Eight Myths on Software Engineering and GenAI

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them;

This is such a weird point to make that doesn't become correct just because everyone makes it, all the time. Why clean the ocean if some magic future tech will clean them? Why save the world now if some benevolent AI is 'just around the corner' and will do it for us? And people have been making this point for years now, and it's not like my job got any easier. I just got more AI.

https://www.poetryfoundation.org/poems/51294/waiting-for-the...

And I say that as someone who uses Claude Code in complex environments almost hourly; I, as the human, still have to do the thinking as Claude still 'can't jump' [1] and I have seen no evidence that they (or similar AI, any time soon) will 'jump' like a human brain does.

[1] https://www.tomzahavy.com/files/llms-cant-jump.pdf

> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent.

Anyone else finding they're spending more time writing code (or at least driving agents to write code) now?

14% used to feel about right for me - I'd spend the rest of the time researching approaches and libraries, planning things out in issues, or sometimes just thinking really hard about problems I ran into.

Now... I still do those things, but I'm doing many of them faster - and I'm often doing them while my coding agents are churning away on code.

There's also this weird effect where the harder a problem is the more I can get done in parallel with it, because an agent might need to spend 20 minutes on it without my involvement.

I've found that using LLMs for significant amounts of code generation completely drain the result from any dopamine I would get doing it myself.

Have others noticed this as well? This is going so far as to me losing interest in side projects because I have "lost touch" with the code base.

>>>Recent research shows that developers—especially women and older engineers—face a “competence penalty” when using AI

and why did this study (performed in China I might add) find that women and “mature age” (which is not defined in the study) have less usage of AI tools?

>>>? We suggest a new barrier: using technology to assist task completion signals a lack of competence to perform the task independently.

So basically the study suggests that older engineers and women are reluctant (and especially women) to use AI tooling because they feel they are being judged more on non-technical competencies.

This study may have a strong cultural influence but I would say that one thing they noted I’ve also seen. 41% of engineers in the study had used AI 12 months after the initial rollout. This aligns with my observations. Some people are struggling figuring out how to adopt in their day to day while others are full in.

I feel like all you need to know about how seriously to take this is that they cite that ancient early-2025 METR study, and describe it in the text as "recently one even found..."

> f developers spend only about 15 percent of their time typing in the editor

I think this is missing an important detail. Lots of time was spent on non-coding stuff, because coding used to be more committal and hence expensive. With how quickly one can code up a quick prototype or even production-ready code these days, the code becomes the communication tool as well.

If you can spin up a new software project with little to no effort, the most important part of the job becomes making the right decisions and this is where we will be spending 99.9% of your time.

I don't understand Myth 1 (Developers Spend Most of Their Time Writing Code).

They quote a study in which developers report to spend 11-14% of their day coding. The rest is stuff like solution design and meetings. The insinuation is that AI can at most automate 14% of your day.

The problem with this argument is that once you have code, some (not all) of the precursors to code go away.

Like many others in the comments, I feel there are a lot of assumptions in this piece. Before, coding is only 14% therefore, small slice. I think that's a very superficial assumption. That was because coding was expensive and we needed to be sure we didn't code the wrong thing. If code is as cheap as it is now, we will optimize differently, we will structure around it. Instead of so many meetings we will code 5 different versions of the same thing and choose, etc.

>a “good” workday, engineers spent 18 percent of their time “coding” (not including bug fixing, testing, etc.)

that's such a weird metric, why exclude bug fixing and testing? depending on the phase of the project I might spend 100% of my coding time bug fixing

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Writing Code Isn't the Bottleneck, until writing code is the bottleneck, until it's not again.

I think the paper would have been stronger if it acknowledged how quickly the underlying evidence is becoming outdated. AI-assisted development in 2026 isn't just better models. The way many devs including myself work has changed and matured quite a bit as compared to last year

Some experience from my work:

- In biz development, a dev usually spends 30-40% time on coding, and more time on requirement discussion, integration testing (especially when the tests involves mobilephone or car)

- coding time can be reduced to 30%, which means reduce 20%-30% time of the full pipeline

- meanwhile, every phase and role is using LLM now, for example, product manager can produce longer requirement doc easily (we can use LLM to read it anyway:) Meeting sometimes is more than before, because more document output leads to more reading and discussion.

- I hope to find new ways to express biz requirements, in a more efficient and automatic manner.

- Shorten the requirement-dev-test-deploy loop is very important. OUTPUT is not OUTCOME. It is equal when we can see the final result, instead of intermediate metric.

- Agentic infra is extremely useful, or every one will find a way to access the database, report and ops system, in some weird fragile method.

> Myth 2: Writing Code Is the Bottleneck

Writing code is indeed the bottleneck for same resource constrained companies.

Rapid code development creates more opportunities for trial and error, providing companies with more information for decision making, that previously might have been addressed by meetings.

Of course, this might bring other problems, but it might not right to generally speaking that writing code is not a bottleneck.

I hope that some of the executives out there will read the list. I know they won't spend the time to read the full blog, but at least the headers should be enough

It seems that this could have been expanded or contracted to any Fibonacci number of myths.

This is actually true at my company. They expect employees to be 10× more productive now that we have AI.

11-18% of time spent in coding is still very high number I think. For a large org with lots of process and risk aversion, this number could be as low as 5%. Even for 14%, the 10x improvement could mean 86+(14/10) => 87.4/100 => 12.6% overall time saved.

With all the myths and hyperbolae circulating regarding AI, I'd love to know what it's like at large software companies adjusting to this brave new world.

It's easy for a small team to adjust workflows and roles, but I just imagine the office politics must be a waking nightmare in big organisations right now.

A lot of this rings true, but I think it's still too narrow. Sure, coding does not equal productivity, that is well debunked already. But I would argue that productivity is a product of engineering delivery + product decision making. Now where is the line between product and engineering? It varies by company, team and individual, but I don't think productivity can be measured for those functions independently, and in fact I see gains from AI on both the coding AND the product management side.

Basically as a senior tech lead in a large company engineering org, I don't have the bandwidth to individually validate every assertion from engineers on other teams OR from every product manager that comes with a half-baked ask. In the past I would be limited by the influence I could get through human relationships to strong SMEs with good judgment, and those folks always thin out as a company grows and calcifies. The number of creative and innovative thinkers dwindles, and the number of people protecting their turf and doing the minimum not to get fired increases. As a result many good ideas can get blocked by random gatekeeprs with poor imagination, poor expertise or both. However with AI I can follow up on gut instincts and fact check a lot more things, and ask incisive questions that can cut through a lot of organizational bullshit.

That's where I think most of the AI gains are today. Of course once AI plateaus and normalizes I think it will be baked into the org structures of tomorrow. But for now it offers real competitive advantage to those with the expertise to ask the right questions.

> A June 2025 study of Microsoft developers

A year ago feels like forever

> studies at Microsoft and elsewhere showing it’s closer to 14 percent

This is a depressing stat. The real productivity gains come from leaving soul sucking big tech companies where nothing gets done with any sort of urgency.

I mostly agree with the part where it is stated that AI is a tool which received massive investments without knowing how to maximize its utility. I think that we will pay for it in the near future.

This reads like a critique of 2023 tooling published in 2026. Their Amdahl-style arithmetic (speed up a 14% slice, cap your gains at 14%) holds only if "AI" means autocomplete. Current frontier models do far more than that: research, code comprehension, review, test authoring, debugging, exploratory prototyping, ideation. That's most of the rest of the working day or "86%".

The only point that still holds is that organizational policies and procedures that automate AI use and lower the barrier to entry are more efficient than leaving it up to each individual. Every other point they make is either stale or was never true to begin with.

one still has to think. Also about this ai automating stuff and humans playing around. I believe there is a time for this and time for that

>a “good” workday, engineers spent 18 percent of their time “coding” (not including bug fixing, testing, etc.)

I must be a crap developer, because I probably spend twice as much time bugfixing and testing than "coding". (Both of which actually involve coding stuff, so I really don't like that distinction they make)

This is stuff AI can be really good at, so brushing that part under the table distorts the picture.

Having said that, I do agree with most of the myths they present.

It's kind of weird how we blame agents for hallucinations as if humans don't fall for that as well, while agents can run the build-fail-fix-repeat in much faster cycles than coders.

even an AI assist that makes coding twice as fast would, in theory, improve developers’ overall productivity by less than 15 percent. The other 85 percent of their time remains untouched

I stopped reading after this. AI has massively impacted most aspects of my non-coding work including the mentioned planning, understanding legacy code bases, setting up environments, etc etc.

Either this article is written by people with skill issues or - given the platform - its a biased and protectionist take that will fall quickly under the march of reality.

All very sensible points which I think all senior programmers who have used AI would largely to agree with.

For those more junior - keep in mind that a lot of the maximalist rhetoric are from people either selling models, or the cottage industry of people selling you courses or tools to help you use the models. Try and keep in mind software is not a mature industry, it's an immature one, and it's prone to hype and fads.

> Despite this decade-old research, many organizations still rely on lines of code as a measure of developer productivity.

No, they don't! It's easy to dispel myths when the myths are built on straw men. Dumb article.

The 14% coding time figure is one of those stats that sounds surprising until you actually track your own time. When I started building a coding agent with persistent state, I realized how some days are spent with minimal actual typing, most of it is design, reading code, debugging, problem solving, and context-switching.

But I'd push back on one thing the article implies that AI is automatically a productivity win. It's not. Some days I've shipped two months of work in a few days with AI. Other days, like today, I've burned a whole day and gotten almost nothing done because the proper research was not done by me or multiple agents.

The bottleneck for AI can be the human understanding of how to optimally use the tool. While the bottleneck for the human can be not maximizing multiple agents, or the input the user enters, then the retention of the output. If the user's input is lost, the output falters. If the user doesn't understand what the AI output is, there is going to be a problem eventually.

The article touches on adoption barriers (Myth 7), but it doesn't really get into the ego piece. There's still a wave of experienced devs who either refuse to adopt AI, or use it quietly and don't share what they're doing. That slows the whole team's learning curve. At this point, I think it's pretty much understood that you should be using AI as a dev — not to replace your skills, but to accelerate them. That means still learning new languages, still writing code, still troubleshooting. The tools change, but the craft doesn't.

I think the article is right that the real leverage is organizational, not individual. The teams that succeed with AI aren't the ones giving everyone a license — they're the ones rethinking how they review, test, and maintain code.

What I'm still uncertain about is how to measure whether AI is actually making systems better, not just faster. Lines of code is clearly a bad metric, but I haven't seen a good alternative yet. What metrics are people actually using that feel meaningful?

I'm getting tired of these articles telling me what AI will or won't do to my career when every day I see something different first hand. I'm about to stop arguing with people. If you think it's all BS then fine. Good luck.

I don’t trust point estimates like 14%. It’s like calculating an average salary and saying it’s $120k. Completely meaningless. What does the actual distribution look like that this was pulled from? No standard deviation. Is it even symmetric? What’s the 10th and 90th percentiles? Just giving a statistic on its own tells me nothing.

I feel like all you need to know about how seriously to take this is that they cite that ancient early-2025 METR study, and describe it in the text as "recently one even found..."

It seems that this could have been expanded or contracted to any Fibonacci number of myths.

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them;

https://www.poetryfoundation.org/poems/51294/waiting-for-the...

[1] https://www.tomzahavy.com/files/llms-cant-jump.pdf

> I, as the human, still have to do the thinking as Claude still 'can't jump'

I still have to do quite a bit of thinking but the amount of of thinking I do per task is trending down. I agree LLMs are not good at abduction but very few humans are either and very few jobs/tasks require it. I can't talk for researchers jobs though. But perhaps fewer researchers would be desired by these labs (not none).

although, if i'm out of tokens and have to wait a full day, i won't bother doing some things manually because the day i'll spend doing something won't take more than 1 hour the next day when tokens are available again.

Same reason some think preserving the environment is pointless because the believers will ascend to heaven, either way. It’s a religion. It’s dogmatic nihilism.

I think you meant to post this in response to https://news.ycombinator.com/item?id=49174900?

Also, if you believe your well-paying job is eventually going to be automated you would be prudent to bank the money while you prepare for the future.

> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent.

Anyone else finding they're spending more time writing code (or at least driving agents to write code) now?

Now... I still do those things, but I'm doing many of them faster - and I'm often doing them while my coding agents are churning away on code.

There's also this weird effect where the harder a problem is the more I can get done in parallel with it, because an agent might need to spend 20 minutes on it without my involvement.

I used it to write SQL and make dashboards. Back in the day, I would spend a lot of time doing that, then I changed roles. I dipped my toe in it recently and used AI exclusively. I would send a prompt, see the output, decide if that is what I wanted or not. I kept my brain in "what-if mode" and I let the LLM handle the technical specs.

Yeah. I spend most of my day driving agents to write code, verifying the results, orchestrating work streams, and so on. The rest of the time, a Fable agent is organizing work in Linear/Jira and making sure coworkers are getting their stuff done in a way that won’t conflict.

I don't understand Myth 1 (Developers Spend Most of Their Time Writing Code).

The problem with this argument is that once you have code, some (not all) of the precursors to code go away.

Yeah, this seriously drives me nuts.

That meeting that you spent an hour in to understand the requirements? You don't need that meeting if you're not writing the code. That sync up with the QA engineer you did to hand it off to them? Don't need that meeting if you're not writing the code. That half hour you spent installing vim extensions? Don't need 'em if you don't open vim anymore.

There are engineers whose jobs go well beyond coding, of course. Staff engineers and principal engineers have had their jobs radically change because of AI, but not because it's writing all their code.

But there are also a lot of engineers -- your standard mid-level engineer, or even senior engineers at a lot of orgs with title inflation -- whose job is almost entirely about delivering code, and who spend all day either writing code or engaging in scaffolding around code-writing activities. Let's not pretend that automating away that code writing is a 15% boost.

Okay.

Show me the evidence that AI has an impact on productivity when doing design work. Or reducing meeting load.

My own experience is that AI doesn't tighten the design cycle, and in fact might extend it by encouraging gold plating.

If Claude told you to work on a task that you don't want to work on, or make a design choice that you think is wrong, would you do it? If not, then it can't really replace things like design or meetings. (Note that this is subtly but importantly different than the "vibecoding" model, where you just don't bother to supervise Claude's decisions.)

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Writing Code Isn't the Bottleneck, until writing code is the bottleneck, until it's not again.

Getting a usable PRD is often the bottleneck.

You forgot to add "coordination" to that pipeline. That is easily far and away the biggest source of delays.

That includes talking to vendors, meetings with every layer of stakeholder when just one person digs their heels, etc.

That is truly the final frontier for "AI", and one that it will likely never cross. That would be when even the execs and upper management feel threatened by "AI". But, since they also delegate so much, you often see someone at the bottom of the totem pole in those meetings. This is why nobody is getting replaced by "AI". We really need to move this discussion away from the scifi stupidity already. There is no singularity or godlike AGI about to take over the world.

I hate to use awful terms like "synergy" and "teamwork", but they do have a lot more substance and truth to them than any perceived threat from "AI".