Really nitpicky I know but GPT-3 was June 2020. ChatGPT was 3.5 and the author even gets that right in an image caption. That doesn’t make it any more or less impressive though.
It is interesting that most of our modes of interaction with AI is still just textboxes. The only big UX change in that the last three years has been the introduction of the Claude Code / OpenAI Codex tools. They feel amazing to use, like you're working with another independent mind.
I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
Grok has been integrated into Tesla vehicles, and I've had several voice interactions with it recently. Initially, I thought it was just a gimmick, but the voice interactions are great and quite responsive. I've found myself using it multiple times to get updates on the news or quick questions about topics I'm interested in.
When we have really fast and good models it will be able to generate a GUI on the fly. It could probably be done now with a fine-tune on some kind of XML-based UI schema or something. I gave it a try but couldn't figure it out entirely, consistency would be an issue too.
Personally I find the information density of text to be the "killer feature". I've tried voice interaction (even built some AI Voice Agents) and while they are very powerful, easy to use and just plain cool, they are also slow.
Nothing beats skimming over a generated text response and just picking out chunks of text, going back and forth, rereading, etc.
Text is also universal, I can't copy-paste a voice response to another application/interface or iterate over it.
My personal view is that the search for a better AI User Interface is just the further dumbing down of the humans who use these interface. Another comment mentioned that the most popular platforms are people pointing fingers at pictures and without a similar UI/UX AI would never reach such adoption rates, but is that what we want? Monkeys pointing at colorful picture blobs?
I get what you’re saying here, and you’re right that other UIs will be a big deal in the near future… but I don’t think it’s fair to say “just” textboxes.
This is HN. A lot of us work remotely. Speaking for myself, I much prefer to communicate via Slack (“just a textbox”) over jumping into a video call. This is especially true with technical topics, as text is both more dense and far more clear than speech in almost all cases.
If you are interested in UX a youtube series I found enjoyable and thought provoking is "liber indigo" (sorry, on mobile)
What comes after the desktop metaphor and mobile? There is VR but... no one is sure it will get anywhere. It's cool but probably won't supplant tradition.
Maybe the ability of AI to accept somewhat imprecise inputs will help us get away from text. Multimodal gesture, voice, and touch perhaps?. So we would all be sort of body acting like players on a stage, in order to convey to a machine what direction you wish to turn its attention
> But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
I feel like I've been hearing this for at least 1.5 years at this point (since the launch of GPT 4/Claude 3). I certainly agree we've been heading in this direction but when will this become unambiguously true rather than a phrase people say?
> So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
As a current graduate student, I have seen similar comments in academia. My colleagues agree that a conversation with these recent models feels like chatting with an expert in their subfields. I don't know if it represents research as a field would not be immune to advances in AI tech. I still hope this world values natural intelligence and having the drive to do things heavily than a robot brute-forcing into saying "right" things.
> if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student.
With coding it feels more like working with two devs - one is a competent intermediate level dev, and one is a raving lunatic with zero critical thinking skills whatsoever. Problem is you only get one at a time and they're identical twins who pretend to be each other as a prank.
> Again, we have moved past hallucinations and errors to more subtle, and often human-like, concerns.
From my experience we just get both. The constant risk of some catastrophic hallucination buried in the output, in addition to more subtle, and pervasive, concerns. I haven't tried with Gemini 3 but when I prompted Claude to write a 20 page short story it couldn't even keep basic chronology and characters straight. I wonder if the 14 page research paper would stand up to scrutiny.
I find Gemini 3 to be really good. I'm impressed. However, the responses still seem to be bounded by the existing literature and data. If asked to come up with new ideas to improve on existing results for some math problems, it tends to recite known results only. Maybe I didn't challenge it enough or present problems that have scope for new ideas?
Novel solutions require some combination of guided brute-force search over a knowledge-database/search-engine (NOT a search over the models weights and NOT using chain of thought), combined with adaptive goal creation and evaluation, and reflective contrast against internal "learned" knowledge. Not only that, but it also requires exploration of the lower-probability space, i.e. results lesser explored, otherwise you're always going to end up with the most common and likely answers. That means being able to quantify what is a "less-likely but more novel solution" to begin with, which is a problem in itself. Transformer architecture LLMs do not even come close to approaching AI in this way.
All the novel solutions humans create are a result of combining existing solutions (learned or researched in real-time), with subtle and lesser-explored avenues and variations that are yet to be tried, and then verifying the results and cementing that acquired knowledge for future application as a building block for more novel solutions, as well as building a memory of when and where they may next be applicable. Building up this tree, to eventually satisfy an end goal, and backtracking and reshaping that tree when a certain measure of confidence stray from successful goal evaluation is predicted.
This is clearly very computationally expensive. It is also very different to the statistical pattern repeaters we are currently using, especially considering that their entire premise works because the algorithm chooses the next most probable token which is a function of the frequency of which that token appears in the training data. In other words, the algorithm is designed explicitly NOT to yield novel results, but rather return the most likely result. Higher temperature results tend to reduce textual coherence rather than increase novelty, because token frequency is a literal proxy for textual coherence in coherent training samples, and there is no actual "understanding" happening, nor reflection of the probability results at this level.
I'm sure smart people have figured a lot of this out already - we have general theory and ideas to back this, look into AIXI for example, and I'm sure there is far newer work. But I imagine that any efficient solutions to this problem will permanently remain in the realm of being a computational and scaling nightmare. Plus adaptive goal creation and evaluation is a really really hard problem, especially if text is your only modality of "thinking". My guess would be that it would require the models to create simulations of physical systems in text-only format, to be able to evaluate them, which also means being able to translate vague descriptions of physical systems into text-based physics sims with the same degrees of freedom as the real world - or at least the target problem, and then also imagine ideal outcomes in that same translated system, and develop metrics of "progress" within this system, for the particular target goal. This is a requirement for the feedback loop of building the tree of exploration and validation. Very challenging. I think these big companies are going to chase their tails for the next 10 years trying to reach an ever elusive intelligence goal, before begrudgingly conceding that existing LLM architectures will not get them there.
In fairness, how much time did you give it? How many totally new ideas does a professional researcher have each day? or each week?
A lot of professional work is diligently applying knowledge to a situation, using good judgement for which knowledge to apply. Frontier AIs are really, really good at that, with the knowledge of thousands of experts and their books.
I have Gemini Pro included on my Google Workspace accounts, however, I find the responses by ChatGPT, more "natural", or maybe even more in line with what I want the response to be. Maybe it is only me.
I recently (last week) used Nano Banana Pro3 for some specific image generation. It was leagues ahead of 2.5. Today I used it to refine a very hard-to-write email. It made some really good suggestions. I did not take its email text verbatim. Instead I used the text and suggestions to improve my own email. Did a few drafts with Gemini3 critiqueing them. Very useful feedback. My final submission about "..evaluate this email..." got Gemini3 to say something like "This is 9.5/10". I sorta pride myself on my writing skills, but must admit that my final version was much better than my first. Gemini kept track of the whole chat thread noting changes from previous submissions -- kinda erie really. Total time maybe 15 minutes. Do I think Gemini will write all my emails verbatim copy/paste... No. Does Gemini make me (already a pretty good writer) much better. Absolutely. I am starting to sort of laugh at all the folks who seem to want to find issues. Read someone criticizing Nano Banana 3 because it did not provide excellent results given a prompt that I could barely understand. Folks that criticize Gemini3 because they cannot copy/paste results. Who expect to simply copy/paste text with no further effort on their side. Myself, I find these tools pretty damn impressive. I need to ensure I provide good image prompts. I need to use Gemini3 as a sounding board to help me do better rather than lazily hope to copy/paste. My experience... Thanks Google. Thanks OpenAI (I also use ChatGPT similarly -- just for text). HTH, NSC
for whatever reason gemini 3 is the first ai i have used for intelligence rather than skills. I suspect a lot more will follow, but its a major threshold to be broken.
i used gpt/claude a ton for writing code, extracting knowledge from docs, formatting graphs and tables ect.
but gemini 3 crossed threshold where conversations about topics i was exploring or product design were actually useful. Instead of me asking 'what design pattern should be useful here', or something like that it introduces concepts to the conversation, thats a new capability and a step function improvement.
First, the fact we have moved this far with LLMs is incredible.
Second, I think the PhD paper example is a disingenuous example of capability. It's a cherry-picked iteration on a crude analysis of some papers that have done the work already with no peer-review. I can hear "but it developed novel metrics", etc. comments: no, it took patterns from its training data and applied the pattern to the prompt data without peer-review.
I think the fact the author had to prompt it with "make it better" is a failure of these LLMs, not a success, in that it has no actual understanding of what it takes to make a genuinely good paper. It's cargo-cult behavior: rolling a magic 8 ball until we are satisfied with the answer. That's not good practice, it's wishful thinking. This application of LLMs to research papers is causing a massive mess in the academic world because, unsurprisingly, the AI-practitioners have no-risk high-reward for uncorrected behavior:
That's also why I don't use these tools that much. You have big AI companies, known for harvesting humongous amount of data, illegally, not disclosing datasets. And they you give them control of your computer, without any way to cleanly audit what's going in and out. It's seriously insane to me that most developers seem to not care about that. Like, we've all been educated to not push any critical info to a server (private key and other secrets), but these tools do just that, and you can't even trust what it's gonna be used for. On top of that, it's also giving your only value (writing good code) to a third party company that will steal it to replace you with it.
Every time I see an article like this, it's always missing --- but is it any good, is it correct? They always show you the part that is impressive - "it walked the tricky tightrope of figuring out what might be an interesting topic and how to execute it with the data it had - one of the hardest things to teach."
Then it goes on, "After a couple of vague commands (“build it out more, make it better”) I got a 14 page paper." I hear..."I got 14 pages of words". But is it a good paper, that another PhD would think is good? Is it even coherent?
When I see the code these systems generate within a complex system, I think okay, well that's kinda close, but this is wrong and this is a security problem, etc etc. But because I'm not a PhD in these subjects, am I supposed to think, "Well of course the 14 pages on a topic I'm not an expert in are good"?
It just doesn't add up... Things I understand, it looks good at first, but isn't shippable. Things I don't understand must be great?
Well, that's why people still have jobs but I appreciate the idea of the post that the neat demo was a coherent paragraph or silly poem. The silly poems were all kind of similar, not very funny, and the paragraphs were a good start but I wouldn't use them for anything important.
Now the tightrope is a whole application or a 14 page paper and the short pieces of code and prose are now professional quality more often than not. That's some serious progress.
You don't use it that way. You use it to help you build and run experiments, and help you discuss your findings, and in the end helps you write your discoveries. You provide the content, and actual experiments provide the signal.
The author actually discusses the results of the paper. He's not some rando but a Wharton Professor and when he is comparing the results to a grad student, it is with some authority.
"So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student. The idea was good, as were many elements of the execution, but there were also problems..."
So when should we start to be worried, as developers ? Like, I don't use these tools yet for cost + security. But you can see it's getting there, mostly. It could take a day before to find a complex algorithm, understand it, and implement it to your code, now you can just ask an AI to do it for you and it could succeed in a few minutes. How long before the amount of engineers needed to maintaint a product is divived by 2 ? By 10 ? How about all the boring dev jobs that were previously needed, but not so much anymore ? Like, basic CRUD applications. It's seriously worrying, I don't really know what to think.
For anyone giving full access to an AI agent, only do so from within the confines of a VM or other containerized environment and back up everything somewhere the agent can't reach.
Like the warning at the bottom says, they can delete files without warning.
The great transition and technological advancement we see. % years ago, it was just a dream, 3 years ago, everything seemed magical, and today AI is everywhere, which is far superior to no time
I start to genuinely wonder where the place for us humans are in this. All I see is human beings being crowded out. Capital via LLMs taking the place of humans.
52 comments
[ 2.4 ms ] story [ 64.9 ms ] thread[1] https://finance.yahoo.com/news/alphabet-just-blew-past-expec...
I am curious what the user interfaces of AI in the future will be, I think whoever can crack that will create immense value.
Google seems to be making good progress [1] and it seems like only a matter of time before it reaches consumers.
1. https://research.google/blog/generative-ui-a-rich-custom-vis...
My personal view is that the search for a better AI User Interface is just the further dumbing down of the humans who use these interface. Another comment mentioned that the most popular platforms are people pointing fingers at pictures and without a similar UI/UX AI would never reach such adoption rates, but is that what we want? Monkeys pointing at colorful picture blobs?
This is HN. A lot of us work remotely. Speaking for myself, I much prefer to communicate via Slack (“just a textbox”) over jumping into a video call. This is especially true with technical topics, as text is both more dense and far more clear than speech in almost all cases.
What comes after the desktop metaphor and mobile? There is VR but... no one is sure it will get anywhere. It's cool but probably won't supplant tradition.
Maybe the ability of AI to accept somewhat imprecise inputs will help us get away from text. Multimodal gesture, voice, and touch perhaps?. So we would all be sort of body acting like players on a stage, in order to convey to a machine what direction you wish to turn its attention
I feel like I've been hearing this for at least 1.5 years at this point (since the launch of GPT 4/Claude 3). I certainly agree we've been heading in this direction but when will this become unambiguously true rather than a phrase people say?
As a current graduate student, I have seen similar comments in academia. My colleagues agree that a conversation with these recent models feels like chatting with an expert in their subfields. I don't know if it represents research as a field would not be immune to advances in AI tech. I still hope this world values natural intelligence and having the drive to do things heavily than a robot brute-forcing into saying "right" things.
With coding it feels more like working with two devs - one is a competent intermediate level dev, and one is a raving lunatic with zero critical thinking skills whatsoever. Problem is you only get one at a time and they're identical twins who pretend to be each other as a prank.
From my experience we just get both. The constant risk of some catastrophic hallucination buried in the output, in addition to more subtle, and pervasive, concerns. I haven't tried with Gemini 3 but when I prompted Claude to write a 20 page short story it couldn't even keep basic chronology and characters straight. I wonder if the 14 page research paper would stand up to scrutiny.
See prompt, and my follow-up prompts instructing it to check for continuity errors and fix them:
https://pastebin.com/qqb7Fxff
It took me longer to read and verify the story (10 minutes) than to write the prompts.
I got illustrations too. Not great, but serviceable. Image generation costs more compute to iterate and correct errors.
All the novel solutions humans create are a result of combining existing solutions (learned or researched in real-time), with subtle and lesser-explored avenues and variations that are yet to be tried, and then verifying the results and cementing that acquired knowledge for future application as a building block for more novel solutions, as well as building a memory of when and where they may next be applicable. Building up this tree, to eventually satisfy an end goal, and backtracking and reshaping that tree when a certain measure of confidence stray from successful goal evaluation is predicted.
This is clearly very computationally expensive. It is also very different to the statistical pattern repeaters we are currently using, especially considering that their entire premise works because the algorithm chooses the next most probable token which is a function of the frequency of which that token appears in the training data. In other words, the algorithm is designed explicitly NOT to yield novel results, but rather return the most likely result. Higher temperature results tend to reduce textual coherence rather than increase novelty, because token frequency is a literal proxy for textual coherence in coherent training samples, and there is no actual "understanding" happening, nor reflection of the probability results at this level.
I'm sure smart people have figured a lot of this out already - we have general theory and ideas to back this, look into AIXI for example, and I'm sure there is far newer work. But I imagine that any efficient solutions to this problem will permanently remain in the realm of being a computational and scaling nightmare. Plus adaptive goal creation and evaluation is a really really hard problem, especially if text is your only modality of "thinking". My guess would be that it would require the models to create simulations of physical systems in text-only format, to be able to evaluate them, which also means being able to translate vague descriptions of physical systems into text-based physics sims with the same degrees of freedom as the real world - or at least the target problem, and then also imagine ideal outcomes in that same translated system, and develop metrics of "progress" within this system, for the particular target goal. This is a requirement for the feedback loop of building the tree of exploration and validation. Very challenging. I think these big companies are going to chase their tails for the next 10 years trying to reach an ever elusive intelligence goal, before begrudgingly conceding that existing LLM architectures will not get them there.
A lot of professional work is diligently applying knowledge to a situation, using good judgement for which knowledge to apply. Frontier AIs are really, really good at that, with the knowledge of thousands of experts and their books.
i used gpt/claude a ton for writing code, extracting knowledge from docs, formatting graphs and tables ect.
but gemini 3 crossed threshold where conversations about topics i was exploring or product design were actually useful. Instead of me asking 'what design pattern should be useful here', or something like that it introduces concepts to the conversation, thats a new capability and a step function improvement.
Second, I think the PhD paper example is a disingenuous example of capability. It's a cherry-picked iteration on a crude analysis of some papers that have done the work already with no peer-review. I can hear "but it developed novel metrics", etc. comments: no, it took patterns from its training data and applied the pattern to the prompt data without peer-review.
I think the fact the author had to prompt it with "make it better" is a failure of these LLMs, not a success, in that it has no actual understanding of what it takes to make a genuinely good paper. It's cargo-cult behavior: rolling a magic 8 ball until we are satisfied with the answer. That's not good practice, it's wishful thinking. This application of LLMs to research papers is causing a massive mess in the academic world because, unsurprisingly, the AI-practitioners have no-risk high-reward for uncorrected behavior:
- https://www.nytimes.com/2025/08/04/science/04hs-science-pape...
- https://www.nytimes.com/2025/11/04/science/letters-to-the-ed...
I feel like these should run in a cloud enviroment, or at least on some specific machine where I don't care what it does.
Is it impossible for them to mess up your system? No. But it does not seem likely.
Then it goes on, "After a couple of vague commands (“build it out more, make it better”) I got a 14 page paper." I hear..."I got 14 pages of words". But is it a good paper, that another PhD would think is good? Is it even coherent?
When I see the code these systems generate within a complex system, I think okay, well that's kinda close, but this is wrong and this is a security problem, etc etc. But because I'm not a PhD in these subjects, am I supposed to think, "Well of course the 14 pages on a topic I'm not an expert in are good"?
It just doesn't add up... Things I understand, it looks good at first, but isn't shippable. Things I don't understand must be great?
Now the tightrope is a whole application or a 14 page paper and the short pieces of code and prose are now professional quality more often than not. That's some serious progress.
It’s like the Gell-Mann amnesia effect applied to AI. :)
https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
"So is this a PhD-level intelligence? In some ways, yes, if you define a PhD level intelligence as doing the work of a competent grad student at a research university. But it also had some of the weaknesses of a grad student. The idea was good, as were many elements of the execution, but there were also problems..."
https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
Would we not expect similar levels of progress in other industries given such massive investment?
Like the warning at the bottom says, they can delete files without warning.