> In the 2010s, if you had technical gaps (say, you couldn’t write CSS), you had to either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet.
You could read some general reference/guide/tutorial documentation on CSS, and then probably solve your problem (without searching for "how to center a div", or whatever your exact problem was, and copy&pasting the answer and moving on), also becoming more knowledgeable in the process.
The rest of the short blog post has some good points, but the first sentence sounds like it's targeted at the percentage of developers who did StackOverflow copy&paste to close Jira tickets, never becoming experts.
Delegating to LLM-ish AI is just a natural evolution of that. The question is whether they can still add value if kept in the loop.
The article author suggests that the answer is to be expert, and is addressing people who... "either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet."
Not sure I agree with this. The math guy at anthropic's prompts are essentially:
"suppose you’ve gotta resolve the $CONJECTURE, like absolutely have to, everything depends on it. think really hard, and try to come up with a bunch of ideas to try. but remember to trust yourself and not necessarily in conventional wisdom!!"
https://claude.ai/share/25740bd5-aa97-4bd7-bf58-c4df3793fda7
https://xcancel.com/__alpoge__/status/2083855298239078748
Tao's chat was for him to gain intuition, not to solve the problem from the outset.
What's funny is that every other person gets a different conclusion about who these models reward/empower. I've seen people say that the generalist stands to gain the most and others say that it's the experts. Like all of life, maybe the "winner" is the person who just does stuff.
It depends on the levels. People with differing fitness levels and ages run at very different paces. Now, do cars make them more equal or less? On the bottom end, the tide lifts all boats. Most healthy people can learn to drive and will drive "fine", they get from A to B. Out there in the city streets the car flattens the differences, everyone roughly takes the same time to get from A to B in a car.
But at the top of top, the gap probably widens. A professional F1 driver will drive laps around some random guy. It amplifies reflexes etc, because at that speed little differences in timing make a big difference.
Now, AI coding isn't exactly analogous, but I think it also has these two regimes. It flattens things for simple tasks. If your task is to shovel data, do some trivial compiler wrangling staring at badly designed error messages, looking through GitHub issues hunting for the comment with many tadaa emojis to fix an issue etc, those things can now be done by anyone. Just as grandpa can also drive to the grocery store. But if you're pushing at things on a higher level, now only your above-AI ability matters. If all the things that AI can do well are subtracted out, how much other expertise do you have left? This will be proportionally a bigger and bigger difference between different people.
This works better for math because math is self-verifiable. Once you have a proof it needs no outside evidence.
Expertise is needed to evaluate model outputs where it can't verify itself, or at the very least one's expertise can help steer the model in the right direction.
However this is irrelevant if models themselves are better at evaluating/leveraging expertise/information.
Corollary to this is an important part of LLM usage is what I call pinning it to reality. That is, designing verification steps that interact with the real world in some way not easy to hallucinate or work around. This means things like having code that interacts with the physical world, round trip tests, arriving at the same result using different paths, interoperability / replication with external libraries / competing products, performance improvement projects that start with robust performance test suites, and similar sorts of things that reduce to "how do I provide evidence that's difficult to fool myself about".
This includes things like "before you start fixing this bug, write two tests that fail proving it exists".
Expertise is good, but a wise expert will set up methods for the machine to prove to itself that a desired result is achieved removing the expert from the tight development loop.
Never once have I typed in a prompt for anything complicated and expected the LLM to spit out "the answer". Not once. It's a back-and-forth.
More and more I'm becoming convinced that the AI skeptics have simply not figured out how to use these tools successfully. Of course, they will never admit that. Instead they'll drop into every thread and talk about how it can't write code or do math or whatever.
This isn't about technology, it's about problem solving. I guess there are lot of "smart" people out there who don't get it?
(Sorry, I'm in a crappy mood, but what on Earth are we supposed to take away from this? Everyone who disagrees with you is secretly an idiot, or worse, they're smart enough to know they're idiots but too proud to admit it?)
On a more helpful note, I think your "confusion" if honest can probably be resolved by realizing that "skeptics" are not a monolith.
I think it's the same thing as it was with googling. "Oh you're such a wizard problem solver, how did you fix my printer??" "I just typed the most important words the problem as you told it to me, the printer's model number and 'solution' and a forum link was on the first page, searched the page for 'solved' and followed the 3 simple steps of which buttons to press."
Then you saw how other people used Google, by filling the search bar with utterly irrelevant words, missing the one key word that's most important to what they are trying to do, then not be able to evaluate the returned search results and triage for which is most "solution-shaped", and they get drawn into wrong search hits, reading a clearly irrelevant page instead of quickly backing out to the search results page to try another page etc.
Or see how people couldn't formulate questions on StackOverflow, other than dumping a huge code chunk and saying "it doesn't work".
Now, AI makes these easier. You can now really just type natural language into the textbox, not just key words, you don't have to know about quote marks and plus signs etc. You can paste the code and say it doesn't work, and the AI just might actually spot a bug.
But having general problem solving common sense will still give you very good dividends.
There was one math proof that was AI generated going around Twitter and the chat transcript to generate it was basically the human prompting "keep going" until it solved it.
Tao's chat was fascinating because the questions he was asking belied expert knowledge of the subject that only a handful of people could have asked.
Yes, we're in the low hanging fruit stage where folks can just say "here's the problem" and "keep going" until a result is found and it will work sometimes.
The low hanging fruit will run short. Ultimately mathematics is a field of subjective selections of problems and proofs as beautiful and interesting. Machines absolutely will struggle with what to study, what theorems are desirable, and when do be done with a proof.
LLMs are a collection of biases. Humans are also a collection of biases. So we project our biases as input through the biases of an LLM and get an output. Hence why I think getting optimal output requires being an optimal person. And in that sentence there are many points of expression.
Finally, we train our LLMs on who we are. Another reinforcement of biases.
I do find that "signalling expertise" is important. "I have a significant background in biblical scholarship. You can assume I've read the most important works in NT studies in particular. Do not translate Greek, Latin, Hebrew, or Syriac. Now, I would like to know . . ." That changes things significantly. So does telling it you have 20+ years of experience with C programming, that you have a robust understanding of machine organization, memory layouts, embedded systems, etc.
Yes. I agree with most, if not all of this. For instance, I am seeing folks either relying in the LLM as an _assumed_ expert or, assuming someone - who knows the structure of skill definitions - also has some expertise (in the area of the skill).
It's a difficult situation; there is not much point in explaining _why_ the LLM output or skill in use (on a domain problem) isn't what the person actually _needs_ to address the domain problem, because the person isn't a domain expert or indeed, adjacent to domain expertise.
But, it is an interesting experiemnt to arm folk with little domain expertise with the _skill_ necessary to be able to extract the right solution from the model.
This is also why people's experience with LLMs/AI varies so much, because some people can see a use for AI for their needs and go about using the tool, while others do not as it relates to whatever they're working on and so they may say "LLMs/AI are useless" (it doesn't mean they're not experts though, although some people who have totally no expertise might also see no use for AI for themselves).
Overall, I agree, when I ask things I'm an expert in and do professionally every day. I get very good useful answers. When, for example, our marketing people, ask about the science, they often get confusing and wrong answers.
I am feeling this a lot lately. Getting the most out of agents seems to require being able to ask the right question. And how can you ask the right questions without deep domain expertise?
Yes sometimes it’s a matter of just using the right word. You can talk to an agent about a general concept for hours and hours and it may never mention $Concept_X, but you mention $Keyword_Y and all of a sudden the AI is going on about how $Concept_X is foundational to understanding the whole thing.
The counterexample of the Dinitz-Garg-Goemans conjecture was basically just "keep going" and finally "enough of partial results. now finish with a complete unconditional counterexample" lol
I often do my best to represent a genuine interest in the subject at hand and learning in general to models. Imagine the model's response prose and mannerisms being on the other polar end of answering questions simply to get the correct answers as they're often scoped for on quantitative benchmarks. Not sure I explained this well, sorry. An LLM could help
I have lengthy conversations with my LLM, almost like an interview. I agree on the expertise part, because I wouldn't be able to go in depth on a subject with it if I lacked the expertise.
Some work is a result of design and negotiations in those designs.
I don't think Tao's style works with everyone/thing, especially if we don't know what style he's tuned his LLM on.
This matches my experience. Just Talk To It is the best method for working with LLMs if you're an expert.
I've seen this at work (as eng manager/lead/principal/whoevenknowsanymore) – all the big APIs give you stats. We see how much people burn in tokens and we know how much output they produce. There is a pretty strong inverse correlation between token burn and output.
The more tokens people burn, the less likely they are to produce a good outcome.
In my experience (scientific programming) AI is a giant multiplier for people with specialized knowledge.
But it’s also a giant devaluer for that same knowledge as people with no idea what they’re doing can clog the field with plausible bullshit.
It’s now the case that if someone tells me they’ve done something, and I look into it and find out it’s completely AI slop, then I will have spent more time on the project than the person who “made” it. The situation is completely untenable and only serves to drain time and resources from people with better things to do.
this will have educational consequences (that I'm trying to solve). I don't think that we can adjust without rapid education and making extreme specialists of us all.
This requires coordination, certification, licensing, and other tiers of authenticity. False experts can ruin sample gathering, can ruin training. False expertise is exemplified by the current American Administration. Look at Robert F. Kennedy Jr.; he's a false expert. He is responsible for the measles outbreak. He is responsible for ivermectin abuse by humans. False expertise is overtaking real expertise. And the results are continuously disastrous and large-scale.
Many, including myself, report having a lot of success with braindumping and not structuring anything. Just talking into speech recognition for 2-10 minutes as a stream of consciousness about what my context is, what I want, what I know already, what I have a vague hunch about, how it fits into a bigger picture, what aspects are most important to me, any footguns I already know about, really like having a chat with a person on the phone, with someone you have to guide remotely because they have to implement the thing right now but you have to be out of office and so your only interface is speech. Except you can be more structureless because the AI won't be offended. Just keep on rambling, and press enter, don't even correct mistranscriptions. It will understand it anyway.
Now, the key is, that while rambling without structure, you do have to drop the key facts into your speech, and you have to know what you're talking about in at least a good portion of it.
I think people are afraid of doing it, because it seems "not the right way" or "not scientific" or whatnot. They want to believe there is some magic to writing the right prompt. So let me tell you, it works.
I don't completely disagree with the concept of giving a free association thought process ramble into context. But I also bet that when you start getting it to actually generate code and link modules of things together, subroutines, functions, code structure and filenames, you still pay attention to what it does and you guide it into the architecture that makes logical sense to you.
Unfortunately the software industry is saying things like "don't look at the code", "LLMs have made developers 10-100x faster", etc. The only way they can make such claims is by saying what you said above: "Claude, make me Microsoft Flight Simulator, make no mistakes".
Additionally engineers are facing pressures via deadlines to work in the paradigm of "Claude, make me Microsoft Flight Simulator, make no mistakes"...
The question i wonder about is, when will an event come along that persuades everyone that human understanding is still required? Or will it never come?
I wonder the same thing. I think we've already seen some of this happening, however the consequences haven't been large enough to the organization, for example:
Such an event would have to be pretty catastrophic at this point to slow down the inertia. Perhaps the tech debt will just pile up until someone's product implodes, or there's a massive safety issue that causes loss of life, or some big hedge fund goes bust.
> In the 2010s, if you had technical gaps (say, you couldn’t write CSS), you had to either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet.
You could read some general reference/guide/tutorial documentation on CSS, and then probably solve your problem (without searching for "how to center a div", or whatever your exact problem was, and copy&pasting the answer and moving on), also becoming more knowledgeable in the process.
The rest of the short blog post has some good points, but the first sentence sounds like it's targeted at the percentage of developers who did StackOverflow copy&paste to close Jira tickets, never becoming experts.
Delegating to LLM-ish AI is just a natural evolution of that. The question is whether they can still add value if kept in the loop.
The article author suggests that the answer is to be expert, and is addressing people who... "either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet."
Not sure I agree with this. The math guy at anthropic's prompts are essentially:
Tao's chat was for him to gain intuition, not to solve the problem from the outset.What's funny is that every other person gets a different conclusion about who these models reward/empower. I've seen people say that the generalist stands to gain the most and others say that it's the experts. Like all of life, maybe the "winner" is the person who just does stuff.
It depends on the levels. People with differing fitness levels and ages run at very different paces. Now, do cars make them more equal or less? On the bottom end, the tide lifts all boats. Most healthy people can learn to drive and will drive "fine", they get from A to B. Out there in the city streets the car flattens the differences, everyone roughly takes the same time to get from A to B in a car.
But at the top of top, the gap probably widens. A professional F1 driver will drive laps around some random guy. It amplifies reflexes etc, because at that speed little differences in timing make a big difference.
Now, AI coding isn't exactly analogous, but I think it also has these two regimes. It flattens things for simple tasks. If your task is to shovel data, do some trivial compiler wrangling staring at badly designed error messages, looking through GitHub issues hunting for the comment with many tadaa emojis to fix an issue etc, those things can now be done by anyone. Just as grandpa can also drive to the grocery store. But if you're pushing at things on a higher level, now only your above-AI ability matters. If all the things that AI can do well are subtracted out, how much other expertise do you have left? This will be proportionally a bigger and bigger difference between different people.
This works better for math because math is self-verifiable. Once you have a proof it needs no outside evidence.
Expertise is needed to evaluate model outputs where it can't verify itself, or at the very least one's expertise can help steer the model in the right direction.
However this is irrelevant if models themselves are better at evaluating/leveraging expertise/information.
Corollary to this is an important part of LLM usage is what I call pinning it to reality. That is, designing verification steps that interact with the real world in some way not easy to hallucinate or work around. This means things like having code that interacts with the physical world, round trip tests, arriving at the same result using different paths, interoperability / replication with external libraries / competing products, performance improvement projects that start with robust performance test suites, and similar sorts of things that reduce to "how do I provide evidence that's difficult to fool myself about".
This includes things like "before you start fixing this bug, write two tests that fail proving it exists".
Expertise is good, but a wise expert will set up methods for the machine to prove to itself that a desired result is achieved removing the expert from the tight development loop.
> Like all of life, maybe the "winner" is the person who just does stuff.
Someone who just does stuff still has to be able to deal with errors and failures. That’s where an expert or a generalist may have an advantage.
Never once have I typed in a prompt for anything complicated and expected the LLM to spit out "the answer". Not once. It's a back-and-forth.
More and more I'm becoming convinced that the AI skeptics have simply not figured out how to use these tools successfully. Of course, they will never admit that. Instead they'll drop into every thread and talk about how it can't write code or do math or whatever.
This isn't about technology, it's about problem solving. I guess there are lot of "smart" people out there who don't get it?
He's on to us, boys! Call in a code 43.
(Sorry, I'm in a crappy mood, but what on Earth are we supposed to take away from this? Everyone who disagrees with you is secretly an idiot, or worse, they're smart enough to know they're idiots but too proud to admit it?)
On a more helpful note, I think your "confusion" if honest can probably be resolved by realizing that "skeptics" are not a monolith.
I think it's the same thing as it was with googling. "Oh you're such a wizard problem solver, how did you fix my printer??" "I just typed the most important words the problem as you told it to me, the printer's model number and 'solution' and a forum link was on the first page, searched the page for 'solved' and followed the 3 simple steps of which buttons to press."
Then you saw how other people used Google, by filling the search bar with utterly irrelevant words, missing the one key word that's most important to what they are trying to do, then not be able to evaluate the returned search results and triage for which is most "solution-shaped", and they get drawn into wrong search hits, reading a clearly irrelevant page instead of quickly backing out to the search results page to try another page etc.
Or see how people couldn't formulate questions on StackOverflow, other than dumping a huge code chunk and saying "it doesn't work".
Now, AI makes these easier. You can now really just type natural language into the textbox, not just key words, you don't have to know about quote marks and plus signs etc. You can paste the code and say it doesn't work, and the AI just might actually spot a bug.
But having general problem solving common sense will still give you very good dividends.
This is becoming such a common statement on here it’s almost copy pasta.
Maybe the answer is more along the lines of “people are using them for different things and getting different results”?
Why does it have to be snark and “these people must be stupid”
[delayed]
And what about problems that cannot be one-shotted but helped along?
There was one math proof that was AI generated going around Twitter and the chat transcript to generate it was basically the human prompting "keep going" until it solved it.
Tao's chat was fascinating because the questions he was asking belied expert knowledge of the subject that only a handful of people could have asked.
Yes, we're in the low hanging fruit stage where folks can just say "here's the problem" and "keep going" until a result is found and it will work sometimes.
The low hanging fruit will run short. Ultimately mathematics is a field of subjective selections of problems and proofs as beautiful and interesting. Machines absolutely will struggle with what to study, what theorems are desirable, and when do be done with a proof.
LLMs are a collection of biases. Humans are also a collection of biases. So we project our biases as input through the biases of an LLM and get an output. Hence why I think getting optimal output requires being an optimal person. And in that sentence there are many points of expression.
Finally, we train our LLMs on who we are. Another reinforcement of biases.
> who these models reward/empower
The easy, straightforward answer is "the people who own the models". Who else benefits feels like a more complex question and we'll have to see...
I do find that "signalling expertise" is important. "I have a significant background in biblical scholarship. You can assume I've read the most important works in NT studies in particular. Do not translate Greek, Latin, Hebrew, or Syriac. Now, I would like to know . . ." That changes things significantly. So does telling it you have 20+ years of experience with C programming, that you have a robust understanding of machine organization, memory layouts, embedded systems, etc.
I cant have an overall opinion but in my personal experience i have analysed that LLMs do reward concreteness
Yes. I agree with most, if not all of this. For instance, I am seeing folks either relying in the LLM as an _assumed_ expert or, assuming someone - who knows the structure of skill definitions - also has some expertise (in the area of the skill). It's a difficult situation; there is not much point in explaining _why_ the LLM output or skill in use (on a domain problem) isn't what the person actually _needs_ to address the domain problem, because the person isn't a domain expert or indeed, adjacent to domain expertise. But, it is an interesting experiemnt to arm folk with little domain expertise with the _skill_ necessary to be able to extract the right solution from the model.
This is also why people's experience with LLMs/AI varies so much, because some people can see a use for AI for their needs and go about using the tool, while others do not as it relates to whatever they're working on and so they may say "LLMs/AI are useless" (it doesn't mean they're not experts though, although some people who have totally no expertise might also see no use for AI for themselves).
Agree with this. LLMs multiply the human user's ability. More ability, more impact!
And unfortunately, more ineptitude, more chaos.
Overall, I agree, when I ask things I'm an expert in and do professionally every day. I get very good useful answers. When, for example, our marketing people, ask about the science, they often get confusing and wrong answers.
I am feeling this a lot lately. Getting the most out of agents seems to require being able to ask the right question. And how can you ask the right questions without deep domain expertise?
Yes sometimes it’s a matter of just using the right word. You can talk to an agent about a general concept for hours and hours and it may never mention $Concept_X, but you mention $Keyword_Y and all of a sudden the AI is going on about how $Concept_X is foundational to understanding the whole thing.
Skilled use may or may not matter, depending on the task. Do you need to do what Terence Tao is doing?
The counterexample of the Dinitz-Garg-Goemans conjecture was basically just "keep going" and finally "enough of partial results. now finish with a complete unconditional counterexample" lol
https://x.com/DmitryRybin1/status/2079904005652893709
https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...
I often do my best to represent a genuine interest in the subject at hand and learning in general to models. Imagine the model's response prose and mannerisms being on the other polar end of answering questions simply to get the correct answers as they're often scoped for on quantitative benchmarks. Not sure I explained this well, sorry. An LLM could help
You're basically playing the role of team lead to the LLM's junior dev.
I love larping as a vacant scrum master
I have lengthy conversations with my LLM, almost like an interview. I agree on the expertise part, because I wouldn't be able to go in depth on a subject with it if I lacked the expertise. Some work is a result of design and negotiations in those designs.
I don't think Tao's style works with everyone/thing, especially if we don't know what style he's tuned his LLM on.
This matches my experience. Just Talk To It is the best method for working with LLMs if you're an expert.
I've seen this at work (as eng manager/lead/principal/whoevenknowsanymore) – all the big APIs give you stats. We see how much people burn in tokens and we know how much output they produce. There is a pretty strong inverse correlation between token burn and output.
The more tokens people burn, the less likely they are to produce a good outcome.
This is true but also false.
In my experience (scientific programming) AI is a giant multiplier for people with specialized knowledge.
But it’s also a giant devaluer for that same knowledge as people with no idea what they’re doing can clog the field with plausible bullshit.
It’s now the case that if someone tells me they’ve done something, and I look into it and find out it’s completely AI slop, then I will have spent more time on the project than the person who “made” it. The situation is completely untenable and only serves to drain time and resources from people with better things to do.
we are slowly punishing reading comprehension
this will have educational consequences (that I'm trying to solve). I don't think that we can adjust without rapid education and making extreme specialists of us all.
This requires coordination, certification, licensing, and other tiers of authenticity. False experts can ruin sample gathering, can ruin training. False expertise is exemplified by the current American Administration. Look at Robert F. Kennedy Jr.; he's a false expert. He is responsible for the measles outbreak. He is responsible for ivermectin abuse by humans. False expertise is overtaking real expertise. And the results are continuously disastrous and large-scale.
Prompt an image or video generator without knowledge in photography or art skills and your results will look sloppy.
LLMs reward architecture knowledge of how to structure things and how to not just say "Claude, make me Microsoft Flight Simulator, make no mistakes".
Many, including myself, report having a lot of success with braindumping and not structuring anything. Just talking into speech recognition for 2-10 minutes as a stream of consciousness about what my context is, what I want, what I know already, what I have a vague hunch about, how it fits into a bigger picture, what aspects are most important to me, any footguns I already know about, really like having a chat with a person on the phone, with someone you have to guide remotely because they have to implement the thing right now but you have to be out of office and so your only interface is speech. Except you can be more structureless because the AI won't be offended. Just keep on rambling, and press enter, don't even correct mistranscriptions. It will understand it anyway.
Now, the key is, that while rambling without structure, you do have to drop the key facts into your speech, and you have to know what you're talking about in at least a good portion of it.
I think people are afraid of doing it, because it seems "not the right way" or "not scientific" or whatnot. They want to believe there is some magic to writing the right prompt. So let me tell you, it works.
I don't completely disagree with the concept of giving a free association thought process ramble into context. But I also bet that when you start getting it to actually generate code and link modules of things together, subroutines, functions, code structure and filenames, you still pay attention to what it does and you guide it into the architecture that makes logical sense to you.
Unfortunately the software industry is saying things like "don't look at the code", "LLMs have made developers 10-100x faster", etc. The only way they can make such claims is by saying what you said above: "Claude, make me Microsoft Flight Simulator, make no mistakes". Additionally engineers are facing pressures via deadlines to work in the paradigm of "Claude, make me Microsoft Flight Simulator, make no mistakes"...
The question i wonder about is, when will an event come along that persuades everyone that human understanding is still required? Or will it never come?
I wonder the same thing. I think we've already seen some of this happening, however the consequences haven't been large enough to the organization, for example:
- https://www.theguardian.com/technology/2026/mar/20/meta-ai-a...
- https://tech.yahoo.com/articles/ai-code-wreaked-havoc-amazon...
- https://alexeyondata.substack.com/p/how-i-dropped-our-produc...
We can only hope that engineers working in safety critical systems haven't fallen to these working conditions.
Such an event would have to be pretty catastrophic at this point to slow down the inertia. Perhaps the tech debt will just pile up until someone's product implodes, or there's a massive safety issue that causes loss of life, or some big hedge fund goes bust.