Another commenter had a great note, that you should take this opportunity to advance your career, given you are in an excellent position to do so. It made me giggle, but then think that you really need to make sure your PoC was executed thoughtfully and seriously, that you understand what the SoTA is wrt using assistants and their various modalities (chat, agent, copilot etc) and despite your expertise it was a no go. Because if you don’t, somebody else who is taking it dead seriously is going to take that commenters advice and demonstrate the value that you didn’t, and this may reflect poorly on you. Execs are getting hype from their back channels or vendors like I have never seen in my career, and you are going to go against that. ($bigco perspective there)
Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
21–28 of 28 posts
Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#22Then I come to check out Ask HN and see this as top post.
Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#23Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#24I would look at why you're outsourcing this thinking to HN instead of putting together data and an understanding of your own environment. Or just ask the LLM's to write it for you.
I tried Copilot and found the answer it gave was unsatisfying, the best para I can lift is "While gen AI can assist with generating code snippets, documentation, and automating certain tasks, it may not be as effective in solving complex, domain-specific problems that require deep understanding and critical thinking." all accurate but not really compelling to somebody who is under the spell of magic beans. This famou…
Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#25Most AI coding tools can generate a Todo app from a small prompt. This is because that problem is well understood.
When you try to use AI coding tools on your own projects you need to start writing a prompt that teaches the AI about your current architecture and decisions.
So the initial prompt is large.
Often the task needs knowledge of other files in your project. You can add them by hand or some AI tools will search the code base.
The prompt is now huge.
When you run that prompt you may or may not get what you expected.
So now the issue is how much time do you spend getting the prompt correct vs just writing the code yourself.
This area is brand new and there are very few resources on how to use AI coding tools effectively.
I have yet to see one demonstration of effective AI coding tool use on a project of reasonable complexity.
Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#26Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#27Biggest cause is ineffective reasoning in a large context in my experience (more than 350 lines). Im my experience, ChatGPT breaks down when it needs to consider more than 350 lines and its performance is sloppy before that. To get solid performance out of it, I essentially need to specify the important areas and changes as well as the desired approach. That being said, I’ve found it has cut my development time for m…
Re: Ask HN: How to explain to execs why gen AI hasn't 10x'd feature dev
#28You still need to do code reviews, especially on the AI. It's quite bug free, more than human code, but you still need to explain the product. v0 doesn't quite replace a good product designer.
It's not yet at the level it can do architectural work and it doesn't understand the scope or goal of products. It doesn't understand roadmaps. It can't plan the code around where it needs to be in 1 year. You need a proper architect to do this better.
The average code it's trained on is from 2019 or so. New models has people write new data for it, but most of this data is not production data. So you're likely using an old design too and it tends to recommend these until encouraged otherwise.
Also if you're not using the newer tools like Cursor, Aider, Windsurf, a lot of the contribution of AI is better test coverage. The value that comes with the "agent" tools is they will write and edit code across multiple files, and they save you the trouble of explaining context when you can just share the source code.