I did a test a few months ago. A friend of mine wanted to develop what i understood to be a simple single page web app. But since she didn’t have any software engineering experience she asked me to help. Around that time everyone was talking about how literally anyone can develop software with LLMs i asked her if she could give it a try first, and if I could watch the attempt.

I was fully expecting that writing the code will pose no problem for the AI. But i was curious if the AI will realise that my friend is a novice and needs extra help with things like: copy pasting the code into a text file and saving it with an html extension, helping her host the file online so she can share it with others, buying a domain for it, etc. I assumed they will get there eventually, but i also assumed that it will take a lot of stumbling around and misunderstandings.

But i was completely wrong. They didn’t even get to that point. Because my friend didn’t have the vocabulary to ask the AI to write code. They were just going around in circles where the AI was brainstorming with her about possible features and getting thints more and more complicated. We terminated the experiment after one and a half hours and many many messages exchanged between her and the LLM.

Whereas to me who knows the terminology would have probably just taken a single exchange of messages to get the result she described to me. I would have prompted with something like “Please write to me an html page which does X, Y, Z.” But since she didn’t know the right terminology she got into a vortex of feature discusion, and she didn’t find a way to tip the AI into “just do it, write it now” mode. In other words in that case the LLM would have rewarded even just a little bit of expertise, but without it there was a confusion about goals between the human and the machine.

The amplifying mirror analogy works best here. LLMs are ultimately a reflection of your own interactions with its weights, the tone you use, the structure with which you construct your prompt, aspects of an issue you tend to focus on, your breadth of vocabulary and world knowledge and whatnot.

People who (carefully) use it as an extension of their own mind and senses will very likely thrive, and those who use it as a replacement for their minds and their senses will struggle.

One of the Claude skills I made Claude itself generate was the 'learning a concept across tiers' skill -- from ELI5 level to a PhD level, and it triggers whenever I ask it a very general question on a complex topic that isn't my bread-and-butter. The fact that I'm able to choose explanation level from a super smart LLM (that's available 24x7) that can explain any topic under the sun would've been mind-bogglingly sci-fi-ish just 4 years ago in 2022.

canine - dog = expertise

It's pretty obvious that for some questions a novice wont be able to drive the conversation towards an "answer". A novice may also not be able to understand an answer either. But there's a more subtle failure mode.

The vernacular used by an expert and novice to describe the exact same problem lead to different traversals of the information space.

For example, I recently asked ChatGPT a medical question using plain english. It gave me an imprecise vague response and told me to call 911. Repeated prodding did not fix this, so I asked the exact same question using medical jargon and in one shot I got what I wanted.

> Of course both are useful, but I’d rather have familiarity with the codebase than a deep general understanding of software systems.

In my experience, getting that familiarity with a particular codebase in a way that isn't surface-level has always been a hands-on process. E.g. just because I know many general things about software, I need to know the particulars of the current codebase I'm in to know what is reasonable to actually apply to it.

This is a chicken and egg problem I find hard to resolve with LLMs. If we're pushed to delegate most work to them, how do you build that expertise? Sure you can ask questions about the codebase, but IMHO that falls under surface-level information, and the devil is often in the deeper details. Hmm.

This is something that really needs to be formally studied.

I'm inclined to say that this matches my own experience, but I can't rule out confirmation bias on my part.

As a meticulous person generally looking for a very specific code outcome, I prompt in a way intended to get exactly the thing I have in mind, and my results reflect that. But on the other hand, I have coworkers who type ten-word prompts with very limited specificity, and they seem to get results that way as well, and that makes me wonder.

It would certainly be beneficial for my career and financial well-being for the assertion to be true, because it means I don't have to worry about being pushed out of my job by an army of $15/hr vibe coders. But the convenience of that assumption is exactly why I think it's important to be skeptical.

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.

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.

I've been equating them to graphing calculators since the first LLM launched. It's an amazing tool if you know how to use it. If you don't know how to use it, it's still a tool, but you won't be doing anything amazing with it.

> 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."

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...

The short version I give to non-technical people who ask me about whether "AI will replace coding" is this: it accelerates you. You can get much further much more quickly.

If you don't know where you're going or how to get there, or even if you're just not paying enough attention, it will get you very far in the wrong direction before you've realised.

At work we call this implicit steering. To use webdev metaphor, if a non-technical person describes making a web page with a big block at the top and some things to click on and then my pictures below that, that will eventually get somewhere. Meanwhile, if you know industry jargon, you might describe a hero, with call to action buttons, and then below a 3x3 grid of images of my portfolio photos—that's likely going to generate something entirely different and likely richer. It can assume things about you (it doesn't think), it can ask you specific questions a web personal might know, it can infer domain context that is otherwise omitted with a basic conversation. Everyone wants to capitalize on corporate vibe coding but the tech literacy is hardly there, let alone more advanced topics.

The LLM is like the death star. If you don't know exactly where to point it, you will likely miss your target and have no/negative effect. The further away the target, the more accurate your firing solution needs to be. If all you need to do is add something like a dark mode theme to an existing product, this is probably a point blank shot in this metaphor. Building an entire codebase from zero, or even refactoring a legacy codebase into a new codebase, are lightyears away by comparison. You can still land the shot, but you need to deeply understand the metrology and astrodynamics.

The information system required to encode the aesthetic preferences needed to make a technology experience not suck is likely in excess of what any near-term solution will offer. Knowing when to say "no" is perhaps the most important skill here. You can't just say it arbitrarily either. You really have to mean it and be willing to fight other humans for it.

I totally see this. I just did 3 hours of bot sitting to put together some thrash loops that thrash our provisioning working flow for a BLE gadget we make. It was pretty straightforward and productive. But then, I have a lot of experience with BLE, and a quite a bit of experience with python and shell scripting. So I was able to guide the process through stages, do some intermediate testing, make some adjustments, and proceed. Domain experience made this really easy and straightforward. Me two junior engineers who have only superficial/high level knowledge of BLE and some of the other pieces, couldn't have done this as effectively.

Where my angst comes, is worrying that no one will ever get that experience anymore. They might have had some eventual success, who knows what monstrosity a much less guided LLM would have done, but experential learning may be mostly a thing of the past. And it creates a real tension between the person with experience and the person without.

A good moment to remind everyone that if we took the promise for granted, that AI will in fact prevail and prompting is the one skill that will rule them all... we'll lose all domain experts in one generation.

It's less of "signaling expertise" and more about actually having said "expertise".

In my experience with LLMs it's not uncommon to be having a deep conversation about making pasta, only to be told, after asking for a sample recipe, to get a bucket of paint and a bag of concrete. Of course these hallucinations are way more subtle and easy to miss for someone who doesn't have deep domain knowledge.

That's why when people like Pieter Levels tweet "I cancelled and then vibecoded 100% of my SaaS subscriptions", you need to take it with a huge grain of salt because you're not Pieter Levels, you cannot vibe code your SaaS subscriptions.

This is absolutely a case where you can't get any output that better than the input, and the input is you.

I think this is just the nature of LLMs as predictive generators.

The model is predicting the type/level of conversation based on what the other party is saying.

The most typical types of conversation are of two peers, so by default the LLM is likely to respond to you at your own level, unless you ask it to behave differently.

As always, prediction goes deep. The best response to Terrance Tao is Tao-level math.

It reminds me of reading how LLMs continue chess games if given a partial game - they have learnt to assess player strength based on the moves they make, and will predict game continuations based on the perceived strength of each player, predicting (generating) poor quality moves for a weaker player. This isn't an AI playing chess to win - it's an expert predictor predicting what comes next.

Wow

There's a growing and fascinating divide between people who see LLMs as more of a "bicycle for the mind" in the vein of Jobs vs those who see them as whollly supplanting the role of human intelligence. I can't help but wonder if these aren't primarily two human archetypes more than anything - the LLMs can be both and they erect a mirror of the human using them. Some humans really don't want deep individual expertise and intelligence to be the deciding factor because they don't identify with that. Others are completely the opposite. We really can't tell which will be more effective yet, because LLMs are very good in both modes. But most of the predictions currently are people executing on wishful thinking about what they hope will be the outcome.

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?

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.

Keep telling yourself that, right up to the layoff.[1]

[1] https://www.linkedin.com/posts/ademola-adelakun_pov-you-get-...

I actually don't feel like Tao's recently published conversation is the best example of this idea. As intelligent as Dr. Tao is, and surely more so than me, I got the feeling that he wasn't running up against failure states of the model, which I'm not sure you could attribute entirely to his expertise. I honestly think it was more a matter of luck that the model apparently had so much training data on the topic or that it was architecturally so well suited for it. On the other hand, I've had really surprising moments where Claude was just failing terribly to execute simple dev ops tasks having to do with log processing. And I'd be so bold to say that I don't think it could have been explained by a lack of expertise on my part, or even a misuse of the model.

So yeah, sometimes LLMs reward expertise, sometimes they don't. I guess either way it helps to have it.

"The most important skill in the AI era may not be prompting. It may be learning how to solve problems using the right kind of help." [1]

Context - I have over 25+ years in software, and I have this observation - being introduced to a new codebase as a human is difficult, especially depending on the scale/size and complexity of it.

Yes, you do start to learn it as you work through it, but if the scale is truly huge, it may just not be possible to fully read and understand all the code and paths etc.

I have found systems-thinkers (I believe I am one, sometimes they are architects) to be able to kind of "see the whole picture" while not knowing all the details, to the point of being able to guess how the system/software should be behaving, even if it is not actually yet. This is a hugely valuable skill and I think takes a certain kind of brain too.

That said, I think recently I may have realized something - we rely on statistics and confidence levels in order to make statements about larger populations. If we can represent a codebase as a, perhaps stratified population of code, interfaces, docs, etc. etc. etc. we may be able to take a valid random sample, review portions of the code, and make some kind of assertions about the state of the larger system - potentially, from that.

I am trying to implement this as a side project right now to see if there is anything to it, basically, a combination of AI/LLM + stats/sampling + facilitated expert human review.

I'd be interested to know if anybody is doing anything similar.

[1] https://www.actinginbalance.com/p/the-right-tool-rule

Agree with this. LLMs multiply the human user's ability. More ability, more impact!

You're basically playing the role of team lead to the LLM's junior dev.

Prompt an image or video generator without knowledge in photography or art skills and your results will look sloppy.

Domain knowledge will stand alone as the sole differentiator. Because LLM benefits can be reaped by almost anyone and it’s a force multiplier. Now those who have the strongest initial force will have a far bigger edge than before.

Love this idea of reading prompts that lead to new discoveries and figuring out how the person got the LLM there. It truly is an art and I’m always reminded of “I, Robot” and the scene about “you must ask the right questions.”

Well, nice post. Actually, there may be some truth behind it, but basically, it captures what I—as a programmer—want to read: expertise will remain valuable.

But how I am observing is different, though. Since LLMs the gap between experts and non-experts has been shrinking. And yes, there is still a gap, but vanishing.

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.

This feels like a moment in time, not the end state of AI.

Like I read there was a time when teams of people + AI could beat pure AI at chess. But that these days, pure AI wins.

For all the things people say about "how AI works" you have to add the missing piece "how current AI works".

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.

Same thing I personally am not a front end guy but I have dabbled with it in the past but I am writing a front end app and a chrome extension, but besides a few pages of code I have reviewed I really do not know what the fck is written (its for an MVP I am building) and I am feeling really conflicted as to what the fuck do I do. At work, the stuff I write has a decent mix of my code, AI code and a few things I do the old way of copying from stackoverflow and seeing what works/doesn't work.

Agree so much with this! In domains I know well I get much better results then someone who doesn't know the domain because I know where to challenge the LLM. LLMs need to be pushed because otherwise their answers are typically average.

So we could run a lighter LLM in front of humans, which translates from 'no domain knowledge' to 'domain expert' and in turn prompts over to the larger LLM.

Then the larger LLM gets all the right lights on, yields better outputs and we translate back into user domain.

I kinda thought the chain-of-thought reasoning already did this, no?

This has been my experience as well. I’ve also been thinking a lot about Terrence Tao and his chats and presentation.

I agree. When I talk to LLMs about fields I am familiar with, I can push back on bad suggestions and ignore faulty/incorrect advice and assumptions, which is much harder for unfamiliar subjects.

Of course, simple common sense and extremely basic Googling on unfamiliar subjects can produce similar results, but it's much faster if you are truly understanding what the AI is suggesting.

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).

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.

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

Might be a very noob question but in this era of LLMs, let me ask the reverse, how do you gain expertise? It seems this rewards people who had expertise pre LLMs, but what about people who don’t have that in a specific domain? What approaches are viable now in this current system?

I cant have an overall opinion but in my personal experience i have analysed that LLMs do reward concreteness

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 find this post re-assuring (as who doesn't like to feel like they are an expert at something and llm definitely strips that away)... but it still feels like you are rewarded just as much for being a 6/10 expert as you are for being a 9/10 expert. It definitely is an equalizer it is just a question of to what degree.

The fact that Claude knows I wrote the Virgil compiler makes it be on its best behavior when working on it. I force it to not write too much code, and to write more tests. I push back on slop and just adding another special case. It has a surprisingly deep understanding of floating point.

I think if we have a large population of generalists, then none of them are generalists after all

Skilled use may or may not matter, depending on the task. Do you need to do what Terence Tao is doing?

Yes. This is called the Matthew Principle:

> For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.

I'm surprised nobody mentioned (including the author) the Gell-Mann Amnesia Effect [1]... Just substitute "LLM" for "journalist" and there you have it.

And to be honest, I have seen it, as I'm sure (almost) everyone has, who has demonstrated experience/expertise in their own fields, and correct the LLM's responses one time or another...

[1] https://en.wikipedia.org/wiki/Michael_Crichton#%22Gell-Mann_...

Is someone keeping a list of the excuses and varying instructions on how to hold it right? It would be fascinating historic documentation

LLMs reward architecture knowledge of how to structure things and how to not just say "Claude, make me Microsoft Flight Simulator, make no mistakes".

You got to know how to use the model+harness+prompt to achieve the results you want, but honestly for many projects and questions all the models already pump out their same best version of an answer.

Sometimes it is really akin to a git clone, although it was a LLM request.

This rewarding expertise is somewhat wishful thinking.

At the end of a day, it feels and is more like gambling, even with the recommended expertise and a good approach, don't delude yourself you're simply pulling the lever too, as any novice.

Now everyone who feels rewarded by LLMs will conclude that it demonstrates their expertise. ;)

Yes. If you use the right technical terms together it’s lights up more specific feature spaces to your task.

Specificity matters to LLMs a lot.

Cows

[flagged]

[dead]

[dead]