4 ms·
Actual intelligence is useless when decision makers send new weekly AI rules to be better employees. It’s race to the bottom. Race to an endless technical debt.
by lnsru 4mo ago
Actual intelligence is useless when decision makers send new weekly AI rules to be better employees. It’s race to the bottom. Race to an endless technical debt. Some companies will implode when codebases stop being manageable. The small minority will thrive. But majority not. I see it used in hardware world. Clever dudes without prior experience with software craft working Python scripts, automate tests, control hardware from rudimentary GUIs. That’s awesome. I see software companies sending internal memo requiring all code to be produced from prompts… It’s like steroids - cleverly used they bring more advantages, though one shouldn’t take double dose with every meal.
- Oras 4mo agoIt’s not like code base written by developers before AI were manageable. The term tech debt was there way before AI coding, and was mainly due to changes made by developers. I see the point of your argument when this is done by inexperienced developers, as they wouldn’t know what’s happening but for those who knows and guide what has to be done, I don’t see much difference. It’s about understanding the outcome, and evaluating the risk.
- bayindirh 4mo agoTech debt is a debt taken to reduce development time. It's a time debt actually. Patching something that would work until the team has the time to do it correctly. ...and that time never comes in most cases. Because monies are earned in exchanged for that debt and, management cares about monies. They don't see that debt as important, or as debt at all.
- acdha 4mo agoIt’s a question of degree: technical debt has a carrying cost trying to balance features against your ability to support the codebase. LLMs change both sides of that equation but I think most companies are going to struggle with maintaining a balance when it’s so easy to push past concerns and get something which seems to work.
- throwatdem12311 4mo agoTechnical Debt is not a developer skill issue. It’s a management planning, capacity and budget issue. It’s a bet that the cost of servicing the debt will be less than the cost of paying for it outright with cash. I’ve been in the industry for decades and 95% of the dysfunction in an engineering organization is always management. AI doesn’t really fix that or is really even that suited for it. In many cases it makes it worse. That’s why you see software quality going down. Developers aren’t told to make better quality software even though AI does really make that easier. Instead they’re told to make more software faster for cheaper. Cheap, Fast, Quality. Pick two. Business will pick cheap (short term) and fast every single time.
- Jtarii 4mo agoCompanies that use AI well will replace the companies that use AI badly. There is no world in which AI is not used extensively in all employment going forward.
- datsci_est_2015 4mo agoI agree, with the caveat that I don’t think any company is using AI well at the moment, specifically because I think our tooling around AI is woefully inadequate and immature. Right now the AI marketing paradigm is to create rockstar superusers who can (supposedly) do the job of hundreds of individuals at the speed of light! Which bleeds into the design paradigm, which is trash. I’m bullish on AI that can be used more cooperatively and collectively by a company.
- ungreased0675 4mo agoRight now LLMs are heavily subsidized. When that ends, the actual cost of the service may exceed its usefulness for many use cases.
- almostdeadguy 4mo agoI'm less sure of the fact that ending subsidized token consumption (in isolation) will happen and change this. I think we've seen this play out before with other tech companies where discounting early use ends up entrenching demand and allowing the company to build larger and more efficient infrastructure. I'm slightly _more_ convinced (still not all that strongly) that the rising cost of memory and chips, data center construction that gets outpaced by computing demand, increasing energy costs, and low switching costs for customers will force the model labs to make changes that increase the barrier to entry (either via higher pricing, more restrictive rate limiting, etc.). or force their customers into longer term commitments.
- foobarian 4mo ago> I think we've seen this play out before with other tech companies where discounting early use ends up entrenching demand and allowing the company to build larger and more efficient infrastructure. We've also seen failures who were convinced "they would make it up in volume." I guess the bet is that infra will get that much more efficient, but it's not clear how much slack there is.
- eloisius 4mo agoIt may be useful outside the current tech rat race. One possibility is that a decade of openly user-hostile business decisions will reach their logical conclusion even faster, and those that haven’t fried our brains with CC may be in a position to pick up customers from these behemoths as they disintegrate.
- paganel 4mo ago> internal memo requiring all code to be produced from prompts That is absolutely insane. Thing is I can honestly believe that it happens, which makes it even more insane.
- pjmlp 4mo agoThis is basically the next step of all the AI trainings and hacktons that many of us are now required to take part into, with KPI metrics on how each one is using their tokens.
- vips7L 4mo agohttps://github.com/dtnewman/burn-baby-burn https://github.com/dtnewman/burn-baby-burn
- baal80spam 4mo agoOh it happens all right.
- locopati 4mo agoIt is also possible to walk away from tech. To stop chasing the demands of anything for a buck and focus on something real.
- liotier 4mo agoYes - it is easier than ever thanks to AI !
- ponector 4mo agoYou don't even need to do anything: layoffs will hit you anyway.
- liotier 4mo agoThanks to AI too - wow, it really is versatile !
- blowscum 4mo agoIndeed, here’s a prompt snippet to help you afterwards”. “Create me a resume for [newjob]. Ensure that it is properly embellished so that my two years of superficial, directionless AI-driven learning seem equivalent to the multi-decade experience and domain expertise the company is actually hiring for”.
- seanclayton 4mo agoSome people live paycheck to paycheck in tech. Where do they walk away to that isn't or won't be impacted by AI? Or are you assuming they have the financial support for such a risky switch?
- kuerbel 4mo agoI work in infrastructure (backups, networking etc) and no longer in software. I just don't see llms being that useful right now. If I have a problem and ask an LLM the answer is either fabricated or useless, rarely does it know what it's talking about. And yes I know how to describe the problem so that it has a chance to give an useful answer. Also even with agents, you can't just try and error your way out of some (most) of the problems I encounter without doing harm if the solution fails. Might be different if used for infrastructure as code or ansible or some such. That I can see.
- jve 4mo agoWell Coding agents are being tackled. Infrastructure agents that would read your host event logs, device configuration, ilo, etc, etc - that is probably the missing piece. Having a chat with chatgpt may give you clues or ideas when you have gone throught your own checklist of what could have went wrong, but can go only as far. Agent on the other side will decompile .dll to find out issues if needed to go deep enought.
- kuerbel 4mo agoMight be but I just can't imagine a customer being fine with a loose cannon agent in their environment. E.g. coding agents are ignoring instructions. Who is to say that Claudes solution to a, say, slow backup isn't deleting the backup?
- foobar10000 4mo agoImagine an agent shadowing all your terminals, providing ideas and asking to run commands that will let it verify the hypotheses it comes up with, while at the same time doing research on vendor docs, etc... Quite safe, and already a force multiplier - this would be a harness. Maybe have it be able to write to a shadow system with similar (ideally same) hardware to verify it's hypothesis on how the system works, etc...
- ratorx 4mo agoProviding access to the data is easy. It is just an MCP or equivalent, and coding such CRUD is cheap now. Applying the actions is unsolved. Unless you YOLO the LLMs, taking stateful actions automatically requires a lot of protective infrastructure, solid testing infra etc. It’s all just more code, but a “create me a shopping website” LLM is likely not going to be doing the infrastructure level thinking required to handle it for now.
- 827a 4mo agoThis too shall pass. Among my software engineering friend group bubble: Every single individual (~12 of us) are actively and seriously tokenmaxing. We have middle-managers who have been given an AI mandate, upper-managers saying "uhh...maybe that brush stroke was too broad" when they look at the bill every month, and zero people in that chain have the authority or even ability to roll it back. This week one of my friends cobbled together an agent that runs in an infinite loop, grabs whatever song they're actively listening to on Spotify, writes it in a file, then instructs the agent to emit tokens for 2-3 minutes on what that song and previous songs that day might mean for that person's mental state, like a little music-based diary. Repeat, run all day, 24/7. Kinda cool. But its just a way to use tokens, because the first thing all these AI labs built was a good coding model, and the second thing they built was a dashboard for admins to track how much their users are using the good coding model. A TON of companies are getting looted by the AI labs and AI users. Many will not survive. I think Meta will be one of them (a shell of their former selves by 2030). The ones who survive to thrive in the 2030s will be the ones that are relentlessly focused on their customers and products, not the process. If you don't regularly hear both "AI would be awesome for that" and "actually AI probably won't be good for that", your company won't make it. You'll either get lapped by the companies who find the strong use-cases, or you'll get looted by infinite and aimless tokenmaxing. The path through the middle is far more narrow than most companies realize, and some major, major companies are waking up to that harsh reality; for some, too late.
- RJIb8RBYxzAMX9u 4mo agoIn case this anecdote is not made up, I would implore you or your friend be a bit more subtle at tokenmaxing (ugh). At $JOB, I'm under the same mandate, and it turns out that every prompt is logged and aggregated. When someone else at $JOB asked the team PM who's in charge of the logging, s/he replied that the log is only used to correlate with commits, and nothing else, trust us (wink). I doubt this is unique to my $JOB. Therefore, sigh burn those tokens, but make sure your prompts are at least superficially defensible, in the unlikely event that you get audited. Use multiple models for the same prompt / task, for instance. It's well know that LLMs are prone hallucinations, so it's only prudent to double / triple cross-check the results with multiple models.
- archagon 4mo agoIf you run your own company — even a tiny one — you don't have to do any of that shit (unless you want to).