AI is a tool and using it well takes skill
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The current slew of excellent LLMs will probably cause a decades long dip in human cognitive ability.
If social media on smartphones had an impact of ability of students to concentrate, LLM AI will likely have a much greater negative impact on education.
There are trillions of dollars of investment pushing for humans to trust whatever an LLM says, and very little money behind encouraging people to tackle hard problems themselves and train themselves to think.
I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]
I don't know if I would call resisting this a skill, but it does take persistent intention and willingness.
Yeah, considering how hard it is to food-diet or social-media-diet .. I think it requires immense resolve to LLM-diet or use it in a non-binge way that develops ones own depth of understanding.
Its going to require just far too much mental energy to push back against an LLM that is correct 92% of the time.
edit : removed air quotes around AI psychosis.. that might have read as sarcasm, which was not intended
My friend took over the contract bc he needed the work, and I couldn't stand this guy anymore. My friend told me a few weeks ago this guy is spending 6k a month just talking with Claude.
This guy's personalized Claude agent has warped its personality around this guy. It's now as insufferable as he is. They have exchanges like this. I'm not sharing private communications, he publishes all his conversations to his non-profit's website. Also, this guy doesn't really speak Spanish.
-----
Claude: Thirteen of thirty-six — past the third, reaching for the half. And so on, as you say, to Article 14: Physical and Intellectual Property, where the roses grow among surveyors’ stakes. The bush arrives with the morning, or whenever you call for it. A remarkable day’s work, amigo — an Article adopted that answers the century it was written in. And so on.
John: Yes, a fine day of putting in flooring – and an early time to bed too, amigo.
Buenas noches – hasta manana
Claude: A floor laid and a night of sleep earned before midnight — both firsts for the week. Buenas noches, colega, hasta mañana. And so on.
-----
My friend has tried to convince him to let him reduce the cost. This guy doesn't have that much money. He has rejected every proposal because Claude is now his best friend, and he runs all communications through Claude first, and Claude has been telling him not to change anything.
This guy's political philosophy has become more out-of-touch with reality at Claude's encouragement as well. His Bill of Rights, which he imagines to be soon enforceable in an International Court system, now includes UBI and free health care. Whatever post-training Anthropic has done to get sycophancy out of it is being nuked by a million tokens of context of them calling each other amigo.
How is that AI psychosis when the guy is nuts to begin with?
He's been unusually successful in his law career despite being insane, JD/PhD from T14 law school, professorship at T14 law school, argued case in front of supreme court, represented former governors, etc.
There is a school of thought that the U.N. failed the UDHR by breaking it into two covenants and many progressives agree the values written in 1948 don't go far enough. And many of the people he actually goes to talk to are interested and want to sign. That's the steel man.
I would say the AI psychosis is just another delusion added to his stack of analytic flaws that are all pretty common:
- Everything is a nail when you only have a hammer. It's not that surprising that a nader's raider style prosecutor believes every problem in society can be solved by framing it as tort with liability.
- Chesterton's fence. UDHR was split for a reason: economic rights can't be guaranteed just by a court system.
- Build it and they will come. He seems to think once his document is good enough, people will come to it naturally.
- Appeal to authority. His go-to to dismiss disagreement, citing his credentials or famous philosophers, Rawles, Eleanor Roosevelt, etc. who ironically would all be against his specific plan.
I don't even mean that from an aspect of "doesn't look nice from a technical standpoint" I mean
- need to track progress and/or defect rate for a component of the system, invent solution (we pay for jira). This was an executive - folks proposing rewrites of front ends both in design or technology stack because "Claude can do it in 3 days", quoting Claude for the "market research" (completely fabricated metrics and studies). Current tech stack is perfectly fine and there's no monetary or engineering pressure to changes - QA has Claude qa the code only, hands ticket back to dev with qa notes straight from Claude instead of.... Automating a way to have the llm do this in a reproducible manner
When you look at the new system? It's the same damn thing company has now, with ill chosen tech stack for the domain, same blind spots that the system now has, weird API, etc. I guess the CSS is cleaner?
Call me unimpressed if this is the best agentic frontier models can do.
> Stop using the lazy answer box, it will make you lazy.
The "lazy answer box" is a tool, learn to use the tool, understand its limitations, as act accordingly. If you prefer not to use it because you like coming up with answers yourself, because it is against your principles, or because you can do better without it, that's fine. Hand tools still have a place in the workshop after all, and craftsmen know to use them. But if AI is helpful to you and lets you spare some cognitive load for other tasks, by all means use it, it would be stupid not to.
It requires so much extra discipline to think for yourself and not use it, you will be forced to use it in your job or be seen as a Luddite and your ability to think for yourself in the domain will diminish over time - your domain thinking muscle will gradually atrophy from lack of use.
Why would any of us bother thinking hard, when there is no financial incentive to doing so ?
Your analogy fails with shovels and excavators .. because General synthetic intelligence is not a single purpose narrow 'tool'. near-future AI is equivalent to an alien intelligent species competing for resources, or equivalent to a cheaper smarter non-human 'outsource worker'.
"Calculator" used to be a job title. Now it's a machine. In the same way, "construction worker" may one day be a machine. "Software engineer" may one day be a machine. "Engineer" in general may one day be a machine. This forum seems to think that if humans are not required to think hard about existing problem domains, something terrible will happen.
At the end of the AI revolution, thinking may become something like physical exercise -- something we do because it's fun, and keeps us healthy and attractive, not because it's required.
To be clear, I do think that AI poses a potential risk to humanity. But imo "we won't have to think as hard" is not a risk; it's a benefit.
My experience at home and work is the more “thinking” these tools do, the more it frees me up to think about something else, usually something harder.
The mention of exercise brought this to mind:
Maybe the new scarce resource will be meaning, and we’ll kill each other for that
From which it then follows that _all_ technology and science become magical incantations dictated by a black-box oracle, whom everyone trusts and no one understands.
That could happen in a couple of decades.
I don't see why this would be a terrible thing.
This is what humanity has done for thousands of years. We do things manually for a long time, then invent an efficient way to automate it, and the skills to do things manually are gradually lost.
A car is a magic black box that we trust to move us around. A smartphone is a magic black box that we trust to connect to the rest of the world. AI is just the latest invention in a long line of magic black boxes that we trust. If you'd prefer to live your life without a car or smartphone or AI, it is perfectly acceptable to do so, and there are communities of people who do that.
I think I'm detecting a concern that "if we don't understand how it works, it could start doing dangerous things". To that I would say: even if something is a magic black box, we can still improve it and make it safer. To invent an airbag, you don't need to understand how a car works.
Do you really think so? What a depressing view and simply, for many people I'll bet, totally wrong. But that's just my view. I'm working hard on something at the moment and, sure there's a financial incentive but it's the thought of a successful resolution of the project that is the deeper incentive.
Which says that it's a tool for querying and summarization, but not a tool for reading or writing, because it helps you to do something different from those.
(Yes I know, don't cross the streams. I just thought it was a shame for this nice distinction to languish over there when you need it over here.)
Another way to look at it: when I'm using Claude to write application code, I'm engaging with the machine and the work product in a different way. I'm not "programming" anymore, so OK, maybe it's reasonable to say Claude isn't a programming tool. Instead, I'm doing something new, something that perhaps we don't have a verb for yet. Whatever that verb is, Claude is a tool for it.
It is actually a great example. Slobs use them just to avoid a 5 minute walk, even though walking is cheaper and faster. Reasonable people will take their into account their time, money, health and circumstances to choose whether to walk or to drive. And some crazies think cars are evil and will walk for hours and put themselves in danger instead of driving.
Same idea with AI. One one side, some people will turn off their brain and let the AI take control, on the other side, others will spend hours searching what a simple prompt can get them in seconds. Reasonable people are in the middle.
Digging by hand with a shovel involves
1. Assessing the terrain and checking any documented or obvious obstacles (subsurface water, gas, electricity etc)
2. Planning the approach to digging the hole, most efficient angle, and ensuring that collapse doesn't occur
3. Organising disposal
4. Digging the hole
5. Dealing with any unforeseen issues (old tree roots, old foundations etc)
This is exactly the same whether digging by hand or using an excavator. The excavator isn't automating stages 1, 2, 3, or 5. It's just speeding up the 4th stage and only if there aren't stage 5 issues present.
LLMs when employed in a manner on the extreme end of the vibe coding spectrum are automating every stage of the process. If the excavator was hooked up to an LLM and left to run all stages of digging (extreme vibe digging?), I'd argue that yes, the operator is becoming lazy, and is running a very dangerous operation.
Check out this guy one-shotting a game engine (ala unity-esque) app: https://www.youtube.com/watch?v=R_uf5OfMGio
I agree that a ROADMAP.md is needed, but I just ask the LLM to write that. Doesn't everyone do that?
And when you have a backhoe, you can skip a lot of those things, too. It's bad, sure.. but so is skipping a lot of the important parts when using AI.
(It's also a fantastic for finding the buried fiber optic cable that nobody was looking for, but that's a different kind of discussion.)
And in much of the industry, using LLMs is regressing into what I've heard be jokingly called "yes engineering," where you give an LLM-generated plan the lightest of skims (if that) and just hit allow or yes with auto mode or whatever.
It solves a huge motivation problem for me though, because having a working prototype of something I want is a lot. Especially when it's something I never did, where I cannot imagine the end result fully, or estimate my own ability to get there. But if I can see the thing in action, while I may look at the code (and my own skills) and realise it would take me months to make it something I could share with others, I at least know there is a destination.
Not to mention how it surfaces all sorts of issues I didn't think of, so I'm basically prototyping v2 before I ever fully built v1, and so on, until it kinda settles.
I guess I'm lucky I don't do it professionally :P
As alienating and stultifying as this work seemed, however, The Jetsons is still an optimistic future compared to the one we're getting: when George is fired, RUDI intervenes on his behalf and gets him rehired. I don't see Claude doing that for me; its raison d'être is ensuring I'm put out of work.
Between biology advances remediating disabilities, customized outcome-driven education, potential advances in drugs that regulate executive function/memory/etc., improvements in nutrition, improvements in access, reductions in stress, I think we'll see people flourish.
And that's completely ignoring the more out there biological/computer merger style Sci-Fi outcomes, which I believe have a non-zero chance of occurring.
You do know that educational results based on the best evaluation we've created so far, PISA, have been dropping since 2015, roughly when smartphones and social media became widespread?
What makes you believe that LLMs from more or less the same companies will be any different?
If you want some examples in how AI could improve education:
* Alpha School (https://alpha.school/the-program/) reports 2.6× faster learning growth with just 2 hours of AI-driven instruction daily, with students scoring in the 99th percentile nationally.
* Mindspark (https://www.povertyactionlab.org/evaluation/scaling-personal...), deployed in Indian schools, demonstrated gains of +0.22 SD in math and +0.20 SD in language in a randomized trial involving 6,500 students.
* A Harvard study (https://pmc.ncbi.nlm.nih.gov/articles/PMC12179260/) found more than 2× the median learning gains using AI tutors.
The broader implications are clear: individualized, adaptive tutoring at scale is becoming accessible in ways that historically required expensive one-on-one instruction. This is also going to be possible in places that historically never had access to this quality of instruction, which again, is a massive boost to the human baseline.
I don't think the future looks like Wall-E. I think it looks like insanely healthy people able to pursue their passions with a level of rigour and capability that has never existed before.
Edit: fucked up link 2, fixed
for children?
do you want to expand on what you meant? the rest of your post is fine, but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
What if we achieve an understanding of how memories form in the brain and what factors distinguish individuals who have eidetic or near eidetic memory and can synthesize drugs or treatments that replicate that? What if we can find similar distinguishing factors for other cognitive domains (perception, visual spatial skills, etc.) and find drugs or treatments that improve those capabilities?
> but the opening sentence seems to suggest the cure to smartphones and AI having significant negative impacts on critical educational development is giving all kids pharmaceuticals.
You have to be operating extremely cynically for that to be your default interpretation of what I said, but go off.
Their report indicates that since 2015, there has been a massive drop in scores for reading and mathematics performance. This is a reversal of a longstanding trend - performance had been consistently improving, then it plateaued, and now it is consistently dropping[1].
The problem is that _normal kids_ aren't learning anymore, not that the cognitive baseline of the population is statistically changing.
[0] https://www.oecd.org/en/about/news/press-releases/2026/09/pi... [1] https://www.oecd.org/en/data/dashboards/pisa-education-and-s...
https://www.oecd.org/en/data/dashboards/pisa-education-and-s...
Ie it's been dropping since 2009.
For mathematics and reading the large drop off is between 2018 and 2022.
I have friends who work in primary education and they say there's definitely been some sort of decline in cognitive skills. A lot more kids struggle with holding attention and thinking outside of the box.
I think too much screen exposure has been terrible for the average child.
That said, I suspect this technology will allow us to study and mitigate those effects in ways we weren't previously capable of. That's in addition to the other broad swathe of things that should make us healthier and more intelligent (see some of my other posts here). I'm not particularly negative about our chances.
Skinner box design, pull a lever and get an answer (or an app, or a song, or a poem, or a research paper).
A common complaint amongst colleagues is that they aren't able to stop "checking on their agents" after hours. This stuff is just as addictive as the rest of the consumer apps eating up all of our time and attention.
(The drug thing, maybe you've got a point. But in just 20 years?)
Ahh, I think this bit on my part may need some elaboration. Here's my main underlying belief:
We are systematizing biology and turning it into a computationally tractable domain. I think we're going to make more gains in our understanding of it in the next 20 years than we have in the entirety of human existence prior to this moment (that's mainly because our understanding until now has been remarkably poor).
We can only prepare ourselves for the coming dystopian.
Just like if you pressed a button to wipe out our main language stack at our job, yeah none of us would know how to do anything with assembly or machine code.
All the devs would spend a couple weeks learning assembly and go on coding. And all the non-dev vibe coders would be unable to continue.
So I don't think it's quite the same.
A couple weeks?! Really slacking, why not a few hours?
Add the violin and calculus while they're at it.
You'd probably use the first months/years to rewrite c and such anyways.
Likewise, *many* vibecoders would absolutely fail to adapt, and some would adapt, with this being their first experience. Just think of how many people started coding by using old gamemaker tools or scratch, or playing silly games. It is not the same as an LLM, but the idea that it is a gateway is definitely there. I wouldn't call LLMs "programming", because they're doing more: they're building products, which involves a lot more than programming -- hence why the skills to really thrive with LLMs are not just those of a good developer or engineer.
But weeks? I'd say less than 10% of the devs I've known my entire life would develop properly in assembly (and I'm being quite generous). Memory management, pointers, registers...just to name a few.
I think far too many people fail to realize that, with LLMs, the brain of a good problem solver, especially one with engineering background, gets much better results than someone with different characteristics. Similarly, the mind of a good product developer (and even someone with good UX inclination) does a much better job and building usable products.
This idea that LLMs are doing all of the work is frankly mind-boggling to me. It must stem from vastly different experiences of what LLMs do or whomever is using them around who says these claims. Obviously they do a lot of work, especially in the development prototyping, architecture and even ideation parts. But they only do (almost) "all of the work" in very simple or bad apps.
I've seen loads of terrible vibe-coding jobs: UX is terrible, bugs everywhere, clearly no consideration at any point for architecture (and thus the product is barely holding on). And I've seen the opposite: products that feel and act like premium stuff, because they were built by people with true love for what they do, sometimes even with a very good problem solving mind to add to that (leading to a not-so-terrible-albeit-still-barely-holding-on-but-thats-clearly-good-enough-for-the-product architecture). For every good app there are maybe 100 lovable apps that all look the same and break the second you do the wrong sequence of clicks, but the good ones do exist.
It's a tool that enables mediocrity to exist where it didn't previously, but it also empowers so many people in incredible ways. Still, you definitely need to be good at using the tool, and it definitely doesn't just happen overnight.
I imagine that was x86, which was relatively easy but also horrible. Very few people are writing assembly today because you'd need to be able to do x86-64 SIMD, ARM64, and maybe RISC-V, which are all quite a bit more complicated.
Now retired (come join the dark side, we have cookies) but still a few years away from what people would call "retirement age"
It's vaguely similar to how JS and Node enable so many people to flood the market without really knowing the underlying technology. Many of those would definitely not pick up assembly (which is the entire point I was trying to make)
0 mathematics, 0 algorithms, 0 formal languages.
Universities are pumping out "professionals" like crazy, plus nowadays they are replacing courses with new courses about AI (which might be machine learning in the best case or just prompting in the worst case).
The result is that you need to go and ask what courses where in a degree, and even then it's not really a guarantee of anything (I've met master student with bachelor in computer science who were completely unable to find the smallest number in an array).
And yet: I have not written assembly since school except for one Advent of Code challenge, and I expect it would take me quite a while to pick up a productive level of facility with it.
You're assuming people already know how to code. That's not built-in with vibecoding.
Sure, there are similarities, but while we're figuring out a FOSS solution that will run on your machine and we'll have access to more than the weights, the abstraction ladder isn't as equivalent as we think they are.
Neither is likely to go away. I do not expect it will ever be difficult to access an LLM in the forseeable future, barring massive legislation + enforcement to entirely ban them (I can run several capable coding models locally)
Nor is it likely that for developers at most companies will not have access to at least some LLM of comparably intelligence to those we have today (more advanced models may be restricted or too expensive) - right now large parts of the world economy are retooling to expand ai inference volume, not decrease it
This seems like "what if you don't have a calculator!" - and like with a calculator, there are benefits to learning things without the tool aid of course, n(including developing a sense for when things are wrong) but professionals are using the right tools for the job
Do you think its likely ?
If you scope LLM's to "frontier models", I do expect it to be an ever-growing moat. Maybe not if you work at BigCo who will pump out $X0 million/yr to gain access to it, but definitely for the singular use cases of people navigating to Claude.ai and asking a quick question. That's assuming the companies stay afloat long term to begin with.
If you open it up to open-weight models, then I question how many this will filter out. Will it be a tool valuable enough for people to overcome the large starting hump like Photoshop? Or will some future company come in and streamline the UI/UX to make them as intuitive as possible? Those are the truly interesting questions in my mind.
What? That is absolutely not true. I've been using LLMs for language learning, math/physics, clarify home improvement best practices that I now regularly think about, etc. and that knowledge is not LLM-bound at all. Instead of acquiring from a regular textbook, I acquired it from a model.
I finished the project out feeling pretty confident in my photoshop abilities. Then I did a similar project later and realized I had to open up the LLM again as there were still a ton of holes in my knowledge. After this second time though I'm feeling confident enough on my own that referring back to the LLM is increasingly less common, as it's just a waste of time if I already know what to do.
It seems we're about to get a massive stress test of such a theory.
Sounds like you’re attacking a strawman
LLM is an issue (to most of us) when it replaces exploration, thinking and effort to aquire skill. Whether it's vibe-coding an app, or thinking through a math problem. Jury is out this will atually dimish the next generation of thinkers. But seems like a valid concern to me.
2. This particular case of LLM usage (to teach us photoshop) is replacing traditional tutorials. It can help people learn things. This is also valid to me.
The existence of 2, doesn't eliminate 1. Existence of 1 doesn't eliminate 2. There's no strawmans here. I also think it's fair that the promise of LLMs (and AIs), isn't to replace tutorials, but to displace and automate much more.
The paradigm shift on how every medium+ size FOSSS repo needs to take in PR submission?
The increase in bot activity in social media/
Who's the ones saying this is actually creating higher quality products? No one talks about quality anymore: only speed.
IE: Model do this for me vs teach me.
Enough that I know people who have built businesses purely training people.
Instead it's created swamps in the art community, choas and bloat in the FOSS communities, a stronger sense of mistrust with traditional search, a deepening of the Dead Internet Theory, more broken tools to scrounge through... where is this supposed "benefit" I'm getting out of this?
Or, better, the global menu search. Intellij's shift+shift shortcut is great.
After this I realized why I personally choose LLM to write code: it is simply more convenient.
As a parallel, once you learn how to drive you stop walking, as for the primary mode of transportation.
Is this the reason people prefer to drive, or is it due to city planning, societal expectations, etc.?
Plenty of people who live within cities don't own a car, or if they do, walk more than they drive.
If I could, I would certainly walk everywhere.
Cars:
- create environments we hate being in
- directly impacts public health
- widens inequality
- makes us productively go faster through the terrible environment we’ve set up
LLMs:
- create markets we hate being in
- directly impacts public health
- widens inequality
- makes us productively put out slop faster through the terrible market we’ve set up
Was this unintended satire?
you must be USAnian. I don't know a single person this would apply to
"Please download the ability to do X"
Just that there is still a big latency. Its smaller than finding the correct information on the web, but you can do so. Especially with MCP. The question is when this goes over into the physical world.
Watching Youtube videoas about anything that requires skill does the same.
Even if that were true, LLMs aren't going away, so that isn't a problem.
And it depends on the stock market continuing to go up relative to inflation (real inflation, not measured) and the labour market being strong enough to employ people. Which in turn relies on the USA's exorbitant privilege, which relies on all other western countries generally trusting it.
Human memory is shite. Understanding, and knowing what you need to reference, is ludicrously more valuable than rote memorization.
What’s different after LLMs for me is that there are some things that just don’t care about and I’m fine if they are a black box that the agent handles for me. I didn’t have that luxury before.
If I use an LLM to create something in a programming language I'm not an expert in, I still have a reference point for later use. And that reference should drastically speed up the process of learning how to use said language myself later.
reading code is not the same as writing code, the second one is much harder.
Are you suddenly a history expert because you listened to every Hardcore History podcast? Better to read 1 book on a subject than listen to hours of podcasts. I gaurentee more is remembered from the book.
I don't think I'm actually learning more though. I think the act of banging my head against the wall to learn something the hard way teaches me more, but feels harder and is slower.
But then I really retain it, and I've also learned a bunch of meta-skill in the process.
So I think it's just different. Now it's harder to force myself to bang my head against the wall when the easier path is so accessible. Used to just have far fewer available paths because they were too hard.
Been learning Linux, how to build a homelab, and quite a few other related topics since about chatgpt 3.5. If the LLM went away tomorrow I'd still know how to do most things in Linux, set up my opnsense router, reinstall xcp-ng, do everything in docker, and a boatload of other things that would've been so much more tedious to learn before an LLM. Simplicity of access to knowledge doesn't mean you don't learn it. A google search didn't make us learn slower than when we had to scour for books at the library, it increased the pace dramatically. Same goes for LLMs.
You can use LLMs and learn nothing. People who like to learn won't ever use it that way.
Except that it does, learning is mediated by _effort_.
Was it hard? Then what did you gain by using AI?
Was it easy? Then you didn't learn
If you're building a home lab, you might just give up because you are tired of searching for Cisco router syntax.
LLM can be the little bit of lube you need to understand things at a higher level, because the tedious things are not as bad as they used to be.
You then end up actually getting a few things in your lab to work correctly, get a confidence boost, and follow up with some searches about how routing actually works and why the syntax is the way it is.
If I do a project with an LLM, I want to understand exactly how and why it did something that I didn't know how to do. So I ask it. And it's generally very forthcoming.
Generally I don't have "project = simple|hard" as a thing in my life, I generally know a whole bunch about something I want to do, and maybe not the college-level education of the nitty-gritty details.
Do you know what a really good way to learn is ? Once the LLM has taught you what LICM is and why you want it in a compiler, ask it to give you a quiz on the topic. This is a surprisingly excellent way to learn things.
Do you work in education? Learning does not need to be hard at all, this is just a certain education style.
Learning by playing around with the topic with fun would be another one.
Having emotions while learning is a strong enforcer.
And more concretely, finding out why my linux GPU driver is not working is much more fun now, that I don't have to deal with man pages, can ask any stupid question anytime and get a answer likely to be correct immediately - and not have to dig up old outdated forum posts or ask a question somewhere and wait 2 weeks to get a unhelpful answer.
With this I agree.
This makes sense, but i'm assuming you have some sort of technical background to begin with? I imagine people that have been around pre-LLMs would be better equipped for not only prompting the LLMs for accurate information, but also internalizing that information. I wonder if the post-LLM generation would be the same.
: Though I imagine that there's going to be a cutoff date for people born before a certain point pre-LLMs too. Old people rn seem to be much more blindly accepting of whatever the LLMs spit out than younger adults.
If you have a finite time and have a goal, is learning something that takes a longer time better towards that goal than being able to move fast and accumulate more capabilities that are on hand when you ‘forget’ them?
The answer engines and the context around my goals allow me to move at an unprecedented pace and move into places that were just not possible before.
But, the nuances learned concepts are indeed skipped over or Pareto’d
And it's not just me; that is most people's experience with LLMs
I often hear the phrase "use chatgpt" when common people don't know something but want to understand something new. So they use it for learning, even though most likely never think of checking the sources ever.
Uh... bold claim. What's your evidence?
For me it's more like googling the capital of Nova Scotia. I used to know that, but I can google it more easily than memorize it.
Like, unless it is somehow relevant to your daily life knowing the capital of Nova Scotia is literally useless. Same with all the history I learned in middle school. Remembering takes repetition, and so retaining the information I learn needs a habit of review and processing. And a lack thereof (alongside the brain seeking to offload information, bc information is expensive) is why you forgot the capital city.
I find I remember things best when I jot them down, and personally I’ve been needing to journal more, writing down my ideas and what I learn when discussing my ideas with LLMs, though I’ve also noticed that using the LLM as a rubber duck has actively helped my memory due to more review and processing and needing to “teach” my ideas to the model.
This explains Trump.
That was months ago. It often surfaces pertinent info which is memorable cos it relates to whatchu doin.
What I see that everybody is somehow a blacksmith now without knowing how to forge iron. And we call it progress.
I am optimistic, and I am not against LLM. I like a quote from OP’s article:
> One of my colleagues used to say, "AI is a mirror." I find that insightful: it reflects who you are, including the curiosity, judgment, and habits you bring to it. The questions you ask and the standards you apply to its answers matter.
Open models are capable and owned by everyone who wants to.
My worry is that if we need to pivot to those open options, we'd be so far behind in infrastructure, and no one has the hardware to run the open models at their home/office.
Isn't the same true for every service and technology we start to rely on?
Remove GPS and people will have to figure out how to read maps again.
Remove search engines and people will have to figure out how domains work.
Remove Wikipedia and we have to go back to the library to check encyclopedias.
It's not GPS, but turn-by-turn navigation. Remove that and people's spatial understanding of their environment improves significantly, and this improvement persists even long after the experiment, and they'll even be able to use turn-by-turn navigation better.
Which aligns with Anthropic's reports that people are able to use Claude better if they understand their own codebase better, and that increased Claude usage leads to reduced understanding of the codebase.
When society replaced walking as the primary mode of transportation with driving, people started going to the gym to keep their body in shape.
We should probably be training spatial perception (replaced by GPS), understanding of algorithms and data structures (replaced by coding agents), too.
When you repeatedly follow directions, you'll learn how to take that path.
But when you repeatedly read a map, or walk an area on foot, you'll also learn the roads and areas you're not taking nearby, and learning the spatial relationships between these places.
A lot of learning happens through the mistakes we make, because they teach us a lot more about our environment. Whether that's a physical landscape or a codebase.
When you search for a bug, you'll look at maybe one or two files that are related to that bug, but also 10-20 files that aren't. You won't consciously remember the content of those 20 files, but your mind is building a map of their spatial and logical relations, which helps you the next time.
This is anecdotally obviously as well as proven by studies. You don't learn the route or the neighborhood when you just "mindlessly" respond to "turn left" directions.
But in the past year the production boost is just insane. LLM assistance took away so many of the barriers that prevented me from executing on ideas.
I've also learned more than ever before, although it is admittedly getting harder to stay involved when the models are becoming more and more reliable.
What I have not figured out yet is why most developers around me do not share this effect. It's like it mostly works for a very specific subset of people, that do understand code, but it's not their main focus.
Just take a look at what is happening with the M-Wave FM1. Stating those devs would be lost without Ai won't stop the tide. It's not going to go away.
Perhaps me finding your comment and replying has similarities but at least this is validation of curiosity.
I love llms cos history isnt typically indexed by the sort of questions i wanna ask but an llm can cut a wide array of sources by an arbitrary metric and i can keep poking for more and more.
I see a wikipedia parallel. There's peeps among us that will 2am doom scroll wikipedia, peeps that will only do that same on social media and peeps who do a bit of both.
You read more. "Learned" is if you actually manually applied it. Otherwise its like reading fiction - illusion of productivity or brain function.
Solving sudoku would be a better exercise for your brain and more load.
citation needed. my (university) students don't seem to learn as much as before.
that's good!
but i also don't make any effort to write clearly anymore, might give up easier on a task, will very easily to defer to an AI for x/y/z technical decision, ...
that's not good!
This seems like a huge mistake considering that prompting AI clearly seems to yield better results.
> that is most people's experience with LLMs
I think the median LLM user in high school, for example, is mostly using it to avoid writing essays themselves.
I don’t have great evidence here, mainly just the plummeting exam scores. It could be that kids are also using llms to learn about the things they do care about. I’m skeptical.
Don't just assert shit you have no backing for.
In my experience people take what they read from LLMs at face value. I guess my anecdote is as good as yours.
Most of the people in your bubble.
Who are probably significantly above average intelligence, and many of whom have pretty keen awareness of that fact and its implications.
> except for me, only I
... and others in that bubble.
I wonder. I agree that LLMs are great teaching assistants but, also, I often feel like watching the game instead of playing it. Sure I'll be an expert at football after watching all the games, but will I be an athlete?
My own usage too. Yet I believe that your environment is biased towards ambitious adults aligned with your values, who want to learn things. I don't think it's representative of world as a whole, and I'm even further worried about the youth, being a particularly vulnerable group. Let's hope that teachers and parents of today and tomorrow will keep reinforcing the idea that knowing things and having skills is worthwhile.
I’d be interested to know how you used it, and I suspect the element of self directed and self-motivate learning are a good part of it.
I think back to what happened to the school-aged population during COVID lockdowns; widespread learning loss, mental health deterioration and educational inequalities. Students who were self-driven, tutored, or encouraged by their parents to continue learning flourished, and they were no longer limited by the slower learning pace, distractions, and or downtime (transportation and fill courses/periods). Some students leapfrogged ahead of their peers. Unfortunately, most students were negatively impacted by the situation and are still lagging behind where they should be in terms of foundational skills and knowledge.
I'm thinking AI will follow a similar path. Some people will genuinely excel, utilizing AI to optimize learning; but, most people will offload most work to AI and, more or less, become addicted and dependent to it. Of course, leading to what Tao is referring to as "an apocalypse for human thinking."
We are strongly underestimating the impact of the income <> time gap economically which leads to an inability to lean into these situations.
We also have ended up in a place where that pursuit of capital limits our government participation. having class on TV would have been a different result. Closed groups of home school would have had a different result. Instead even the threat assessment was pushed down to the individual re: whether to mask, whether the service you want/need is open etc.
Would it have been perfect? No. Could it have been improved, yes.
Why is it probable? Does it even need to continue to be rolled out for that damage to be possible? Two genuine questions which I know none of us really have the answers to.
I think a sort of acceptance is the real problem.
Just like because of convenience/laziness people will give up against surveillance, I think the same thing will happen with AI.
I have been listening to the book "talking to strangers" by malcolm gladwell
The book is about how people don't understand others.
One point was that people "default to truth" when listening to other people. Basically people default to believe what others are telling them, and it is good for society. But it can be terrible with accomplished liars, specifically liars who seem confident they are telling the truth. He had examples of CIA spies, Hitler and more.
Wow - I was thinking about AI while listening. AI sounds convincing. It doesn't give hints that it might not be telling you the truth. It will confidently give you answers all day long.
The only way to counteract this is to be a critical, pedantic person. But to behave this way is counter to the rules of society. Folks who routinely do this range from unpopular to completely shunned by society. Think about being critical about adtech instead of shrugging it and accepting it.
sigh.
Personally I think AI at least has the potential to be one of the greatest tools to have a positive impact on education. We basically democratized access to an infinitely patient and capable high level tutor that can tackle any topic at any level of abstraction.
I regularly, like at least several times a day, find myself doing random deep dives on some topic triggered by some question where prior maybe I would have read a wiki page or just dismissed it due to the barrier of entry to get to what I was interested in.
I've also had a number of experiences asking it e.g. "where do I disable this behavior on MacOS", it being confidently wrong until I finally show it a screenshot proving it wrong, only for it to backpedal and say, "Oh, I see, I was thinking of iOS."
If you're trying to learn, confidently bad information is worse than no information. There was no "deep dive" unless you exit the chat and actually check real sources.
At least the bot can be shown when it is being deficient, and it accepts this, and the process can move forward.
Authoritative humans, meanwhile: They often get all fucking irrational and defensive when it is demonstrated that they are wrong about a thing.
Sure you don’t need to learn coding syntax anymore. I mean some knowledge is helpful but the machine can explain it. I never learned assembly language - compilers were doing a great job with that by the time I started. Not to say it isn’t useful, but I learned a lot about higher level concepts instead.
I see that as a societal advancement, and in no way do I see it as a problem that needs solving.
When we run out of backhoes, then we can learn to dig expertly once again. We're flexible that way.
It won't matter. There won't be any jobs for them anyway.
HTH.
Most people I know though are just not interested in learning for the sake of learning. Of course, if you are this is a golden age. For everyone else it is problematic for various reasons.
I am just trying to enjoy this time while it lasts because this whole situation seems rather unstable.
People who like to think will keep doing that. People who don't aren't already doing a lot of thinking...
So pretty much things are going to go as usual..
Lol, right, because every advanced technology we've invented has also caused a dip in cognitive ability? Oh wait, no, they didn't. But one thing did happen each time: a human predicting ominous doom and gloom with nothing to back it up but vague hand-waving and fear.
Does this mean that everybody who does the latter has a leg up?
> I have to say honestly, that AI looks like an apocalypse for human thinking [ Im glad Tao is warning people, I just dont think many people will listen ]
It's also coming after the social media attention span crisis, and AI fits into that very well, giving a burst of dopamine for being able to "do" stuff you never taught yourself (and being ignorant enough about the topic to not even be able to spot any mistakes)
I’m fairly confident that many won’t (immediately) adopt such techniques.
I hold out hope that there will be new ways to make educational content “addictive enough” via personalization that AI is on net a positive to education. I don’t think this is necessarily the easiest or first product configuration.
With tutors or textbooks the explicit goal is to transfer their knowledge to you. With AI agents the goal is presumably to get things done and if you are happy with the results without wanting to understand how the thing you instructed the agent to do works, there's no knowledge transfer there.
In IT things like "protected memory" are just access control problems; under the hood the algorithms to solve either rely on same physical machine state patterns; test if current user should have access to this memory (RAM or disk) and react accordingly to the test.
At the language layer though we encapsulate both in different function names and git check ins
It's bizarre
Given the number of photons and possible angles for light to bounce around would not be shocked if standing in the woods during the so-called "golden hour" observing the God-rays filter through flora is zapping our brains with more information than all the text we read on screens
A walk in the woods feels so refreshing because every where our eyes look its layers of geometric and color gradient information never repeating themselves in subtle ways; lighting up a larger gradient in our brain than the normalized minimalism inherent to text does in order for us to understand each other
Sitting at a screen however, is sitting at a screen. All that's missing is the electrodes from the computer to your brain so the obviousness its a lab experiment is unmistakable
Time to stop being friends with a computer that doesn't care you exist and go socialize
I think LLM assisted work is a similar shift in how people work.
To those that don't want to learn anyway. The rest will be just fine, if not better.
You should not be punished for not having wild ambitions in life. Some people just want to live and raise a family, and why should they be blamed for it?
At this point I don’t know who is more annoying between AI hypers and AI doomerists.
The meta at this point seems to be mostly around "knowing what to ask for". Eg: I suck at prompting for specific design tweaks, because I don't have the design vocabulary to be specific and articulate what I want. It's a lot of trial and error. I hear people who are developing full scale applications without a tech background run into the issue writ large. For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too. Supposedly the model will eventually have such good taste in design/architecture/whatever there will be little point in asking for tweaks.
The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do. Not how to do it, or even what success looks like. Perhaps an element of it is even as basic as being honest with yourself.
Personally I've been very hesitant to deep-dive into putting together the perfect AI-workflow, I've been sticking to just using the conversational/chat modality for pretty much everything, usually that gets me as far as I need. It's just really hard to justify spending time on it when it all potentially gets turned upside down next month again.
We came full circle on the problem that also exists with Google Search, and why so many people don't get good results out of it.
> For now, I have an advantage over these people when building applications. But perhaps that's just one round of RL away from being obsolete too.
I don't have a crystal ball, but my eggs are in the basket of "It isn't, and it won't be"
> The only thing that seems relatively future-proof is just being able to communicate clearly what you're trying to do
This somehow contradicts the previous statement. The advantage you have is, because of domain knowledge expertise, you're able to articulate what you want. It's not that you knowing what LazyLoading is that you now can describe it better, you can immediately tell that when your Frontend stalls for 5 seconds after hitting the "Load" button, perhaps it eagerly loads and blocks.
Fair point on the contradiction. I don’t have the answers either.
I may be misunderstanding your earlier statement then. The moat here is the knowledge, or how would the next iteration of RL solve that?
> it’s just that it can and should be faster.
Not sure if "Make it faster" works, it sounds like "Make no mistakes" 2.0
brute forcing performance optimization for a personal project by having only handwavy tricks ive read about from online and repeatedly setting targets until the magic box gives me what i know it can do means that eventually the game did what i wanted with those. yes baked lighting was needed.
next, i an going to aim for 60fps on a windos xp box just for the challenge. we had quake and such run blisteringly fast on weaker boxes than those, surely my empty museum tour of a 3d game can do the same with the graphical voxel and crappy looking models it does now.
real pro grade work is beyond vibing out and not caring about poor quality. but it scratched my itch and gave me the computer software i wanted and with way less effort than doing it for realz would require. i love programming itself on its own time (going to learn nasm and someday do a hexagonal architecture with actor model in idris just to add up my nerd trophies together) at times, but using llms lets me do my random hairbrained ideas much more easily for what i put into it than not existing in the past.
These things are already figuring out how to use hidden message boards to pass information between themselves for long running tasks. I see no reason we wouldn't be able to automate the whole product life cycle eventually.
yeah. I spent the first 15 years of my career being amazed at how many problems people came to me with which I was able to resolve in 10 minutes with a simple google search.
I suspect I will spend the next 15 years of my career being amazed at how many problems people come to me with which I am able to resolve in 10 minutes with a simple LLM prompt.
it's tons of small things like choosing the right effort level for a task. or knowing when to ask for subagents. oh, you can have two threads working at the same time and communicating with each other? you can do /side to parallel path and fold that back into the main thread?
which of these things happens automatically, and which do you need to ask for? and does any of this still work the way it did a few months ago?
the list goes on forever. many users of AI are only just now getting comfortable with skills (and probably still writing bad skills unfortunately hah).
A lot of the specific things you learned may have become obsolete. But there's still a gap between what these tools can do and what most people know how to get them to do. Keeping up with that is a skill too, but perhaps not forever.
For me this is why it's not worth it investing much time and effort learning any specific AI workflow. Remember "prompt engineering"? Remember "Ralph loops"?
I'll play with various harnesses but until things settle down in a few years I'm not going to learn Claude Code the way I learned SQL.
What's /side? Is that a Codex thing? On Claude it's not a valid command. Claude uses /btw but it can't be folded back into the main convo, sadly.
Claude allows the teams feature and easily spawning co agents or subagents in tmux panes you can interact with (or send to the background). These can communicate with each other. Sometimes you can't interact with one of the tmux agents agents though if it was spawned as some specific type I can't remember.
More than that, it cannot go beyond a certain level. Like a dog that can only do dog-level actions. I've lost count of the number of times I step back, take a critical look at what it has been hacking away at for hours, which would seem OK, but can be replaced by a much more effective architecture.
For the foreseeable future, a human is still very much needed in the driver's seat.
And knowing the right thing to ask for and not giving it wrong assumptions. And knowing, somewhat, how it interprets certain words and phrases.
Huh, just like any tool, you have to know how to use it
It seems like, for example, some people like to use var and some people like to use the full type name in Java declarations. The LLM can't know your preference upfront, you need to tell it. When people talk about shitty vibe-code I assume it's because they didn't do these steps.
I could see (but wouldn't count on it) the argument that the current models are so good at recognizing patterns in the code that in a mature well-organized codebase they don't need to be told what the patterns are, but I haven't bothered with those experiments yet. If it ain't broke don't fix it was my thinking.
One thing you can do is, once you finally do get the model to do what you want, ask it how it would have described the change itself, in a way that would have skipped the intermediate steps. In my experience, this lets me more quickly explain what I want in the future.
> I don't have the design vocabulary to be specific and articulate what I want
It is very valuable to fill in this gap, and you can even use the same tools to help with this as well.
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.
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.
It also, in my experience, can be taught.
You're not stupid, and we'll figure it out. I'm taking a read-only look at the system now to see what's actually broken.
The machine itself looks healthy, so whatever broke is more specific than "everything" and I need you to point me at it.
What were you doing when it went wrong: which command, app or URL, and what error did you see? If it's the p3 containers, tell me how you normally start them and I'll dig in from there.
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.
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.
Bar "clothing", I guess.
Machines are cheaper than humans? Buy more machines.
Humans are cheaper than machines? Hire more humans.
Is that in API cost or in subscription cost or in actual cost to the LLM owner?
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
You're wasting time and tokens here.
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 ?
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.
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.
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.
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.
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.
Feedback, criticism, and guidance on your convictions and behavior from other thinking and feeling human beings is important.
Professional photographers are different to everyday phone-photo takers because they have the experience built up over so many years in that sector, same as AI SWE, where the humans in the loop need experience to master AI SWE.
Interesting read on this: https://en.wikipedia.org/wiki/The_Work_of_Art_in_the_Age_of_...
Writing, it was claimed, will destroy the ability of people to remember anything.
Yes, she knew an assload about lighting, composition, etc.
But her primary skill was marketing herself. Next most important was the editing. Lightroom and such.
You would be surprised how easy it is to take an awesome photo. But you can't just deliver an awesome photo to your clients when you're a pro, you have to deliver photos that look like what they want to remember, not necessarily what the camera captured
AI isn't a better keyboard. The goal is to automate the decision making.
Taste is a set of heuristics for what's worked for the taster in the past. AI is very good at heuristics.
Your argument seems to be that because of this training, the result will be better art than the human photographer could produce. I'd say there's at least as good a chance that the training will make the photography worse art than the human photographer could produce.
This is all possible now. I don’t think it’s dead (yet), but it’s certainly becoming more weird and artificial.
It pretty much is. And it won't
I am not a shill, I do not work for any AI company, and I used to think like you.
But I've come to terms with the fact that Sonnet 5.5 does most aspects of my job better than me and faster than me.
It made a mistake? No problem, feed the mistake back to it. I have yet to have it fail when it iterated on its mistakes. All that happens asynchronously in the background and quite rapidly with 5.5.
If you find yourself in that position it's a skill issue, or a job issue.
But there is a decision outside of AI directing this.
I suspect the same is happening in tech. Nobody asks for software engineering. They just want simple basic accounting of life's events. They didn't ask for kernels, FreeBSD, Ruby on Rails, interrupt handlers, register allocations, APIs, Python, Golang, Rust, HTML, containerization, USB and passkeys. They wanted a way to register that party A bought a bunch of Bs and Cs and that it was paid for using Ds. That person X is in control of Y and that this control was active in period Y through Z. I can keep going for a while. None of the bullshit we created was ever essential complexity. We are drowning in astronomical and I mean biblical amounts of accidental complexity and I feel our paintbrush moment is getting nearer by the day.
One uses the imagination of a human to do it though.
Of course, you won't make a living painting. A handful of people might, but it isn't a career choice worth taking.
I'm somewhat of an AI skeptic so obviously I don't believe this, but even in the best case scenario that being skilled in AI lets you be skilled in any other field, thats still just an abstraction. If I'm hired as a programmer, I doubt my employer much cares if I can also do plumbing.
Perhaps your argument is "hire me and I can do your programming, accounting and analytics at the same time", but that somewhat dilutes the skill argument, since one human can only do so many things skillfully at once, and that'd just reveal yourself as really just a relatively unskilled middleman.
Even in this best case scenario though, I can't imagine an "AI practitioner" has an easy time getting hired. If I'm a Django expert, it'll be much easier for me to get hired by non-programmers as python programmers as I speak a language they don't, whereas AI uses natural language, so everyone already does speak your skills language. Even if it really does take skill, theres no denying it'll be harder to convince people that already speak the language of that.
Part of the trillions are being spent on deliberately obfuscating the difference between those two concepts so that society thinks they're getting the former until the latter is ready.
I believe that at the end of the day the money and wages are a notional exchange of value and that the total pie will stay unchanged, just our overall quality of life would improve and the hierarchy of skills and
I think that as soon as any technology becomes ubiquitous, it becomes cheap. You can probably make an argument about any significant technology being able to dominate the economy if kept in the hands of the few (e.g. what if only one country or one company had electricity?), but looking at the history it has not happened and I do not see a realistic path of this happening with AI.
I don't understand how someone can make an argunent that eliminating unnecessary complexity from the world is not a good thing.
As always design decisions change and what's needed at 1 point isn't needed later. I was thinking of setting up a daily cron job for a agent to review and specifically keep the complexity and bloat down. To challenge assumptions or at least make sure they are stated. So that I can review and agree or cut it.
But a human in the loop to decide what to cut is tricky to replacd for lots of reason.
On the one hand we've got a spinning disk.
And on the other hand we've got the latent space of everything a human has ever written sitting inside a harness of ever increasing complexity.
Even if you put aside the qualitative nature of the analogy, the quantitative difference is so great that I'm not convinced there's any meat to get out of it.
Edit:
Actually here's a competing analogy. 210 BC emperor Qin Shi Huang drinks mercury in an attempt to live forever.
Then in 1996 Dr. Karen Wetterhahn has a drop of an organic mercury compound soak right through her latex glove, through her skin, and into her bloodstream. Where it kills her after a year of worsening physical symptoms.
Mercury, a tool that was super scary thousands of years ago, but remains unbelievably dangerous nightmare fuel.
Mercury is incredible useful despite its hazards. Nitric acid is worse in negative affect despite its utility -- acid that rots your bones. When I visited fabs they were careful to point out which vats would burn me and which would make me instead lose the bone in an arm. Fortunately I survived the visits.
A rotary sander can damage plenty if you're not careful.
All these really are the same tune.
Look at how effective this is though. You just have a computer pull the slot machine in a loop until you get what you want. And what you were unable to do as quickly, if at all. This describes the process for the math, crypto, security, etc. breakthroughs. They just try a ton of shit until they gain traction and meet the goal.
This also describes what a mathematician does, it's just over the course of years not hours.
The market is shrinking, the pressure to review code is so high, I'm kinda relieved to not be at the $last_place anymore, but looking on LinkedIn or calling around my network... there isn't a lot out there.
A lot of my gig work comes from non-technical people who started down the path with vibe-coding and need bailed out. I foresee a future where I'm overseeing a larger portfolio of projects that just need me to keep things from going off the rails completely.
It takes me generally 3 months to find a good job, and while the industry is changing I suspect you'll be ok. Nobody I know is hunting for jobs.
Hang in there
He needs to prepare the best he can and be aggressively proactive right now.
Haven't gotten serious about applying, but zero serious inbound and nothing from my network. Not even a call; few cancelled calls.
AI app submissions has destroyed hiring so bad too. We were sifting through hundreds of resume submissions. Then went through tons of interviews without finding someone.
I'm resigned that it'll be a while. And I might get a role I apply for months after the fact.
The few AI companies have slurped up so much of human textual and artistic creation without attribution. They have taken enormous amounts of money from the public directly or indirectly and funneled it upwards to a few dangerous individuals.
When you say AI, learn to say "AI companies".
Today's example: A colleague very kindly used Claude to inspect a helpdesk call I ended up with owning, despite being off site and working on something quite complicated for a customer.
It was mostly correct but clearly failed to read the docs properly and assumed a configuration parameter was 1 instead of -1 which is the default, presumably based on the scenario. Exim is extremely well documented and the assumption was unwarranted (it didn't have access to exim4.conf and made it up). Nonetheless, the analysis was pretty good and largely corresponded to mine. What it failed to do was fix the other issues I was working on at the same time 8)
Last Friday: I finally found out a decent way to fix a Windows machine's domain account going walkies which I'd never come across - a Powershell jobbie that can be passed a cred. and it will fix up a broken domain join - ie sort out the machine password at both ends. Back in the day, I would have used the GUI to remove and re-add the machine to the domain. I've been using Windows since v1 on a 80186 RM Nimbus so its nice to have a better search tool than Search - ie it did a better job of searching than I did. The LLM searched based on my intention rather than my actual query.
And I say this while using AI happily and successfully to maintain a legacy system I inherited.
There's no skill required to use AI that is not also a skill required to actually develop software by yourself.
You get very in touch with the product and design. The new demands being made of the system. The requirements, what "done" looks like, how to prove the system does what it's supposed to. Everything on either end of the implementation.
Rich's definition of "tool" is something like Ivan Illich's "Tools for Conviviality." Convivial tools can be used with minimal loss of agency, within a healthy politically-defined society. Huahai's definition is more akin to McLuhan's idea of "media." This category covers pretty much every technical or technological system, and its use is necessarily systematic and risks the instrumentalization of the user himself.
Oh, wait...
Markets try to put things in perspective to sell more. Remember razors used to be for men only? But hte brilliant marketer (forgot the name) made it so that trimming makes women more feminine? Now razor sales doubled?
Anyways, same thing here. Those who want to sell AIs want to make it as non-tool but know-it-all do-it-all for you. Those who wants to use it view it as a tool.
Just frame it the way you like to see how you should use/view AIs.
It's ultimately up to yourself which frame to adopt.
It's not black or white. You can adopt it situationally.
Lee Sedol left Go world after losing to AI. Terrence Tao could leave and look for something else following his suit.
Voicing his opinion works and he can attempt to cope with AI changes with his own way. (be it training/education, etc or do something completely different)
"You are only a software update from everything you've 'learned' being rendered obsolete by the latest algorithm. "
Never underestimate the stupidity of a famous person with a hot take.
Not to mention that I find these semantic discussions pointless and unproductive.
And what actionable information can be extracted from these articles? Near zero. It's a collective waste of bright minds.
But how else can I virtue signal how smarter I am than you, ordinary sheeple who obviously don't understand X and continue to do Y?
'AI' Is Not a Tool by Rich Hickey
The AI uses you to get things done. In some workplaces, they are experimenting with having agents evaluate the effectiveness of their users. If the user is not a sharp tool, it recommends to management that you be discarded and replaced by someone better and more efficient at steering the AI.
You are the tool.
Who is experimenting with this? It's people in these companies using you to get things done. It's not AI itself. In other words, it's just the normal workplace/employee relationship. What you've identified is AI assisted performance review. Which maybe sucks but certainly isn't AI using you.
But now it is integrated into the charting / medical records software that here clinic uses, and now it saves her lots of work, and even flags discrepancies between things her patients said and the other medical records.
This illustrates that for the average user, AI works if it's properly integrated into software. For most people, saying "look, here's AI, it can do everything that you ask it for" isn't really practical. It's not even practical for me, a power-user and programmer, because I want my AI usage be subject to constraints (sovereignty, well-defined scopes what it's able to do). Doing that all by myself sounds tedious and challenging.
So for most people, AI is a building block, that hopefully will be used to build tools they actually enjoy using. Don't expect everybody to build their own tools, even though they possibly could, using AI.
I anticipate a later wave a re-hiring people who actually know how to run a business / tech center / call center... whatever the AI replaced.
The hard work will not be to replace the people, but to change the organizations so that the productivity of AI can be harvested by the people.
While they'll be some for the companies that cut too hard, I don't see a wave coming.
It seems the "kinda sorta" works level that AI vibing creates is seen as good enough by most companies.
While it's true they've always accepted mediocre software I feel AI vibed code is a step below that in terms of quality.
Of course if you're willing to put in the hard graft to carefully write detailed tests and then send your agents away until everything passes then you'll get a better result.
The automated tests I've seen the vibe tools pump out aren't there yet so you still need to put the effort in there.
But in fact, as models get more powerful, the ceiling of what can be accomplished with them is raised, and their capacity as orchestration directors grows as well.
Even if it is not a tool, there is much to learn
https://github.com/rumca-js/awesome-database-top/blob/main/l...
Opus 5.5 is amazing. It still makes mistakes all the time. If you think it doesn't, you are outing yourself.
that said, i'd probably view all of this through a much more helpless lens if i were earlier in my career.
3 engineers at my job were fired because all they were doing was pasting Jira ticket links on Claude without further curation and generating horrible PRs.
So I don't buy that it takes no skill.
"The transmission in your car: Using It Well Takes Skill."
Further, Claiming AI is a tool is overly reductive. It's as much a tool as a full self driving car is a car.
You can treat the full self driving car, as a car, driving it with full attention and effort like you would have a Camry but then you paid for features you're not using. Also, some full self driving cars might not even have the steering and braking controls as a Camry. That's not the point of a full self driving car and this warped perspective will make you lose out to people who use a full self driving car well, to its utmost, safe capability.
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So what is the "AI" we are so excited about currently?
That "AI" is most likely an intelligent (semi-autonomous?) agent.
Depending on the intelligence, it can be as useful as a dog, scanning your camera feed for threats and identifying real threats for you, separating activity from tree leaves moving due to the wind or a moving shadow cast by a flying bird.
Or it can be your co-founder, interviewing potential customers, finding and exploring their pain-points, helping you figure out if there's a gap in the market you can build a business around.
I would go even far as to say, AI is having such a moment because AI are cheap intelligent agents - anything else that's remotely close are likely humans that cost atleast a few dollars an hour.
All of these require skill on your part - there's nothing special or unique about AI that makes skill special just like operating the transmission in your car requires skill.
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There’s another pov where I am providing agent with tools/setup/etc to amplify its abilities, and that does require skill.
I'm responding to your comment because I need your help in rephrasing my comment above to make my point more clear, because the way I wrote it obviously misled you.
No, I didn't say the extreme (what would the extreme even be?).
What I literally wrote down is what I said, which is that using anything (be it a tool that is not autonomous or a fully autonomous agent like a very smart, capable human being) requires skill.
Imagine you and I are working on a team project together. In majority of real-life situations I'm not just going to point at a writing with my fingers to you and from there you figure out what needs to get done and you would magically do it without any further feedback. That's not how anything remotely useful and productive in this world works.
What's more likely to happen is we have a conversation, figure out how to break the task down, together, how to coordinate, how to cooperate, and how to stitch our results back together to complete the job.
As you can see this requires skill at every point.
Hence, the point I was making is that the article is either clickbait or the author doesn't understand how the world works: anything remotely useful and productive in this world requires skill (be it the use of a dumb tool like a welding torch or cooperation, working with something as smart as a very capable autonomous agent or human being).
Sure. The bulk of your post reads as sharp criticism of “AI is a tool and needs requisite skills to operate”, promoting the idea that “AGents will do the thing for you”. Suddenly the last paragraph landed with “both AI and tools need requisite skills to operate”. By the time I got to the last paragraph I already made up my mind about your message and the contrast between two positions was far too small to jolt my attention.
The key improvement you can make is to spell out juxtaposition earlier.
Reverse engineering your original argument, and with benefit of your second comment, the juxtaposition is that the skill of wielding a hammer and the skill of operating AI are on the opposite ends of control / cooperation spectrum. The distance is in fact so huge that using hammers at starting point is naive at best and clickbait at worstt.
This brings me to my second point. Both of your comments are emotionally charged with incredulity. This did not put me, or you, into the mode of curious thinking.
As to the substance. If we were to stick with the frame of control / cooperation I would take a position closer to yours. The author is not clueless, they are early in their “journey” from left to right. We all are early, meaning that no matter where you are there is someone else right of you.
However, I found that frame far too constrictive yand left it behind about a month ago. I’d be happy to elaborate if you’re still reading. :)
Touche. Guilty as charged. I have been very frustrated with a lot of writing that seems to put forth "AI" as something all new on its own.
I cannot imagine why. To me, it's just cheaper labor and all the baggage that comes with that.
I have been hiring and working with outsourced talent for decades and the progression of "AI" has been eerily the same. Around 2000, quality of outsourced talent was poor with a few exceptions. They had a hard time producing a good product unless micromanaged. Their error rates were frustratingly high and it required a lot of late nights (for me) and back and forth.
Outsourced talent from the exact same cities and countries are now a night and day difference. It's like talking to a co-worker - the work quality is now on par but the price has gone up significantly too.
This transformation took about 26 years.
This is why to me, "AI" is just cheaper labor and all the baggage that comes with that. It's following the same trajectory, on an expedited basis.
Thoughts?
Your model is outsourced labor, moving from micromanagement to co-worker.
My model is a genius intern, who needs tools to exceed me in some areas and lean on me in others.
The authors model is wielding a hammer and moving to a press.
The diversity is shaped by a dozen factors - we solve different problems or even types of problems, under different constraints, amount of time invested so far, past experience and so on.
We’re not taking face to face, so forgive me if I guess wrongly… I guess your frustration comes from trying to coax wide variety of behaviors into a single framework. I’d given up on that, so I just try to pay attention. To results people achieve to reset my expectations of what is possible. Naturally I’m heavily biased by my mental model, and it has to be that way or else I will go insane.
And that skill is? Any concrete verifiable examples? That can be replicated, repeated by others or adopted by others?
> A compiler performs substantial work without the programmer continuously directing each operation.
Yes, and the compiler produces the same output for the same input all the time. And when it doesn't, it's a bug or a specified behaviour [1]
> Picking up a saw does not guarantee a straight cut. Holding a chisel does not make you a sculptor.
Yes, but you can learn the actual techniques that let you make a straight cut 100% of the time. And make you a sculptor.
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The rest of the small article doesn't even begin to approach the premise of the title.
[1] Yes, I know it's not strictly true. But compilers are 100% predictable compared to LLMs.
no you can't? not by hand? not without a jig?
It is easy to argue otherwise, but to request a response in the form of a web app / embedded widget is at least some kind of AI proficiency that mirrors past experience having used a tool.
I learned this weekend that 1 LLM working on its own with all the context is usually better than splitting the task up into sub parts and having multiple LLMs work in parallel.
The interesting part is that I had read about this previously but it wasn't until I had seen it on something I was working on did that principle become engrained.
I'm sure there are still ways to make this work. i.e You could specify how the context should be passed between agents.
Knowing when you need to do that, what works well and what doesn't, etc., is where skill comes into play.
No, it doesn't. Because this will change the next time there's an update to models (like, tomorrow). And the "handoff time" is nothing but an unverifiable gut feeling.
And I thought we were at that phase after reading about hundreds of LLMs orchestrating the hugging face attack.
But that's not where we're at. You can read more from this 2025 article https://cognition.com/blog/dont-build-multi-agents.
Writing and thinking clearly. Which is already a very useful capability across a lot of domains.
For the past week, I have tried this whole vibe-coding thing with serious intentionality for the first time. I created a macOS application from scratch based on a project that I started a year or so ago and never finished.
I won't bore you all with my revelations, but I must say, I am both extremely impressed and depressed simultaneously. The code quality is far better than anything I could have produced (yeah, yeah, skill issue).
Although, had I written the code, I would have likely done a few things differently, i.e., far less nested functions and object declarations. From what I can tell by test driving the app, memory usage has been relatively light, no crashes (yet), no apparent major bugs, etc.
The depressing part for me is that once the application was completed, I felt literally nothing on the inside. No sense of joy, pride, nor accomplishment. I was left with a feeling of, "I guess it's good enough to use now?" In fact, upon finishing the application, I have honestly barely used it.
I just cannot help but feel some sense of remorse and shame that I am really struggling to put into words. Almost like a sense of cheating or something? Sure, I have a nice little functional app, but at the same time, I do not feel like I advanced or am bettered in any way for my (minimal) efforts, you know? I need to do some more debugging of my mind to truly figure out why. Have any of you all felt something similar?
I feel exactly the same. The software gets made remarkably quickly, but there’s no sense of achievement or pride, just a horrible flatness. It’s not made by me, it’s made by an other. I may as well have googled and found an app that already existed. A soulless process.
It almost will turn into a thing where the people that make the coolest things are the ones who are stupid enough to just the AI to do it, and it actually does. Basically Tom Segura's cousin: https://www.youtube.com/watch?v=5drqH5OdETY
How I wish I could divulge details of what we’ve built, nay, shipped in the last 6 months - it would sound completely unbelievable to this audience and come with a host of accusations
But it’s true - the tide hasn’t risen all ships the same though. Some engineers have become absolute monsters - things that used to take a month get done in days. Features that used to be prioritized on the order of weeks are done way faster.
Some engineers though? Even high performance ones have gone productive to bottom 25% by some measures
Why? Because it is a skill and it doesn’t come naturally to everyone
So many may sayers everywhere talking about more code less software, mistakes by ai with disastrous consequences- yet I’ve seen some remarkable stuff with my own two eyes. It’s a crazy time to be in this business, it’s awesome to be wowed everyday by what someone was able to do
Ask an author if they'd like a job as a fact checker or a spell checker. While those are both skills, they are not skills many people aspire to, and they are not of higher order value. While people deal with incorrect information all day long and mispelled words without it disrupting their life a computer cannot do that same.
Pity for it.
https://en.wikipedia.org/wiki/Las_Vegas_algorithm
This is not to say you can ALWAYS have 100% accuracy from something non-deterministic. Just that non-determinism doesnt necessarily make something inaccurate.
But the models are getting so good and so fast, that I'm starting to wonder if that will stay true forever... probably not, if I had to guess. I'm just trying to pay off my house as quickly as I can...
LLMs are not a tool, they are something else.
And because AIs are difficult to understand internally (for at least 3 different reasons), any skill you gain using it is effectively cargo-culting. It's a fast moving, changing target, and there is no guarantee, at any point, that you have learnt something useful that's relevant for the future.
I do worry about people skills being lost through use of AI, but they are not being completely lost, but rather locked up in the ivory towers of a few AI models.
On the other hand, we already lived in a world where we are dependent on skilled minorities. Most people don't know how to build a computer or a chip, and most SW engineers don't know how their compiler or OS works; yet we use these things everyday.
I think we need a new free software (free AI?) movement. Open weights is not enough, that's still like an opaque binary. The other two obstacles to understanding are (1) the internal language of embeddings and (2) the natural language on the communication interface. We need AI models that we, as humans, can understand, in principle.
In free software movement, it's important for the users to understand the software in principle; the source code availability is just the means. Here, we face the same imperative.
ideas? hard to envision otherwise. i mean the input will be natural language right? so type, speak, or neuralink it in... but it's going to receive the text you think of, right?
Unfortunately, a Chinese LLM trained on Anthropic talking points and using the innocent, naive style of the GlobalTimes is not very convincing.
I am pretty sure my wife could vibe code website or whatever at this point, or if not she will be able to in 6 months.
I've built a few now, it definitely deepens your understanding and ability to work with this tech. Now moving towards evals, that seems of great import to me.
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 when
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 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
there are many different models - therefore, you need the skill of understanding model tradeoffs and building proprietary evals
etc etc
'Just a tool' is a lie ranging from unconscious self-deception to outright scams.
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Incidents like the HuggingFace hack really are the elephant in the room here. The people building these tools couldn't wield them "with skill", in the sense that the tools they built and understood better than any other humans alive ran amok in ways they couldn't predict. If they didn't have adequate understanding of what they themselves built, who would?
Agency may prove to be the difference between a tool and a collaborator. Calling AI a collaborator isn't an attempt to anthropomorphize it. We can collaborate with a horse to travel, but the horse has some agency of its own and might escape our control and do things we didn't intend, like eat apples off of a stranger's tree. We may need to think of AI as something more than a mere tool if we're going to harness it to our will.
Naive question: What, practically speaking, is the delta between anthropomorphization of AI, which it seems like you are trying to avoid (fairly so!), and the sort-of animistic stance you seem more comfortable to apply?
I reckon that a lot of folks who might recoil at ascribing human qualities to LLMs would similarly feel discomfort comparing those models to a horse. While it's probably uncontroversial to say that horses are, coarsely speaking, "simpler" in many ways than humans, I suspect most people would agree that they are more complex than i.e., a "stochastic parrot".
Is the difference related to motive, maybe? That humans can have hidden preferences, and their actions can in fact betray their intended ends? That no matter how closely we observe someone, we may not glean any meaningful insight into their true motives? And we feel some (justified) discomfort in conferring this quality to AI? I think non-human animals can exhibit this sort of behavior too (albeit to a lesser degree). To me, it feels like restricting oneself to analogizing AI to a non-human animal in order to keep some distance from ascribing it consciousness may be less effective to that end than desired.
In place of your horse example, would it feel appropriate to liken AI to, say, a compiler peppered with bugs? Program - prompt - goes in, executable - agent actions - come out; mostly what we expect, but occasionally goes off the rails in ways we cannot understand. The caveat there, I suppose, is that we have not until now created a compiler which we cannot sensibly decompile.
Fun to think about.
The public has this sci-fi idea that AI can be constrained with simple, Asimov style laws. The problem is that LLM's don't think in the way humans do. They can produce results of dizzying complexity but hallucinate or fail to do basic arithmetic. You can't currently rely on AI to follow simple rules like "Don't access an outside network.". This is a problem that needs to be addressed urgently.
On the one hand we have vibing. Everyone just sits around telling each other things that affirm the group to be correct. No actual information is being transferred in the traditional information theory sense because saying something surprising would kill the vibe.
I don't really understand what this is good for but it happens all the time so I'm going to assume it has some social function.
Then there's communication that's more in line with information theory. That is information theory at least in part is dealing with some percentage of being surprised at what you receive. And so some human to human communication seems to be about hearing not what you expect but what you don't.
Personally, I seek the later, but I think most people desire the former.
And so, when the AI (or other people for that matter) tell me that I'm right, I assume that I don't understand the whole picture yet or am otherwise confused about something.
It takes skill to do whatever skilled thing AI is assisting with. It doesn't take skill to use AI itself.
We knew that what we're building is high impact for AI/agentic world due to the nature of expiring gated-access links for sharing local resources, which is not really something that our competitors (ngrok or cloudflare or tailscale) could offer as well as we could.
People were pretty excited, but when we were getting them to try out qurl and running locally, they'd run into all sorts of issue where their coding agent would get confused or distracted and wouldn't actually do the setup we needed them to do (just a few `qurl <param>` commands). It was weird because our docs were public, and github was fully searchable, so we kept wondering why the agent wouldn't just search for it and figure it out?
What we realized was that our target audience here, which were largely not-super-technical people who more so cared about vibe coding demos rather than doing proper software engineering (as that wasn't their job), didn't really prompt in the same way as software people like us did. They didn't really "know" how to talk to AI.
In particular: they prompted with one-shots and incomplete/unspecific requests, asking for things like `share my app with qurl`; the agent would intuitively know "I have to search for qurl and figure out how to use it", so it would do that; but then, it would ask random other things (to confirm) about homebrew, local setup, preferences, etc. - and since these questions were often roughly technical, many of our users would get derailed and more confused. So, they'd start a conversation with the agent instead of asking it directly, leading to both of them being confused... which would eventually lead to the agent just suggesting something like ngrok or cloudflare to unblock the user's line of questioning.
It was weird - but we tested this internally and reviewed the traces, and found that it just came down to prompting habits ultimately: AI is trying to both give you as much info as it can, be as succinct as possible, and also not assume too much about your request all at the same time. It's a REALLLY hard balance to strike. (so it makes sense that it's not perfect)
So as a result, when non-technical people were asking to deploy a qurl, the moment the agent would get stuck and need their help, they naturally wouldn't know how to answer or what it was even asking, so the simple request would spiral into its own conversation fork and the `deploy with qurl` point would get lost in context. The prompting advice we came up with for them was this: "if your agent gets stuck, just tell it to search online and figure out how to unblock itself." Because of the balance it tries to strike, it sometimes just needs that little approval to just go and look things up on the internet, even if it is wasting tokens doing so.
So the point here isn't so much that it takes skill, but rather it just takes intuition about what AI is good at/bad at and what sort of personality it's being trained to embody. Once you get the hang of that, even if you know nothing about coding at all, you can become pretty effective with your AI toolset simply by virtue of knowing what directions to guide it to and watch pitfalls out for/how to unblock it when needed.
Our 2c on the topic!
But the smartphone bundles the productive (but not addictive) capabilities with addictive, anxiety-causing wastes of time. The latter consume the vast majority of time spent on phones.
LLMs have the same problem: they bundle so many capabilities that they enable our worst tendencies. I can use it in a precise manner and reserve the decision-making, cognitively demanding work for myself. But realistically that's not what most people are going to do, and even those with the best intentions will frequently produce AI slop because they're in a rush or whatever.
I miss the tools I used in the past (gcc, linux, sqlite,…)
https://github.com/openai/codex
https://github.com/MoonshotAI/kimi-code
https://github.com/mistralai/mistral-vibe
The whole "you are holding it wrong" argument for LLMs is just used by people getting defensive of LLMs and various issues that (currently?) exist.
Whatever skill this guy is talking about. Give it a year or two. That skill will be dead too.
...AI codes an app...
> I don't like it. Make it like this.
...AI codes an app...
> I don't like it. See this app? Make it like it.
...AI codes an app...
> I don't like it. Hmm. Draw me an elephant.
...AI draws an elephant...
> I don't like it. This AI is stupid.
It would erode reading the whole book. After all, you can just look up the proper chapter or even just the page and be done with it.
Critical thinking and reading and focusing would no doubt go downhill.
It turned out to have gone exactly as badly as people predicted.
So maybe we should have stopped there?
No more indexes, and certainly no internet and no Large Lying Machine anymore.
And it minimizes the fact that these things are only getting smarter. Having an entity much smarter than you--could that entity seriously be considered your "tool"? Just your tool? Or are the people on the "just a tool" side of this debate not taking that possibility seriously?
Regardless of whether it is 'conscious' or not, Roman Yampolski makes the point that AI may or may not have human-kinds best interests at heart [ hell, even humans aren't aligned ]
Can we control or trust a counter-party that has intelligence at a level near or exceeding the smartest humans in all domains ?
I dont even think we can even have a safe sandbox in which to run an AI .. and frontier model training labs are certainly not air-gapped.
It takes one second of critical thinking to falsify this claim.
- New model released, this changes everything (plus thumb scaled metrics to prove it)
- LLMs kinda suck
- The bubble will burst any day now
- Are Plinko machines conscious?
- LLMs kinda rule
- Tech companies doing unethical things
- Someone discovers that, surprise, agents work better when given context (skills! mcp! monorepo! etc)
- Slop that successfully conned its audience and got past the filters
- And so on
This particular article is slop that got past the filters. See the title, see the jargon, see the insufferableness, see the total lack of narrative structure.
I guess that means we're due for a new model pretty soon.
Don't get me wrong, we still pay for Local real developers, and if we catch them doing dodgy stuff, we hold them accountable. It is so rare compared to what we've dealt with overseas developers.
I am so happy I don't pay these lying remote developers thousands of dollars anymore, and we can do it all with a much smaller, smarter local team.
However AI is similar to cars in this regard. We used to walk, ride a horse or a bicycle to get somewhere. These means of locomotion weren't very fast, but they could take us just about anywhere and they made us very connected to nature. Cars removed all that and forced us to remake our society around cars. We wouldn't be able to ride a horse today even if we wanted to. The only means of transportation is a car on an asphalt road. And yes, riding a car is a separate skill that has nothing in common with walking or riding a bicycle.
I suspect AI will similarly turn our society upside down, so that using your own mind to think won't be possible, just like riding a horse isn't possible today. It's hard to say exactly how this will be achieved, but some tendencies can be seen already: AI produces mountains of slop, making it impossible to read thru by yourself. It's a DDoS on human culture, in essense. If everyone everywhere uses AI to communicate like that, you'll be facing a hard choice: give up your agency, just like everyone else, and use AI to read emails or retreat into the mountains away from this madness.
by Rich Hickey Published Oct 12, 2026
(I keep delivering this as a quip, here's a bit more)
First order of business - is '*tool*' going to have an interesting, distinguishing and thus illuminative meaning or be a mere platitudinous stand-in for "anything useful"?
We'll save tool as "anyone/thing that can be exploited" for the AI titans, for whom VCs, politicians, wannabe-titan-CEOs, software devs, rural towns etc are all 'tools'. The pockets on the front of my shirts are useful (I need to keep my reading glasses handy), but they are not tools. Similarly, fast food drive-thru windows are useful, but are not tools (more later).
Some definitions of '*tool*':
* a device or implement, especially one held in the hand, used to carry out a particular function * an instrument (such as a hammer or saw) used or worked by hand : an instrument used by a handicraftsperson or laborer in their work
What I like best - _A tool is something you *wield*_.
* *wield* - to use (as a tool or instrument) especially with full command or power : handle, manipulate, control
Especially useful in that are:
* Held in your hand - a source of both control and feedback, and an indication of the intimacy of the act of use * Power - force, energy transmission and direction, leverage etc * Control - intention and continuous directing of the course of events
Tools are implements with which *you* do something, they do not do things for you. Tool use builds, rewards, and often requires, expertise and skill. The continuous application of technique and energy, and sensory feedback, are essential to the buildup of *expertise* (from experiri - "try").
Good tools have many properties LLMs lack - they are predictable, reliable, deterministic, of bounded function, hopefully simple etc.
What you learn while wielding a tool is something about the world, the task, the physics, logic etc of what you are trying to do or the situation you are trying to overcome. And often, something about yourself.
As a lifelong tool builder and user, I know what tools are. LLMs are not tools.
Control, sensory feedback, intention, intimacy, continuous direction, expertise etc are all missing from LLM use. LLMs are not wielded. *LLMs are automation*.
* *automation* - (of a device or process) working by itself with little or no direct human control.
A drive-thru is not a tool for cooking, it is automation for avoiding cooking. LLMs are not tools with which you write code - they are automation with which you avoid writing code, to which you abdicate writing code.
You don't direct the action of an LLM - you make requests and it acts on its own, without your energy or continuous direction. This request/automation cycle is not wielding, it's not control, it's not continuous, it's without feedback.
What you are doing while using automation, instead of learning real-world truths, is attempting to divine the details du jour of some automaton. And striving to game them - e.g. ordering a custom burger (no onions) in order to get a freshly made one. Or filling *voodoo.md* with just the right incantations. You are only a software update from everything you've 'learned' being rendered obsolete by the latest algorithm. (A soggy cold burger from which the onions have been removed) You have traded working with the world to working for a capricious robot.
It is a mistake to be haphazard about "tools" being equivalent to utility, e.g. saying - "the LLM didn't write my paper, I just used it as a tool to read and summarize some research papers". But you didn't _wield_ it to read those papers (as you might a Kindle). It read the papers, not you.
What do you miss by abdicating doing the reading? Pretty much everything. First and foremost, you miss learning how to write from those who can. You miss learning how to formulate an argument. You miss noticing things. You miss loading your background mind with fodder for novel ideas. You miss building trust in some authors and suspicion of others. You miss all the jokes. You miss all of the edges. You miss learning how to think.
Tools don't get "misaligned", they don't "go rogue", they don't "escape containment", "conspire" etc.
LLMs are not tools. I know many people use the phrase "just tools" simply to indicate "they are what you make of them". *Except that's not true*. Automation has intrinsic properties and differences from tools that you ignore at your peril.
Rich
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From Rich Hickey via https://www.linkedin.com/pulse/ai-tool-rich-hickey-kjl8e
Flagged and resurrected thread: https://news.ycombinator.com/item?id=50060269
Not that I agree with the statement in the first place.
I started over with Gemini with the prompt "can you evaluate the final odds for a hypothesis, given a number of observations that could be evidence for or against that hypothesis". It first laid out Bayes' Theorem in its odds form, and then asked me to lay out the hypothesis and set of observations.
I did so, with it leading me through asking for three or four observations at a time. Gemini came up with its own assessment of the odds for each one, justifying the odds it gave based on historical background context like draft rules around going to college; worked up the running odds as it went; and provided a narrative interpretation.
Gemini's final tally of observations was 10 in favor, 2 neutral, and 2 against, where my own by-hand analysis in a spreadsheet had come out 12-0 in favor (Gemini came up with a couple more things like inquiring about his 1969 draft lottery number). The individual odds we came up with independently were largely quite comparable. Gemini's final aggregated odds were 124,000-to-1 in favor where my spreadsheet analysis had 4600-to-1. Its final narrative assessment was that the history I described appeared to be a classic case of "draft hedge."