I consider myself senior engineer. When talking with junior colleagues they often are surprised how little I care about some things and how much I care about others.

These internal "attention weights" are highly influential parameters of how I work with LLM.

E.g. when working with Rust I often hold strict control over structures and lifetimes. But when lately I've been doing token-based bind generation I didn't care about anything outside of high level patterns like RAII and ultimately - API ergonomics which was verified in consumer app.

I've been in position of porting real-code to vibe-code platform and seeing non-technical people prompt-stream (they were shared across accounts) I know why they engaged engineer to run this work.

Their efforts took 6 weeks, I ported app within 4 days and (to be honest with myself) without LLM I that'd be 3M+ work pre-LLM.

In short: I observed same effect as claimed.

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.

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.

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.

> Because my friend didn’t have the vocabulary to ask the AI to write code

Is it possible that the effectiveness of an LLM user with respect to the expertise of the user is like a sigmoid function or at least a step function in that shape? That is, one has to know something like the basic concepts and the vocabulary to bootstrap a programming project, but one does not have to know too much to do lots of meaningful work, and then again one needs to be en expert to build something extraordinary. Since most of the work is somewhere middle, most of us mere mortals are still concerned or stressed out for the possibility that LLMs will squeeze out too many job opportunities.

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".

If you go back to Alan Turings' paper, its all about chastisement! honestly, last pages are all about postive and negative (child!) reenforcement - 1950's style (I do not condone ... etc). Simple as that. I conduct high level litgation in the courts, and win because of a good LLM, with a good version of me, keeping it in line! also crypto and cyber sec. Of course, child rearing, and dealing with former spouses is also very useful. The orginal paper 1950 https://tinyurl.com/yuszahpw (punish is mentioned six times). Just say-ing-like. TTFN.

The actual "prompting trick" that dramatically improves your results is often to add just two or three words, like "use library foo", "write in <language>", "<bar> algorithm". To know which two or three words apply in your situation, you need a deep understanding of both the problem and the solution space.

Your prompt might look almost the same as the one from somebody with a good understanding of the requirements but no technical competency, plus maybe one or two sentences. Those one or two sentences dramatically change the results, and what those sentences are differs from prompt to prompt.

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.

> The model outputs are much more concise than when I try and talk to GPT-5.6 Sol about mathematics. By signalling expertise, Tao shunts the model into “talking-to-mathematicians”

Maybe, but FWIW my first thought when I skimmed Tao's session was that he probably has a personal system prompt requesting this style.

E.g. even if you get it into "talking to an expert" mode I've found AI waffling through filler like "given your background in Linux kernel engineering, I'll skip the surface level and go straight to the technical meat". You do have to explicitly tell them if you don't want this.