I'm on the same page. Do people not analyze the problems themselves? Are they just copy/pasting their entire ticket description into Claude Code and having it iterate until they land on something that works?
I don't get it.
I don't get it.
That is exactly what they are doing, yes
Also one engineer is treating the code as assembly. I've asked some pointed questions about code in his PR and the response was "yeah, I don't know that's what the agent did".
Edit:
To everyone freaking out about the second guy. Yeah, I think being unable to answer questions about the code you're PRing is ill advised. But requirement gathering, codebase untangling, and acceptance testing are all nontrivial tasks that surround code gen. I'm a bit surprised that having random change sets slurped up into someone else's rubber stamped PR isnt the thing that people are put off by.
I just can't make the joke work. There really are people that think they can get paid to press the agent's on button. How long before their checks stop clearing and it "just works itself out naturally"?
This is honestly the mindset of the people on here who proudly proclaim that they haven't written a line of code in six months and are excited about what programming is "evolving" into. Naturally, _their_ AI skills aren't something that an "idea guy" can use to build a product without looping in a developer, so _his_ job is safe and will never go away -- "I understand system design, an LLM will never be able to do that!" Sure thing buddy.
These comments are hilarious.
The high level language code is a prompt for the compiler. Consider that there is parsable C code whose behavior is not even defined. There are still bugs in compilers today, where the code produced is not what you intended. And further, modern compilers do lots of work to optimize performance. You usually don’t even look at the resulting code, you just gratefully accept the rewrite for the extra oomph.
The only difference is that this is happening to us.
On typesetters and investment: the WYSIWYG word processor is on almost every home and office desk in the world.
But it's like a kid running a lemonade stand. Total DIY weekend project quality stuff that they are demanding go live. Hardcoded credentials, no concept of dev/qa/prod environments, no logging, no tests, no source control.
I'm not really sure teaching basic SWE practices / SDLC / system design to people whose day job is like.. accounting makes sense compared to just accelerating developer productivity.
Bringing code does not help, but a validated user story with flow diagrams, a UI suggestion, and a valid ticket could. That’s the bridge to gap.
Were I that CTO I’d explain that code carries liability, SWEs can end up in jail for malfeasance, fines, penalties, and lawsuits are what awaits us for eff-ups. “Coders” get fired if their code doesn’t work. Same speech to the devs, do exactly as much unsolicited Accounting as you wanna get fired for. Talk fences, good neighbours.
Non-technical people are not writing tickets, they are just slinging slop.
Another anecdote of things I've seen - a non technical person setting up some web scraping monstrosity with 200k lines of code. They beat their chest about how they didn't need the IT org. 1 month goes by and of course it breaks as soon as anything on the website changes and now they have a gun to ITs head to "fix it" and take it over.
This outcome for a DIY brittle web scraper is obvious to anyone that's ever written code, but shocking to someone who thinks LLMs are magic.
I can do so much more with my spare time now. I throw agents at problems and get way more done.
$1k in tokens every day is easy to hit.
It’s not like AI is the first time this happened. CI/CD and extensive preflight and integration and canary testing is also a way of saving engineer time and improving throughput at the cost of latency and compute resources. This is just moving up the semantic stack.
Obviously as engineers we say “awesome more features and products!” but management says “awesome fewer engineers!” either way pasting the ticket in and letting a machine do the work for a fraction of the cost was the right choice. There’s no John Henry award.
If it were producing equivalent outcomes, sure. So far I haven't personally seeing strong evidence for that. LLMs do write code pretty competently at this point, but actually solving the correct problem, and without introducing unintended consequences, is a different matter entirely
If you're not doing the design of the solutions for problems as an engineer or at least making the decisions and owning the maintenance of that architecture/design, what even is your job at that point?
So are many corporations but that doesn't stop them from being economically successful.
Unfortunately the people who offload the work of understanding and interacting with tickets just end up offloading the consequences to everyone else who has to do extra work to make sure their LLM understands the task, review the work to make sure they built the right thing, and on and on.
The same thing happens when people start sending AI bots to attend meetings: The person freed up their own time, but now everyone else has to work hard to make sure their AI bot gets the right message to them and follow up to make sure what was supposed to happen in the meeting gets to them.
OTOH, I try hard to provide all possibly relevant context, manually copy/paste logs to reduce context overhead, always ask to produce an implementation plan and review it before making any code changes. Yet I often feel like a dinosaur here, all coworkers who tout "LLM productivity" just type a few words in and let the agent spin for hours without any guidance.
There's also the pattern of creating an army of agents to solve problems. Human write a plan. One agent elaborates on it. Another reviews it and makes changes. Another splits it up into tasks and delegates out to multiple agents who make changes. Yet another agent reviews the changes, and on and on. All working around the clock.
"Their ticket" = that was AI generated. After which they will wait their AI generated PR be checked by an automated AI QA that will validate against the AI generated spec.
It feels like important metric of "corporate AI adoption" should be how effective the human in steering the AI.
IF THE HUMAN ISN'T EFFECTIVE, THE HUMAN NEEDS TO GO.
After that we use AI to translate the tasks to a more technical view.
After that we use AI to implement the tasks.
After that we use AI to review the tasks.
After that a human QA tests the tasks.
If all is good, the code is merged and lands in production.
And yes, we burn a lot of tokens but the process is very fast. It takes months instead of years.
If it manages to solve the working solutions - then it's great! why would you waste your time on it?
It it fails - then it's great! you find your value by solving the ticket, which can be a great example where human can still prevail to the AI (joke: AI companies might be interested to buy such examples)
(All assuming that your time cost is pricier than token spending. Totally different story if your wage is less than token cost)