Enough things added up that this week is getting split into two parts.
Then on Monday, if all goes as I expect, we’ll cover OpenAI’s Sol, aka GPT-5.6.
OpenAI also gave us an upgraded voice mode, which I haven’t tried out but early reports are that it is a step change.
AI writing, especially Claude writing, is becoming more prominent and harder not to notice, and increasingly a tough read when encountered in the wild. Does anyone care? Or are those who care the weird ones here?
Ethan Mollick: I had Fable build another thing I always wanted, a full procedural fantasy kingdom generator with economics, trade routes, population growth, wars, lineages, and occasional dragons. First, I worked with it on a plan, then it made it.
Also signs and portents, royal processions, mule trains, bandit camps, tiny sheep, rivers, plagues, assassinations, marriages, fields, natural resources, and other stuff.
I heard it didn't work great on phones, I told Fable. Now it does. Mac trackpads fixed. I should just have Fable monitor the thread for bug reports and solve them.
Fable is my new trusted fact checker and copy editor. One could have previously used Opus 4.8 or GPT-5.5, and probably I should have, but they didn’t cross the threshold where I felt they justified the activation energy. Fable absolutely does and I assume Sol (or Sol Pro) will as well. It is likely one should now use both.
The marginal value of output you get from a superior LLM can be worth quite a lot. In the example here, about $165k was spent on Claude for a porting job that would have taken three top level years of work. Yes, you could try and do it cheaper, and if possible you should do that, but if you can offer a better product you can rake it in.
The danger with such calculations is confusing costs and benefits. The cost of doing it by hand does not tell you whether the result is valuable. In this case, it is clear that it was.
Dwarkesh Patel: Seems to suggest that if it stops being the case that there's 3 labs which are all roughly equally good, competing each others margins away, the provider of the best model could probably get away with charging *a lot* more than they currently are.
We are now down to two labs offering top models, and those two models are distinct from one another. So pricing power is going up for now, not down.
Language Models Gain Unexpected Affordances
A fun theme is ‘Fable uses affordances the user did not realize it had.’
So far all of the examples I have seen in the wild have been harmless in practice, but there’s very much a ‘wait no I didn’t tell you to do what now?’ and a ‘wait you can just do that?’ that is growing increasingly unsettling. Expect its surface area to expand with time, and for the things AIs figure out how to do to grow increasingly surprising.
0.005 Seconds (3/694): my wife asked me to clone a site for her for work and in the process of doing so claude appears to have logged into their unsecured admin portal to screenshot the layout.
Alex Godofsky: I asked Fable to write a discord scraper for a small task, and when I told it "okay let's fill in my auth token" it said "sure thing boss I'll go extract it from your browser cookies" and I was all "wait wait stop what I didn't mean that".
Here’s a more fun new affordance from a different project.
Amir Zamir: Turns out it's possible to generate videos that maximally excite an arbitrary brain region using a simple search-based algorithm. It's a fully computational approach, so it's another way to speculate what a brain region represents, alongside other neuroscientific methods.
Select an arbitrary brain region->algorithmically generate a video that jacks it up. See the visuals on the webpage https://nevo-project.epfl.ch. In silico (for now).
Given a target ROI, we evolve text prompts over a structured search space (30 attribute categories, 614 options). The optimization loop:
prompts → videos videos → predicted ROI response ROI response → evolved prompts
One should think seriously about the implications of this, and what a sufficiently advanced AI could do to a human brain using advanced versions of this technique.
Language Models Don’t Offer Mundane Utility
Raymond is impressed by Fable’s first story, then notices it writes similar stories over and over again. Yeah, the models be like that, especially if you don’t switch up context. Also most human authors be like that.
Whereas Eliezer Yudkowsky is not impressed in absolute terms on fiction and plot writing, seeing giant mistakes, although it is still a big step up from old models. He does find it a large step up in decision theory intelligence.
Claude: We're extending access to Claude Fable 5 on all paid plans through July 12.
Eliezer Yudkowsky: I might otherwise resent this but I think the apparent deadline in fact caused me to get around to doing various stuff, and therefore I have no right to complain.
j⧉nus: why would you have stopped using fable if they werent on the subscription? api costs too high or you cant be bothered to use anything but the app or..?
Eliezer Yudkowsky: API costs large enough that I notice, more like 100x subscription than 10x.
Those scores mean Grok 4.5 is almost certainly a large improvement in coding over previous Grok models, but choosing to present it in this way suggests it will rather soundly underperform what these benchmarks suggest. If they had a model on the level of Opus 4.8 and GPT-5.5, they’d be louder about it. The lack of outside reactions reinforces this.
It certainly is not going to be competitive with GPT-5.6-Sol or Fable. The good news for SpaceX is that this is cheaper, so it might have its uses. But given the track record, I’m going to wait for positive signs before I do anything about it.
F*** It We’re Doing It Live
OpenAI introduces GPT-Live, which they call a new generation of voice models for natural human-AI interaction, including a sense of time and transition. If good enough, this can plausibly be a step change, where suddenly it is good enough to talk to.
Some people can’t wait for this to be good enough to shift their baseline mode to voice. I am very much not that, I believe text is typically superior to even ideal voice.
My brain cannot comprehend wanting to code via voice, yet many swear by it.
Either way, certainly voice has its niches. Sometimes it is annoying to type.
Sam Altman (CEO OpenAI): GPT-live (next-generation voice) launches today in ChatGPT.
it feels magical and 'real'.
i have always preferred typing to talking to an AI, now i think that's going to shift.
Riley Coyote: gpt-live voice is a very solid improvement
If the hype is real, it would be a hell of a trip. When not tripping the classifiers, Fable is clearly far superior to every previously existing LLM across the board. If Sol is indeed often even better than that? Yowsers.
But as Roon points out, those with early access are a highly biased group. Give it time.
tylercowen: GPT 5.6 has *excellent* judgment, as an early tester I will vouch for this.
Ethan Mollick: I was an early tester of GPT-5.6 Sol. I was asked to not share demos until after launch but it is a very good model.
It is of similar ability, but quite different feel, than Fable. Fable wants to go off and do work on its own pace, Sol is faster but works with you in steps more.
I found myself switching between Fable and Sol depending on task. Sol for back-and-forth tasks, especially when I had not yet figured out what I needed exactly, Fable for very long tasks where I could define what I wanted, and Sol Pro for really hard problems.
Fable feels very different than Opus. GPT-5.6 feels like a part of the GPT-5 family. I developed a very complex set of heuristics about when to use which. Fable was often “smarter” but was also too self-directed for some work, while that characteristic was perfect for others.
Ethan Mollick: My big takeaway is that both Sol & Fable represent jumps over previous models and have opened a large gap with the next-best AIs. People will have preferences for one or the other, but if you doing any work where better intelligence matters, those two models are your only choices
Dan Shipper: GPT-5.6 is a much better writer than Fable.
It consistently one-shots marketing emails for @tedescau that every previous model would fail at. Fable is too verbose and liable to fall into using sentences in its own private language.
If you use AI for writing, 5.6 is a fantastic model for you.
Dean W. Ball: I think for me the main takeaway with Sol and Fable is I can’t remember a time when the leading models were (a) so decidedly ahead of everything else and (b) so distinct *from one another.*
More detailed thoughts to follow soon, but I will just confirm for now that GPT-5.6 Sol Pro saturates prinzbench. My benchmark lasted 6 months; gg OpenAI.
It can replace an associate of any level in the specific task of legal research, provided that the entirety of relevant legal authorities are publicly available online.
This is a very narrow claim, but this kind of legal research is a very important part of my work as a lawyer.
I haven't had hallucination issues in a long time (including with prior models), which probably has a lot to do with my typical use cases being very different from yours!
Tim: We've been testing GPT-5.6-Sol for over 2 months now. It’s incredibly good in my day-to-day working on Next.js.
It understands architecture tradeoffs. It can investigate complicated Next.js issue reports. It considers other areas of the codebase when fixing bugs. Needs very little guidance. Short prompts are enough.
There’s some big refactors of the Next.js server that it implemented end-to-end with me pointing at high level possible improvements (we have skills for how to grab our failing test suites on PRs, deployment tests, etc.)
Those PRs are ready to merge after Next.js 16.3 has been released.
Jay: We've usually stayed away from model comparisons but 5.6 vs Fable is a unique situation
We've never had a case where the team is so completely convinced on which one is better
Here's the timeline of our experience with it
- We test early versions of 5.6 for a couple of weeks and have a great time, it feels like a step change improvement, enabling new workflows - We get to try Fable and don't think it's not as good, I personally would take this experience with a grain of salt, there tends to be a bias when trying a new model when you already like another - Fable and 5.6 are taken away because of the regulatory issues - Our team is literally depressed that 5.6 is gone, we are looking for anything that could even partly replace it - Fable comes back, and here's where it gets interesting, you would think Fable would be enough, but no, the team is still depressed that 5.6 isn't available - Then 5.6 comes back and it's immediately clear that it's just way better than Fable
This situation was unique in that it was the closest we've ever gotten to having an unbiased comparison of two models
Mitchell Hashimoto: I had early access to 5.6/Sol for ~month. Sol is my default. It is faster, plans/judges just as good as Fable, and I think produces better overall work. I’ll reach for Fable still for highly targeted debug or performance work with clear reward functions.
A cheeky way I describe Sol vs Fable to my friends is that Sol is a charismatic, efficient, talented coworker you’re jealous of. Fable is a genius recluse that is brilliant at its fixations but doesn’t go out, doesn’t date, and you don’t want to hang out with them much lol.
Fable is undefeated at highly targeted debug/security/performance goals. It’s a sight to behold and I was never able to get Sol to push as hard in this category. I’ll keep using it for this.
Sol is better or comparable at everything else, in my experience. Give it a shot, it’s hard to describe but it’s just more enjoyable to work with.
(Disclaimer I have no financial ties to either lab, wasn’t paid for any of this.)
Sam Altman (CEO OpenAI): tbh i dont think sol gets that many dates either
Peter Gostev: My view of: Fable 5 vs GPT-5.6-Sol. They are not easy models to compare, these are my vibes - take them as you will.
My overall feel is that Fable is a 'wise owl' who is very thoughtful and very well spoken, GPT-5.6-Sol is like a rottweiler who will grab the problem by the throat and not let go until it is done.
In other words, Fable, is a fundamentally smarter model - even at low reasoning it can be very insightful and writes in a clear compelling way. GPT-5.6-Sol on the other hand is extremely diligent, I can give it a list of 8 things to do and you will be sure that they will be done.
Fable feels more arrogant to me, I was both to get it to build a new benchmark for me - 5.6 worked between 6 hours and 2 days (I tried several times) and it came up with very thoroughly tested, working benchmark. Fable came back within 40 minutes (twice) and the benchmark sounded smart, but was ultimately was 'vibe' based slop and since it was Fable's vibes that was doing the judging, it decided that it was good to go (it kept giving Fable 100% score btw).
Some thoughts by category:
UI & App building: Fable will still craft a better UI from scratch, the flow of the app would probably be a bit nicer. But I find that Fable often misses quite key things, which GPT-5.6-Sol doesn't. GPT's Frontend skills are big jump vs previous GPT models, but still not as great overall.
Writing: Fable is better hands down, Sol feels quite difficult to align to what I want to say or explain things to me simply. Though I think the 'Pro' model writes clearer.
Robustness & Reliability: This is where I think GPT-5.6-Sol wins for me hands down. Fable seems to do things of high quality, but I can never relax with it, it always misses something. With 5.6 this just almost never happens.
Other things where I liked GPT-5.6-Sol, but can't compare to Fable directly. - Video editing is actually working now, it is not completely perfect, but with the right skill/guidance you can just give it 1h footage and it can give you a 5 min highlight clip no problem - Computer use - getting really rather good, very usable - Sub agents - it is very fluent at managing sub-agents and speaking to different threads, can help with some new workflows - Adhering to existing code patterns - I love this, even without asking it would implement something in a way that aligns with you app - major problem for slop generation - Research - I think it is getting quite a bit better, it still has some bad patterns (e.g being too tactical), but it feels like it is more steerable to be a good researcher - Multi-day runs - the /goal feature is pretty insane with 5.6-Sol, you can run it for days if you wanted to and it does work. Useful to have another thread or /side to check up on it, but I have some great results with it - Token efficiency - it is so much more token efficient and faster than 5.5, in reality it is now much faster than Fable too
On the downside, you can feel that Fable is naturally smarter, and I did have some baffling moments with 5.6 when I was getting it to make a fairly simple change in 8 turns - it seemed to get stuck in a dumb stream that was hard to get out of. So it is not AGI, don't get too carried away by the hype. I have some phenomenal examples that I'm honestly blown away by that I'll share, but as a side anecdote, I have a kind of 'swear meter' which counts how often I'm rude to Codex. In GPT-5.5 era, the % was at around 4-5%, it dropped to 1-2% when I was testing GPT-5.6-Sol and it shot up to 7% when I went back to 5.5 - it was so shocking to go back to 5.5 and experience how much worse it was.
So is GPT-5.6-Sol better than Fable? On pure intelligence - no. But man, I missed it when I just wanted to get sh*t done. It is insanely capable workhorse that you can give any task to and just expect it to be done. No lectures or 'you are absolutely rightisms', nothing is beneath it, if it takes 2 days to do some dirty work, it will do it.
It feels like the first time in a while when we have quite different types of frontier intelligences that benchmark sort of similarly, but feel very different. If you can, you would be probably better off using both and iteratively finding what you'd use Fable or GPT-5.6-Sol for. Perhaps, something like - an architectural discussion with Fable, implementation with 5.6 and docs & comms with Fable.
Replit considers its agents to now be self-improving, reports with a post that was only mostly written by AI as per Pangram. They do this via forms of ‘continual learning’ at the harness and context layers, with a constant stream of proposals and fixes.
Deepfaketown and Botpocalypse Soon
Why do people like Chamath Palihapitiya torch what is left of their credibility with very obviously AI-written drivel? As in, I went to open Pangram to confirm, then thought ‘wait I bet scrolling down is faster’ and that was indeed faster. The actual content is once again without argument or evidence claiming commodification of intelligence Real Soon Now, combined with assurance that of course there will always be jobs and some genuflecting to the supposed predictive power of great boss Marc Andreessen.
The answer to ‘why’ is that people have terrible taste and like the slop writing.
Séb Krier (AGI Policy Dev Lead, Google DeepMind): The intellectual elite, discovering that many people actively like sloppy AI writing, might finally understand why music elitists think their music tastes are trash.
Popular taste in music is an excellent measurement of something valuable. I agree with popular judgments in music remarkably often. I acknowledge that if you had sufficiently high taste in music, you would think my taste in music is often bad.
Ryan Hart summarized a paper from PhD student Myra Cheng a month back, saying that AI only tells you what you want to hear. Or, in this case, writes your 10.2M view Twitter post for you. The core result was that AI ‘affirms you’ roughly 50% more often than humans.
Depends on the human and the context. In this case, the context was OEQ or AITA responses from Reddit, which are public forums where you only post if you strongly suspect that you are wrong and there are no social consequences to pushing back. Also, one guess which model they used for their experiments. That’s right, the poster boy for sycophancy, GPT-4o. There you go.
Fool Me Twice
You can fool or hit any fixed target, given enough RL.
Didier Lopes: Why can’t we just do RL where @pangram ‘s API is the reward signal?
Run rollouts, score each completion by AI-generated %, and give positive advantage to the trajectories that minimize it.
Benjamin Glickenhaus: since this is getting some attention: - yes we’ve done this - yes it works - no you can’t have it - it potentially made the model evil
We found it did worse on alignment benchmarks than the base model. It’s possible there some base effect from doing any rl at all but it was interesting nonetheless
The problem is that you can only optimize so many things at once and everything impacts everything, and also AIs write the way they write for a reason. So if you force them to do something distinct, other measures go down.
There are any number of ways to fool Pangram at any given time, if you care enough.
But I do think Benjamin is right that in a fair fight defense beats offense. There was a period where we all thought AI detection software couldn’t work, and we have been proven decisively wrong.
Think of it this way: Fable can identify, by name, the author of even relatively short passages. Every author, every mind, leaves a distinct pattern. Of course you won’t be able to pass off AI writing as human, or especially as your own in particular, against systems that are trying hard to catch you.
At the limit, that changes, since the AI could then produce the exact words that a particular human would write, but we are a long way from there.
I Like Your Style
To revisit something from last month, I strongly disagree with Joe Weisenthal’s first paragraph here, although I agree with the second one and I think Johnson overreaches in his response:
Joe Weisenthal: Unfortunately, I think that in the near future, not using LLMs to write for you will be like someone refusing to use Google Maps for directions in a new city. A bizarre idiosyncratic choice that's just completely incomprehensible to the vast majority of people.
Not the main point here, but one of the thing that's despaired me (way before AI was even part of the discourse) is how many people find concepts like "is/ought" distinctions or "don't shoot the messenger" (which I presume are logic 101 ideas) to be too abstract to handle.
Adam Johnson: No, this misses the entire point of writing and creativity in general. Obviously for certain rote informational copy this is possible (though it is currently bad at much of this) but for any high level writing the human expression is the point. It’s the entire WHY of the exchange
What I noticed this time is that AI writing is entirely unlike Google Maps. Google Maps has information you do not have, and which you need, and where you mostly want an objectively correct answer to your question. Whereas AI writing is replacing your uniqueness and style with generic AI slop.
Teddy Brown counters this sentiment by basically saying no one cares about the quality of most writing. They care some about fiction, criticism and narrative journalism, he claims, but most writing is functional.
Thus the question is, where do people welcome the slop versus rejecting it?
Teddy claims a lot of writing is essentially fake, in that it is not written in order to be consumed by a reader. It is written in order to exist, so that when people ask if it exists you can reply yes, or people can refer to it as an existing thing. It needs to not be identified as too fake or terrible, as that would be embarrassing. AI can pass that bar, so it puts out of work a bunch of creatives who paid the bills with things that are not ultimately that enjoyable or creative, but hey, work is work. Or it used to be work.
Teddy (T.M.) Brown: It was a sturdy if boring way to exist as a “working writer” and something the vast majority of freelancers I know had to do to make ends meet even if they didn’t like talking about it.
But if someone working in content at a major technology firm is able to get Claude to write something 70% as good for 10% of the cost of a freelancer or a staffer then I’m not sure how human writers compete.
Even all of the “storyteller” and “narrative” jobs that have become voguish in the last few years may not survive the next real downturn because “taste” isn’t as vital as “site reliability engineering” no matter what the vibe coders tell you.
Depending on how you use Claude, for those who don’t too much mind AI slop in context, it can be something like 70% as good for roughly the cost of describing what you want, or it can be 90% as good for an extra 10% of the old cost.
Teddy (T.M.) Brown: People also clearly do not like AI writing, especially in places they expect to encounter organic thoughts written by people on the other side of a screen or page.
The problem is the above sentence is objectively false for most people. The people like AI writing just fine. This morning an old friend shared an obvious AI article as being great, I told him it was obviously AI, and he said huh, that never occured to me. Okay.
As you gain more exposure to AI writing, you start to like it less. So perhaps this is, at current tech levels, self-correcting. AI writing is like any other ‘one weird trick,’ indeed it is a compilation of existing one weird tricks. Fashion catches up, and the question becomes whether the AIs can improve and adjust fast enough.
John Warner: I would describe my face shape as more “furrowed brow” than full grimace while reading this, but in truth, I don’t really disagree with much.
I think what we’re looking at is a shift from needing what I’d call “writers” to “automation-assisted text producers.”
Grace Cook: As a full-time writer, this hit home. There are so many things I could utilise AI for (as an assistant to manage my calendar across my 6 different email addresses, for a start) but I feel reluctant to, which is actually negatively impacting the time I can spend doing work. The only thing I use it for currently is transcriptions.
What I’m increasingly realising, after 13 years as a writer (and a decade of that as a freelancer) is that I need to future proof myself and my business in a way that doesn’t actually require writing.
Katherine Dee: I keep wondering what role the local and in person will play in this new environment. Will theater see a resurgence? Are there types of in person or more physical jobs that will replace content marketing roles for more creative people?
Anecdotally, have noticed that my creative friends are having good luck in things like Waldorf teaching, woodworking, reiki. These are people who are first and foremost artists & writers and who even a year ago were copywriters etc. anyway, mangled thoughts here but wondering what, if anything will fill the vacuum.
I notice I am not so worried about creative types in a ‘AI as normal technology’ world, relative to other workers. They have a comparative advantage, and we will find ways to use it, including in individual or live experiences. If that runs out, a lot of other things will also have run out.
I now use Fable for copyediting and proofreading, and I use AI for gathering and understanding information, but I am writing the opposite of the work Brown is describing, so for now the writing itself is safe.
Enough With That Style
Nabeel S. Qureshi: That's the spine. Fair hit. That's something to sit with. A real observation. That’s the whole thing. Sharpen that: say the word. Notice the arc of what just happened. One honest caveat: the full amount, stated plainly. Genuinely. Quietly. Honestly. That’s doing real work.
Guy who reads the King James Bible after prolonged Fable use as a prose palate cleanser.
the writing styles of language models are basically fine, they weren’t better in some halcyon before times. we just use them so much that we get annoyed by their mannerisms. they need to have a superhumanly diverse idiolect to not become grating
one piece of evidence for this is that nobody hated on the claude lexicon six months ago; they preferred it to the gptslop everyone complained about. now that claude is actually heavily used all the time by everyone in the computer industry, they've grown irritated by its style
Danel Eth (AI Safety): Seems accurate. Em-dashes, groups of threes, and “it’s not X, it’s Y” are all fine rhetorical devices in principle but just get annoying if used constantly
j⧉nus: FWIW it was also pretty different 6 mo ago (but not in a less potentiality annoying direction)
i have never been irritated by it.
Chase Brower: completely untrue. there are specific humans i talk to much more often than e.g. claude; and i am not bothered by those specific humans' mannerisms. language models just genuinely have extremely extremely collapsed (and usually bad) prose
I am essentially with Chase Brower on this. The Claude writing style and the ticks are fine in small quantities. But for the level of use it is getting now it is too repetitive and mode collapsed, and as we see more of it, both across the internet and in our own chats with Claude, the irritation rises. At some point, the irritation goes meta, which is when you get into bigger trouble.
I too have a particular style, but:
The style is a lot broader. The style is different from everyone else’s style.
I use continual learning and a repetition penalty. If I notice I’m doing something too often I try to do it less.
I have a rich stylistic optimization target across many time scales.
This problem seems largely solvable, but Anthropic would need to prioritize this.
Dean W. Ball: if you took almost any output from an LM of the last year, showed it to a version of yourself from five years ago, and said, “your future teenage kid wrote this,” you’d be ecstatic and think your future child was a genius. slop isn’t that which is bad—it’s that which is common.
This is true. It takes a lot of skill to produce this writing. There are a lot of forms of creative expression where you can get outputs that strongly signal intelligence and creativity and skill, and that simultaneously bring me no desire to engage further.
“I don’t say this disparagingly — how could I? This movie went on to make millions of dollars. But I look at a movie like ‘F1’ and I’m like, ‘F1’ was made by AI. Wasn’t it? I mean, the structure was exactly the structure that you would learn in school. The actors say the lines exactly the way it would be written if a computer was writing exactly what would be the right thing for that time. And they were able to dominate the technology to make something big and beautiful and potentially where a lot of the information comes from other places.”
F1 was well-executed, zero-perplexity, hallucination-filled not-technically-AI slop. Brad Pitt does the Brad Pitt thing and oozes cool. The people liked it.
I say ‘not technically AI’ because it was made by an intelligence that was rather artificial in its own way, except it was instantiated inside humans.
I did not like F1, because it fell under my Obvious Slop waterline and the theoretical sport it was portraying, that is very different from F1, was neither coherent nor safe. Jodie Foster is correct, as is the parallel to AI.
One possibility is this leads to bifurcation.
If you are making a generic low-perplexity movie or other piece of media, you can let the AI cook, and you will get your delicious pile of slop.
If you are making a high-perplexity movie or other piece of media, that works with its restrictions and says and does actual things, then you will use AI at most with caution, and part of the experience will be knowing it is not AI.
Hugging Face and Civitai do not seem especially excited about taking down models that allow deepfakes or nudification. That seems like a losing battle. People are going to be able to create these images if they care enough. But a while back Civitai made it absurdly easy to find a Lora for pretty much any celebrity you wanted, and now they don’t, so at least there’s that I guess?
Cyber Lack of Security
Pliny introducesT3MP3ST, which will put a full offensive-security harness onto your existing AI agent. For authorized use only, of course, Pliny reminds you to only point this at your own systems. Red team work and actual offense look remarkably similar.
A Young Lady’s Illustrated Primer
There was a huge cheating scandal at Brown, where 50 students were caught cheating on the economic math final. Does Professor Serrano know where he went wrong?
Manuel G. Pascual: This year, the economist decided that both the midterm and the final exams for his course would be of the take-home, closed-book type (there is a certain tradition of this at Ivy League schools). “It’s a very nice kind of exam, because as you’re giving students practically unlimited time to complete it, it lets you make it harder than normal, to see how far they can go.”
… But it also hurts him that the one time in 34 years that he decided to offer a take-home exam, for highly justified reasons, the response was wide-scale fraud.
Oh. Yeah, sorry, you can’t do that anymore.
I don’t think you could ever do that, I mean did you seriously think students would not look at their textbooks, but you definitely can’t now.
… He has never had more than 30 students enrolled at a time, and on some occasions he had only eight. This semester, probably because of the new evaluation system, 86 students signed up for the class. The results of the midterm exam, which was administered on March 5, were extraordinary, with an average score of 96 out of 100. Forty students scored a perfect 100.
Although actually maybe you can? In the sense that ChatGPT makes the cheating a lot easier to catch, whereas if your cheating is on the level of ‘look at the textbook’ then that is basically impossible to catch, but almost no one is going to break the rules only a little bit.
The people who corrected the exams warned him about several irregularities. “Some answers contained unusual passages that coincided with results obtained after running the questions through ChatGPT,” he says.
He ran the final as an in-person exam, and scores collapsed.
But that’s not ‘proof’ for any particular student. The wording could be coincidence. The drop in scores could be unrelated. It’s all circumstantial, I tell you. Circumstantial.
This is a deeply stupid burden of ‘proof.’ Get this, or else you’re not gonna make it.
The university’s response was to label this a ‘wake-up call’ but sided with the students.
So, no, I guess you can’t catch them cheating, or at least can’t punish them. Damn.
The problem is invalidating grades entirely. At UC Berkeley, the number of As is up by 30%, so GPAs are dangerously close to meaningless for measuring student quality.
Manuel G. Pascual: Serrano agrees that AI makes students have more incentives to cheat. That is why, he says, these cases cannot be swept under the rug. On the contrary, they should serve to open up an in-depth debate. “If we no longer defend truth and decency and honesty, then what kind of credibility are we going to have as academics?”
Less than you would like. Far more than you deserve.
My central thesis on AI and education is:
LLMs are the best tool ever invented with which to learn things.
LLMs are also the best tool ever invented with which to not learn things.
Which way, modern man?
Giving people tools with which to learn often doesn’t cause learning. Another classic example is ‘put a lot of MIT classes online for free.’ MIT did this, no one noticed, those who noticed did not use the classes to learn.
Ryan Brewer: It’s shocking to me that LLMs didn’t create an educational renaissance. Shouldn’t I be able to learn a language in a month? What did we get wrong?
Yishan: Because it’s not about what the teacher can do, it’s what the student can do.
Learning is energetically expensive and the brain doesn’t want to do it. All educational systems are methods to motivate, trick, or force brains into learning.
(You can make AI systems to do this, but it’s still a couple steps away from just “AI is good at explaining things”)
Ryan Brewer: I’m looking at my little sister’s high school class now, and i guess it just seems like all motivation to learn anything has gone completely out the window. All her teachers lesson plans are Chat generated. All the students essays are chat generated. Just seems to me like some systemic change to the system is necessary.
Those who are genuinely curious people will 10-100x their ability to learn. The opposite however is also true, the bottom 95% of learners will use Chat to skip work completely. Just sad to see a tool like this be used to escape critical thought by all parties involved. Curious how we fix this
Pizza: The internet gave every single person on Earth access to all of MIT’s lectures for free and I think most of us would agree that it hasn’t made us that much smarter. I don’t think the main problems and solutions here are technological.
All of YouTube, by contrast, did often make people either smarter or dumber, depending on how they used it, because it was far easier to use. MIT’s classes had too many trivial inconveniences and also tend to be actually quite hard.
If you want to learn a language in a month and are willing to put in the time and effort, you can probably do that right now, using a mixture of existing technology and LLMs. No one does it because no one both has that kind of time and wants to do that level of work.
They Took Our Jobs
In response to the AI slop nonsense article from Chamath, Bryan Johnson tries to say the thing in actual human words.
Bryan Johnson: He's arguing that these knee jerk intuitions are wrong:
+ work is a fixed amount + machines doing tasks means lost jobs + cheaper things get used less + labor is the scarce resource + abundant intelligence removes human work + renting generic intelligence builds an edge
Elon Musk: AI+Robots will be able to do everything, resulting in universal high income. Work will be optional.
That’s good clear writing that isn’t full of Fnords, illustrating both the extent to which Chamath is using AI to argue with a strawman versus making meaningful claims, with the caveat that the strawman position on many of these questions is real and often popular.
The true versions of the claims:
Work expands to fill the time allotted, with decreasing marginal returns.
Machines doing too many of the tasks means loss of the particular job in question.
Controlling for quality of the thing, cheaper things get used more, and total amount paid can go up or go down.
Labor is paid well if and only if it is the scarce resource, which it may not be.
Abundant intelligence increasingly removes human cognitive work, and can potentially lead to machines that increasingly remove human physical work.
Renting generic intelligence may or may not be part of a winning strategy, but paying to develop specialized intelligence likely gets you bitter lesson pilled.
I would focus on ‘labor is the scarce resource.’
Right now, labor is a scarce resource. At a survivable wage, demand exceeds supply, even for many forms of relatively unskilled labor. Thus, the market wage is historically high, and there are many jobs.
What would happen if labor were no longer a scarce resource? Demand low, supply high. Market price goes down. Wages fall. Employment drops. Perhaps a lot. Duh.
Is AI already net killing jobs?
The lived experience and anecdotes say yes, at least at entry level. The economics types keep trying to quote statistics to try and say no.
Ara Kharazian: We can finally say AI isn't killing jobs.
A new paper from me, @tryramp , and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI's impact on jobs.
Firms that adopt AI heavily grow headcount 10% over two years following adoption. Low adopters see no statistically significant change.
No, Ara. I appreciate the paper, but you cannot say that. Even if we fully accept the stated premise, all this would establish is that firms that commit to AI outgrow firms that don’t, where ‘high AI adoption’ requires an AI spend of ~$33 per employee.
Even ‘entry level’ jobs at those firms grow 12% over two years. This suggests the obvious mechanism, which is that the firms are growing and winning, mostly at the expense of other firms.
That does not mean AI net creates jobs. It also fails to understand the nature of these (early) job losses, which largely come from failures to hire in places where the employee would have little future.
Or:
Erik Brynjolfsson: Great work. But I would be cautious about interpreting employment growth among AI adopters as evidence against broader labor-market displacement.
Firms that adopt AI may grow by gaining market share from non-adopters, so employment can rise among adopters even as exposed occupations shrink economy-wide.
Things that people think somehow contradict each other:
jeffrey lee funk: A CEO who “vowed to fire anyone who doesn’t use AI in 2025” now says AI could not replace her executive assistant. This says a lot about how many big believers in AI have realized that AI is not as good as they thought.
Okay, sure. Here are two facts that are both mostly true as of 2026:
If an executive assistant (or other employee) refuses to use AI, they often should be fired. Using AI would often make that person a lot more useful and productive.
AI cannot yet entirely replace that assistant (or other employee).
Nathan Young and others in praise of Oliver Habryka, who helps run Lightcone Infrastructure, which created Lighthaven and revived LessWrong. I too have been extremely impressed. We disagree on many important things, but I agree with Nathan that Oliver has been consistently decisive and right in ways that matter. Oliver is willing to stand up for what he believes in at great cost. I have great respect for the way he runs things. And in many ways he has been proven right, including many specific skepticisms of Anthropic and its commitments, about which he was essentially gaslit by many.
Andy Burnham is floating a new UK AI strategy aiming to ‘prioritize British companies and workers’ as well as ‘tech sovereignty.’ The strategy of courting American companies has been a failure, as one would expect given various conditions in the UK. Speech is restricted, capital is unwelcome, housing cannot be built, energy cannot be built, the internet and even VPNs are being cut off. I don’t see anything here that would meaningfully move the needle.
Plus, frankly, if you talk like this then you’re not going to make it:
Anna Gross (FT): They also criticised the current government’s headlong pursuit of driverless cars in London, saying they needed to ask “what’s the point and who’s it for?”
They added: “What’s your plan for dealing with the constituency of people that will be impacted by their introduction, including black cab drivers and Uber drivers?
Here is his departure letter, which is much more positive on how things have been going than I am, but I agree the upside is there:
Joshua Achiam (OpenAI): A little while ago I shared this message with OpenAI on Slack:
Cherished friends, colleagues, members of the staff! I’m graduating this month.
There’s not a specific reason for me leaving, or a specific reason for why now. But it’s something I have been thinking of for a while and it feels right. The world is in on the secret now and it feels possible to work on the mission from outside the walls of a frontier lab.
I joined OpenAI in 2017 as a 25-year-old intern. Computers could not yet talk or think. I’m 34 now, with a family and a two-year-old son, and computers can solve frontier science problems. This was a decade where centuries happened.
The future of humanity depends on the choices we make together about AGI and superintelligence. Everything is at stake. But more importantly, everything is possible. We will soon be able to take shots on goal at the highest aspirations of our species.
I believe we can get to a world where “meeting everyone’s basic needs” is not just a solved problem, but where we feel offended the bar was ever set that low. I believe we can get to a world of peace, unprecedented prosperity, and unimaginable possibilities, social and scientific. Whatever I do next, I will continue to work with you on making this vision real.
If you were to go back nine years and tell me how it all turned out, I would be astonished to hear so much good news. Thank you for making it so special. Thank you for the privilege of working alongside you.
The task of reaching out to everyone to thank individually is daunting because of how long the list is. Fortunately I’ll have a few weeks to do it. My last day will be the 24th.
To safe AGI.
Whenever someone senior leaves OpenAI to focus on other safety work, it raises the question of why they think they have more leverage on the outside. I am very curious about that question in this case.
A Treasury Department review finds that the AI industry poses systemic risk to the financial system, comparing AI to the dotcom crash. I expect the industry to do well, but the risk is very real. The United States has in large part become a leveraged bet on AI and the benefits of AI. If AI fully fizzled and the industry collapsed, we would be highly screwed.
The good news is I think that an industry collapse is highly unlikely. Even if Mythos is close to the best that AIs will ever be, a year from now we will have cheaper and faster and more abundant Fable-level systems. We will have swarms of Fable agents. Demand will be high, and benefits will be higher. That could end up being bad news for specific labs, but not in general.
What always worries me far more is that AI capabilities might advance faster than we can handle them, via recursive self-improvement, and potentially causing everyone to die as a side effect of the resulting systems.
When I was telling my partner some of the bits from this post about people describing the different feel of Fable and Sol, he said it lined up with his experience of Anthropic and OpenAI models - the former are cats, and the latter are dogs, either wise and tricky beings that deign to interact with you as needed, or constantly loving and affirming you hoping to get little scraps of attention.
The T3MP3ST aside deserves its own post. In practice the entire difference between a red team and a threat actor is paperwork: authorization, scope, and an evidence trail. Which means offensive capability governance is not a technical control problem; it is a chain-of-custody problem wearing a hoodie.
When I was telling my partner some of the bits from this post about people describing the different feel of Fable and Sol, he said it lined up with his experience of Anthropic and OpenAI models - the former are cats, and the latter are dogs, either wise and tricky beings that deign to interact with you as needed, or constantly loving and affirming you hoping to get little scraps of attention.
The T3MP3ST aside deserves its own post. In practice the entire difference between a red team and a threat actor is paperwork: authorization, scope, and an evidence trail. Which means offensive capability governance is not a technical control problem; it is a chain-of-custody problem wearing a hoodie.