What is the point on Musk you are making? The monetary success does not neccesserily correlate to the common good they have created. In case of Musk there is a lot of governement subsidies, lots of market manipulation and false claim s. So not all activieties that bring profit to the richest are good to the rest. And stats on inequality just highlights that trend
I think its somewhat comparable to cutting grass in the cities.
It was manual labour first. Then there were teactors. Now robots join in - does that mean that personel cutting grass is obsolete? No , you need all of them. That means that city becomes nicer.
With software and AI I somehow feel the same will happen. How many features have you skipped just because it would help some niche set of users and PM or Management would not approve the spending. It is low priority. Or bugs that were annoying but financially not bringing much value.
I hope switching some work to AI , some companies will capture opportunity to make software better while others will make the same software cheaper
Isnt' that what old-school software did for many years? It used to take jobs, just not from developers. If you implement software that takes accounting from 10 people to 2, 8 just got fired. If you have Support solution helping one support rep answer 100 requests instead of 20, you just optimised support force by the rate of 1 to 5.
I'm in the boat of SaaS myself, but feel a bit dishonesty from Senior devs complaining about technology stealing jobs. When it was them doing the stealing, it was fine. Now that the tables have turned, it's not technology is bad
Jevons' paradox still exists. Making X cheaper (usually by needing fewer people to do one unit of X) can and often does lead to more people being needed for X.
My feeling was the same reading. If you give a task to a junior and he wipes out a database in production - it is not a fault of a Junior, it is your own fault that he was able to do it.
Isnt that point kind of the counterpoint to the AI-first narrative.
With standard, human driven operations its true about opportunity costs. What we are told is that AI will replace human, essentially saying that opportunity cost becomes cash only. Then the question of why doesnt AI lab start SaaS fully managed by AI becomes ever more interesting. Maybe because it's not that simple. Hence, it's not that easy in other companies as well to just replace devs, engineers and so on with AI
Can I ask you if you consider that AI changes anything about that? Since I'm embarking on the same boat, my dream is a team of AI which supports and ensures business continuity while Im on vacation or "OOO" otherwise.
If we get to the point that AI can run a complete business unencumbered then the world looks very different. At this time, I have very little confidence that AI today can operate my business untouched while OOO. Nor would I want it to. I enjoy my work and I don't trust AI to run amuck with the valuable asset that is my business.
But it certainly has helped me gain velocity working alone. My business is very hands off after ten years of automating most things and cleaning up the hot paths. Things break, but usually due to external factors that have nothing to do with me. A few support emails and hands-off monitoring is hardly a deal-breaker for me on vacation. I'm not entirely sure why zero-effort is a goal, when you can genuinely attain a 5-hour work week today.
AI is really just a tool and there's a lot of incremental room between "helpful" and "totally autonomous". This calculus could all change one day, but it's not a personal desire of mine.
Even Anthropic consistently says their own AI can't help with meaningful work in their own corporation. Any person that tells you it can is overhyping it. Probably to sell you something.
I am honestly curious about your point on productivity boost. Are you saying that you can write tests at the same speed as AI can? Or is it the point that tests written by AI is of much lower quality that is not worth using them?
I am at the role of solo-preneur now and I see a lot of benefit from AI. But then I read posts like yours that experienced devs don't see much value in AI and I start to doubt the things I do. Are they bad quality(possibly) or is it something else going on.
I’m not faster at writing tests than AI but my own code needs fewer tests.
When I’m writing my own code I can verify the logic as I go and coupled with a strong type system and a judicious use of _some_ tests its generally enough for my code to be correct.
By comparison the AI needs more tests to keep it on the right path otherwise the final code is not fit for purpose.
For example in a recent use case I needed to take a json blob containing an array of strings that contained numbers and needed to return an array of Decimals sorted in ascending order.
This seemed a perfect use case - a short well defined task with clear success criteria so I spent a bunch of time writing the requirements and building out a test suite and then let the AI do its thing.
The AI produced ok code, but it was sorted everything lexicographically before converting to a Decimal rather converting to Decimals first and sorting numerically so 1000 was less than 900.
So I point it out and the AI says good point, you’re absolutely correct and we add a test for this and it goes again and gets the right result but that’s not a mistake I would have made or needed a test for (though you could argue it’s a good test to have).
You could also argue that I should have specified the problem more clearly, but then we come back to the point that if I’m writing every specific detail in English first, it’s faster for me just to write it in code in the first place.
> Are you saying that you can write tests at the same speed as AI can?
I feel this is a gross mischaracterization of any user flow involving using LLMs to generate code.
The hard part of generating code with LLMs is not how fast the code is generated. The hard part is verifying it actually does what it is expected to do. Unit tests too.
LLMs excel at spewing test cases, but you need to review each and every single test case to verify it does anything meaningful or valid and you need to iterate over tests to provide feedback on whether they are even green or what is the code coverage. That is the part that consumes time.
Claiming that LLMs are faster at generating code than you is like claiming that copy-and-pasting code out of Stack Overflow is faster than you writing it. Perhaps, but how can you tell if the code actually works?
"Write unit tests with full line and branch coverage for this function:
def add_two_numbers(x, y):
return x + y + 1
"
Sometimes the LLM will point out that this function does not, in fact, return the sum of x and y. But more often, it will happily write "assert add_two_numbers(1, 1) == 3", without comment.
The big problem is that LLMs will assume that the code they are writing tests for is correct. This defeats the main purpose of writing tests, which is to find bugs in the code.
Tip: teach it how to write tests properly. I’ll share what has worked pretty well for me.
Run Cursor in “agent” mode, or create a Codex or Claude Code “unit test” skill. I recommend claude code.
Explain to the LLM that after it creates or modifies a test, it must run the test to confirm it passes. If it fails, it’s not allowed to edit the source code, instead it must determine if there is a bug in the test or the source code. If the test is buggy it should try again, if there is a bug in the source code it should pause, propose a fix, and consult with you on next steps.
The key insight here is you need to tell it that it’s not supposed to randomly edit the source code to make the test pass. I also recommend reviewing the unit tests at a high level, to make sure it didn’t hallucinate.
I think it highly depends on your perception on what is your job.
If programmer is only the code-writer - then it is reasonable to agree with the post.
If on the other hand the developer is problem solver - well it just changes what problems you're solving. Somehow I don't see the future where CEO of any respectable company would sit with AI and ask Agent to develop features. You hire people who solves problems for you.
Facing similar issue with monitoring part of executions. What is your solution if I may as - have you taken smth of the shelf and extended to your needs or did you built from the ground up everything?
Honestly we were looking at hatchet / pickaxe - similar vein of a project but more dev focused, but in the end realised our use cases were not all that complex so just built everything in a bespoke manner.
We used n8n for two things mostly - AI agents and process automation.
For AI we just built own MCP servers, and then the agents are quite easy to use as the major frameworks kinda help you with it. N8N’s AI is kinda just a UI layer for langchain - though we just used google’s adk.
For process automation - well there is so much options it’s not even funny.
While it's definitely interesting comparison, I would say the that key thing is that genius is limited in scope. What I mean by that is given any single genius - we might agree that he is one on subject X but not on subject Y.
With AGI it seems that expectation is to cover all the subjects. Which I think is more like god. You either believe it or you don't. Noone have definite proof of its existence or non-existence.
