I can understand skepticism to a degree, and even fundamentally believing that AI is bad for all sorts of reasons, but I am becoming more and more perplexed at the certainty behind statements like this one. How are you so certain that AI development is this doomed? It just hasn't matched my experience at all, and I wonder what your experience is that has driven you to this level of certainty about the certain doom of AI coding?
Is it just a philosophical belief that AI is morally bad? Or have you actually used AI to build things and feel confident that you have explored the space enough to come to such a strong conclusion?
I have been writing code every day for over 30 years, and have been doing it professionally for over 20. I have seen fads come and go, and I have seen real developments that have changed the way I do what I do numerous times. The more experience and the more projects I create with AI, the more certain I am that this is a lasting and fundamental change to how we produce software, and how we use computers generally. I have seen AI get better, and I have seen myself get more proficient at using it to get real work done, work that has already been tested with real world, production, workloads.
You can hate that it is happening, and hate the way working with AI feels, but that doesn't mean it is not providing real value for people and doing real work.
I dont know any serious engineers thay are doing real work with AI agents. I know some that are building features for web applications and just punching a clock, but I don't think that constitutes real work or provides much value to the world.
I like thinking, solving problems and typing out code myself. Im going to keep putting tons of care into my craft and I promise I'll have more impact than the guy running 3 agents to build the 500th version of some web concept.
Rolex has a much bigger impact on the world than white label mass manufacturers in China.
You really don't know any serious engineers using AI for real projects? Donald Knuth himself has written about his success using AI to solve long standing problems he has been interested in. I dont know if there is anyone more deserving of the "serious engineer" label than him.
I personally know of very serious AI projects running at some of the biggest, most successful tech companies in the world. These aren't "another version of a web concept", they are serious projects solving serious problems.
There are all sorts of valid criticisms about AI, but you aren't making them.
It is real work, just 90% of it is either net negative for society or provides nuetral value. Most web applications that are piling on features now because they have agents, are piling on features that we never needed in the first place, hence why they weren't prioritized previously. Junk junk junk.
> I like thinking, solving problems and typing out code myself.
I get this, I totally do, and I kind of hate relegating myself to doing "project manager" work instead of "software engineer" work, but the productivity gains make it no contest on whether to use AI here. Once I comprehensively validate the spec for a new feature, Codex just one-shots it basically every time. I'm talking thousands of lines of code in a single 3-hour session, with much of my time being spent browsing the internet while I wait for Codex to run in 15-20 minute sessions.
I'd estimate at least a 20x speedup in my ability to ship.
(and before you say it, yes, I review every single line of code before merging anything, so no - it's not AI slop)
I’m a bit curious with these takes. Arguing in good faith - is the general assumption that people who use AI/agents/harnesses don’t ship features? We’ve been all in Claude Code since ~Septemberish, and have been able to successfully track the boost. Like the features that we ship that get used in production. Both from infrastructure side, and business logic implementations. Frontend and backend.
I don’t think people are wasting too much time. Although, I do agree most of these posts are just bs, including this one. But AI-development has been a thing across a lot of companies in the world.
Ignore the people who haven't found out how to use ai yet or don't want to.
AI is a powerful tool. Depending on what I need I use chatgpt, in-ide agents, or a platform like Devin.ai.
I use it when it helps me advance my goals. I don't when it doesn't. Sometimes it misses the mark and I scale back and have it do a specific piece and I'll do the rest.
Sometimes I use it to analyze the code base in seconds vs minutes. Sometimes I use it to pinpoint a bug fast.
Ive solved customer issues in seconds and minutes with it vs hours.
I worked on a banking app with deeply domain specific data issues. AI was not very helpful on that team. My current work on consumer web apps mean my problems are more mundane and AI is a big accelerant.
Being and engineer means solving the problems with the right tools with the right tradeoffs as well. It's why I use an idea vs notepad, I use chatgpt for one-off scripts and "chat", and i use agentic workflows for big, repetitive, or "boring" low-stakes tasks.
I can take on a slightly weaker form in good faith: professionally it’s a non-starter until private, open source inference can be self-hosted and the ROI is clear enough to invest in that.
And on the ROI side, trying things out regularly, I haven’t seen the positive ROI in the limited time I’ve dedicated to exploring the tools. I’ve restricted experimenting to 4 hours per month, because spending more than 2.5% of the month chasing productivity improvements that realistically seem to be 10-20%, will quickly eat into those gains. After accounting for token costs, it ends up being a wash.
I think I should also clarify, I work in the training of encoder-decoder transformer models. Before the ChatGPT era I worked on on encoder-only transformer models. I'm not unfamiliar with the literature and general discourse. I just do not use LLMs for programming.
The poster provided numbers and thresholds they used to evaluate the utility of a business product.
With infinite time anything is possible, but since we live within constraints, discussing practical, real world thresholds or evaluation methods is a worthwhile use of our time.
I suspect some devs don't want AI to succeed - and it's understandable, as it will fundamentally change the way they work, and possibly put them out of a job as we need less developers.
So they convince themselves AI can't work because they don't want it to.
For my team, it has been easy. We deal with infrastructure for the entire org, so have tickets created for every request. We also gave our own backlog for internal project, so can see burn rate, and etc. Team hasn’t changed, a lot of similar/same tasks that have taken half a day has been completely automated to a point where we just do PR review after an initial ticket is created by other teams.
There are a lot of little things we’ve tracked, and it’s just faster to implement things now. To be fair, everyone on my team has decade+ professional experience (many more non-prodessional), and we understand limitations of AI fairly well.
> to be fair, everyone on my team has decade+ professional experience (many more non-prodessional), and we understand limitations of AI fairly well.
I see this appear quite often in discussions on productivity, to the point that a conclusion may be made regarding its centrality for productivity gains.
What is your definition of faster to implement? Is it producing a plausible implementation, or is it faster at producing a correct and high quality implementation? Are you including time spent refactoring and fixing bugs in your metrics? If not, I think you are tracking a gut feeling rather than cold hard facts. I’m not saying this is easy to track, just saying that it’s hard to know for sure that you are really more productive with AI.
Not the same person, but it really depends on projects. E.g. I have some projects that involve working to large specification sets where we can measure rate of delivery against the spec. If your spec is fuzzy and incomplete, then it gets hard, but then you have little insight into human productivity for those projects either.
Initial drop, as people learn to use the tools, and while they keep babysitting their harnesses. Then significant boost once people start getting used to running the agents in the background, especially once they start running multiple sessions in parallel. I'd say you need a ~6 month push of getting people trained if they are not used to this way of working, and to customised setups etc. for your organisation, and then you start seeing significant payoff.
i treat it like Minecraft automation - it's just for funsies and to pass the time haha
I don't think agentic workflows are there yet, but implementing skills to manually call and use while working side by side with an AI is definitely nice - our company is focused a lot on sandboxing right now and having safe skills
I don't think we've gotten feature development well yet, but the review skills + grafana skills they wrote have been pretty solid
Trick is to not burn too much time worrying about the perfect skills and this and that. See a lot of people filling skills with LLM junk, or overdoing rules that start confusing the LLM. Just try Vanilla, see something you don't like? Then you make a skill and funnel the LLM to use it for the style of task it's working on. E.g. database work is a mixed bag with LLMs, they tend to do work in totally different styles if you leave them unconstrained.
Agents are unbelievably useful at helping takeover and refactor messy codebases though. I just started taking over this monstrous nightmare of a codebase, truly ancient code the bulk of it written over 10+ years ago in PHP. With the use of Claude / Codex I was able to port over the vast majority of the existing legacy storefront and laid the groundwork for centralizing the 10-20k LOC mega-controller logic over to reusable repo/service patterns.
Just shit that would've taking years previously, is achievable in under a month.
Everything needs an element of human touch, I would somehow only run vanilla things. But if, let’s say, I’m creating backup scripts, I meticulously outline the plan.
Is it just a philosophical belief that AI is morally bad? Or have you actually used AI to build things and feel confident that you have explored the space enough to come to such a strong conclusion?
I have been writing code every day for over 30 years, and have been doing it professionally for over 20. I have seen fads come and go, and I have seen real developments that have changed the way I do what I do numerous times. The more experience and the more projects I create with AI, the more certain I am that this is a lasting and fundamental change to how we produce software, and how we use computers generally. I have seen AI get better, and I have seen myself get more proficient at using it to get real work done, work that has already been tested with real world, production, workloads.
You can hate that it is happening, and hate the way working with AI feels, but that doesn't mean it is not providing real value for people and doing real work.