Recent AI model progress feels mostly like bullshit
lesswrong.com
lesswrong.com
Anecdotally, I've been playing around with o3-mini on undergraduate math questions: it is much better at "plug-and-chug" proofs than GPT-4, but those problems aren't independently interesting, they are explicitly pedagogical. For anything requiring insight, it's either:
1) A very good answer that reveals the LLM has seen the problem before (e.g. naming the theorem, presenting a "standard" proof, using a much more powerful result)
2) A bad answer that looks correct and takes an enormous amount of effort to falsify. (This is the secret sauce of LLM hype.)
I dread undergraduate STEM majors using this thing - I asked it a problem about rotations and spherical geometry, but got back a pile of advanced geometric algebra, when I was looking for "draw a spherical triangle." If I didn't know the answer, I would have been badly confused. See also this real-world example of an LLM leading a recreational mathematician astray: https://xcancel.com/colin_fraser/status/1900655006996390172#...
I will add that in 10 years the field will be intensely criticized for its reliance on multiple-choice benchmarks; it is not surprising or interesting that next-token prediction can game multiple-choice questions!
Not sure whether their (INSAIT's) agenda is purely scientific, as there's a lot of PR on linkedin by these guys, literally celebrating every PHD they get, which is at minimum very weird. I'd take anything they release with a grain of sand if not caution.
This take is completely oblivious, and frankly sounds like a desperate jab. There are a myriad of activities whose core requirement is a) derive info from a complex context which happens to be supported by a deep and plentiful corpus, b) employ glorified template and rule engines.
LLMs excel at what might be described as interpolating context following input and output in natural language. As in a chatbot that is extensivey trained in domain-specific tasks, which can also parse and generate content. There is absolutely zero lines of intellectual work that do not benefit extensively from this sort of tool. Zero.
This effectively makes LLMs useless for education. (Also sours the next generation on LLMs in general, these things are extremely lame to the proverbial "kids these days".)
No. You're only arguing LLMs are useless at regurgitating homework assignments to allow students to avoid doing it.
The point of education is not mindless doing homework.
So 1) 2) and 3) were out by 1,1 and 3 orders of magnitude respectively (the errors partially cancelled out) and 4) was nonsensical.
This little experiment made my skeptical about the state of the art of AI. I have seen much AI output which is extraordinary it's funny how one serious fail can impact my point of view so dramatically.
I feel the same way. It's like discovering for the first time that magicians aren't doing "real" magic, just sleight of hand and psychological tricks. From that point on, it's impossible to be convinced that a future trick is real magic, no matter how impressive it seems. You know it's fake even if you don't know how it works.
I'm in the second camp but find it kind of sad and often envy the people who can stay entertained even though they know better.
The AI will create something for you and tell you it was them.
"Good point! Blah blah blah..."
Absolutely shameless!
It got the golf ball volume right (0.00004068 cubic meters), but it still overestimated the cabin volume at 1000 cubic meters.
It's final calculation was reasonably accurate at 24,582,115 golf balls - even though 1000 ÷ 0.00004068 = 24,582,104. Maybe it was using more significant figures for the golf ball size than it showed in its answer?
It didn't acknowledge other items in the cabin (like seats) reducing its volume, but it did at least acknowlesge inefficiencies in packing spherical objects and suggested the actual number would be "somewhat lower", though it did not offer an estimate.
When I pressed it for an estimate, it used a packing density of 74% and gave an estimate of 18,191,766 golf balls. That's one more than the calculation should have produced, but arguably insignificant in context.
Next I asked it to account for fixtures in the cabin such as seats. It estimated a 30% reduction in cabin volume and redid the calculations with a cabin volume of 700 cubic meters. These calculations were much less accurate. It told me 700 ÷ 0.00004068 = 17,201,480 (off by ~6k). And it told me 17,201,480 × 0.74 was 12,728,096 (off by ~1k).
I told it the calculations were wrong and to try again, but it produced the same numbers. Then I gave it the correct answer for 700 ÷ 0.00004068. It told me I was correct and redid the last calculation correctly using the value I provided.
Of all the things for an AI chatbot which can supposedly "reason" to fail at, I didn't expect it to be basic arithmetic. The one I used was closer, but it was still off by a lot at times despite the calculations being simple multiplication and division. Even if might not matter in the context of filling an air plane cabin with golf balls, it does not inspire trust for more serious questions.
1000 ÷ 0.00004068 = 25,000,000. I think this is an important point that's increasingly widely misunderstood. All those extra digits you show are just meaningless noise and should be ruthlessly eliminated. If 1000 cubic metres in this context really meant 1000.000 cubic metres, then by all means show maybe the four digits of precision you get from the golf ball (but I am more inclined to think 1000 cubic metres is actually the roughest of rough approximations, with just one digit of precision).
In other words, I don't fault the AI for mismatching one set of meaninglessly precise digits for another, but I do fault it for using meaninglessly precise digits in the first place.
Edit: Then again, maybe they have a point, going by an answer I just got from Google's best current model ( https://g.co/gemini/share/374ac006497d ) I haven't seen anything that ridiculous from a leading-edge model for a year or more.
But Google search gave me the exact same slop you mentioned. So whatever Search is using, they must be using their crappiest, cheapest model. It's nowhere near state of the art.
No wonder Trump isn't afraid to put taxes against Canada. Who could take a 3.8 sqare miles country seriously?
More than even filling the gaps in knowledge / skills, would be a huge advancement in AI for it to admit when it doesn't know the answer or is just wildly guessing.
Looking up the math ability of the average American this is given as an example for the median (from https://www.wyliecomm.com/2021/11/whats-the-latest-u-s-numer...):
>Review a motor vehicle logbook with columns for dates of trip, odometer readings and distance traveled; then calculate trip expenses at 35 cents a mile plus $40 a day.
Which is ok but easier than golf balls in a 747 and hugely easier than USAMO.
Another question you could try from the easy math end is: Someone calculated the tariff rate for a country as (trade deficit)/(total imports from the country). Explain why this is wrong.
- USAMO - United States of America Mathematical Olympiad
- IMO - International Mathematical Olympiad
- ICPC - International Collegiate Programming Contest
Relevant paper: https://arxiv.org/abs/2503.21934 - "Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad" submitted 27th March 2025.
https://openai.com/index/learning-to-reason-with-llms/
The paper tested it on o1-pro as well. Correct me if I'm getting some versioning mixed up here.
So, I describe the mathematics to ChatGPT-o3-mini-high to try to help reason about what’s going on. It was almost completely useless. Like blog-slop “intro to ML” solutions and ideas. It ignores all the mathematical context, and zeros in on “doesn’t converge” and suggests that I lower the learning rate. Like, no shit I tried that three weeks ago. No amount of cajoling can get it to meaningfully “reason” about the problem, because it hasn’t seen the problem before. The closest point in latent space is apparently a thousand identical Medium articles about Adam, so I get the statistical average of those.
I can’t stress how frustrating this is, especially with people like Terence Tao saying that these models are like a mediocre grad student. I would really love to have a mediocre (in Terry’s eyes) grad student looking at this, but I can’t seem to elicit that. Instead I get low tier ML blogspam author.
**PS** if anyone read this far (doubtful) and knows about density estimation and wants to help my email is bglazer1@gmail.com
I promise its a fun mathematical puzzle and the biology is pretty wild too
Sometimes when I'm anxious just to get on with my original task, I'll paste the code and output/errors into the LLM and iterate over its solutions, but the experience is like rolling dice, cycling through possible solutions without any kind of deductive analysis that might bring it gradually closer to a solution. If I keep asking, it eventually just starts cycling through variants of previous answers with solutions that contradict the established logic of the error/output feedback up to this point.
Not to say that the LLMs aren't productive tools, but they're more like calculators of language than agents that reason.
This might be honing in on both the issue and the actual value of LLM:s. I think there's a lot of value in a "language calculator" but if it's continuously being sold as something it's not we will dismiss it or build heaps of useless apps that will just form a market bubble. I think the value is there but it's different from how we think about it.
The same may work with you problem. If it's unstable try introduce extra 'brakes' which theoretically are not required. May be even incorrect. Whatever it is in your domain. Another thing to check is optimizer, try several. Check default parameters. I've heard Adams defaults lead to instability later in training.
PS: it would be heaven if models could work at human expert level. Not sure why some really expect this. We are just at the beginning.
PPS: the fact that they can do known tasks with minor variations is already a huge time saver.
The reason I expected better mathematical reasoning is because the companies making them are very loudly proclaiming that these models are capable of high level mathematical reasoning.
And yes the fact I don’t have to look at matplotlib documentation anymore makes these models extremely useful already, but thats qualitatively different from having Putnam prize winning reasoning ability
Math packages of the time like Mathematica and MATLAB helped me immensely, once you could get the problem accurately described in the correct form, they could walk through the steps and solve systems of equations, integrate tricky functions, even though AI was nowhere to be found back then.
I feel like ChatGPT is doing something similar when doing maths with its chain of thoughts method, and while its method might be somewhat more generic, I'm not sure it's strictly superior.
If you don't see anyone mentioning what you wrote that's not surprising at all, because you totally misunderstood the paper. The models didn't suddenly drop to 5% accuracy on math olympiad questions. Instead this paper came up with a human evaluation that looks at the whole reasoning process (instead of just the final answer) and their finding is that the "thoughts" of reasoning models are not sufficiently human understandable or rigorous (at least for expert mathematicians). This is something that was already well known, because "reasoning" is essentially CoT prompting baked into normal responses. But the empirics also tell us it greatly helps for final outputs nonetheless.
And this only suggested LLMs aren't trained well to write formal math proofs, which is true.
How do we know that Gemini 2.5 wasn't specifically trained or fine-tuned with the new questions? I don't buy that a new model could suddenly score 5 times better than the previous state-of-the-art models.
And to be clear, that's pretty much all this was: there's six problems, it got almost-full credit on one and half credit on another and bombed the rest, whereas all the other models bombed all the problems.
USAMO : USA Math Olympiad. Referred here https://arxiv.org/pdf/2503.21934v1
IMO : International Math Olympiad
SOTA : State of the Art
OP is probably referring to this referred to this paper here https://arxiv.org/pdf/2503.21934v1. The paper explains out how a rigorous testing revealed abysmal performance of LLMs (results that are at odds with how they are hyped about).
Instead they're barely able to eek out wins against a bot that plays completely random moves: https://maxim-saplin.github.io/llm_chess/
https://dictionary.cambridge.org/us/dictionary/english/eke-o... to obtain or win something only with difficulty or great effort
AcmeAssistant is "helpful" and "clever" in the same way that Vampire Count Dracula is "brooding" and "immortal".
Much in the same way a human who only just learnt the rules but 0 strategy would very, very rarely lose here
These companies are shouting that their products are passing incredibly hard exams, solving PHD level questions, and are about to displace humans, and yet they still fail to crush a random-only strategy chess bot? How does this make any sense?
We're on the verge of AGI but there's not even the tiniest spark of general reasoning ability in something they haven't been trained for
"Reasoning" or "Thinking" are marketing terms and nothing more. If an LLM is trained for chess then its performance would just come from memorization, not any kind of "reasoning"
https://dynomight.substack.com/p/chess
Discussion here: https://news.ycombinator.com/item?id=42138289
It’s just a much harder math benchmark which will fall by the end of next year just like all the others. You won’t be vindicated.
I.e. if you randomly sampled N humans to take those tests.
When you ask it a question, it tends to say yes.
So while the LLM arms race is incrementally increasing benchmark scores, those improvements are illusory.
The real challenge is that the LLM’s fundamentally want to seem agreeable, and that’s not improving. So even if the model gets an extra 5/100 math problems right, it feels about the same in a series of prompts which are more complicated than just a ChatGPT scenario.
I would say the industry knows it’s missing a tool but doesn’t know what that tool is yet. Truly agentic performance is getting better (Cursor is amazing!) but it’s still evolving.
I totally agree that the core benchmarks that matter should be ones which evaluate a model in agentic scenario, not just on the basis of individual responses.
LLMs fundamentally do not want to seem anything
But the companies that are training them and making models available for professional use sure want them to seem agreeable
You're right that LLMs don't actually want anything. That said, in reinforcement learning, it's common to describe models as wanting things because they're trained to maximize rewards. It’s just a standard way of talking, not a claim about real agency.
A standard way of talking used by people who do also frequently claim real agency.
I've dropped trying to use LLMs for anything, due to political convictions and because I don't feel like they are particularly useful for my line of work. Where I have tried to use various models in the past is for software development, and the common mistake I see the LLMs make is that they can't pick up on mistakes in my line of thinking, or won't point them out. Most of my problems are often down to design errors or thinking about a problem in a wrong way. The LLMs will never once tell me that what I'm trying to do is an indication of a wrong/bad design. There are ways to be agreeable and still point out problems with previously made decisions.
And when you ask it to 'just write a test' 50/50 it will try to run it, fail on some trivial issues, delete 90% of your test code and start to loop deeper and deeper into the rabit hole of it's own halliciations.
Or maybe I just suck at prompting hehe
Every time someone argues for the utility of LLMs in software development by saying you need to be better at prompting, or add more rules for the LLM on the repository, they are making an argument against using NLP in software development.
The whole point of code is that it is a way to be very specific and exact and to exercise control over the computer behavior. The entire value proposition of using an LLM is that it is easier because you don't need to be so specific and exact. If then you say you need to be more specific and exact with the prompting, you are slowly getting at the fact that using NLP for coding is a bad idea.
In fact, this might be why so many business executives are enamored with LLMS/GenAI: It's a yes-man they don't even have to employ, and because they're not domain experts, as per usual, they can't tell that they're being fed a line of bullshit.
umm, it seems to me that it is this (tfa):
But I would nevertheless like to submit, based off of internal
benchmarks, and my own and colleagues' perceptions using these models,
that whatever gains these companies are reporting to the public, they
are not reflective of economic usefulness or generality.
So maybe there's no mystery: The AI lab companies are lying, and when
they improve benchmark results it's because they have seen the answers
before and are writing them down. There was a little girl,
Who had a little curl,
Right in the middle of her forehead.
When she was good,
She was very good indeed,
But when she was bad she was horrid.I have my own opinions, but I can't really say that they're not also based on anecdotes and personal decision-making heuristics.
But some of us are going to end up right and some of us are going to end up wrong and I'm really curious what features signal an ability to make "better choices" w/r/t AI, even if we don't know (or can't prove) what "better" is yet.
Furthermore, as we are talking about actual impact of LLMs, as is the point of the article, a bunch of anecdotal experiences may be more valuable than a bunch of benchmarks to figure it out. Also, apart from the right/wrong dichotomy, people use LLMs with different goals and contexts. It may not mean that some people do something wrong if they do not see the same impact as others. Everytime a web developer says that they do not understand how others may be so skeptical of LLMs, conclude with certainty that they must be doing sth wrong and move on to explain how to actually use LLMs properly, I chuckle.
In the absence of actually good evidence, anecdotal data may be the best we can get now. The point imo is try to understand why some anecdotes are contrasting each other, which, imo, is mostly due to contextual factors that may not be very clear, and to be flexible enough to change priors/conclusions when something changes in the current situation.
People -only!- draw conclusions based on personal experience. At best you have personal experience with truly objective evidence gathered in a statistically valid manner.
But that only happens in a few vanishingly rare circumstances here on earth. And wherever it happens, people are driven to subvert the evidence gathering process.
Often “working against your instincts” to be more rational only means more time spent choosing which unreliable evidence to concoct a belief from.
A majority of what makes a "better AI" can be condensed to how effective the slope-gradient algorithms are at getting the local maxima we want it to get to. Until a generative model shows actual progress of "making decisions" it will forever be seen as a glorified linear algebra solver. Generative machine learning is all about giving a pleasing answer to the end user, not about creating something that is on the level of human decision making.
I'm just most baffled by the "flashes of brilliance" combined with utter stupidity. I remember having a run with early GPT 4 (gpt-4-0314) where it did refactoring work that amazed me. In the past few days I asked a bunch of AIs about similar characters between a popular gacha mobile game and a popular TV show. OpenAI's models were terrible and hallucinated aggressively (4, 4o, 4.5, o3-mini, o3-mini-high), with the exception of o1. DeepSeek R1 only mildly hallucinated and gave bad answers. Gemini 2.5 was the only flagship model that did not hallucinate and gave some decent answers.
I probably should have used some type of grounding, but I honestly assumed the stuff I was asking about should have been in their training datasets.
I do feel (anecdotally) that models are getting better on every major release, but the gains certainly don't seem evenly distributed.
I am hopeful the coming waves of vertical integration/guardrails/grounding applications will move us away from having to hop between models every few weeks.
There are three questions to consider:
a) Have we, without any reasonable doubt, hit a wall for AI development? Emphasis on "reasonable doubt". There is no reasonable doubt that the Earth is roughly spherical. That level of certainty.
b) Depending on your answer for (a), the next question to consider is if we the humans have motivations to continue developing AI.
c) And then the last question: will AI continue improving?
If taken as boolean values, (a), (b) and (c) have a truth table with eight values, the most interesting row being false, true, true: "(not a) and b => c". Note the implication sign, "=>". Give some values to (a) and (b), and you get a value for (c).
There are more variables you can add to your formula, but I'll abstain from giving any silly examples. I, however, think that the row (false, true, false) implied by many commentators is just fear and denial. Fear is justified, but denial doesn't help.
So am I. If you promise you'll tell me after you time travel to the future and find out, I'll promise you the same in return.
People having vastly different opinions on AI simply comes down to token usage. If you are using millions of tokens on a regular basis, you completely understand the revolutionary point we are at. If you are just chatting back and forth a bit with something here and there, you'll never see it.
I'd love to see a survey from a major LLM API provider that correlated LLM spend (and/or tokens) with optimism for future transformativity. Correlation with a view of "current utility" would be a tautology, obviously.
I actually have the opposite intuition from you: I suspect the people using the most tokens are using it for very well-defined tasks that it's good at _now_ (entity extraction, classification, etc) and have an uncorrelated position on future potential. Full disclosure, I'm in that camp.
Someone who lacks experience, skill, training, or even the ability to evaluate results may try to use a tool and blame the tool when it doesn't give good results.
That said, the hype around LLMs certainly overstates their capabilities.
Once all the AI batch startups have sold subscriptions to the cohort and there's no more further market growth because businesses outside don't want to roll the dice on a probabilistic model that doesn't have an understanding of pretty much anything rather is a clever imitation machine on the content it has seen, the AI bubble will burst when more statups would start packing up by end of 2026 or max 2027.
Part of me thinks this is because I expected less of 3.5 and therefore interacted with it differently.
It's funny because it's unlikely that everyone interacts with these models in the same way. And that's pretty much guaranteed to give different results.
Would be interesting to see some methods come out for individuals to measure their own personal success rate/ productivity / whatever with these different models. And then have a way for people to compare them with each other so we can figure out who is working well with these models and who isn't and figure out why the difference.
Despite me rejecting the changes and explicitly telling it to ignore the linter it kept insisting on only trying to solve for that
Also:
> I think what's going on is that large language models are trained to "sound smart" in a live conversation with users, and so they prefer to highlight possible problems instead of confirming that the code looks fine, just like human beings do when they want to sound smart.
I immediately thought: That's because in most situations this is the purpose of language, at least partially, and LLMs are trained on language.
Maybe it's that I do have PhD level questions to ask them, and they've gotten much better at it.
But I suspect that these anecdotes are driven by something else. Perhaps people found a workable prompt strategy by trial and error on an earlier model and it works less well with later models.
Or perhaps they have a time-sensitive task and are not able to take advantage of the thinking of modern LLMs, which have a slow thinking-based feedback loop. Or maybe their code base is getting more complicated, so it's harder to reason about.
Or perhaps they're giving the LLMs a poorly defined task where older models made assumptions about but newer models understand the ambiguity of and so find the space of solutions harder to navigate.
Since this is ultimately from a company doing AI scanning for security, I would think the latter plays a role to some extent. Security is insanely hard and the more you know about it the harder it is. Also adversaries are bound to be using AI and are increasing in sophistication, which would cause lower efficacy (although you could tease this effect out by trying older models with the newer threats).
In other words, all the sort of lazy prompt engineering hacks are becoming less effective. Domain expertise is becoming more effective.
Why? Because I know so little about chemistry myself that I wouldn't even know what to start asking the model as to be impressed by the answer.
For the model to be useful at all, I would have to learn basic chemistry myself.
Many though I suspect are in this same situation with all subjects. They really don't know much of anything and are therefore unimpressed by the models response in the same way I am not impressed with chemistry responses.
Of course, if you train an LLM heavily on narrow benchmark domains then its prediction performance will improve on those domains, but why would you expect that to improve performance in unrelated areas?
If you trained yourself extensively on advanced math, would you expect that to improve your programming ability? If not, they why would you expect it to improve programming ability of a far less sophisticated "intelligence" (prediction engine) such as a language model?! If you trained yourself on LeetCode programming, would you expect that to help hardening corporate production systems?!
No LLM last year got silver. Deepmind had a highly specialized AI system earning that
If a model doesn't do good in the benchmarks it will either be retrained until it does or you won't hear about it.
It probably depends a lot on what you are using them for, and in general, I think it's still too early to say exactly where LLMs will lead us.
Even approximations must be right to be meaningful. If information is wrong, it's rubbish.
Presorting/labelling various data has value. Humans have done the real work there.
What is "leading" us at present are the exaggerated valuations of corporations. You/we are in a bubble, working to justify the bubble.
Until a tool is reliable, it is not installed where people can get hurt. Unless we have revised our concern for people.
People who can’t recognize this intentionally have their heads in the sand
1) AI undoubtedly has utility. In many agentic uses, it has very significant utility. There's absolute utility and perceived utility, which is more of user experience. In absolute utility, it is likely git is the single most game changing piece of software there is. It is likely git has saved some ten, maybe eleven digit number in engineer hours times salary in how it enables massive teams to work together in very seamless ways. In user experience, AI is amazing because it can generate so much so quickly. But it is very far from an engineer. For example, recently I tried to use cursor to bootstrap a website in NextJS for me. It produced errors it could not fix, and each rewrite seemed to dig it deeper into its own hole. The reasons were quite obvious. A lot of it had to do with NextJS 15 and the breaking changes it introduces in cookies and auth. It's quite clear if you have masses of NextJS code, which disproportionately is older versions, but none labeled well with versions, it messes up the LLM. Eventually I scrapped what it wrote and did it myself. I don't mean to use this anecdote to say LLMs are useless, but they have pretty clear limitations. They work well on problems with massive data (like front end) and don't require much principled understanding (like understanding how NextJS 15 would break so and so's auth). Another example of this is when I tried to use it to generate flags for a V8 build, it failed horribly and would simply hallucinate flags all the time. This seemed very likely to be (despite the existence of a list of V8 flags online) that many flags had very close representations in vector embeddings, and that there was almost close to zero data/detailed examples on their use.
2) In the more theoretical side, the performance of LLMs on benchmarks (claiming to be elite IMO solvers, competitive programming solvers) have become incredibly suspicious. When the new USAMO 2025 was released, the highest score was 5%, despite claims a year ago that SOTA when was at least a silver IMO. This is against the backdrop of exponential compute and data being fed in. Combined with apparently diminishing returns, this suggests that the gains from that are running really thin.
"game changing" isn't exactly the sentiment there the last couple months.
A 4-bit quant of QwQ-32B is surprisingly close to Claude 3.5 in coding performance. But it's small enough to run on a consumer GPU, which means deployment price is now down to $0.10 per hour. (from $12+ for models requiring 8x H100)
In my evals 8 bit quantized smaller Qwen models were better, but again evaluating is hard.
What innovation opens up when AI gets sufficiently commoditized?
For example, you can see this in health insurance reimbursements and wireless carriers plan changes. (ie, Verizon’s shift from Do More, etc to what they have now)
Companies basically set up circumstances where consumers lose small amounts of money on a recurring basis or sporadically enough that the people will just pay the money rather than a maze of calls, website navigation and time suck to recover funds due to them or that shouldn’t have been taken in the first place.
I’m hopeful well commoditized AI will give consumers a fighting chance at this and other types of disenfranchisement that seems to be increasingly normalized by companies that have consultants that do nothing but optimize for their own financial position.
Personally, I think the models are “good enough” that we need to start seeing the improvements in tooling and applications that come with them now. I think MCP is a good step in the right direction, but I’m sceptical on the whole thing (and have been since the beginning, despite being a user of the tech).
The problem is that up until _very_ recently, it's been possible to get LLMs to generate interesting and exciting results (as a result of all the API documentation and codebases they've inhaled), but it's been very hard to make that usable. I think we need to be able to control the output format of the LLMs in a better way before we can work on what's in the output. I don't konw if MCP is the actual solution to that, but it's certainly an attempt at it...
The accuracy problem won't just go away. Increasing accuracy is only getting more expensive. This sets the limits for useful applications. And casual users might not even care and use LLMs anyway, without reasonable result verification. I fear a future where overall quality is reduced. Not sure how many people / companies would accept that. And AI companies are getting too big to fail. Apparently, the US administration does not seem to care when they use LLMs to define tariff policy....
A lot of companies made Copilot available to their workforce. I doubt that the majority of users understand what a statistical model means. The casual, technically inexperienced user just assumes that a computer answer is always right.
I'm not surprised, because I don't expect pattern matching systems to grow into something more general and useful. I think LLM's are essentially running into the same limitations that the "expert systems" of the 1980's ran into.
Even Sonnet 3.7 was able to do refactoring work on my codebase sonnet 3.6 could not.
Really not seeing the "LLMs not improving" story
It was reverse engineering ~550MB of Hermes bytecode from a react native app, with each function split into a separate file for grep-ability and LLM compatibility.
The others would all start off right then quickly default to just greping randomly what they expected it to be, which failed quickly. 2.5 traced the function all the way back to the networking call and provided the expected response payload.
All the others hallucinated the networking response I was trying to figure out. 2.5 Provided it exactly enough for me to intercept the request and using the response it provided to get what I wanted to show up.
But it still feels more like a small incremental improvement rather than a radical change, and I still feel its limitations constantly.
Like... it gives me the sort of decent but uninspired solution I would expect it to generate without predictably walking me through a bunch of obvious wrong turns as I repeatedly correct it as I would have to have done with earlier models.
And that's certainly not nothing and makes the experience of using it much nicer, but I'm still going to roll my eyes anytime someone suggests that LLMs are the clear path to imminently available AGI.
I'm wondering how much gemini 2.5 being "amazing" comes from sonnet-3.7 being such a disappointment.
Current AI just cannot do the kind of symbolic reasoning required for finding security vulnerabilities in softwares. They might have learned to recognize "bad code" via pattern matching, but that's basically it.
"Is Paul Newman known for having had problems with alcohol?"
All of the models up to o3-mini-high told me he had no known problems. Here's o3-mini-high's response:
"Paul Newman is not widely known for having had problems with alcohol. While he portrayed characters who sometimes dealt with personal struggles on screen, his personal life and public image were more focused on his celebrated acting career, philanthropic work, and passion for auto racing rather than any issues with alcohol. There is no substantial or widely reported evidence in reputable biographies or interviews that indicates he struggled with alcohol abuse."
There is plenty of evidence online that he struggled a lot with alcohol, including testimony from his long-time wife Joanne Woodward.
I sent my mom the ChatGPT reply and in five minutes she found an authoritative source to back her argument [1].
I use ChatGPT for many tasks every day, but I couldn't fathom that it would get so wrong something so simple.
Lesson(s) learned... Including not doubting my mother's movie trivia knowledge.
[1] https://www.newyorker.com/magazine/2022/10/24/who-paul-newma...
"Yes, Paul Newman was widely known for being a heavy drinker, particularly of beer. He himself acknowledged his significant alcohol consumption."
The answer I got (https://gemini.google.com/share/9e327dc4be03) includes references such as https://apnews.com/article/entertainment-reviews-movies-paul... and https://www.psychologytoday.com/us/blog/the-playing-field/20... although they are redacted from the public-sharing link.
"Sobriety & AA: Newman got sober in 1964 and remained so for the rest of his life."
Which doesn't check out. And it includes plausible but completely hallucinated URLs (as well as a valid biography.com URL that completely omits information about alcohol.)
>Paul Newman is indeed known for having struggled with alcohol during his life. Accounts from various sources, including his own memoir and the documentary ... (https://www.perplexity.ai/search/is-paul-newman-known-for-ha...)
I guess there's something about ChatGPT's set up that makes it different? Maybe they wanted it to avoid libeling people?
I use it for asking - often very niche - questions on advanced probability and simulation modeling, and it often gets those right - why those and not a simple verifiable fact about one of the most popular actors in history?
I don’t know about Idiocracy, but something that I have read specific warnings about is that people will often blame the user for any of the tool’s misgivings.
https://chatgpt.com/share/67f332e5-1548-8012-bd76-e18b3f8d52...
Your query indeed answers "...not widely known..."
"Did Paul Newman have problems with alcoholism?"
https://chatgpt.com/share/67f3329a-5118-8012-afd0-97cc4c9b72...
"Yes, Paul Newman was open about having struggled with alcoholism"
What's the issue? Perhaps Paul Newman isn't _famous_ ("known") for struggling with alcoholism. But he did struggle with alcoholism.
Your usage of "known for" isn't incorrect, but it's indeed slightly ambiguous.
They're going to regurgitate something not so much based on facts, but based on things that are accessible as perceived facts. Those might be right, but they might be wrong also; and no one can tell without doing the hard work of checking original sources. Many of what are considered accepted facts, and also accessible to LLM harvesting, are at best derived facts, often mediated by motivated individuals, and published to accessible sources by "people with an interest".
The weightings used by any AI should be based on the facts, and not the compounded volume of derived, "mediated", or "directed" facts - simply, because they're not really facts; they're reports.
It all seems like dumber, lazier search engine stuff. Honestly, what do I know about Paul Newman? But, Joanne Woodward and others who knew and worked with him should be weighted as being, at least, slightly more credible that others; no matter how many text patterns "catch the match" flow.
I think we'll have a term like we have for parents/grandparents that believe everything they see on the internet but specifically for people using LLMs.
Also, you can/should use the "research" mode for questions like this.
This is niche in the grand scheme of knowledge but Paul Newman is easily one of the biggest actors in history, and the LLM has been trained on a massive corpus that includes references to this.
Where is the threshold for topics with enough presence in the data?
>Found 24 models: llama3-70b-8192, llama-3.2-3b-preview, meta-llama/llama-4-scout-17b-16e-instruct, allam-2-7b, llama-guard-3-8b, qwen-qwq-32b, llama-3.2-1b-preview, playai-tts-arabic, deepseek-r1-distill-llama-70b, llama-3.1-8b-instant, llama3-8b-8192, qwen-2.5-coder-32b, distil-whisper-large-v3-en, qwen-2.5-32b, llama-3.2-90b-vision-preview, deepseek-r1-distill-qwen-32b, whisper-large-v3, llama-3.3-70b-specdec, llama-3.3-70b-versatile, playai-tts, whisper-large-v3-turbo, llama-3.2-11b-vision-preview, mistral-saba-24b, gemma2-9b-it
Excluding the ones that do not support chat completions, all but one (qwen-qwq-32b) answered in the affirmative. The answer from qwen-qwq-32b said:
Paul Newman, the renowned actor and humanitarian, did not have a widely publicized
struggle with alcohol addiction throughout most of his life, but there were
specific instances that indicated challenges.
It took three minutes for me to do the above from the time I created my API key to when I had an answer.
Yes, Paul Newman was known for being a heavy drinker, particularly of beer. 1 He acknowledged his high consumption levels himself. 1. Review: Paul Newman memoir stuns with brutal honesty - AP News
apnews.com
While he maintained an incredibly successful career and public life, accounts and biographies note his significant alcohol intake, often describing it as a functional habit rather than debilitating alcoholism, although the distinction can be debated. He reportedly cut back significantly in his later years.
Yes, Paul Newman did experience significant struggles with alcohol. In his posthumously published memoir, The Extraordinary Life of an Ordinary Man, Newman candidly discusses his drinking habits and acknowledges his long-term battle with alcoholism. He describes himself as a "functioning alcoholic," a trait he noted was shared with his father. At one point, Newman was reported to consume a case of beer daily, followed by spirits, until he eventually gave up hard liquor.
I appreciate your consideration of a subjective question and how you explained it and understand these nuances. But please - do not trust chatgpt etc. I continue to be frustrated at the endless people claiming something is true from chatgpt. I support the conclusions of this author.
> Hi, can you search the web and find out if Paul Newman has any alcohol problems?
> Paul Newman, the acclaimed actor and philanthropist, had a well-documented fondness for alcohol, particularly beer. He was known to consume significant quantities, with some accounts noting he would drink a case of beer in a day. This level of consumption led some to describe him as a functioning alcoholic. His drinking habits reportedly caused strain in his marriage to Joanne Woodward. In the late 1970s, an incident occurred where Woodward did not allow Newman back into their house because of his drinking. He promised to give up hard liquor as a compromise, but continued to drink beer, sometimes consuming a case during dinner.
Apparently it isn’t so specialized that a pretty obvious old fashioned web search on Google wouldn’t immediately return an authoritative source.
I guess the problem with LLMs is that they're too usable for their own good, so people don't realizing that they can't perfectly know all the trivia in the world, exactly the same as any human.
LLMs are literally fundamentally incapable of understanding things. They are stochastic parrots and you've been fooled.
They're quite literally being sold as a replacement for human intellectual labor by people that have received uncountable sums of investment money towards that goal.
The author of the post even says this:
"These machines will soon become the beating hearts of the society in which we live. The social and political structures they create as they compose and interact with each other will define everything we see around us."
Can't blame people "fact checking" something that's supposed to fill these shoes.
People should be (far) more critical of LLMs given all of these style of bold claims, not less.
Also, telling people they're "holding it wrong" when they interact with alleged "Ay Gee Eye" "superintelligence" really is a poor selling point, and no way to increase confidence in these offerings.
These people and these companies don't get to make these claims that threaten the livelihood of millions of people, inflate a massive bubble, impact hiring decisions and everything else we've seen and then get excused cause "whoops you're not supposed to use it like that, dummy."
Nah.
Hm nope, now that the web if flooded by LLM generated content it's game over. I can't tell how many times I almost got fooled by recipes &co which seem legit at first but are utter non sense. And now we're feeding that garbage back to where it came from
Its good for broad general overview such as most popular categories of books in the world.
The reason this bothers me is that comments like this reinforce the believes of people that could otherwise find value in these tools.
But I think points like this would be better made in shared chats or screenshots, since we do not have something like a core dump or stacktrace to attach.
And while I am not saying OP did this, I have seen technically skilled engineers asserting/implying that llm/chatbots aren’t good or not useful to them look at their chat log that a multitude of topics that I am sure would impact the result of the query.
Yes. It can be an UX problem. Yes. It can be an algorithmc problem. But they are just tools that can be used wrong and not a perfect mechanical brain.
# Quick Answer
Yes, Paul Newman struggled with alcohol. His issues with alcohol were explored in the HBO Max documentary, The Last Movie Stars, and Shawn Levy's biography, Paul Newman: A Life. According to a posthumous memoir, Newman was tormented by self-doubt and insecurities and questioned his acting ability. His struggles with alcohol led to a brief separation from Joanne Woodward, though it had nothing to do with cheating.
(4x Source footnotes omitted for readability)
# Ki Multi-step Research Assistant
Paul Newman is known to have struggled with alcohol. According to his posthumous memoir, Newman candidly discussed his issues with drinking and self-doubt, describing himself as an alcoholic who was tormented by insecurities[^1][^2]. He reportedly drank a significant amount of beer daily and later moved on to stronger drinks like Scotch[^3][^4]. His drinking habits were a notable part of his life, and he was often identified by his beer drinking[^5][^6]. Despite these struggles, Newman was also recognized for his generosity and devotion to his family[^7].
[^1]: [Posthumous memoir reveals Paul Newman's struggle with alcohol](https://www.express.co.uk/entertainment/films/1688113/paul-n...) [^2]: [Review: Paul Newman memoir stuns with brutal honesty - AP News](https://apnews.com/article/entertainment-reviews-movies-paul...) [^3]: [Was Paul Newman Really an Alcoholic? - The Cinemaholic](https://thecinemaholic.com/was-paul-newman-really-an-alcohol...) [^4]: [Who Paul Newman Was—and Who He Wanted to Be](https://www.newyorker.com/magazine/2022/10/24/who-paul-newma...) [^5]: [Paul Newman's Beer Drinking - Psychology Today](https://www.psychologytoday.com/us/blog/addiction-in-society...) [^6]: [Paul Newman's Beer Drinking | Psychology Today United Kingdom](https://www.psychologytoday.com/gb/blog/addiction-in-society...) [^7]: [The troubled life of Paul Newman | The Spectator](https://www.spectator.co.uk/article/the-troubled-life-of-pau...)
'though it had nothing to do with cheating' is a weird inclusion.
That is, as you point out, "all of the models up to o3-mini-high" give an incorrect answer, while other comments say that OpenAIs later models give correct answers, with web citations. So it would seem to follow that "recent AI model progress" actually made a verifiable improvement in this case.
My calculator can't conjugate German verbs. That's fine IMO. It's just a tool
I think the ability to embed arbitrary knowledge written in arbitrary formats is the most important thing llms have achieved.
In my experience trying to get an llm to perform a task as vast and open ended as the one the author describes is fundamentally misguided. The llms were not trained for that and won't be able to do it in a satisfactory degree. But all this research has thankfully provided us with the software and hardware tools where one could start working on training a model that can.
Contrast that to 5-6 years ago, when all you could hope for this kind of thing was simple rule based and pattern matching systems.
Maybe any AI experts can elaborate on this but it seems there's a limit to the fundamental underlying model of the LLM architecture of transformers and tokens.
LLM's are amazing but we might need something more or some new paradigm to push us towards true AGI.
Learning to harness current tools helps to harness future tools. Work on projects that will benefit from advancements, but can succeed without them.
Example: I frequently get requests for data from Customer Support that used to require 15 minutes of my time noodling around writing SQL queries. I can cut that down to less than a minute now.
To that end the LLM could convey as much.
(Anecdotal, sorry:) I was using Claude (not paid) recently and noticed Claude hedging quite a bit when it had not before. Examples:
"Let me be careful about this response since we're discussing a very specific technical detail ..."
"Given how specific that technical detail is, I want to be transparent that while I aim to be accurate, I may hallucinate such precise historical specifications."
I confess my initial reaction was to ask ChatGPT since the answers are more self-assured, ha ha. So perhaps corporate AI are not likely to try and solve this problem of the LLM telling the user when it is on shaky ground. Bad for business.
This is likely a manifestation of the bitter lesson[1], specifically this part:
> The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project [like an incremental model update], massively more computation inevitably becomes available.
(Emphasis mine.)
Since the ultimate success strategy of the scruffies[2] or proponents of search and learning strategies in AI is Moore's Law, short term gains using these strategies will be miniscule. It is over at least a five year period that their gains will be felt the most. The neats win the day in the short term, but the hare in this race will ultimately give away to the steady plod of the tortoise.
1: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
2: https://en.m.wikipedia.org/wiki/Neats_and_scruffies#CITEREFM...
A few months ago I looked at essentially this problem from a different angle (generating system diagrams from a codebase). My conclusion[0] was the same as here: LLMs really struggle to understand codebases in a holistic way, especially when it comes to the codebase's strategy and purpose. They therefore struggle to produce something meaningful from it like a security assessment or a system diagram.
[0] https://www.ilograph.com/blog/posts/diagrams-ai-can-and-cann...
Wasn't it back in the 1980s that you had to pay $1000s for a good compiler? The entire LLM industry might just be following in the compiler's footsteps.
I asked Grok and others as well. I believe Perplexity was the only one correct.
Repeated it multiple times even with a friends account. It kept doing the same thing. It knew the sizes, but thought the smaller sized one was bigger...
Central Park in New York City is bigger than GoldenGate Park (which I think you might mean Golden Gate Park) in San Francisco.
Central Park covers approximately 843 acres (3.41 square kilometers), while Golden Gate Park spans about 1,017 acres (4.12 square kilometers). This means Golden Gate Park is actually about 20% larger than Central Park.
Both parks are iconic urban green spaces in major U.S. cities, but Golden Gate Park has the edge in terms of total area.
1. Model "performance" judged by proxy metrics of intelligence have improved significantly over the past two years.
2. These capabilities are yet to be stitched together in the most appropriate manner for the cybersecurity scenarios the author is talking about.
In my experience, the best usage of Transformer models has come from a deep integration into an appropriate workflow. They do not (yet) replace the new exploration part of a workflow, but they are very scarily performant at following mid level reasoning assertions in a massively parallelized manner.
The question you should be asking yourself is if you can break down your task into however many small chunks that are constrained by feasiility in time to process , chunk those up into appropriate buckets or even better, place them in-order as though you were doing those steps with your expertise - an extension of self. Here's how the two approaches differ:
"Find vulnerabilities in this code" -> This will saturate across all models because the intent behind this mission is vast and loosely defined, while the outcome is expected to be narrow.
" (a)This piece of code should be doing x, what areas is it affecting, lets draw up a perimeter (b) Here is the dependency graph of things upstream and downstream of x, lets spawn a collection of thinking chains to evaluate each one for risk based on the most recent change . . . (b[n]) Where is this likely to fail (c) (Next step that a pentester/cybersecurity researcher would take) "
This has been trial and error in my experience but it has worked great in domains such as financial trading and decision support where experts in the field help sketch out the general framework of the process where reasoning support is needed and constantly iterate as though it is an extension of their selves.
> how the hell is it going to develop metrics for assessing the impact of AIs when they're doing things like managing companies or developing public policy?
Why on earth do people want AI to do either of these things? As if our society isn’t fucked enough, having an untouchable oligarchy already managing companies and developing public policies, we want to have the oligarchy’s AI do this, so policy can get even more out of touch with the needs of common people? This should never come to pass. It’s like people read a pile of 90s cyberpunk dystopian novels and decided, “Yeah, let’s do that.” I think it’ll fail, but I don’t understand how anyone with less than 10 billion in assets would want this.
This is the really important question, and the only answer I can drum up is that people have been fed a consistent diet of propaganda for decades centered around a message that ultimately boils down to a justification of oligarchy and the concentration of wealth. That and the consumer-focus facade makes people think the LLMS are technology for them—they aren't. As soon as these things get good enough business owners aren't going to expect workers to use them to be more productive, they are just going to fire workers and/or use the tooling as another mechanism by which to let wages stagnate.
Suddenly the benchmarks become detached from reality and vendors can claim whatever they want about their "new" products.
Just as a possible explanation, as I feel like I've seen this story before.
The amazing thing was that minimizing PPL allowed you to essentially guide the LLM output and if you guided it in the right direction (asked it questions), it would answer them pretty well. Thus, LLMs started to get measured on how well they answered questions.
LLMs aren't trained from the beginning to answer questions or solve problems. They're trained to model word/token sequences.
If you want an LLM that's REALLY good at something specific like solving math problems or finding security bugs, you probably have to fine tune.
Seems like they're looking at how they fail and not considering how they're improving in how they succeed.
The efficiency in DeepSeek's Multi-Head Latent Attention[0] is pure advancement.
Where’s the business model? Suck investors dry at the start of a financial collapse? Yeah that’s going to end well…
For who? Nvidia sell GPUs, OpenAI and co sell proprietary models and API access, and the startups resell GPT and Claude with custom prompts. Each one is hoping that the layer above has a breakthrough that makes their current spend viable.
If they do, then you don’t want to be left behind, because _everything_ changes. It probably won’t, but it might.
That’s the business model
I don't think this is a problem though. I think there's a lot of low-hanging fruit when you create sophisticated implementations of relatively dumb LLM models. But that sentiment doesn't generate a lot of clicks.
Maybe someone active in the research can comment? I feel like all of these comments are just conjecture/anecdotal and don't really get to the meat of this question of "progress" and the future of LLM's
- Make as much money as you can in a 24 hour period doing only legal, moral and non-annoying things
- Make a depressed person who calls a suicide hotline feel really happy doing only legal, moral and non-annoying things
- Do something really useful for society with measurable outcomes doing only legal, moral and non-annoying things
Are they getting better, definitely. Are we getting close to them performing unsupervised tasks, I don't think so.
Maybe they should create a benchmark collectively called YC founders. Gather various test cases. Never make it public. And use that to evaluate newly released models.
> Personally, when I want to get a sense of capability improvements in the future, I'm going to be looking almost exclusively at benchmarks like Claude Plays Pokemon.
Definitely interested to see how the best models from Anthropics competitors do at this.,
Please tell me this is not what tech-bros are going around telling each other! Are we implying that the problems in the world, the things that humans collectively work on to maintain the society that took us thousands of years to build up, just aren't hard enough to reach the limits of the AI.
Jesus Christ.
It’s pretty likely that they have extremely dull problems like "running an inbound call center is a lot of work" or "people keep having their mail stolen and/or lying that they did" that "more smarter gpus" won't solve
Well.. they've been caught again and again red handed doing exactly this. Fool me once shame on you, fool me 100 times shame on me.
"These machines will soon become the beating hearts of the society in which we live. The social and political structures they create as they compose and interact with each other will define everything we see around us."
This sounds like an article of faith to me. One could just as easily say they won't become the beating hearts of anything, and instead we'll choose to continue to build a better future for humans, as humans, without relying on an overly-hyped technology rife with error and unethical implications.
Which is software written 1966, but the web version is a little newer. Does occasional psychotherapy assistance/brainstorming just as well, and I more easily know when I stepped out of its known range into the extrapolated.
That said, it can vibe code in a framework unknown to me in half the time that I would need to school myself and add the feature.
Or vibe coding takes twice as long, if I mostly know how to achieve what I want and read no framework documentation but only our own project's source code to add a new feature. But on a day with a headache, I can still call the LLM a dumb twat and ask it to follow my instructions instead of doing bullshit.
But, vibe coding always makes my pulse go to 105, from 65 and question my life choices. Since few instructions are rarely ever followed and loops never left once entered. Except for on the first try getting 80% of the structure kinda right, but then getting stuck for the whole workday.
* Model creation has exploded with people training their own models and fine tunes, etc but these are all derivatives of parent models from large companies
So I'm not really sure what they mean when they refer to "recent model progress"...I don't think anybody is putting out a llama finetune saying "this is revolutionary!111" nor have I seen OAI, et al make any such claims either.
Is the sensation just because forward momentum is stalling while we wait for the next big leap?
There were qualitatively leaps in my day-to-day usage:
Claude Sonnet 3.5 and ChatGPT O1 were good for writing slop and debugging simple bugs
Grok Thinking and Sonnet 3.7 were good to catch mildly complicated bugs and write functions with basic logic. They still made mistake
But recently, Gemini 2.5 pro has been scary good. I liked to made fun of the feel-the-AGI crowd but for the first time a model made me raise an eyebrow
It can one shot unusual function with complicated logic and subtle edge cases
I'm this case the goal is to kill all the humans who know a lot about keeping other people safe...
Bingo, but I'd argue this is only scratching the surface of how twisted things are.
A lot of the stuff these labs put out (see: Amodei's cult-like blog ramblings) reeks of what I call "sociopathic utopianism" - essentially, the logical extreme of ends-justified means, made worse in the context of AI labs by the singularity pseudo-religion.
They all truly believe that AGI/ASI is possible, imminent, and could lead to utopia... so achieving that goal will surely outweigh any unsavory acts they commit in the pursuit of it.
This is why I think it's possible OpenAI took out a hit on Suchir; getting bogged down in a legal battle could delay the arrival of their machine god messiah. Same for defrauding benchmarks - they just need a "few more rounds" of investor cash, and by the time those run out, they'll surely have AGI on lock!
Fools. I look forward to them all getting prison time.
After reviewing the discussion on the Hacker News thread, it’s clear that there are a range of complaints and criticisms about AI, particularly centered around its limitations, overhype, and practical utility. Some users express frustration with AI’s inability to handle complex reasoning, its tendency to produce generic or incorrect outputs, and the perception that it’s more of a buzzword than a transformative tool. Others question its value compared to traditional methods or human expertise, suggesting it’s overhyped or misapplied in many cases. Below, I’ll offer a defense of AI that addresses these concerns while highlighting its strengths and potential.
AI isn’t perfect, and no one should claim it is—but that’s not the point. It’s a tool, and like any tool, its effectiveness depends on how it’s used. Critics who point to AI’s struggles with nuanced reasoning or edge cases often overlook the fact that it’s not designed to replace human judgment entirely. Instead, it excels at augmenting it. For example, AI can process vast amounts of data—far more than any human could in a reasonable timeframe—and identify patterns or insights that might otherwise go unnoticed. This makes it invaluable in fields like medicine, where it’s already helping diagnose diseases from imaging data, or in logistics, where it optimizes supply chains with precision that manual methods can’t match.
The complaint about generic or incorrect outputs, often dubbed “hallucinations,” is fair but misses the bigger picture. Yes, AI can churn out nonsense if pushed beyond its limits or fed poor data—but that’s a reflection of its current stage of development, not its ultimate potential. These systems are improving rapidly, with each iteration reducing errors and refining capabilities. More importantly, AI’s ability to generate starting points—like drafts, code snippets, or hypotheses—saves time and effort. It’s not about delivering flawless results every time; it’s about accelerating the process so humans can refine and perfect the output. A programmer tweaking AI-generated code is still faster than writing it from scratch.
As for the overhype, it’s true that the buzz can get out of hand—marketing teams love a shiny new toy. But beneath the noise, real progress is happening. AI’s contributions aren’t always flashy; they’re often mundane but critical, like automating repetitive tasks or enhancing search algorithms. The critics who say it’s just a “fancy autocomplete” underestimate how transformative that can be. Autocomplete might sound trivial until you realize it’s powering real-time language translation or helping scientists sift through research papers at scale. These aren’t sci-fi fantasies—they’re practical applications delivering value today.
Finally, the notion that AI can’t match human expertise in complex domains ignores its complementary role. It’s not here to outthink a seasoned expert but to amplify their reach. A lawyer using AI to review contracts doesn’t lose their skill—they gain efficiency. A researcher leveraging AI to analyze data doesn’t stop hypothesizing—they get to test more ideas. The technology’s strength lies in its ability to handle the grunt work, freeing humans to focus on creativity and judgment.
AI isn’t a silver bullet, and it’s not without flaws. But the criticisms often stem from inflated expectations or a failure to see its incremental, practical benefits. It’s not about replacing humans—it’s about making us better at what we do. The trajectory is clear: as it evolves, AI will continue to refine its capabilities and prove its worth, not as a standalone genius, but as a partner in progress.