Maybe it's due to a more R&D-ish nature of my current work, but for me, LLMs are delivering just as much gains in the "thinking" part as in "coding" part (I handle the "communicating" thing myself just fine for now). Using LLMs for "thinking" tasks feels similar to how mastering web search 2+ decades ago felt. Search engines enabled access to information provided you know what you're looking for; now LLMs boost that by helping you figure out what you're looking for in the first place (and then conveniently searching it for you, too). This makes trivial some tasks I previously classified as hard due to effort and uncertainty involved.

At this point I'd say about 1/3 of my web searches are done through ChatGPT o3, and I can't imagine giving it up now.

(There's also a whole psychological angle in how having LLM help sort and rubber-duck your half-baked thought makes many task seem much less daunting, and that alone makes a big difference.)

> One thing I find frustrating is that management where I work has heard of 10x productivity gains. Some of those claims even come from early adopters at my work.

Similar situation at my work, but all of the productivity claims from internal early adopters I've seen so far are based on very narrow ways of measuring productivity, and very sketchy math, to put it mildly.

> One thing I find frustrating is that management where I work has heard of 10x productivity gains.

That may also be in part because llms are not as big of an accelerant for junior devs as they are for seniors (juniors don't know what is good and bad as well).

So if you give 1 senior dev a souped up llm workflow I wouldn't be too surprised if they are as productive as 10 pre-llm juniors. Maybe even more, because a bad dev can actually produce negative productivity (stealing from the senior), in which case it's infinityx.

Even a decent junior is mostly limited to doing the low level grunt work, which llms can already do better.

Point is, I can see how jobs could be lost, legitimately.

> overall my productivity is 10-15% better. That is nothing to sneeze at, but it isn't 10x.

It is something to sneeze at if you are 10-15% more expensive to employ due to the cost of the LLM tools. The total cost of production should always be considered, not just throughput.

On my personal projects it's easily 10x faster if not more in some circumstances. At work where things are planned out months in advanced and I'm working with 5 different teams to figure out the right way to do things for requirements that change 8 times during development? Even just stuff with PR review and making sure other people understand it and can access it. idk sometimes it's probably break even or that 10-15%. It just doesn't work well in some environments and what really makes it flourish (having super high quality architectural planning/designs/standardized patterns etc.) is basically just not viable at anything but the smallest startups and solo projects.

Frankly even just getting engineers to agree upon those super specificized standardized patterns is asking a ton, especially since lots of the things that help AI out are not what they are used to. As soon as you have stuff that starts deviating it can confuse the AI and makes that 10x no longer accessible. Also no one would want to review the PRs I'd make for the changes I do on my "10x" local project... Especially maintaining those standards is already hard enough on my side projects AI will naturally deviate and create noise and the challenge is constructing systems to guide that to make sure nothing deviates (since noise would lead to more noise).

I think it's mostly a rebalancing thing, if you have 1 or a couple like minded engineers who intend to do it they can get that 10x. I do not see that EVER existing in any actual corporate environment or even once you get more then like 4 people tbh.

Ai for middle management and project planning on the other hand...

It's just another tech hype wave. Reality will be somewhere between total doom and boundless utopia. But probably neither of those.

The AI thing kind of reminds me of the big push to outsource software engineers in the early 2000's. There was a ton of hype among executives about it, and it all seemed plausible on paper. But most of those initiatives ended up being huge failures, and nearly all of those jobs came back to the US.

People tend to ignore a lot of the little things that glue it all together that software engineers do. AI lacks a lot of this. Foreigners don't necessarily lack it, but language barriers, time zone differences, cultural differences, and all sorts of other things led to similar issues. Code quality and maintainability took a nosedive and a lot of the stuff produced by those outsourced shops had to be thrown in the trash.

I can already see the AI slop accumulating in the codebases I work in. It's super hard to spot a lot of these things that manage to slip through code review, because they tend to look reasonable when you're looking at a diff. The problem is all the redundant code that you're not seeing, and the weird abstractions that make no sense at all when you look at it from a higher level.

I don't disagree with your assessment of the world today, but just 12 months ago (before the current crop of base models and coding agents like Claude Code), even that 10X improvement of writing some-of-the-code wouldn't have been true.

Its great when they use AI to write a small app “without coding at all” over the weekend and then come in on Monday to brag about it and act baffled that tasks take engineers any time at all.

How much of the communication and meetings are because traditionally code was very expensive and slow to create? How many of those meetings might be streamlined or entirely disappear in the future? In my experience there is are a lot of process around making sure that software on schedule track and that it's doing what it is supposed to do. I think that the software lifecycle is about to be reinvented.

The reports from analysis of open source projects are that its something in the range of 10%-15% productivity gains... so it sounds like you're spot on

Wait till they hear about the productivity gains from using vim/neovim.

Your developers still push a mouse around to get work done? Fire them.

Canva has seen a 30% productivity uplift - https://fortune.com/2025/06/25/canva-cto-encourages-all-5000...

AI is the new uplift. Embrace and adapt, as a rift is forming (see my talk at https://ghuntley.com/six-month-recap/), in what employers seek in terms of skills from employees.

I'm happy to answer any questions folks may have. Currently AFK [2] vibecoding a brand new programming language [1].

[1] https://x.com/GeoffreyHuntley/status/1940964118565212606 [2] https://youtu.be/e7i4JEi_8sk?t=29722

I’m a tech lead and I have maybe 5X output now compared to everybody else under me. Quantified by scoring tickets at a team level. I also have more responsibilities outside of IC work compared to the people under me. At this point I’m asking my manager to fire people that still think llms are just toys because I’m tired of working with people with this poor mindset. A pragmatic engineer continually reevaluates what they think they know. We are at a tipping point now. I’m done arguing with people that have a poor model of reality. The rest of us are trying to compete and get shit done. This isn’t an opinion or a game. It’s business with real life consequences if you fall behind. I’ve offered to share my workflows, prompts, setup. Guess how many of these engineers have taken me up on my offer. 1-2 and the juniors or ones that are very far behind have not.

Making a comparison to crypto is lazy criticism. It’s not even worth validating. It’s people who want to take the negative vibe from crypto and repurpose it. The two technologies have nothing to do with each other, and therefore there’s clearly no reason to make comparative technical assessments between them.

That said, the social response is a trend of tech worship that I suspect many engineers who have been around the block are weary of. It’s easy to find unrealistic claims, the worst coming from the CEOs of AI companies.

At the same time, a LOT of people are practically computer illiterate. I can only imagine how exciting it must seem to people who have very limited exposure to even basic automation. And the whole “talking computer” we’ve all become accustomed to seeing in science fiction is pretty much becoming reality.

There’s a world of takes in there. It’s wild.

I worked in ML and NLP several years before AI. What’s most striking to me is that this is way more mainstream than anything that has ever happened in the field. And with that comes a lot of inexperience in designing with statistical inference. It’s going to be the Wild West for a while — in opinions, in successful implementation, in learning how to form realistic project ideas.

Look at it this way: now your friend with a novel app idea can be told to do it themselves. That’s at least a win for everyone.

Each FTE doing that manual data pipelining work is also validating that work, and they have a quasi-legal responsibility to do their job correctly and on time. They may have substantial emotional investment in the company, whether survival instinct to not be fired, or ambition to overperform, or ethics and sense to report a rogue manager through alternate channels.

An LLM won't call other nodes in the organization to check when it sees that the value is unreasonable for some out-of-context reason, like yesterday was a one-time-only bank holiday and so the value should be 0. *It can be absolutely be worth an FTE salary to make sure these numbers are accurate.* And for there to be a person to blame/fire/imprison if they aren't accurate.

>I would guess that for every 1 software engineer there are 100 people doing this kind of 'manual data pipelining'.

For what time of company is this true? I really would like someone to just do a census of 500 white collar jobs and categorize them all. Anything that is truly automatic has already been automated away.

I do think AI will cause a lot of disruption, but very skeptical of the view that most people with white collar jobs are just "email jobs" or data entry. That doesn't fit my experience at all, and I've worked at some large bureaucratic companies that people here would claim are stuck in the past.

You're vastly underestimating the complexity of these types of jobs.

Well; I can't speak to your specific experience (current or past) but I'm telling you that while I'm skeptical as hell about EVERYTHING, it's blowing my expectations away in every conceivable way.

I built something in less than 24h that I'm sure would have taken us MONTHS to just get off the ground, let alone to the polished version it's at right now. The most impressive thing is that it can do all of the things that I absolutely can do, just faster. But the most impressive thing is that it can do all the things I cannot possibly do and would have had to hire up/contract out to accomplish--for far less money, time, and with faster iterations than if I had to communicate with another human being.

It's not perfect and it's incredibly frustrating at times (hardcoding values into the code when I have explicitly told it not to; outright lying that it made a particular fix, when it actually changed something else entirely unrelated), but it is a game changer IMO.

I tried the "thinking partner" approach for a while and for a moment I thought it worked well, but at some point the cracks started to show and I called the bluff. LLMs are extremely good at creating an illusion that they know things and are capable of reasoning, but they really don't do a good job of cultivating intellectual conversation.

I think it's dangerously easy to get misled when trying to prod LLMs for knowledge, especially if it's a field you're new to. If you were using a regular search engine, you could look at the source website to determine the trustworthiness of its contents, but LLMs don't have that. The output can really be whatever, and I don't agree it's necessarily that easy to catch the mistakes.

I've been programming for 40 years and have started using LLM's a few months ago, and it has really changed the way I work. I let it write pieces of code (pasting error messages from logs mostly result in a fix in less then a minute), but also brainstorming about architecture or new solutions. Of course I check the code it writes, but I'm still almost daily amazed at the intelligence and accuracy. (Very much unlike crypto).

I say this as an LLM skeptic.

All code, including stuff that we experienced coders write is inherently probabilistic. That’s why we have code reviews, unit tests, pair programming, guidelines and guardrails in any critical project. If you’re using LLM output uncritically, you’re doing it wrong, but if you’re using _human_ output uncritically you’re doing it wrong too.

That said, they are not magic, and my fear is that people use copilots and agentic models and all the rest to hide poor engineering practice, building more and more boilerplate instead of refactoring or redesigning for efficiency or safety or any of the things that matter in the long run.

There's one thing that I find LLM extremely good at: data science. Since the IO is well defined, you can easily verify that the output is correct. You can even ask it write tests for you given that you know certain properties of the data.

The problem is that the LLM needs context of what you are doing, contexts that you won't (or too lazy) to give in a chat with it ala ChatGPT. This is where Claude Code changes the game.

For example, you have PCAP file where each UDP packet contain multiple messages.

How do you filter the IP/port/protocol/time? Use LLM, check the output

How do you find the number of packets that have patterns A, AB, AAB, ABB.... Use LLM, check the output

How to create PCAPs that only contain those packets for testing? Use LLM, check the output

Etc etc

Since it can read your code, it is able to infer (because lets be honest, you work aint special) what you are trying to do at a much better rate. In any case, the fact that you can simply ask "Please write a unit test for all of the above functions" means that you can help it verify itself.

Treating it like a thinking partner is good. When programming, I don't treat it like a person, but rather a very high programming language. Imagine exactly how you want the code to be. Then find a way to express that unambiguously in natural language; the idea is that you'll still have a bit of work to do writing things out, but it will be a lot quicker than typing out all code by hand. Combined with iterations of feedback as well as having the AI build and run your program (at least as a sanity check); and asking the AI to check the program behaviour in the same way you would, gets you quite far.

A limitation is the lack of memory. If you steer it from style A to B using multiple points of feedback, if this is not written down, the next AI session you'll have to reexplain this all over.

Deepseek is about 1TB in weights; maybe that is why LLMs don't remember things across sessions yet. I think everybody can have their personal AI (hosted remote unless you own lots of compute); it should remember what happened yesterday; in particular the feedback it was given when developing. As an AI layman I do think this is the next step.

You don't trust the code coming out of the probabilistic machine. You build a validation cage around it with hard interfaces and you also review the output.

Hmm. Ok so you're basically quoting the line from The Weatherman "60% of the time, it works all of the time."

I also use gpt and Claude daily via cursor.

Gpt o3 is kinda good for general knowledge searches. Claude falls down all the time, but I've noticed that while it's spending tokens to jerk itself off, quite often it happens on the actual issue going on with out recognizing it.

Models are dumb and more idiot than idiot savant, but sometimes they hit on relevant items. As long as you personally have an idea of what you need to happen and treat LLMs like rat terriers in a farm field, you can utilize them properly

Your comment is no better than the comment in the article that the author is calling out.

"90%" also seems a bit suspect.

Same experience here, probably in a slightly different way of work (PhD student). Was extremely skeptical of LLMs, Claude Code has completely transformed the way I work.

It doesn't take away the requirements of _curation_ - that remains firmly in my camp (partially what a PhD is supposed to teach you! to be precise and reflective about why you are doing X, what do you hope to show with Y, etc -- breakdown every single step, explain those steps to someone else -- this is a tremendous soft skill, and it's even more important now because these agents do not have persistent world models / immediately forget the goal of a sequence of interactions, even with clever compaction).

If I'm on my game with precise communication, I can use CC to organize computation in a way which has never been possible before.

It's not easier than programming (if you care about quality!), but it is different, and it comes with different idioms.

But you just confirmed everything the blogpost claimed.

You didn't share any evidence with us even though you claim unbelievable things.

You even went as far as registering a throwavay account to hide your identity and to make verifying any of your claims impossible.

Your comment feels more like a joke to me

I find that the code quality LLMs output is pretty bad. I end up going through so many iterations that it ends up being faster to do it myself. What I find agents actually useful for is doing large scale mechanical refractors. Instead of trying to figure out the perfect vim macro or AST rewrite script, I'll throw an agent at it.

> I audit everything myself before making PRs and test rigorously

How do you audit code from an untrusted source that quickly, LLMs do not have the whole project in their heads and are proned to hallucinate.

On average how long are your prompts and does the LLM also write the unit tests?

> I started a job at a demanding startup and it’s been several months and I have still not written a single line of code by hand

Damn, this sounds pretty boring.

> I’ve shipped non trivial greenfield products

Links please

> there’s a process of research and planning and perusing in careful steps, and I set the agent up for success

Are there any good articles you can share or maybe your process? I’m really trying to get good at this but I don’t find myself great at using agents and I honestly don’t know where to start. I’ve tried the memory bank in cline, tried using more thinking directives, but I find I can’t get it to do complex things and it ends up being a time sink for me.

Web dev CRUD in node?

> but my productivity was still at least 3x

How do you measure this?

I use Claude code for hours a day, it’s a liar, trust what it does at your own risk.

I personally think you’re sugar coating the experience.

Please re-read the article. Especially the first list of things we don't know about you, your projects etc.

Your specific experience cannot be generalized. And speaking as the author, and who is (as written in the article) literally using these tools everyday.

> But I’m just so floored how anyone could not be extracting the same utility from it. It feels like there’s two articles like this every week now.

This is where we learn that you haven't actually read the article. Because it is very clearly stating, with links, that I am extracting value from these tools.

And the article is also very clearly not about extracting or not extracting value.

Translation, transcription, and code generation (up to some scale) were borderline intractable problems?

Google Translate, Whisper and Code Generators (up to some scale) have existed for quite some time without using LLMs.

> but to be quite frank more rampant amongst detractors than promoters, who often have to deal with the flaws and limitations on a day to day basis.

"detractors" usually point to actual flaws. "promoters" usually uncritically hail LLMs as miracles capable of solving any problem in one go, without giving any specific details.

I think you are underestimating the negative impacts that overhype cause. It's distorting the market, causing over investment, preemptively slashing departments, and creating an expectation that will never be meet. These articles are important for cooling expectations. When people sell LLMs, they usually aren't talking about summarizing customer support calls, they are trying to sell the idea of firing customer support staff.

This very much. People who claim there is no real use of LLM never faced the problem of processing a lot of data in kinda reliably way.

For years most of the translations on the web didn't have context. Now they can have.

You don't need consensus on the meaning across the board. I maintain my own, more permissive milestone for what constitutes "AGI", but I have no expectations that others will share it. Much like "crypto" to me is still cryptography, not cryptocurrency - sometimes the mainstream will just have a different opinion.

We have a definition.

"AI is whatever hasn't been done yet."[1]

1. https://en.wikipedia.org/wiki/AI_effect

"People claim to have productivity increases anywhere between a random number I invented to solve other number I invented. We should believe these claims uncritically"

And of course it says first law that applies here.

The people I have talked to at length about using AI tools claim that it has been a boon for productivity: a nurse, a doctor, three (old) software developers, a product manager, and a graduate student in Control Systems.

It is entirely believable that it may not, on average, help the average developer.

I'm reminded of the old joke that ends with "who are you going to believe, me or you're lying eyes?"

Wow. Quite a conclusion from an article that actually doesn't reach for that conclusion

It makes no sense to compare the current AI hype to the tulip mania of crypto/NFTs. A much better parallel is to cloud computing hype in 2009.

Are those LLMs in the room with us now? ;)

Actually, I did try asking ollama running locally. That should've reduced the amount of non-determinism and whatever layers providers add, and the uncertainty of computer availability.

I asked it for a list of Javascript keywords ordered alphabetically. Within 5 minutes it produced a slightly different list

Asking a model for 2x2 is moot because 2x2=5 is statically highly unlikely. Anything more complex though?

> Everything looks good to the untrained eye

thats the trick with bullshit in general.

> LLM abuse is going to be the end of so many tech companies

and is also going to provide a lot of opportunities for experienced engineers to cleanup the mess.

I have had SOTA models stray from factual content in documents I provided them with within 2-3 prompts.

They haven't solved anything. They are just fast and look good doing what we ask them to do. But they corrupt data with a passion and to that hype just responds: "just give us 10x as much money and compute".

>Yes, they are non-deterministic

The LLM is more like a Ouija board than a reliable tool.

>I can ask it to write user stories or prototypes

By the time I write enough to explain thoroughly to an LLM what to write in "user stories" or "prototypes", I could have just written it myself, without the middleman(bot), and without the LLM hallucinating.

If half the time I spend with an LLM is telling it what to do, and then another half is correcting what it did, then I'm not really saving any time at all by using it.

I don't think you got the author's point.

They didn't say "AI is bad". Take another look.

Can you elaborate on your situation? Which country are you in? How is crypto used there?

This is basically the only use case for crypto, and one for which it was explicitly designed: censorship resistance. This is why people have so much trouble finding useful things for it to do in the legal economy, it was explicitly designed to facilitate transactions the government doesn't want or can't facilitate. In some cases, there are humanitarian applications, there are also a lot of illicit applications.

I don't think you actually disagree with the authors quip. You seem to want to use crypto as a currency, while OP was most likely referring to the grifting around crypto as an investment. If you're using it as a currency, then the people trying to pump and dump coins and use it for a money making vehicle are your adversaries. You are best served if it's stable instead of a rollercoaster of booms and busts.

It's a valid use case in the sea of nonsensical hype where "you are a moron if you don't believe in some true meaning of crypto".

"You had to be there to believe it" https://x.com/0xbags/status/1940774543553146956

AI craze is currently going through a similar period: any criticism is brushed away as being presented by morons who know nothing

Said this in another thread and I'll repeat it here:

It's the same problem that crypto experiences. Almost everyone is propagating lies about the technology, even if a majority of those doing so don't understand enough to realize they're lies (naivety vs malice).

I'd argue there's more intentional lying in crypto and less value to be gained, but in both cases people who might derive real benefit from the hard truth of the matter are turning away before they enter the door due to dishonesty/misrepresentation- and in both cases there are examples of people deriving real value today.

> But similar to crypto these movements only have value if the value is widely perceived.

The difference in the AI case is that companies that are actually able to use it to boost productivity significantly will start to outcompete those who don't.

That's why, unlike crypto/blockchain, so many mainstream companies are pouring money into AI. It's not FOMO so much as fear of extinction.

Was this text generated by an LLM?

Re. 2 - I find it fascinating that people using coding agents have seemingly no problem to reveal their full codebases to AI companies.

AI is here to stay, and you do not have agency over that. You can choose not to use it, but that has zero impact on the broader adoption rate. Just like when the automobile was introduced and society as a whole evolved.

In my codebase in my proprietary project, it’s possible that LLMs write around half the code, but that’s just because a lot of the trivial parts of the project are quite verbose.

The really difficult and valuable parts of the codebase are very very far beyond what the current LLMs are capable of, and believe me, I’ve tried!

Writing the majority of the code is very different from creating the majority of the value.

And I really use and value LLMs, but they are not replacing me at the moment.

Regardless of the usefulness of llms, if you don't work at anthropic, how gullible are you to believe that claim at face value?

> Ok. Claude Code produces most code at Anthropic.m

Does it? Or does their marketing tell you that? Strange that "most code is written by Claude" and they still hire for actual humans for all the positions from backend to API to desktop to mobile clients.

> How much babysitting and reviewing is undetermined; but the Ants seem to tremendously prefer the workflow.

So. We know nothing about their codebase, actual flows, programming languages, depth and breadth of usage, how much babysitting is required...

Truly, how could they have the valuation they have and do anything else?

> but the Ants

Is that the official cutsie name people working there are called? Those feels so 2020 ...

computer programming and law are very similar. computer code is called code because it is the law that dictates the behavior of the computer. law is a bit different because it is a program that runs on people, who aren't as deterministic as machines, but in theory law and the interpretation of law are also supposed to be completely logical, and you can translate back and forth directly from the logic of law to a logical expression in a computer program.

i specialize in programming, and LLMs are very good right now, if you set them up with the right tooling, feedback based learning methods, and efficient ways of capturing human input (review/approve/suggest/correct/etc).

with programming you have compilers and other static analysis tools that you can use to verify output. for law you need similar static analysis tooling, to verify things like citations, procedural scheduling, electronic filing, etc, but if you loop that tooling in with an llm, the llm will be able to correct errors automatically, and you will get to an agent that can take a statement of fact, find a cause of action, and file a pro se lawsuit for someone.

courts are going to be flooded with lawsuits, on a scale of 10-100X current case loads.

criminal defendants will be able to use a smart phone app to represent themselves, with an AI handling all of the filings and motions, monitoring the trial in real time, giving advice to the defendant on when to make motions and what to say, maximizing delay and cost for the state with maximum efficiency.

with 98% of convictions coming from guilty pleas (https://www.npr.org/2023/02/22/1158356619/plea-bargains-crim...) which are largely driven by not being able to afford the cost of legal services the number of criminal defendants electing to go to full jury trial could easily explode 10-20X or more very quickly.

fun times!

uh

https://www.technologyreview.com/2025/05/20/1116823/how-ai-i...

https://hai.stanford.edu/news/hallucinating-law-legal-mistak...

https://www.reuters.com/technology/artificial-intelligence/a...

There are more of these stories every week. Are you using AI in a way that doesn’t allow you to be entrapped by this sort of thing?

> I'm honestly started to get pissed seeing these articles on HN every day. Aren't we technologists?

Funnily enough, that's exactly what the article asks.

> I'm an expert in my field with decades of experience, and a damn good programmer, and Cursor is like zipping down the street on a powered bike.

Oh look, an unverified claim. Go through the first list in the article, and ask "what we, as technologists, know about this claim".

You can also actually read the article and see that I actually use these tools every day.

- superficial emotion

- cliché phrasing

- em dashes

- abundant alliteration

- all comments suspiciously similar in length

- all posts pointing to the same website

Does HN not have a policy against vapid AI comment spam? If not, it needs one.

edit: It does:

https://news.ycombinator.com/item?id=37617714

Amen.

At my job right now there is an imminent threat from a team empowered to say "what if we asked an AI to just build X instead of having a team build and maintain it?"

X is something where it's straightforward when N is below 50 but deeply complex when N is in the thousands, which for our team it is, and there is a huge risk that this team will get a demo with N=15 that attracts leadership attention and trying to explain why the AI-generated solution does not scale is a career-limiting move framing me as a naysayer, but this AI team would deliver the demo and go away and the inevitable failure of their solution at scale would ALSO be my team's problem, so..... I hate the future.

I've always looked at it as we're not making software that can think, we're (quite literally) demonstrating that vast categories of things don't need thought (for some quality level). The problem is, it's clearly not 100%, maybe it's 90-some percent, but it doesn't matter, we're only outsourcing the unimportant things that aren't definitional for a task.

This is very typical of naive automation, people assume that most of the work is X and by automating that we replace people, but the thing that's automated is almost never the real bottleneck. Pretty sure I saw an article here yesterday about how writing code is not the bottleneck in software development, and it holds everywhere.

These percentage estimates of AI's proximity to "human reasoning" are misleading abstractions that mask fundamental qualitative differences in how LLMs and humans process information.

> AI (LLMs) is probably 90% of the way to human levels of reasoning

Considering LLMs have 0 level of reasoning, I can't decide if it's a bad take, or a stab at the average human's level of reasoning.

In all seriousness, the actual numbers vary from 13% to 26%: https://fortune.com/2025/02/12/openai-deepresearch-humanity-...

My take is that there are fundamental limitations to try to pigeon-hole reasoning to LLMs, which are essentially a very very advanced autocomplete, and that's why those % won't jump too much too soon.

>The best way I’ve heard this described: AI (LLMs) is probably 90% of the way to human levels of reasoning. We can probably get to about 95% optimizing current technology.

We don't know enough about how LLMs work or about how human reasoning works for this to be at all meaningful. These numbers quantify nothing but wishes and hype.