AI usage patterns in software teams

This looks like measuring what is easy to do, rather than what really matters.

PR open counts, issues created , ceos/founders spending more time on linear don't automatically lead to better outcomes (in my experience they are often negatively correlated:-) )

Usage does not correlate with valued output or ROI. This article smells like a bad attempt to say hey guys our customers see ROI without any actual evidence that they do.

Also, somewhat amusingly, the “founder” consistently being at the top of the use curve might just have something to do with everyone else also using it, but that more implies people are using it because the chief at the top wants them to use it… not because it’s actually useful. A pattern that’s typical of bad AI deployments.

These days, my work has become: generate code for 20 minutes, then spend an hour reading it.

this seems inappropriate. I think its a bad paradigm that just because you use a platform's service, they get intimate details about your usage. And for them to be so bold about publishing the statistics they've stolen from their customers data? Gives me a reason to never recommend my org use this platform.

"Pull requests are up 111% in two years". Would be more honest to say that the number of pull requests "detected" by linear are up XXX%. Because it only works if you setup git repo tracking and use it properly. And at that point it is not obvious if more teams are using linear and using it correctly, or if the number of PR really increased that much!

This looks like a "We are so AI native and efficient!!" article. At least, they could delve deeper into how they define the metrics and how they collected the data.

This is pretty interesting but I wish it would have been refined in two ways:

- The prose before the data appears to be AI generated. Not a big deal but it makes the reader work harder to figure out what's actually being said.

- Linear didn't control for their platforms AI changes over the past year. The platform has become much more AI integrated, so a lot of numbers will move. I'm not sure how you do it but this is only useful signal with a control.

> Time spent on customer requests, docs, and projects held steady [..] AI has so far changed how teams execute far more than how they decide what to build

I think the measurement for this may be flawed. We do mostly use AI to decide how to build. But what we build is influenced by AI-driven research into a problem or task. That's largely done in coding and desktop AI tools, not Linear Asks/AI.

I'm working on accelerating my team's work by implementing AI-driven code pipelines with guardrails to eliminate as much unnecessary review time as possible. Also making a chatbot for turning repetitive tasks & PRs into buttons, and an "architectural guidance" chatbot that gives advice tailored to our business, software/system architecture, cloud, standards, etc. This puts AI and automated jobs in the center of both how (automated task) and what (architecture guidance).

But this has a not-so-great implication for Linear. With my tools, a human never has to touch a ticket, so we could use any ticketing system with an API or CLI. Linear is a great product because they made a great interface. What happens when I replace their interface with a chat bot?

I think it's worth noting that headlines like "AI adoption has spread to every function" is only limited to roles covered/tracked by Linear.

Recent studies by e.g. Google show a much broader range of adoption.

Nice data!

Tim (author) if you're there: it'd be amazing to see the split of which agents people are using, if you have that data.

Founders topping the usage chart says more about org pressure than product value. Adoption driven from the top looks different from adoption driven by results.

I didn't know Linear has "AI features". Linear is boring, but that's actually fine by me.

I use LLMs to write my code, but this does not show up in this data.

The AI Slopologists strike again. More garbage by garbage people.