I think that over time people will start looking at AI-assisted coding the same way we now look at loosely typed code, or at (heavy) frameworks: it saves time in the short term, but may cause significant problems down the line. Whether or not this tradeoff makes sense in a specific situation is a matter of debate, and there's usually no obviously right or wrong answer.
I can see where productivity could be higher if all I did was type in programs to some spec, or bootstrapping new apps all day - but that's like not the reality of "programming", at least for me past 25 years. Sorting through what to even make and interpreting "requirements" is what takes the most time
You're only as fast as your biggest bottleneck. Adding AI to an existing organization is just going to show you where your bottlenecks are, it's not going to magically make them go away. For most companies, the speed of writing code probably wasn't the bottleneck in the first place.
Blunt opinion: Most devs are not that good and really only execute what they are told to do.
The threat of AI for devs, and the way to drastically improve productivity is there: keep the better devs who can think systemically, who can design solutions, who can solve issues themselves and give them all the AI help available, cut the rest.
I think some AI companies are just now starting to feel the pressure to profit.
Soon, I predict we will see a pretty significant jump in price that will make a 10% productivity gain seem tiny compared to the associated bills.
For now, these companies are trying to reach critical mass so their users are so dependant on their tech that they have to keep paying at least in the short term.
Unsurprising for multiple reasons. Most organizations have other bottlenecks and limiting factors than “how fast can you develop”.
Regardless, if you’re a dev who is now 2x as productive in terms of work completed per day, and quality remains stable, why should this translate to 2x the output? Most people are paid by the hour and not for outcomes.
And yes, I am suggesting that if you complete in 4 hours that which took you 8 hours in 2019, that you should consider calling it a day.
Apparently "AI is speeding up the onboarding process", they say. But isn't that because the onboarding process is about learning, and by having an AI regurgitate the answers you can complete the process without learning anything, which might speed it up but completely defeats the purpose?
This is self-reported productivity, in that devs are saying AI saves them about 4 hours per week. But let’s not forget the METR study that found a 20% increase in self-reported productivity but a 19% decrease in actual measured productivity.
(It used a clever and rigorous technique for measuring productivity differences, BTW, for anyone as skeptical of productivity measures as I am.)
But let’s not forget the METR study that
found a 20% increase in self-reported productivity
but a 19% decrease in actual measured productivity.
Counting "time per PR" is as useless as counting lines of code.
An industry I think we spend ~10% of our time writing code and ~90% of our time maintaining it and building upon it.
The real metric is not "how long did that PR take" but "how much additional work will this PR create or save in the long run." -- ie did this create tech debt? Or did it actually save us a bunch of effort in the long run?
My experience with ChatGPT these last few years is that if used "conscientiously" it allows me to ship much higher quality code because it has been very good at finding edge cases and suggesting optimizations. I am quite certain that when viewed over the long haul it has been at least a 2X productivity gain, possibly even much more, because all those edge cases and perf issues it solved for me in the initial PR represent many hours of work that will never have to be performed in the future.
It is of course possible to use AI coding assistants in other ways, producing AI slop that passes tests but is poorly structured and understood.
I read this article as the CTO being the bottleneck if he's only seeing 10% productivity boost at his organization.
I dont think this is a purely AI problem more with the legacy costs of maintaining many minds that can't be solved by just giving people AI tools until the AI comes for the CTO role (but not CEO or revenue generating roles) too and whichever manager is bottlenecking.
I imagine a future where we have Nasdaq listed companies run by just a dozen people with AI agents running and talking to each other so fast that text becomes a bottleneck and they need another medium that can only be understood by an AI that will hold humans hand
This shift would also be reflected by new hardware shifts...perhaps photonic chips or anything that lets AI scale up crazy without the energy cost....
Exciting times are ahead AI but it's also accelerating digital UBI....could be good and bad.
My biggest road blocks as an engineer has almost never been the authorship of code but everything else around it.
* Getting code reviewed
* Making sure its actually solving the problem
* Communicating to the rest of the team whats happening
* Getting tests to pass
* Getting it deployed
* Verifying that the fix is implemented in production
* Starting it all over when there is a misunderstanding
Slinging more code faster is great and getting unit testing more-or-less for free is awesome but the separation between a good and great engineer is one of communication and management.
AI is causing us to regress to thinking that code velocity is a good metric to use when comparing engineers.
The real takeaway here -- also corroborated by the DORA 2025 report https://dora.dev/research/2025/ -- is that more than anything, AI amplifies your current development culture. Organizations with strong quality control discipline enjoy more velocity, those with weak practices suffer more outages.
Expecting AI to magically overcome your development culture is like expecting consultants to magically fix your business culture.
Furthermore, by various estimates, engineers only spend 10 - 60% of their time on actual code. So, given that currently AI is largely used only for coding activities, 10% is actually considerable savings.
Also this is the result of retro-fitting AI into existing workflows; actual "AI-native" workflows would probably look very different, likely having refactored in other parts of software engineering. Spotify's "Honk" workflow is probably just a starting point.
10% feels right for the median case but wildly wrong for specific workflows.
Where I see massive gains (50%+ time savings): boilerplate generation, test writing, regex/config syntax I'd otherwise have to look up, and exploring unfamiliar codebases. These are high-frequency, low-creativity tasks where AI genuinely excels.
Where I see near-zero gains: debugging complex distributed systems, architectural decisions, understanding why code exists the way it does (not what it does), and navigating ambiguous requirements. These require context that doesn't fit in a prompt.
The 10% average likely masks a bimodal distribution. Developers who restructured their workflow around AI assistance probably see 30-40%. Developers who use it as a fancy autocomplete see 5%. Measuring "productivity" as a single number hides all the interesting variation.
Also worth noting: the biggest unlock isn't code generation — it's using AI to quickly evaluate multiple approaches before committing to one. That's hard to measure but extremely valuable.
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[ 25.0 ms ] story [ 942 ms ] threadThe threat of AI for devs, and the way to drastically improve productivity is there: keep the better devs who can think systemically, who can design solutions, who can solve issues themselves and give them all the AI help available, cut the rest.
Soon, I predict we will see a pretty significant jump in price that will make a 10% productivity gain seem tiny compared to the associated bills.
For now, these companies are trying to reach critical mass so their users are so dependant on their tech that they have to keep paying at least in the short term.
Regardless, if you’re a dev who is now 2x as productive in terms of work completed per day, and quality remains stable, why should this translate to 2x the output? Most people are paid by the hour and not for outcomes.
And yes, I am suggesting that if you complete in 4 hours that which took you 8 hours in 2019, that you should consider calling it a day.
(It used a clever and rigorous technique for measuring productivity differences, BTW, for anyone as skeptical of productivity measures as I am.)
An industry I think we spend ~10% of our time writing code and ~90% of our time maintaining it and building upon it.
The real metric is not "how long did that PR take" but "how much additional work will this PR create or save in the long run." -- ie did this create tech debt? Or did it actually save us a bunch of effort in the long run?
My experience with ChatGPT these last few years is that if used "conscientiously" it allows me to ship much higher quality code because it has been very good at finding edge cases and suggesting optimizations. I am quite certain that when viewed over the long haul it has been at least a 2X productivity gain, possibly even much more, because all those edge cases and perf issues it solved for me in the initial PR represent many hours of work that will never have to be performed in the future.
It is of course possible to use AI coding assistants in other ways, producing AI slop that passes tests but is poorly structured and understood.
I dont think this is a purely AI problem more with the legacy costs of maintaining many minds that can't be solved by just giving people AI tools until the AI comes for the CTO role (but not CEO or revenue generating roles) too and whichever manager is bottlenecking.
I imagine a future where we have Nasdaq listed companies run by just a dozen people with AI agents running and talking to each other so fast that text becomes a bottleneck and they need another medium that can only be understood by an AI that will hold humans hand
This shift would also be reflected by new hardware shifts...perhaps photonic chips or anything that lets AI scale up crazy without the energy cost....
Exciting times are ahead AI but it's also accelerating digital UBI....could be good and bad.
* Getting code reviewed
* Making sure its actually solving the problem
* Communicating to the rest of the team whats happening
* Getting tests to pass
* Getting it deployed
* Verifying that the fix is implemented in production
* Starting it all over when there is a misunderstanding
Slinging more code faster is great and getting unit testing more-or-less for free is awesome but the separation between a good and great engineer is one of communication and management.
AI is causing us to regress to thinking that code velocity is a good metric to use when comparing engineers.
Expecting AI to magically overcome your development culture is like expecting consultants to magically fix your business culture.
Furthermore, by various estimates, engineers only spend 10 - 60% of their time on actual code. So, given that currently AI is largely used only for coding activities, 10% is actually considerable savings.
Also this is the result of retro-fitting AI into existing workflows; actual "AI-native" workflows would probably look very different, likely having refactored in other parts of software engineering. Spotify's "Honk" workflow is probably just a starting point.
In other words: notionally, if not literally, by the time trailing numbers are collected they are out of date.
This is of course axiomatic, but, that staleness is a serious matter in this particular moment.
It's a cliché that six months can be a lifetime on the bleeding edge of tech.
This is the first time in my career that is more or less literally true.
Humans reason poorly with non-linear change.
This entire article is a demonstration of that.
Where I see massive gains (50%+ time savings): boilerplate generation, test writing, regex/config syntax I'd otherwise have to look up, and exploring unfamiliar codebases. These are high-frequency, low-creativity tasks where AI genuinely excels.
Where I see near-zero gains: debugging complex distributed systems, architectural decisions, understanding why code exists the way it does (not what it does), and navigating ambiguous requirements. These require context that doesn't fit in a prompt.
The 10% average likely masks a bimodal distribution. Developers who restructured their workflow around AI assistance probably see 30-40%. Developers who use it as a fancy autocomplete see 5%. Measuring "productivity" as a single number hides all the interesting variation.
Also worth noting: the biggest unlock isn't code generation — it's using AI to quickly evaluate multiple approaches before committing to one. That's hard to measure but extremely valuable.