3, 2024 25 8
Nov 10, 2024 27 9
Nov 17, 2024 26 8
Nov 24, 2024 28 9
Dec 1, 2024 23 8
Dec 8, 2024 27 9
Dec 15, 2024 28 9
Dec 22, 2024 26 8
Dec 29, 2024 10 3
Jan 5, 2025 11 4
Jan 12, 2025 25 8
Jan 19, 2025 27 9
Jan 26, 2025 27 8
Feb 2, 2025 28 8
Feb 9, 2025 29 9
Feb 16, 2025 30 9
Feb 23, 2025 29 9
Mar 2, 2025 30 9
Mar 9, 2025 30 9
Mar 16, 2025 30 9
Mar 23, 2025 31 9
Mar 30, 2025 31 9
Apr 6, 2025 30 8
Apr 13, 2025 32 9
Apr 20, 2025 28 8
Apr 27, 2025 28 8
May 4, 2025 28 8
May 11, 2025 29 8
May 18, 2025 32 9
May 25, 2025 31 9
Jun 1, 2025 28 8
Jun 8, 2025 31 8
Jun 15, 2025 31 8
Jun 22, 2025 30 8
Jun 29, 2025 32 8
Jul 6, 2025 29 8
Jul 13, 2025 32 8
Jul 20, 2025 31 8
Jul 27, 2025 32 8
Aug 3, 2025 32 8
Aug 10, 2025 32 8
Aug 17, 2025 31 7
Aug 24, 2025 33 8
Aug 31, 2025 32 8
Sep 7, 2025 31 8
Sep 14, 2025 34 8
Sep 21, 2025 34 8
Sep 28, 2025 34 8
Oct 5, 2025 35 8
Oct 12, 2025 34 8
Oct 19, 2025 34 8
Oct 26, 2025 34 8
Nov 2, 2025 36 8
Nov 9, 2025 36 8
Nov 16, 2025 35 8
Nov 23, 2025 37 8
Nov 30, 2025 31 7
Dec 7, 2025 37 8
Dec 14, 2025 38 8
Dec 21, 2025 37 8
Dec 28, 2025 16 3
Jan 4, 2026 13 3
Jan 11, 2026 35 7
Jan 18, 2026 40 8
Jan 25, 2026 39 8
Feb 1, 2026 42 8
Feb 8, 2026 44 8
Feb 15, 2026 46 9
Feb 22, 2026 44 8
Mar 1, 2026 49 9
Mar 8, 2026 50 9
Mar 15, 2026 51 9
Mar 22, 2026 50 9
Mar 29, 2026 52 9
Apr 5, 2026 48 9
Apr 12, 2026 49 9
Apr 19, 2026 54 9
Apr 26, 2026 55 9
May 3, 2026 55 8
May 10, 2026 57 9
May 17, 2026 60 10
May 24, 2026 62 10
May 31, 2026 57 9
Jun 7, 2026 65 10
Jun 14, 2026 63 9
Jun 21, 2026 65 10

The clearest indication of AI’s influence on product development is the dramatic output gains experienced by teams using coding agents over the last two years. We have no way of knowing whether this increased output led to positive business outcomes, but it shows a very clear correlation between AI adoption and acceleration.

Perhaps more intriguing is the makeup of that adoption, and how it appears to be blurring roles. Senior leaders are doing more of the hands-on IC work, adopting AI aggressively to help them do it, and non-engineers are committing code. The suggestion that everyone in an organization is becoming a “builder” seems to be directionally true.

Those gains haven’t shown up as time saved, though. Time spent on existing tasks in Linear held while AI usage appeared as a new layer of work, meaning the overall time spent on product development is going up rather than down. As far as we can observe, teams are working more, not less, suggesting AI has a Jevons paradox quality beyond token consumption.

Many will rightfully argue that looking at pull requests indicates motion rather than value, which is certainly true, but it’s still a step forward from measuring tokens. A mechanical refactor might burn lots of tokens while a meaningful bug fix or code review doesn’t, so token spend and value don’t line up at all, and using one as a proxy for the other will be remembered as a relic of AI’s early days.

In future reports we intend to go deeper on the full lifecycle of work, from token spend all the way to outcomes, something we can newly observe now that code and code review run through Linear as well.

Appendix #

Methodology

This report uses aggregated product data from Linear. The data includes AI conversations, agent sessions, issue activity, comments, and pull requests. It covers only paid workspaces and the users in them. We report all metrics in aggregate to show broad patterns in how teams use AI to build software, not individual behavior. We measure each metric in a fixed time window. A window is one calendar month or the last 30 days. The year‑over‑year charts use June 2025 and June 2026. Adoption metrics use a trailing 30‑day window, and time‑series charts aggregate to weekly points. Both steps reduce short‑term noise. Some charts keep only the users who are active in both windows.

Definitions

AI-active. A user with at least one AI interaction, an in-app or Slack conversation or an agent session, in a 28-day window.

Agent team. A workspace with a coding agent connected.

Pull request. A code change opened against a repository connected to Linear. We count pull requests opened, not merged.

Paid workspace. A workspace on a paid plan, active during the relevant period.

Agent issue. This includes delegating an issue to an agent or starting a session.

Company size. Full-time employees at the company, from third-party enrichment.

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