I am a physics professor and often use Gemini to check my papers. It is a formidable tool: it was able to find a clerical error (a missing imaginary unit in a complex mathematical expression) I was not able to find for days, and it often underlines connections between concepts and ideas that I overlooked.
However, it often makes conceptual errors that I can spot only because I have good knowledge of the topic I am discussing. For instance, in 3D Clifford algebras it repeatedly confuses exponential of bivectors and of pseudoscalars.
Good to know that ChatGPT 5.5 Pro can produce a publishable paper, but from what I have seen so far with Gemini, it seems to me that it is better to consider LLMs as very efficient students who can read papers and books in no time but still need a lot of mentoring.
I assume you're using the "regular" Pro version of Gemini 3.1 for the above, rather than the Deep Think mode, which is more comparable to GPT-5.5 Pro. To my knowledge, regular 3.1 Pro is a tier below and often makes mistakes.
Moreover, there's no reason to believe the progress of LLMs, which couldn't reliably solve high-school math problems just 3–4 years ago, will stop anytime soon.
You might want to track the progress of these models on the CritPt benchmark, which is built on *unpublished, research-level* physics problems:
https://critpt.com/
Frontier models are still nowhere near solving it, but progress has been rapid.
* o3 (high) <1.5 years ago was at 1.4%
* GPT 5.4 (xhigh), 23.4%
* GPT-5.5 (xhigh), 27.1%
* GPT-5.5 Pro (xhigh) 30.6%.
https://artificialanalysis.ai/evaluations/critpt.
> there's no reason to believe the progress of LLMs [...] will stop anytime soon
Wrong. Every advancement has followed a s curve. Where we are on that curve is anyones guess. Or maybe "this time its different".
Great. You see a shape in graphs. And that shape tells you that _at some unknown point in the future_ progress will slow (but likely not stop).
Now back to the point, what reason do you have to believe progress will stop soon? If you have no reason, then it sounds like you agree with OP.
Which makes the patronizing sarcasm all that much more nauseating.
Nausea aside, what evidence does anyone have that “super intelligence” of the sort your argument alludes to is even possible? Because that’s what we’re really talking about; greater than human intelligence on this sort of academic task. For example; When llms start contributing meaningfully to their own development, that would be a convincing indicator imo.
This discussion is not about superintelligence, it is about continued progress. Fully general human intelligence at much lower cost than humans is all that is required to profoundly reshape society, but it is not clear even that will happen soon.
As the blog points out - this is one particular subfield where LLMs have much easier prospects - lots of low hanging fruit that “just” requires a couple weeks of PHD candidate research.
Mathematics itself is one of a small handful of endeavors where automated reinforcement training is extremely straightforward and can be done at massive scale without humans.
Neither of these factors place a structural bound on the kind of thing LLMs can be good at, but we are far from certain we can achieve performance at this level in other fields economically and in the near future.
> When llms start contributing meaningfully to their own development, that would be a convincing indicator imo.
This has been the case for awhile now already…
https://kersai.com/the-48-hours-that-changed-ai-forever-clau...
And yet the world hasn’t changed all that much except people getting laid off in response to over-hiring prior to the diffusion of llm’s.
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What people miss is that AI isn't one S curve, each capability we try to bake into a model has its own S curve. Model progress might not impact some capabilities at all, but other capabilities might get totally overhauled.
This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently (https://www.nature.com/articles/d41586-024-03214-7).
So if instead of text we come up with a different representation for mathematical or physical problems, that could both improve the quality of the output while reducing the amount of transformers needed for decoding and encoding IO and for internal reasoning.
There are also difference inference methods, like autoregressive and diffusion, and maybe others we haven't discovered yet.
You combine those variables, along with the internal disposition of layers, parameter size and the actual dataset, and you have such a large search space for different models that no one can reliably tell if LLM performance is going to flatline or continue to improve exponentially.
>This could be right for the current architecture of LLMs, but you can come up with specialized large language models that can more efficiently use tokens for a specific subset of problems by encoding the information differently.
That's precisely what happens on the bad side of a S curve.
There are advancements that do not follow s curves - consider for instance total data transmitted over all networks, or financial derivatives volumes.
I think a better question for AI is “is it more like a network effect, liquidity effect, or a biological/physical effect”?
Those are measuring the utility of a technological advancement by looking at usage, not the pace of advancement of said technology.
Yes. But quantity has a quality all its own, as they say — derivatives have gone through at least a few step functions where they have become more important and more useful as their usage grows. I’d call that advancement.
Maybe just to be clear I think that kneejerk “I hate this AI trend, and prefer to believe this will end soon, all exponential growth ends eventually” is intellectually lazy, and dangerous for younger engineers/hackers, a group I hope can benefit from being on HN.
Bitcoin mining went through something like 13 10x growth periods, last I ran the numbers a few years ago. There are physical processes that do have very extended periods of doubling, and there are digital and financial processes that don’t show any signs of doing anything but continuing to keep growing over their multidecade lives. So, like I said, it’s worth thinking carefully, and risk mitigation for things like mental health, career decisions and investment decisions indicates we should be cautious assessing new dynamics.
Got him. That guy always posts with so much bluster lmao.
>There are advancements that do not follow s curves - consider for instance total data transmitted over all networks, or financial derivatives volumes
Or Roman trade volume before the Fall of Rome.
Not to mention what you describe is not technological improvement but increase in data or money flows, not the same.
Sic transit gloria - obviously.
But I don’t that think it’s quite so obvious that model quality / growth / usefulness is definitively and obviously not more like data or money flows than it is like some other process.
Total volume of usage is not an advancement, it’s orthogonal.
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It’s more of a guess if you don’t know about things like scaling laws and RL with verification. The onus of “we’re going to saturate” anytime soon is on that claim because every measurement points to that not being true.
He said "will stop anytime soon". He didn't say forever.
Which still makes no sense. There is the same chance we are flatlining now as that we are flatlining in e.g. 3 years or 5 years.
In what sense are the models flatlining?
In the sense that the incremental improvements in capabilities that we've been seeing in recent models seem to taking exponentially growing amounts of compute to achieve.
But they don't?
Mythos is a 10T model. Opus is a 5T model.
That's not an exponentially growing amount of compute but it is achieving exponential improvements (eg from Mozilla: https://blog.mozilla.org/en/privacy-security/ai-security-zer... )
Compute doesn't necessarily linerarly follow parameters. And with how many active parameters Mythos vs Opus gets its effectivenes from? Is it 1x or 2x? We don't know. We don't even know the parameters (it's more of rumor than confirmed 10T iirc).
But even more so, who said the improvements are "exponential"? Mozilla's single metric, that doesn't even prove anything of the sort?
> but it is achieving exponential improvements
“Exponential” used here is pure hyperbole. Can you justify it?
It can be S curve (and it almost surely is), but on every chart you can plot, you don't see even of an inkling of the bend yet.
Software and hardware have no limits. Theoretically would could bozons for computations and have the same amount of computation available on one cm3 of the current total computation in the entire world. Same with software. Never there was a stop on new algorithms. With LLMs there are so many parts that will get better and are not very far fetched.
What the fuck does that have to do with “soon”?
This is FUD and extremely wrong. None of the advancements have followed an S curve. This time IS different and it should be obvious to you at this point.
There are many indications that model progress is slowing down, so that is not entirely accurate.
Model progress at spitting out unhallucinated facts is slowing down hard. Model progress at solving hard math challenges/programming tasks doesn't seem to be slowing down that I can tell.
Please be specific because outside of anecdotal blog posts by people who don’t know what they’re talking about it’s not true. Look at scaling laws, composite benchmarks from the epoch capability index, nothing at all suggests “model progress is slowing down”
Which indications are that?
The cost factors on the new models compared to the old models.
Qwen3.6 9B is as good as GPT-4o and runs on my M2 MacBook Air. Models are getting stronger and less costly at the same time, but these are somewhat separate branches of research. Frontier labs are spending more because they are still getting marginal returns and there is more capacity to spend than there was a year ago.
You are mixing cost and progress. It’s not because it’s more and more expensive that progress is slowing down by itself.
They are intrinsically linked beyond a certain point. If we're making progress but costs are spiraling exponentially then it stands to reason that we will soon reach a point where we can no longer afford the increasing costs and thus progress will slow.
(barring some breakthrough that reduces costs, which of course may happen, but for which recent model improvements are not strong evidence of)
Investment dollars.
Source for that claim?
Nobody is releasing NEW models
…not only is this not true but it also doesn’t matter. Why would this indicate performance saturating?
What constitutes a NEW model for the purposes of calculating progress?
What? DeepSeekV3 just came out and is incredible for the price. Mythos is also half-released.
The standard networking connection has been called “Ethernet” for more than thirty years, so networking has stagnated, right?
If higher bandwidth networking consisted primarily running more and more ethernet lines in parallel, you would most certainly agree that "networking has stagnated".
"Reasoning" and now "Agentic" AI systems are not some fundamental improvement on LLMs, they're just running roughly the same prior-gen LLMS, multiple times.
Hence the conclusion that LLM improvement has slowed down, if not stagnated entirely, and that we should not expect the improvements of switching to these "reasoning" systems to keep happening.
From TFA:
“ChatGPT came up with an idea which is original and clever. It is the sort of idea I would be very proud to come up with after a week or two of pondering, and it took ChatGPT less than an hour to find and prove”
You misunderstand. I'm not saying that Reasoning/Agentic systems aren't better.
I'm saying they're not an advancement in the tech in the way GPT 1 through 3 were. They're a different kind of improvement.
And as such the rate improvement cannot just be extrapolated into the future.
GPT1 through GPT3 advancement were exactly like using more Ethernet cables in parallel.
All interesting conceptual breakthroughs came after GPT3: RL and reasoning being the main ones.
Deep think still makes many many many more mistakes than gpt 5.5 pro on math
LLMs are at their best when you have an expectation for their output. I generally know the shape of the correct response and that allows me to evaluate it's output on it's "vibes", rather than line by line. If there's no expectation then I have to take everything at face value and now I'm at the mercy of the machine.
Exactly, if I generate a large chunk software, I'm going to have expectations about what it will do, how it will do it, etc. You don't just accept the statement that "it's done" for fact but you start looking for evidence.
A scientific approach here is to look to falsify the statement. You start asking questions, running tests, experiments, etc. to prove the notion that it is done wrong. And at some point you run out of such tests and it's probably done for some useful notion of done-ness.
I've built some larger components and things with AI. It's never a one shot kind of deal. But the good news is that you can use more AI to do a lot of the evaluation work. And if you align your agents right, the process kind of runs itself, almost. Mostly I just nudge it along. "Did you think about X? What about Y? Let's test Z"
> Mostly I just nudge it along. "Did you think about X? What about Y? Let's test Z"
Exactly - you need to constantly have your sceptics glasses on and you need to be exacting in terms of the structure you want things to follow. Having and enforcing "taste" is important and you need to be willing to spend time on that phase because the quality of the payoff entirely depends on it.
I recently planned for a major refactor. The discussion with claude went on for almost two days. The actual implementation was done in 10 minutes. It probably has made some mistakes that I will have to check for during the review but given that the level of detail that plan document had, it is certainly 90-95% there. After pouring-in of that much opinion, it is a fairly good representation of what I would have written while still being faster than me doing everything by hand.
So you have to know the answer and also be an expert in the problem domain?
In my experience you need exactly what you said, and I would add that he probably would have spent half day to do the refactoring himself and it would be sure he did right.
I can speak towards building large-scale systems from scratch with these tools. I've been working since late last year on a project that was barely a tech demo, and the progression of development on that project has seen me go from leveraging co-pilot autocomplete at the start, to full-on vibecoding 100% of the new additions.
I have reasonable eng chops I'd like to think - I have been a senior IC for a while on a reasonably diverse set of challenging systems problems and built out some pretty large-scale pieces of software the old "artisinal" way.
This particular project is a productization of some ideas I had for leveraging a virtual machine to execute high-divergence parallel logic on GPUs, in an effort to move complex things like "unit behaviour in games" (the classical symbolic kind, not NN-based unit behaviour) into the GPU. The project is going well but still quite a ways from release. But it's at about 300k lines of code now across 9 or so rust repositories, and a smattering of typescript on the frontend.
I have had stumbles, but overall I feel I have put together some good strategies and principles for pushing large projects along with these tools in an effective way.
The biggest takeaway for me is that the "feel" is different. Software construction by hand felt like building legos where you put the pieces together yourself. A lot of my focus would be on building and solidifying core components so I could rely on them when I stepped up to build higher-level components. Projects would get mired quickly if you didn't solidify your base.
With agentic development, one of the early challenges I ran into was this issue with something I'll call "oversight inception". It's when at some early point in the process a somewhat low-importance decision is made - an implementation decision, a decision to say.. align a test with the implementation rather than an implementation with a test.
Then, as you build more on top of this, that small decision somehow ends up getting reified into a core architectural policy that then cascades up.
You realize that when you're building a big project, the focus on some particular component is backstopped by a general understanding of local development directionality with respect to the larger level project. And the agent has no idea of directionality.
So small chinks in the design end up getting magnified and blown up as the dev process proceeds, and later on review you find major architectural pieces have just been overlooked, all flowing from some small incidental implementation choice a long time before.
This is one among a number of issues, but it's a big one. Once I saw it happening I tried an approach to mitigate it by developing a set of golden "goal" documents that describe directionality at the project level: what you are working towards and what design components need to exist.
This doesn't eliminate the "oversight inception" issue, but it does catch them earlier.
When I started applying the goal documentation aggressively to re-align the project implementation direction, I found velocity dropped a lot.
And as I progress, I'm balancing this out a bit - to allow the system to diverge a bit, but force reconvergence towards the goals at some specific cadence. I haven't found the right candence yet but I'm getting there.
This new style of development feels more like claymoulding pottery than lego assembly. You sort of "get it into shape". It's a very interesting new set of process assumptions.
I agree, but I would add that they can be very useful even if you do not have clear expectations but have some solid ways to verify their claims. Often in doing this verification I came up with new ideas.
Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn. It does not, and it is for the human brain a formidable task to remember that something as smart as an LLM does not learn. I keep catching myself making the same mistake.
It’s also because it is so annoying to have to manage the memory of the LLM with custom prompts/instructions manually.
I have not yet played with the long term memory feature, but I fear it will be even less reliable than prompts, simply because in one year or two years so much will have changed again that this “memory” will have to be redone multiple times by then.
They can form new associations between concepts via their input prompts and thinking text. That is a form of learning. Just not very durable. I liken it to https://en.wikipedia.org/wiki/Anterograde_amnesia
yeah, I should have been more specific: I meant the type of learning that mentoring fosters, the long term learning.
I hear you. I think we are already seeing some middle ground with agentic systems using RAG, skills.md files, etc. It's a sort of disassociated card catalog memory. An engineer's notebook. Not the integrated, correlated, pre-processed set of relationships in the model. How to go backward from the notebook -> model cheaply without tanking performance is definitely one of those billion dollar questions.
Current LLM architecture doesn't learn - and you're right this is a huge piece that normal folks fail to understand, since in many ways, it's the opposite of what years of AI research has been trying to create.
However, I think it's important to remember that LLMs are embedded in larger systems, and those larger systems do learn.
exactly like you said - the harness might learn.
we do also have training on synthetic data. it might compound.
I mostly agree, though after a mentoring session you can ask it to write skill or a memory and it can be reasonably durable. For Claude at least, the memories work pretty well (though I am still at a small scale with them. As they grow it might start to break somewhat. Doesn't always work, but has often enough that I thought it worth a mention.
> Using the word “Mentoring” is anthropomorphic and subconsciously makes you think it will learn.
I think this is a bit pedantic. Obviously the parent you’re replying to is referring to the concept of “in-context learning”, which is the actual industry / academic term for this. So you feed it a paper, and then it can use that info, and it needs steering / “mentoring” to be guided into the right direction.
Heck the whole name of “machine learning” suggests these things can actually learn. “reasoning” suggests that these things can reason, instead of being fancy, directed autocomplete. Etc.
In other news: data hydration doesn’t actually make your data wet. People use / misuse words all the time, and that causes their meaning to evolve.
I agree it’s pedantic and personally don’t get bent out of shape with people anthropomorphizing the llms. But I do think you get better results if keep the text prediction machine mental model in your head as you work with them.
And that can be very hard to do given the ui we most interact with them in is a chat session.
I'm no physics professor but this aligns with the way I use the tools in my "senior engineer" space. I bring the fundamentals to sanity-check the trigger-happy agent and try to imbue other humans with those fundamentals so they can move towards doing the same. It feels like the only way this whole thing will work (besides eventually moving to local models that do less but companies can afford).
I agree and put it this way: LLMs sound so convincing presenting you the work it does rose colored and promising to give you more if you keep going.
There is a 50/50 chance that it turns out to be right or letting you jump of the cliff.
Only the trip stays the same beautiful 5 star plus travel.
Also, spotting an error and telling LLM makes it in most cases worse, because the LLM wants to please you and goes on to apologize and change course.
The moment I find myself in such a situation I save or cancel the session and start from scratch in most cases or pivot with drastic measures.
Gemini to me is the most unpredictable LLM while GPT works best overall for me.
Gemini lately gave me two different answers to the same question. This was an intentional test because I was bored and wanted to see what happens if you simply open a new chat and paste the same prompt everything else being the same.
Reasoning doesn’t help much in the Coding domain for me because it is very high level and formally right what the LLM comes up with as an explanation.
I google more due to LLMs than before, because essentially what I witnessed is someone producing something that I gotta control first before I hit the button that it comes with. However, you only find out shortly afterwards whether the polished button started working or gave you a warm welcome to hell.
Reusing the same prompt several times is something I've started doing too. The contrast is often illuminating.
In one case, it made a thoroughly convincing argument that an approach was justified. The second time it made exactly the opposite argument, which was equally compelling.
I now see LLMs as persuasion machines.
One thing I've been doing lately -- and I'm in a business function, not a technical one, although I have an engineering background -- is pitting LLMs against each other. For example, if I'm structuring a proposal or a contract with the assistance of Claude, I'll begin my 360 feedback review first by asking Claude how it would react if it were the counter-party receiving the proposal. After some iterative changes, mostly manual, I will then run the same output document past Gemini and ask it to adopt personas from both sides and provide reactive feedback. The result of this is almost always a stronger proposal that I can also accompany with proactive objection handling and a solid FAQ, as well as clear points of negotiation that will likely be acceptable to both parties.
For this sort of thing, using multiple LLMs is extremely helpful.
Before AI happened I watched youtube. Occasionally I encountered there very convincing arguments. Same person often made very convincing arguments on many subjects.
But noticed that the closer the domain they were talking about was to my area of competence the less convincing their arguments were. There were more holes, errors and wrong conclusions.
I recalibrated my bs meter thanks to that.
Since AI came I successfully used this strategy of being extremely cautious towards convincing arguments to not become mislead by AI.
However this year I'm working with AI more in the domain of software development. Where I can see the competence. And I see the competence. This had opposite effect on me. I tend to trust AI outside my domain of expertise much more after I saw what can it do in software.
One caveat though is that there are a lot of areas of human culture where there's very little actual knowledge, but a lot of opinions, like politics, economy, diet, business, health. I still don't trust AI in those domains. But then again, I don't trust humans there either.
For me basically AI achieved the threshold of useful reliability for any domain that humans are reliable at.
I don't really care about sycophancy. I might have a slight advantage that I don't talk to AI in my native language. So its responses don't have a direct line to my emotions.
Ever since they started getting really sycophantic, I’ve been presenting my ideas as “my co-worker says this is a good approach but I disagree, can you help me convince him that it’s wrong?”
>LLM wants to please you
I was using Copilot and asked it a question about a PDF file (a concept search). It turned out the file was images of text. I was anticipating that and had the text ready to paste in.
Instead, it started writing an OCR program in python.
I stopped it after several minutes.
Often Copilot says it can't do something (sometimes it's even correct), that's preferential to the try-hard behaviour here.
> Gemini to me is the most unpredictable LLM while GPT works best overall for me.
This nails an important thing IMHO. I've absolutely noticed this, for better or worse. Gemini can produce surprisingly excellent things, but it's unpredictability make me go for GPT when I only want to ask it once.
We've got a rather extensive AI setup through our equity fund and I've setup a group of agents for data architecture at scale. One is the main agent I discuss with and it's setup to know our infrastructure and has access to image generation tools, websearch, hand off agents and other things. I tend to use Opus (4-6 currently) and I find it to be rather great. As you point out it comes with the danger of making mistakes, and again, as you point out, it's not an issue for things I'm an expert on. What I rely on it for, however, is analysing how specific tools would fit into our architecture. In the past you would likely have hired a group of consultants to do this research, but now you can have an AI agent tell you what the advantages and disadvantages of Microsoft Fabric in your setup. Since I don't know the capabilities of Fabric I can't tell if the AI gives me the correct analysis of a Lakehouse and a Warehouse (fabric tools).
What I do to mitigate this is that I have fact checking agents configured to be extremely critical and non-biased on Opus, Gemini and GPT. Which are then handed the entire conversation to review it. Then it's handed off to a Opus agent which is setup to assume everything is wrong. After this, and if I'm convinced something is correct I'll hand the entire thing off to a sonnet agent, which is setup to go through the source material and give me a compiled list of exactly what I'll need to verify.
It's ridicilously effective, but I do wonder how it would work with someone who couldn't challenge to analytic agent on domain knowledge it gets wrong. Because despite knowing our architecture and needs, it'll often make conceptional errors in the "science" (I'm not sure what the English word for this is) of data architecture. Each iteration gets better though, and with the image generation tools, "drawing" the architecture for presentations from c-level to nerds is ridiclously easy.
Are you using this agent hive for any repeatable tasks? What you described, superficially, seems like a one off. Genuinely curious.
please, sign up for a paid plan of either chatgpt or claude. gemini is while close, still noticeably behind
you deserve opinions shaped by interactions with the best tools that are out there.
Gemini feels deep and philosophical. Especially for product management. Tell him you're a product manager and we're a team of two.
But regular reminder - All LLMs can be wrong all the time. I only work with LLMs in domains I'm expert in OR I have other sources to verify their output with utmost certainty.
Or when you don't care about results being very correct.
When I'm cooking meatballs with sauce and the recipe calls for frying them, I'll have an LLM guestimate how long and which program to use in an air fryer to mimic the frying pan, based on a picture of balls in a Pyrex. So I can just move on with the sauce, instead of spending time browsing websites and stressing about getting it perfect.
I used to hate these non-deterministic instructions, now I treat it as their own game. When I will publish my first recipe, I'll have an LLM randomize the ingredient amounts, round them up to some imprecise units and also randomize the times. Psychologists say we artists need to participate and I WILL participate.
> I only work with LLMs in domains I'm expert in
This. Should become a general rule for any non-trivial use of LLM in a professionel setting.
Agreed, Gemini is clearly a capable model, but the tool use is lagging behind the other two. Ironically it regularly gets things wrong (ie. the current version of some software) because of an unwillingness to use web search.
Gemini is certainly not behind Claude in terms of physics.
ChatGPT and Gemini are actually fairly comparable.
Claude has been utterly useless with most math problems in my experience because, much like less capable students, it tends to get overly bogged down in tedious details before it gets to the big picture. That's great for programming, not so much for frontier math. If you're giving it little lemmas, then sure it's great, but otherwise you're just burning tokens.
Seriously, it’s not worth reaching for less intelligence. Use Extended Pro 100% of the time for things you’d spend the amount of time GP spent writing their post.
LLM’s are the most powerful tool invented to search across a huge information space in response to human input.
That’s all they are. They don’t ‘know’ anything intrinsically and do know ‘know’ what reasoning even is.
Hi ziotom! I wonder about you work in 3D Cifford Algebras. May you share some links to the research you do? I also have interest in this topic I research on my own.
Just in case if you don't want to disclose your name my email is northzen@gmail.com
This doesn't surprise me since the coding agents are similar. I've previously compared them to very fast, ambitious junior programmers. I think they are probably mid-level coders now, but they continue to make mistakes that a senior programmer wouldn't. Or at least shouldn't.
Gemini’s smug and over-confident “this is the gold standard in 2026” definitely leaves little space for nuance if you don’t know the subject matter. Human students would, hopefully, know they don’t know everything.
> Gemini’s smug...
Anthropomorphizing these systems is dangerous, whether coming from the bullish or bearish perspective. The output is statistically generated by a machine lacking the capability to be smug.
>Anthropomorphizing these systems is dangerous
That ship has sailed. Humans will anthropomorphize a rock if you put googly eyes on it.
First I thought to myself, "my daughter does this and it looks so cute". And only as a second thought, that your comment just proved itself.
It's only "statistically generated" in the same way that your brain is just "neurons firing." That's the low-level description of what's happening, but on a higher level, it's correct to say that it's being smug.
Chiming in to agree but clarify that the latest sota models are no better than Gemini.
I put my stuff through several sota models and round robin them in adversarial collaboration and they are all useful even though, fundamentally, they don’t “understand” anything. But they are super useful delegates as long as deciding on the problem and approach and solution all sits safely in your head so you can challenge them and steer them.
So I know the article is about one particular new model acing something and each vendor wants these stories to position their model as now good enough to replace humans and all other models, but working somewhere where I am lucky enough to be able to use all the sota models all the time, I can say that all keep making obvious mistakes and using all adversarially is way better than trusting just one.
I look forward to the day one a small open model that we can run ourselves outperforms the sum of all today’s models. That’s when enough is enough and we can let things plateau.
Basically all Erdos problems that get solved with AI use ChatGPT 5.* Pro, not Gemini/Opus.
I would guess it's because ChatGPT Pro allows for 80min "think". I've never had even remotely similar think times with Gemini Deep Think. It's generally around 10-15min for math problems, and get increasingly shorter for continued interactions.
This is close to my experience with code. LLMs can pick out small mistakes from giant code changes with surprising accuracy, or slowly narrow down a weird. On the other hand I've seen them bravely shoulder on under completely incorrect conceptual models of what they're working with and churn around in circles consequently, spin up giant piles of slop to re-implement something they decided was necessary, but didn't bother to search for, or outright dismiss important error signals as just 'transient failures'. Unlimited stamina, low wisdom.
Any experience with NotebookLM?
Mine has epically bad.
intern that never sleeps
> in 3D Clifford algebras it repeatedly confuses exponential of bivectors and of pseudoscalars.
I have no idea what any of those words even mean. I'm sure LLMs make similar obvious-to-professors mistakes in all the domains. Not long ago, we didn't even have chatbots capable of basic conversation...
Ironically, it's sort of the other way around! Every frontier chatbot since GPT 4 (at least) has had a pretty good understanding of even very esoteric technical concepts.
Bivectors and pseudoscalars (in a 3D context) are "just" signed areas and volumes. Easy!
Back around the GPT 3, 3.5, and 4.0 era I used to ask the bots to explain "counterfactual determinism", which is one of the most complex topics I personally understand.
Then I would lie to the bot about it, and see if it corrected me or not.
This test is useless now, the frontier models can't be fooled any longer on such "basic" concepts.
Conversely, LLMs are basically useless at anything that doesn't have enough (or no) public information for their training. Think: obscure proprietary product config files and the like, even if the concepts involved are trivial.
Similarly, Clifford Algebra is a relatively niche (even "alternative") area of mathematics and physics, with vastly less written material about it than the competing linear algebra. Hence, the AIs are bad at it.
I've been watching the automation of things like flight control systems for the past decade, and the evolution of the fallback to a real pilot in the event of a emergency is what's most concerning about where LLMs are being embedded.
Right now, we have a lot of smart people who have trained for decades to understand where these things go wrong and how to nudge them back, but the pool of people are going to slowly be replaced by less knowledgeable.
At some point, a rubicon will be crossed where these systems can't fallback to a human operator and will fail spectacularly.
Watching a teenager approach their homework, instead of struggling to answer questions they don't know, they ask Gemini. Unfortunately, I think the mental struggle to approach an answer is where much of the learning is. They also miss out on the reward for persistence of seeing things fall together.
It is troubling. It suggests a plateauing of human understanding.
It absolutely is where the learning is, that's pretty well established brain science.
What that means practically is that we've got a generation - 25 years or less - to evolve these things not to need the fallback. If such a thing is possible.
We're on the road to Idiocracy.
I don't think the experience with Gemini will be the same when using GPT.