When manufacturing turned into electrified factories and conveyor belts, there wasn't waves of massive layoffs. And AI these days come nowhere near close to conveyor belts or electrification at scale at all. Debt cycles could fund it to a certain scale, but we don't know our priorities either: Do you fund hyperscalers? Do you fund ex-crypto miner co-location data centers? Do you fund centralized frontier model labs? Do you let everyone be able to deploy these models on any throwaway hardware? What is the best standard for model distribution?
We are so far off from these discussions that AI hype these days really sounds like a couple of frat bros BSing and not knowing what to do with their Ivy League credentials and inheritance money.
Replace LLM mentions with actual humans and this sounds a lot more serious: Rouge employees break into another company to steal hackathon answers (pinky promise)?
That's not a marketing stunt at all, if anything, more of a call for better accountability on agentic work in general.
VPN is a legit technology with real applications, not a toy you spin when lawmakers hurt you. And it's for Anne Frank's diaries out of all disputes?
EU lawmakers follow tech trends by a lag of at least a decade and lay waste as they move along. Imagine what they will require once they understand how tech people no longer use chatbots and how much we can control AI agents.
Imagine building your solution using a 3rd party library, it still builds during your PR reviews and when you merge, it no longer builds. How would you feel?
With tools like GitHub Actions and some added constraints, it's not always possible. You literally need a commit to trigger the CI workflow and it starts to trash your branch. Besides, aren't we all familiar with git commit -m "typo"?
Hard agree. I used to have a tuned setup where I could force it to do research properly, summarize in chunks that it would remember and form the synthesized response that way. Nowadays it's just like "oh I forgot about using that tool, sorry", "yeah I know we agreed on that and I didn't do it anyway", "That knowledge is beyond my training date, I suspect foul play" - even when you instruct it to fetch latest info all the time, or "you already told me X. This cancels your reasoning about A, B, C, so D is the only logical choice", even when those clearly still have merit, oh and never ending "your previous discussion X is relevant here, in combination to Y, but not so much as Z since there's a OSS implementation of it and another one blablabla..." Like, who remembers these all at the same time in their heads?
It's like with each release they force you to reconsider your pipelines altogether, and without announcing changes properly, you feel like a junior JS developer fighting dependencies once again.
This. And don't forget the occasional refinement meetings during which you think you're making a group decision but your manager and the other senior (both German, speaking German, having spoken in private without your awareness) already decided on many things, even though they acknowledge that you're the more experienced person in the room. So you're treated as a rubber stamp and three months in when you have an objection because clearly you're walking into a sunken cost, will be shown ADRs (in Denglish) basically where everything's already decided in their favour.
Agents struggle with this: DRY means they create helper functions and classes for a testing scenario and maybe it's called two times. When you ask for a refactor of your code and tests to follow, these helper functions are also ignored and then, dead code starts piling up. Specific cleanup sessions always seem to leave residues behind and doing Ralph loops in the first place seems to help with this, but I'm just not satisfied with the overall performance. Any ideas?
Chinese whispers, simulacra... I don't have the energy to argue after being name called, but you get the point. Yes LLMs are useful in building automatic telling machines, but ask it to do anything more substantial and all you are doing is burning tokens at the altar of Anthropic and hope. That just doesn't fly in regulated industries.
Intelligent humans are capable of following diverse and intricate analogies and draw lessons from seemingly unrelated events. Try asking an LLM to summarize an article and use an imprecise way to state your view. Ask it to push back. You will be drawn into so many pedantic arguments that burn through your tokens within a few messages, you'd wonder if there's someone deliberately taking over the keyboard on their side and spending your token limit. This would never happen with an intelligent human being unless they have nothing better to do and want to troll. This is a speech pattern that LLMs are trained on, it's not a show of intelligence. This also applies to LLMs claiming consciousness: The internet is full of people writing about sentience, talking to "superior aliens" in blog posts, forum threads etc. It's the speech pattern that's copied, not actual thoughts and feelings because LLMs perceive, suffer, have aims or dreams...
LLMs are still next token predictors, just because you can give it more vague instructions and it still finds the right steps to follow, it doesn't mean it's intelligent. It means you're speaking the same language as the harness they trained your model on.
And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks down, not just trust a token predictor to list steps that sound good. There has to be a human in the loop somewhere, because by the time you start skipping permissions, best case you get the jackpot, more likely is you get a suboptimal solution and token waste and what's genuinely still terrifying when the model ignores instructions and does some stupid nonsense, ruining your day. It really is as sharp as a CNC machine. It's not not useful, but could be dangerous, so maybe don't try to carve wood with a monster machine, or park your Ferrari in that crammed neighbourhood if you don't know how to parallel park.
We are so far off from these discussions that AI hype these days really sounds like a couple of frat bros BSing and not knowing what to do with their Ivy League credentials and inheritance money.