Comment by whatever1
1 day ago
Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
I literally just ask a frontier model : "Pull the latest log and root cause the issue.". That's it, 10' later it comes back with a correct answer. Sometimes I might have to prompt a second time to steer.
No skills, agent interconnections, graphs, vector search or whatever else.
The progress is very scary.
I would agree with you but this is what it was like with search engines as well. Search engines got so good it felt like anyone could just Google anything instead of paying someone.
I think the surprising part is that, no matter how easy it gets, the majority of he population would still rather pay someone else to do it for them.
Honestly the first ten years of my career was just me being baffled I was getting paid to Google stuff. I get worried about AI in the same way but continue to be baffled that I am getting paid to just prompt AI now.
Maybe this time is different but honestly the world is so ridiculous sometimes that I honestly don’t think it will be different.
That's all true when you're working in a space where people have already solved and written about similar problems, for instance with web dev, or simple enterprise application dev. When you work in a problem space that is novel and tricky, googling or AI doesn't answer your questions (correctly).
Google wasn't a magic answer box, it was a collection of things people had written about the problems they faced. AI isn't a magic answer box either, it's trained to spit out the best guesses based on things people have written about the problems they faced. But all this is based on actual humans who are exploring, wrestling with problems, and writing things down. I don't think people can assume that AI will take all that over and push things forward.
Very few of the problems we have to solve are novel and tricky.
Search engines were very interesting and had a lot of myths surrounding them. People claiming that typing out full sentences worked better, vs others entering just keywords. A lot of people learned to utilize the special operators.
I thought I got really good at googling right before the engine started becoming really bad around Covid years.
I'd love to read something about the topic.
About 80% of my career could be generously described as typing what someone asked me into Google and giving them the answer back in a way they can understand. I don't even mean my interactions with non-technical folk. It's far too common with my "peers" as well.
> Maybe this time is different
It is in the sense that you performed tasks for people, and knew which tasks needed to be performed. The advent of agentic programs now means those same tasks can be deduced by the agent and also performed by it. There's a very thin moat between you and that, now.
some people, most people i'd say, don't really enjoy working with computers. if you ask anyone on the street they know chatgpt of course but ask them what an agent is and they will have no idea.
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Search engines couldn’t analyze your local files and give you a specific root cause and fix it. Agents can. I don’t think the two are comparable, really, other than at the most superficial surface level.
They’re comparable in an exponential view. I remember the librarian teaching us how to look through boxes of cards. Then Google came out a few years later. For a couple decades, Google-fu was a superpower that most people didn’t know they needed.
> Search engines got so good it felt like anyone could just Google anything instead of paying someone.
I mean this is probably true enough to have had real economic impact! I've made a whole lot of fixes via YouTube & Google that I would have had to pay someone for otherwise.
> Whoever claims to have skill in AI is very shortsighted. Whatever skill you thought you had six months ago can now be achieved with a simple six-word prompt in a standard workspace.
Apparently. Remember "prompt engineering?" That was the next big job for about 15 minutes.
It takes a lot of tokens to run a multi-agent system, though. AI may hit economic limits. Progress was achieved on hard mathematical problems at a high token cost. More than the operating budgets of some college math departments.
> at a high token cost. More than the operating budgets of some college math departments.
Is that in API cost or in subscription cost or in actual cost to the LLM owner?
Wouldn't it be ironic if, after all this, it was discovered that the most economical way of producing some fundamental 'unit of intelligence' was to feed/clothe/entertain a human?
This is the very core of my issue with "AI". From a resource usage point of view, it cannot hope to compete with a developer armed with a banana.
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You can always put the brain in a jar: https://corticallabs.com/cl1.html
Isn't that the premise of The Matrix?
Bar "clothing", I guess.
It would be. But that's not going to happen.
No, that is just equilibrium economics.
Machines are cheaper than humans? Buy more machines.
Humans are cheaper than machines? Hire more humans.
eh, hardware will get more efficient. moore's law and whatnot.
Moore's law has been dead for 20 years.
the bulk of the skill lies in shoring up the fundamental limitations of llms
they cannot be held accountable - therefore, you need the skill and expertise to verify outputs, or set up a verifier
they have a limit to their context window - therefore, you need the skill of knowing what context is useful and exposing that
they cost money and time to run - therefore, you need the skill of learning what parts can be handed off to code, when you can use a cheaper model, when you tradeoff accuracy vs latency
they are nondeterministic - therefore, you need the skill to set up evals, guardrails, knowing when to reach for the llm vs. code depending on the problem
they are not omniscient - therefore, you need the skill of setting up the processes and infra to supply legible context to the llms
etc etc
We still have yet to find what is possible without a career's worth of experience doing it all by-hand.
My kids' high school is developing an "AI Literacy Program" for all students by grade level. Since I work in tech, I was asked to participate. I attended one session and then decided it was a waste of time. Their objective seems to be to write a massive document that no one will ever actually read let alone implement. The primary focus was on prompt engineering. I told them it's 2026 and every kid in the school can write prompts just as effectively if not more so than the folks in the room. The secondary focus was on "safety" but really, for high school kids it boils down to 1) don't just copy pasta the output into your assignment and hand it in 2) verify everything the model tells you before believing it 3) don't put sensitive info into the prompt 4) don't use it to do anything illegal or stupid. Not sure this requires a committee/document/plan.
Maybe this is covered by (3) but I would also want extensive coverage regarding using the tools for therapy or life advice. One of the hallmarks of being a teenager is "not being understood", which coincidentally is one thing LLMs will dole out in abundance.
Feedback, criticism, and guidance on your convictions and behavior from other thinking and feeling human beings is important.
> I literally just ask a frontier model : "Pull the latest log and root cause the issue.".
While this can work, you also can't rely on it to work every single time, and still need to deal with the failures.
This is the part that gives me pause with all the discourse: you can't give up on the task you describe. You can't tell your team "something failed, but even after 20 prompts the model gives nothing good so let's forget the whole thing and go on with our lives"
So you still actually need the skills for when shit hits the fan. Perhaps it can be done in a more granular fashion with the model, and spending 30~60 min on it will do, but we're pretty far from "just utter 6 words"
If we could call someone else whenever it utterly fails it would also make it a different proposition. Same way people can try to fix their plumbing themselves, and call a pro when it's just too hard.
But who will you call to find the root cause of the issue in production ?
It seems to me as though you're describing a skill...
What would happen if you just screamed: "SAVE ME! EVERYTHING IS BROKEN! I'M STUPID MONKEY!"
I mean, sure, this might not seem like a lot of skill to you, but you at least have a clue what you are doing, and not everyone does.
Is your bar for skill "name the symptom you observed"?
Most sales jobs would be unnecessary if people would be good at communicating their problems.
You also need to be able to type on a keyboard or speak to the AI to prompt it. That’s a skill too.
I mean, if a large fraction of the population seem to lack it...
It also, in my experience, can be taught.
Okay, admittedly this is not necessarily a great test, but I decided to try it anyway. I loaded up Claude Code and then gave it that prompt.
Then it ran some commands, and came back with:
It listed out some stats based on top and some investigating the filesystem numbers, running processes, etc, and then said:
I'm not feeling super creative this afternoon, so I just said "Everything is slow!" It politely told me that it cannot see anything with the CPU, memory, or disk that suggests it is slow, and that I should check out the host. It knows it is in a VM on MacOS. Told me where to go to look for more info.
Now picture it running in Cowork instead of CC, and it would probably tell non-technical users what the real problem is. Or at least give them something to hand to IT.
What a world.
It failed to tell your to power cycle the machine. Our jobs are safe!
Thank you for empirically testing the claim. So many people who really should know better, simply assume what AI can or can't do, write that down on HN, and then believe that their assumptions have been validated when they read similar claims from other people doing the same thing.
"Theory of mind" is all you need...
I mean you could have asked it to build a log classifier instead so next time it happens you either get the solution immediately or it autos into an llm giving you its answer.
You're wasting time and tokens here.
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