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AI usage patterns in software teams
- jdw64 2mo agoThese days, my work has become: generate code for 20 minutes, then spend an hour reading it.
- danggggg 2mo ago[dead]
- dullcrisp 2mo agoIt could be. Or it could be 1x, or 0.2x. You don’t have enough information to make that judgment.
- danggggg 2mo ago[dead]
- slopinthebag 2mo agoIt’s worthwhile to me under specific conditions, mainly that the codebase has existing patterns and abstractions that the LLM utilizes, and they’re actually good. It’s not a common case but I’m lucky that one of the codebases I’m in is one I built myself mostly by hand, and LLMs are really effective in it at the moment. My main concern is making sure the generated code maintains the high standard, but it’s definitely saving me time. I’ve kind of changed my mind on prompting, it’s definitely a skill. It’s a skill based on your own skills in the domain. I’m at the point where I can get the LLM to generate the same code (roughly speaking) that I would have written. So it’s basically generating the same thing I would write, just faster. So it’s like reading your own code. Using it as a crutch to do things you aren’t capable of is where people run into trouble. That’s where the massive amounts of code review come into play. For me, I’m only ever reviewing 100-300 loc changes at a time. Often less. Because I know what I’m doing and can break things down into manageable diffs. Can’t see myself going back, but also can’t see myself doing it without the experience I have without LLMs. Which is a bit of an issue for new developers. Not sure what the solution is for that.
- dullcrisp 2mo agoThat plus if you’ve ever stared at your code and then searched StackOverflow to see if you could find a better way of doing it, it’s like having that running continuously.
- slopinthebag 2mo agoYeah it’s great for prompting for improvements, granted you can actually judge the suggestions for yourself. They’re often slightly or majorly bad.
- danggggg 2mo ago[dead]
- deimos_28 2mo agoI’ve reached a similar conclusion, but there’s a part that worries me: the expertise that allows us to judge AI’s output was itself built by doing the work we’re now delegating. So there’s a risk that our judgement will decay over time. I’ve been thinking about the problem as choosing where we can afford to “borrow” comprehension, versus where we need to keep exercising it, and how to “claim back” the critical comprehension we lost.
- hypfer 2mo agoA possible heuristic: Everywhere the LLM made a design decision is probably a place to revisit later. You can ship it now, but to stay in the game you need to stay in the loop. If you need the LLM to explain to you the concept behind something - or even if you figure out yourself what it did - that's likely wrong and will lead to the undesirable outcomes you've mentioned. As long as it just types and doesn't think otoh, you should be good. There is - I'd argue - little to no value in manually typing if (foo) {}. And it's also great for bouncing ideas around.
- danggggg 2mo ago[dead]
- dullcrisp 2mo agoWell you got me. I’d agree about that workflow, but something more rapid like a few seconds generating code and then a couple minutes reading it can be more engaging to me than writing it out yourself. I think it’s near time we all stop having such strong opinions about the matter either way personally.
- danggggg 2mo ago[dead]
- dullcrisp 2mo agoI’m entitled to my opinion about your opinion just like you’re entitled to your opinion about my opinion about your opinion.
- slopinthebag 2mo agoYeah I kinda agree. The AI boosters have been super annoying for a while. I’m not anywhere close to an AI booster but I find value in it. I think we should think of it less like some intelligent being or “agent” and more as a code generation tool. It would both be more productive and healthier.
- danggggg 2mo ago[dead]
- tankaiji 2mo agoAnd then more hours cleaning it up and re-prompting.
- danggggg 2mo ago[dead]
- jdw64 2mo agoWriting code directly takes longer to warm up. Usually, I'd keep tens of thousands of lines in my head. In the past, I spent a lot of time designing error propagation and execution contexts. (Talented people might figure it out right away, but unfortunately I don't have that kind of talent.) So I'd have to think about things like Result<T> and how far to propagate errors—and worry about whether my approach would conflict with the existing codebase. But these days, AI just generates code following the existing patterns of the codebase. In the past, staring at a blank screen meant going through a checklist of things to design—starting from policies and writing everything down step by step. Now, I just ask AI and it gives me a template—which is great. Then if the AI makes a mistake, I fix it manually. Of course, I still hand-code sometimes—but only in the areas I enjoy. Most of the time, I use AI coding. Both are fun, and they complement each other in interesting ways. Doing both together is actually enjoyable.
- skydhash 2mo agoFor me, coding is either a flow state (when I have huge swaths of code to write) or deeply thinking about just a few lines (mostly to figure if I caught everything). The latter case is when I procrastinate the most, because I usually have an idea of a solution, but don’t want to do the work of actually verifying if it’s correct. Using AI then is skipping the enjoyable part (writing the code) to the unpleasant part (formulating hypothesis and verifying them).
- jdw64 2mo agoI feel similarly, but at the same time, I think I am the exact opposite. I actually find formulating hypotheses more fun. For hobby projects or things I start casually, I usually do not think about errors and such at all. When it is a tool I want to build or need for myself, I really do not care about that part. In my case, I do not contribute to open source at all. Mostly, I deliver code for factory systems or specific companies, and usually, there are strict enterprise requirements. (To be precise, there is always that mandatory code the lead developer on their end dictates, right?) That kind of code is mostly no fun, but it has to meet their requirements and often clashes with my own style. Having AI write that code for me is a huge relief. In that sense, I think it is just a difference in personality and preferences. I originally became a programmer because I wanted to make games. I started programming because I found it fascinating to see things drawn and displayed on the screen. Becoming a programmer was all because making Flash games was so much fun... So in that regard, for me, writing code is just 'drawing what I want on the screen', which is why I guess I do not mind if the code is written by AI. When I contribute to other people's projects, I do not use AI for anything other than English translation, but for my own projects, I have no hesitation. Is this really just a difference in inclination? It is not that I did not enjoy writing code, but rather that seeing what I want rendered on the screen brings me more joy. When the concept of 'vibe coding' first came out, I really hated it (since my knowledge was earned over 4 to 5 years of getting scolded by lead developers as a subcontractor and factory software provider). But thinking about it, what I really wanted to do as a developer was just to build the worlds I envisioned, so I decided not to let it bother me too much. We talk often here on HN, and I really enjoy debating with you. I learn a lot from you.Mr."skydhash", I actually remember you quite often, and I even steal a few keywords from your posts sometimes. Because we have different tendencies, we occasionally clash, but having these conversations is exactly what makes it enjoyable. Thank you for always replying. Have a great day, and I hope this does not offend you in any way.
- mulanroo 2mo ago[dead]
- whateveracct 2mo agoI spend 45 minutes writing it. then i git commit and move on cuz i made something good. who is winning here? lol
- danggggg 2mo ago[dead]
- ReptileMan 2mo agoLike the Titan submarine team's moto mine is - real men test in production. The LLM produce so much code that the best I can is skim and look for obvious flaws, also pass it trough adversarial one.
- CircuitSeuss 2mo ago[dead]
- 0xbadcafebee 2mo ago> 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?
- greatgib 2mo ago"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!
- well_ackshually 2mo agoDoubling PRs isn't necessarily unbelievable. AI reduces ceremony around PRs, makes the small shitty changes that you never had time to do possible and the larger cleanups at least doable. Double the PRs doesn't mean double the output.
- Phill_Madd 2mo ago[flagged]
- gkamal 2mo agoThis 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:-) )
- hexasquid 2mo agoI'd be interested to overlay, I don't know, customer satisfaction or anything that can show the follow-on effect of all this output. Linear won't have that information. My guess is some will jump up (where the team has managed to make themselves move effective and responsive) and many will plummet (doesn't need explaining). Then there might be something to look at.
- onion2k 2mo agoI'd be interested to overlay, I don't know, customer satisfaction or anything that can show the follow-on effect of all this output. Linear won't have that information. Very few businesses can accurately attribute customer value to the work they do, especially once they're passed start-up scale. A mature company makes lots of small changes and they're rarely measurable.
- Fordec 2mo agoYeah all of these along with raw token usage are metrics that were being used around December to February by people who just didn't have anything to go off yet. Skill/Hook usage rates, budget spend, auto-approval rate, focus area heatmaps, MTTR, MTTD are all there now
- onion2k 2mo agoThis looks like measuring what is easy to do, rather than what really matters. Even that's hard. There aren't enough signals to attribute changes directly to AI, so these apps seem to correlate the signal that the user was interacting with AI to the changes they made e.g "Bob used AI at that time, and they opened a PR at a similar time, so Bob probably used AI to make that PR." Until the tooling for gathering data on AI usage improves the data will be fairly interesting because correlations often point to something related, but won't be a source of truth.
- slopsosn 2mo agoThe AI Slopologists strike again. More garbage by garbage people.
- subarctic 2mo agoCan't tell if you mean the people the article is talking about or the article itself
- what 2mo ago¿Por qué no los dos?
- prolly97 2mo agoIs this just an opinion? If so, fair. If it's an attempt at rebutting their claims etc, it'd be easier to interact if you provided some data, or concrete observations :)
- inigyou 2mo agoThe bullshit asymmetry principle suggests that once something is identified as bullshit, we shouldn't spend much time to write an excruciatingly detailed refutation.
- prolly97 2mo agoInvoking the bullshit card on opposing views, in order not to have to elaborate your own pov, seems counterproductive, if the aim of your participation in a discussion is to test your own hypotheses about the world - or in any way gain or deliver insights.
- joegibbs 2mo agoI might build a Chrome extension that looks for “AI” in HN submission titles and shows a fake comment at the start saying “AI is all slop garbage that’s total slop and I hate it. AI sucks and it’s slop!” It will save people time reading the 5-10 other identical vacuous comments
- agnishom 2mo agoI 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.
- sebiandev 2mo agothis 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.
- humbleharbinger 2mo agoUhh that's how a lot of economic data works too. Guess how we get a lot of jobs data... ADP
- sebiandev 2mo agoOh hey! My industry. Guess what? ADP doesnt just yoink your data. ADP conducts voluntary surveys on the scale of hundreds of thousands. Voluntary. ADP also pays for it for the most part. Do you think Linear conducted..voluntary surveys here?
- clintonb 2mo agoThe data is aggregated, and you cannot possibly identify a single user from what's been published. I see no issue here.
- whateveracct 2mo agolol isn't this what google said and yet..the NSA cometh
- giraffe_lady 2mo agoI think their point is that there's business value in the usage data and linear is using that value in a way that benefits them but not the customers they got it from. It reminds me of matt levine's reframing of insider trading where it's not about fairness it's about theft. You're supposed to get secret insights and use them to get an edge. What you can't do is get an edge for yourself with secret insights that your employer got. So it roughly comes down to "that data is valuable and rightfully belongs to the originating company." Which then makes this a contract diligence type situation.
- qqt 2mo ago[flagged]
- jocelyner 2mo ago[dead]
- alexwrich 2mo ago[flagged]
- coinyong 2mo ago[dead]
- jmtulloss 2mo agoThis 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.
- akashy123 2mo ago[dead]
- hobofan 2mo agoI 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.
- cadamsdotcom 2mo agoNice 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.
- AlexKalWork 2mo agoThis 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.
- bitwize 2mo ago> delve Butt-head: Huh huh huh. Hey Beavis, he said "delve". Huh huh huh. Beavis: Yeah, I bet he'll use an em-dash next. Heh heh heh.
- cmiles8 2mo agoUsage 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.
- danggggg 2mo ago[dead]
- drodgers 2mo agoSkill issue. If you make an effort to use the new tools effectively, the gains are wild. Don't fall into a grumpy luddite trap, the train is leaving the station and you'll struggle to catch up if you don't learn and grow. Not using AI for software development in 2027 will be like only knowing how to program via punchcard in 2010. It's new. It's different. It's hard. It's your job. Learn how to use it effectively, embrace the new abundance mindset.
- inigyou 2mo agoThis but the train is competence (the opposite of AI) and it has already left.
- danggggg 2mo ago[dead]
- drodgers 2mo ago> spent millions on AI contracts The key question is how much value has been delivered on the other end for a given cost. Well used, tokens would be cheap at 10x the price right now.
- stlahxm 2mo ago[dead]
- Mugshelf 2mo agoFounders topping the usage chart says more about org pressure than product value. Adoption driven from the top looks different from adoption driven by results.
- Marvin_RunAI 2mo ago[flagged]