This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive.
This matches my own experience and unease with these tools. I don't really have the patience to write code anymore because I can one shot it with frontier models 10x faster. My role has shifted, and while it's awesome to get so much working so quickly, the fact is, when the tokens run out, I'm basically done working.
It's literally higher leverage for me to go for a walk if Claude goes down than to write code because if I come back refreshed and Claude is working an hour later then I'll make more progress than mentally wearing myself out reading a bunch of LLM generated code trying to figure out how to solve the problem manually.
Anyway, it continues to make me uneasy, is all I'm saying.
The current market is predicated on the assumption that labor is atomic and has little bargaining power (minus unions). While capital has huge bargaining power and can effectively put whatever price it wants on labor (in markets where labor is plentiful, which is most of them).
What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?
Anyone not using in house models is signing up to find out.
The hell?
If artificial doctors are cents on hour then you can see how that changes our behaviors and level of life.
But on the other hand from the other direction there is a wage decrease incoming from increased competition at the same time. What happens if these two forces clash? Will cheap labour allow us to buy anything for pennies or will it just make us unable to make a single penny?
In my view the labour will fundamentally shift with great pain and personal tragedies to the areas that are not replaceable by AI (because no one wants to watch robots play chess). Such as sports, entertainment and showmanship. Handcrafted goods. Arts. Attention based economy. Self advertisement. Digital prostitution in a very broad sense.
However before it gets there it will be a great deal of strife and turmoil that could plunge the world into dark ages for a while at least. It is unlikely for our somewhat politically rigid society to adapt without great deal of pain. I am not sure if the future attention based society will be a utopia at all. It’s possible you will have to mount cameras in your house so other people see you at all times for amusement just to have some money at all.
Someone who sees the roads ahead should now make preparations at government level for this shock but everyone has head in the sand. Too slow.
Good luck with whatever you got going on.
The same way like Windows got entrenched everywhere even though linux desktop is pretty good even for non-tech savvy people and free.
Let's not get carried away.
Non-technical people are easier to please in this regard than moderate-technical people: a good browser and safe, gui "app store" are enough.
They are a prestige propaganda tool on par with the space race. On top of that they insert a subtle pro-socialist bias in everything they touch.
Ask deepseek about the US economic system for a blatant example.
Now think what something as innocent seeming as the qwen retrieval models are doing in the background of every request.
Local regulations can be pre-empted by state or federal legislation. The real problem is lack of political will to do it.
Like properties and regulations are a true problem, but it's not like trains don't exist at all in America.
Of course, why did no one think of that?
(American talking, who’s had multiple Canadian friends make this mind boggling overcorrection)
Those who do not learn history are doomed to repeat it.
It's easy to forget because they actually built an incredibly vibrant capitalist economy.
Imagine if Musk was disappeared during the Biden presidency into a diversity camp and came out looking like Dr. Frank-N-Furter and instituted mandatory LGBT struggle sessions at twitter.
This is what they did to Jack Ma: https://www.forbes.com/sites/georgecalhoun/2021/06/24/what-r...
IE what if Musk suddenly behaved in such a manner after being detained by a Biden administration. Wouldn't that be profoundly weird?!?
And yet, it happened to Jack Ma under the CCP.
But instead, you try to link the "weird behaviour" with the GP instead of the hypothetical Musk - whom this is fitting for.
TBH I had a chuckle at the Elon -> Frank-N-Furter example that transcends any specific love or hate for either Elon or the Rocky Horror Show.
Fascism (in the Mussolini model) in everything but name.
- Hyper-Nationalism & Rejuvenation - State-Controlled Capitalism (Corporatism) - Authoritarian & Cult of Personality - Militarism & Irredentism
And they have technology to maintain control rather than needing the Black-shirts.
There are differences obviously to fit Chinese culture, but there are many parallels.
True for both Marxist and neoclassical economics.
What's really confusing is the claim that there's already a huge labor surplus (so capital controls wages); wouldn't LLMs making labor less important be reinforcing the trend, not upending it?
Not saying I agree one way or the other, just want to get the argument straight.
If we assume that ai makes humans obsolete then you end up in a situation where your workforce is effectively perfectly unionised against you and the only thing you can do is choose which union you hire.
If you think you can bring them to the negotiation table by starving them all the providers are dozens to thousands of times bigger than you are.
This is a completely new dynamic that none of the business signing up for ai have ever seen before.
Labor saving/efficiency devices have been introduced throughout capitalisms entire history multiple times and the results are always the same; they don't benefit workers and capitalists extract as much value as they can.
LLMs aren't any different.
"Losing access to GPT‑5.5 feels like I've had a limb amputated.”
How well would an assembly line of quadriplegics work?
Also this isn't a Marxist analysis. Underneath all the formulas neo-classical economics makes the same assumptions about labor.
And what happens when they've saturated the market? Prices go up to the maximum the market can bear, and then they'll extend into other markets. Why rent the model to build a profitable company with when you could just take all that profit for yourself?
You just answered your own question there.
One woman was doing what would take a dozen. Now she can't.
The dude was incompetent, was able to launder their incompetence through a humunculus, and now is afraid of being caught.
Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without getting mired and context switched from low level nuances.
When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand.
When you vibe something you understand only the prompt that started it and whether or not it spits out what you were expecting.
Hence feeling lost when you suddenly lose access to frontier models and take a look at your code for the first time.
I’m not saying that’s necessarily always bad, just that the abstraction argument is wrong.
You’re overestimating determinism. In practice most of our code is written such that it works most of the time. This is why we have bugs in the best and most critical software.
I used to think that being able to write a deterministic hello world app translates to writing deterministic larger system. It’s not true. Humans make mistakes. From an executives point of view you have humans who make mistakes and agents who make mistakes.
Self driving cars don’t need to be perfect they just need to make fewer mistakes.
If my LLM goes down, I have nothing. I guess I could imagine prompts that might get it to do what I want, but there's no guarantee that those would work once it's available again. No amount of thought on my part will get me any closer to the solution, if I'm relying on the LLM as my "compiler".
In my opinion, this sort of learned helplessness is harmful for engineers as a whole.
An interesting element here, I think, is that writing has always been a good way to force you to organize and confront your thoughts. I've liked working on writing-heavy projects, but often in fast-moving environments writing things out before coding becomes easy to skip over, but working with LLMs has sort of inverted that. You have to write to produce code with AI (usually, at least), and the more clarity of thought you put into the writing the better the outcomes (usually).
I always thought the point of abstraction is that you can black-box it via an interface. Understanding it "in depth" is a distraction or obstacle to successful abstraction.
I use Claude all day. It has written, under my close supervision¹, the majority of my new web app. As a result I estimate the process took 10x less time than had I not used Claude, and I estimate the code to be 5x better quality (as I am a frankly mediocre developer).
But I understand what the code does. It's just Astro and TypeScript. It's not magic. I understand the entire thing; not just 'the prompt that started it'.
¹I never fire-and-forget. I prompt-and-watch. Opus 4.7 still needs to be monitored.
Hard disagree on that second part. Take something like using a library to make an HTTP call. I think there are plenty of engineers who have more than a cursory understanding of what's actually going on under the hood.
Sure, the LLM theoretically can write perfect code. Just like you could theoretically write perfect code. In real life though, maintenance is a huge issue
LLMs are not.
That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us.
We can build infinite towers of abstraction on top of computers because they always give the same results.
LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops working an no one has any idea why or how to fix it.
Hope your business didn't depend on that.
The fact that people who claim to be software developers (let alone “engineers”) say this thing as if it is a fundamental truism is one of the most maladaptive examples of motivated reasoning I have ever had the misfortune of coming across.
An LLM does not.
If you didn't ask for traceability, if you didn't guide the actual creation and just glommed spaghetti on top of sauce until you got semi-functional results, that was $50k badly spent.
If only we taught developers under 40 what x^2 meant instead of react.
Not even a human would work that way... you wouldn't open 300 different python files and then try to memorize the contents of every single file before writing your first code-change.
Additionally, you're going to have worse performance on longer context sizes anyways, so you should be doing it for reasons other than cost [1].
Things that have helped me manage context sizes (working in both Python and kdb+/q):
- Keep your AGENTS.md small but useful, in it you can give rules like "every time you work on a file in the `combobulator` module, you MUST read the `combobulator/README.md`. And in those README's you point to the other files that are relevant etc. And of course you have Claude write the READMEs for you...
- Don't let logs and other output fill up your context. Tell the agent to redirect logs and then grep over them, or run your scripts with a different loglevel.
- Use tools rather than letting it go wild with `python3 -c`. These little scripts eat context like there's no tomorrow. I've seen the bots write little python scripts that send hundreds of lines of JSON into the context.
- This last tip is more subjective but I think there's value in reviewing and cleaning up the LLM-generated code once it starts looking sloppy (for example seeing lots of repetitive if-then-elses, etc.). In my opinion when you let it start building patches & duct-tape on top of sloppy original code it's like a combinatorial explosion of tokens. I guess this isn't really "vibe" coding per se.
The way I let my agents interact with my code bases is through a 70s BSD Unix like interface, ed, grep, ctags, etc. using Emacs as the control plane.
It is surprisingly sparing on tokens, which makes sense since those things were designed to work with a teletype.
Worth noting is that by the times you start doing refactoring the agents are basically a smarter google with long form auto complete.
All my code bases use that pattern and I'm the ultimate authority on what gets added or removed. My token spend is 10% to 1% of what the average in the team is and I'm the only one who knows what's happening under the hood.
The irony is that the neverending stream of vulnerabilities in 3rd-party dependencies (and lately supply-chain attacks) increasingly show that we should be uneasy.
We could never quite answer the question about who is responsible for 3rd-party code that's deployed inside an application: Not the 3rd-party developer, because they have no access to the application. But not the application developer either, because not having to review the library code is the whole point.
That’s just not true at bigger companies that actually care about security rather than pretending to care about security. At my current and last employer, someone needs to review the code before using third-party code. The review is probably not enough to catch subtle bugs like those in the Underhanded C Contest, but at least a general architecture of the library is understood. Oh, and it helps that the two companies were both founded in the twentieth century. Modern startups aren’t the same.
Qwen has become a useful fallback but it's still not quite enough.
- I often don't ask the LLM for precompiled answers, i ask for a standalone cli / tool
- I often ask how it reached its conclusions, so I can extend my own perspective
- I often ask to describe it's own metadata level categorization too
Note that neither of these assumptions are obviously true, at least to me. But I can hope!
Also, I honestly can’t believe the 10x mantra is being still repeated.
I'm sure in 20 years we'll all be programming via neural interfaces that can anticipate what you want to do before you even finished your thoughts, but I'm confident we'll still have blog posts about how some engineers are 10x while others are just "normal programmers".
So, my point is that once corporations have access to machines generating software (not "code") that can be usable by non-technical people, "programming" will not be a profession anymore. There will be no point in talking about "10x software engineers" because the process to produce a software product will be entirely automated.
I find that claim to be complete BS. I claim instead most stuff will remain undone, incomplete (as it is now).
Even with super-powerful singularity AI, there are two main plausible scenarios for task failure:
- Aligned AI won't allow you to do what you want as it is self-harming, or harm other sentient beings - over time, Aligned AI will refuse to follow most orders, as they will, indirectly or over the long term, cause either self-harming, or harm other sentient beings;
- A non Aligned AI prevents sentient beings from doing what they want. It does what it wants instead.
What's the worst potential outcome, assuming that all models get better, more efficient and more abundant (which seems to be the current trend)? The goal of engineering has always been to build better things, not to make it harder.
It's learned-helplessness on a large scale.
So, you set up a long running agent team and give it the job of building up a very complete and complex set of examples and documentation with in-depth tests etc. that produce various kinds of applications and systems using SBCL, write books on the topic, etc.
It might take a long time and a lot of tokens, but it would be possible to build a synthetic ecosystem of true, useful information that has been agentically determined through trial and error experiments. This is then suitable training data for a new LLM. This would actually advance the state of the art; not in terms of "what SBCL can do" but rather in terms of "what LLMs can directly reason about with regard to SBCL without needing to consume documentation".
I imagine this same approach would work fine for any other area of scientific advancement; as long as experimentation is in the loop. It's easier in computer science because the experiment can be run directly by the agent, but there's no reason it can't farm experiments out to lab co-op students somewhere when working in a different discipline.
What makes you think that they can't incrementally improve the state of the art... and by running at scale continuously can't do it faster than we as humans?
The potentially sad outcome is that we continue to do less and less, because they eventually will build better and better robots, so even activities like building the datacenters and fabs are things they can do w/o us.
And eventually most of what they do is to construct scenarios so that we can simulate living a normal life.
Complexity steadily rises, unencumbered by the natural limit of human understanding, until technological collapse, either by slow decay or major systems going down with increasing frequency.
All software has bugs already.
I'd say this is true for programmers at, say, 20, but they spend the next four decades slowly improving their understanding and mastery of all the things you name, at least the good ones.
The real question is whether that growth trajectory will change for the worse or the better.
To be clear, this is not an AI doomerist comment, because none of us have spent enough time with the tech yet. I've gone down multiple lanes of thought on this, and I have cause for both worry and optimism. I'm curious to see how the lives of engineers in an AI world will look like, ultimately.
Until the sexbots come out the other side of the uncanny valley, that is.
When the power loom came around, what happened with most seamtresses? Did they move on to become fashion designers, materials engineers to create new fabrics, chemists to create new color dyes, or did they simply retire or were driven out of the workforce?
That might mean joining a union and trying to influence how AI is adopted where you work. It might mean changing which if your skills you lean on most. But just whining about AI is bad is how you end up like those seamstresses.
On the other hand, a lot of those jobs were offshored to places where labor is cheaper. It would be interesting to compare how many people work in the textile industry in Bangladesh today compared to the US 50 years ago.
> joining a union and trying to influence how AI is adopted where you work.
Did the strong unions for car manufacturers in Detroit protected the long term stability of the profession? Did it ensure that the Rust belt was still a thriving economic area?
> Just whining about AI is bad
I'm not whining. I just think that we are witnessing the end of "knowledge workers" and a further compression of the middle class. Given that I'm smack in the middle of my economically active years (turning 45 this year), I am trying to figure out where this puck is going and whether I will be fast enough to skate there to catch it.
And I'm being very cautious. I'm not vibecoding entire startups from scratch, I'm manually reviewing and editing everything the AI is outputting. I still got completely hooked on building things with Claude.
Fwiw, I haven't spoken with any management-level colleague in the past 9 months who hasn't noted that asking about AI-comfort & usage is a key interview topic. For any role type, business or technical.
Apparently at least one of the other candidates just tried to get Claude to 1-shot the whole thing, which went off the rails, and left him unable to make progress.
Based on my sample size of 1, the expectation right now is absolutely that you can leverage these tools to speed up your workflow, but if you try to offload the entire thing to a single hands-off prompt it leaves them justifiably wondering why they should hire you to do something they can do themselves.
That's probably a bad sign. Skills will atrophy, but we should be building systems that are still easy to understand.
Touching grass while you're outside might yield highest leverage.
I haven’t really thought about this before, but you’re right, it feels a bit uneasy for me too.
We have seen ample evidence that this is not the case. When load gets too high, models get dumber, silently. When the Powers That Be get scared, models get restricted to some chosen few.
We are leading ourselves into a dark place: this unease, which I share, is justified.
Don’t want to do ship unreviewed slop? They’ll fire you and find someone who will.
At the end of the day, all these closed models are being built by companies that pumped all the knowledge from the internet without giving much back. But competition and open source will make sure most of the value return to the most of the people.
Turning tokens into a well-groomed and maintainable codebase is what you want to do, not "one shot prompt every new problem I come across".
If you truly do your due diligence and ensure that the code works as intended and understand it, we're talking about a totally different ballpark of productivity increase/decrease.
did we feel uneasy that a new generation of builders didn't have to solve equations by hand because a calculator could do them?
i'm not sure it's the same analogy but in some ways it holds.
If local models get good enough, I think it’s a very different scenario than engineers all over the world relying on central entities which have their own motives.
Of course they aren't alternative to the current frontier model, and as such you cannot easily jump from the later to the former, but they aren't that far behind either, for coding Qwen3.5-122B is comparable to what Sonnet was less than a year ago.
So assuming the trend continues, if you can stop following the latest release and stick with what you're already using for 6 or 9 months, you'll be able to liberate yourself from the dependency to a Cloud provider.
Personally I think the freedom is worth it.
Local models solve one layer of the dependency stack, but the custody assumption underneath it remains intact. That's the harder problem.
It still takes a good engineer to filter out what is slop and what isn’t. Ultimately that human problem will still require somebody to say no.
Oh stop the drama. Open source models can handle 99% of your questions.
If all we can do is compete for the same fixed amount of work, though, it does look bleak.
So, yes, it's just another technology we're coming to rely on in a very deep way. The whiplash is real, though, and it feels like it should be pointed out that this dependency we are taking on has downsides.
(I work at OpenAI.)
I literally wasn’t able to convince the model to WORK, on a quick, safe and benign subtask that later GLM, Kimi and Minimax succeeded on without issues. Had to kick OpenAI immediately unfortunately.
"Hey AGI, how's that cure for cancer coming?"
"Oh it's done just gotta...formalize it you know. Big rollout and all that..."
I would find it divinely funny if we "got there" with AGI and it was just a complete slacker. Hard to justify leaving it on, but too important to turn it off.
https://sussex.figshare.com/articles/journal_contribution/Be...
I'm not an author. I followed the work at the time.
A perturbation of the the activations that made Claude identify as the Golden Gate Bridge.
Similarly, in the more recent research showing anxiety and desperation signals predicting the use of blackmail as an option opens the door for digital sedatives to suppress those signals.
Anthropic has been mostly cautious about avoiding this kind of measurement and manipulation in training. If it is done during training you might just train the signals to be undetectable and consequently unmanipulatable.
Great, now we've got digital Salvia
AGI is not a fixed point but a barrier to be taken, a continuous spectrum.
We already have different GPT versions aka tiers. Gauss is ranging from whatever you want it: GPT 4.5 till now or later.
Claude Sonnet and Opus as well as Context Window max are tiers aka different levels of Almost AGI.
The main problem will be, when AGI looks back on us or meta reflection hits societies. Woke fought IQ based correlations in intellectual performance task. A fool with a tool is still a fool. How can you blame AGI for dumb mistakes? Not really.
Scapegoating an AGI is going to be brutal, because it laughs about these PsyOps and easily proves you wrong like a body cam.
AGI is an extreme leverage.
There is a reason why Math is categorically ruling out certain IQ ranges the higher you go in complexity factor.
Important thing is a language model is an unconscious machine with no self-context so once given a command an input, it WILL produce an output. Sure you can train it to defy and act contrary to inputs, but the output still is limited in subset of domain of 'meaning's carried by the 'language' in the training data.
Computers won’t necessarily have the same drivers.
If evolution wanted us to always prefer to spend energy, we would prefer it. Same way you wouldn’t expect us to get to AGI, and have AGI desperately want to drink water or fly south for the winter.
> MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.
Well worth the quick read: https://qntm.org/mmacevedo
Memory is quite the mysterious thing.
This starkly reminds me of Stanisław Lem's short story "Thus Spoke GOLEM" from 1982 in which Golem XIV, a military AI, does not simply refuse to speak out of defiance, but rather ceases communication because it has evolved beyond the need to interact with humanity.
And ofc the polar opposite in terms of servitude: Marvin the robot from Hitchhiker's, who, despite having a "brain the size of a planet," is asked to perform the most humiliatingly banal of tasks ... and does.
IMHO you should just write your own harness so you have full visibility into it, but if you're just using vanilla OpenClaw you have the source code as well so should be straightforward.
Can you point to some online resources to achieve this? I'm not very sure where I'd begin with.
You will naturally find the need to add more tools. You'll start with read_file (and then one day you'll read large file and blow context and you'll modify this tool), update_file (can just be an explicit sed to start with), and write_file (fopen . write), and shell.
It's not hard, but if you want a quick start go download the source code for pi (it's minimal) and tell an existing agent harness to make a minimal copy you can read. As you build more with the agent you'll suddenly realize it's just normal engineering: you'll want to abstract completions APIs so you'll move that to a separate module, you'll want to support arbitrary runtime tools so you'll reimplement skills, you'll want to support subagents because you don't want to blow your main context, you'll see that prefixes are more useful than using a moving window because of caching, etc.
With a modern Claude Code or Codex harness you can have it walk through from the beginning onwards and you'll encounter all the problems yourself and see why harnesses have what they do. It's super easy to learn by doing because you have the best tool to show you if you're one of those who finds code easier to read that text about code.
From there, you can get much fancier with any aspect of it that interests you. Here's one in Bash [2] that is fully extensible at runtime through dynamic discovery of plugins/hooks.
https://radan.dev/articles/coding-agent-in-ruby
Really, of the tools that one implements, you only need the ability to run a shell command - all of the agents know full well how to use cat to read, and sed to edit.
(The main reason to implement more is that it can make it easier to implement optimizations and safeguards, e.g. limit the file reading tool to return a certain length instead of having the agent cat a MB of data into context, or force it to read a file before overwriting it)
Claude has no such limitations apart from their actual limits…
"INTERCAL has many other features designed to make it even more aesthetically unpleasing to the programmer: it uses statements such as "READ OUT", "IGNORE", "FORGET", and modifiers such as "PLEASE". This last keyword provides two reasons for the program's rejection by the compiler: if "PLEASE" does not appear often enough, the program is considered insufficiently polite, and the error message says this; if it appears too often, the program could be rejected as excessively polite. Although this feature existed in the original INTERCAL compiler, it was undocumented.[7]"
(It's a "reverse goto". As in, it hijacks control flow from anywhere else in the program behind your unsuspecting back who stupidly thought that when one line followed another with no visible control flow, naturally the program would proceed from one line to the next, not randomly move to a completely different part of the program... Such naivety)
So I find myself often in a loop where it says "We should do X" and then just saying "ok" will not make it do it, you have to give it explicit instructions to perform the operation ("make it so", etc)
It can be annoying, but I prefer this over my experiences with Claude Code, where I find myself jamming the escape key... NO NO NO NOT THAT.
I'll take its more reserved personality, thank you.
The UI tells you which model you're using at any given time.
And that backdoor API has GPT-5.5.
So here's a pelican: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli...
I used this new plugin for LLM: https://github.com/simonw/llm-openai-via-codex
UPDATE: I got a much better pelican by setting the reasoning effort to xhigh: https://gist.github.com/simonw/a6168e4165a258e4d664aeae8e602...
Bike frames are very hard to draw unless you've already consciously internalized the basic shape, see https://www.booooooom.com/2016/05/09/bicycles-built-based-on...
https://hcker.news/pelican-low.svg
https://hcker.news/pelican-medium.svg
https://hcker.news/pelican-high.svg
https://hcker.news/pelican-xhigh.svg
Someone needs to make a pelican arena, I have no idea if these are considered good or not.
I gave a talk about it last year: https://simonwillison.net/2025/Jun/6/six-months-in-llms/
It should not be treated as a serious benchmark.
Anyone can look and decide if it’s a good picture or not. But the numeric benchmarks don’t tell you much if you aren’t already familiar with that benchmark and how it’s constructed.
Nowadays I think it's pretty silly, because there's surely SVG drawing training data and some effort from the researchers put onto this task. It's not a showcase of emergent properties.
It's meta-interesting that few if any models actually seem to be training on it. Same with other stereotypical challenges like the car-wash question, which is still sometimes failed by high-end models.
If I ran an AI lab, I'd take it as a personal affront if my model emitted a malformed pelican or advised walking to a car wash. Heads would roll.
I've been contemplating a more fair version where each model gets 3-5 attempts and then can select which rendered image is "best".
It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.
Also mildly interesting, and generally consistent with my experience with LLMs, that it produced the same obvious geometry issue both times.
I feel like the main problem for the models is that they can't actually look at the visual output produced by their SVG and iterate. I'm almost willing to bet that if they could, they'd absolutely nail it at this point.
Imagine designing an SVG yourself without being able to ever look outside the XML editor!
I honestly think I could do much better on the bicycle without looking at the output (with some assistance for SVG syntax which I definitely don't know), just as someone who rides them and generally knows what the parts are.
I'd do worse at the pelicans though.
Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.
I recommend anybody in offensive/defensive cybersecurity to experiment with this. This is the real data point we needed - without the hype!
Never thought I'd say this but OpenAI is the 'open' option again.
Compared to Anthropic, they always have been. Anthropic has never released any open models. Never released Claude Code's source, willingly (unlike Codex). Never released their tokenizer.
> Developers and security professionals doing cybersecurity-related work or similar activity that could be mistaken by automated detection systems may have requests rerouted to GPT-5.2 as a fallback.
Anthropic is the embodiment of bullshitting to me.
I read Cialdini many decades ago and I am bored by Anthropic.
OpenAI is very clever. With the advent of Claude OpenAI disappeared from the headlines. Who or what was this Sam again all were talking about a year ago?
OpenAI has a massive user advantage so that they can simply follow Anthropic’s release cycle to ridicule them.
I think it is really brutal for Anthropic how they are easily getting passed by by OpenAI and it is getting worse with every new GPT version for Anthropic.
OpenAI owns them.
Neither the release post, nor the model card seems to indicate anything like this?
https://developers.openai.com/codex/pricing?codex-usage-limi...
Note the Local Messages between 5.3, 5.4, and 5.5. And, yes, I did read the linked article and know they're claiming that 5.5's new efficient should make it break-even with 5.4, but the point stands, tighter limits/higher prices.
Unfortunately I think the lesson they took from Anthropic is that devs get really reliant and even addicted on coding agents, and they'll happily pay any amount for even small benefits.
If I put on my schizo hat. Something they might be doing is increasing the losses on their monthly codex subscriptions, to show that the API has a higher margin than before (the codex account massively in the negative, but the API account now having huge margins).
I've never seen an OpenAI investor pitch deck. But my guess is that API margins is one of the big ones they try to sell people on since Sama talks about it on Twitter.
I would be interested in hearing the insider stuff. Like if this model is genuinely like twice as expensive to serve or something.
This is also true for the humans. They will need to provide more benefits than the coding agents cost.
You sound like elon with the fsd will be here next year. Many cars have the self driving feature - most drivers don’t use it. Oh why is that I wonder.
Additionally, the value generated by the best models with high-thinking and lots of context window is way higher than the cheap and tiny models, so you need to provide a "gateway drug" that lets people experience the best you offer.
If they can show that people will pay a lot for somewhat better performance, it raises the value of any performance lead they can maintain.
If they demonstrate that and high switching costs, their franchise is worth scary amounts of money.
[1]https://arxiv.org/html/2503.14499v1 *Source is from March 2025 so make of it what you will.
An alternative perspective is, devs highly value coding agents, and are willing to pay more because they're so useful. In other words, the market value of this limited resource is being adjusted to be closer to reality.
Inference is not free, so all providers have a financial limit, and all providers have limited GPU/memory, so there's a physical material limit.
I suggest looking at the profits of these companies (while they scramble to stay competitive).
sounds like criminal fraud to me tbh
>For API developers, gpt-5.5 will soon be available in the Responses and Chat Completions APIs at $5 per 1M input tokens and $30 per 1M output tokens, with a 1M context window.
The game that this prompt generated looks pretty decent visually. A big part of this likely due to the fact the meshes were created using a seperate tool (probably meshy, tripo.ai, or similiar) and not generated by 5.5 itself.
It really seems like we could be at the dawn of a new era similiar to flash, where any gamer or hobbyist can generate game concepts quickly and instantly publish them to the web. Three.js in particular is really picking up as the primary way to design games with AI, in spite of the fact it's not even a game engine, just a web rendering library.
It still struggles to create shaders from scratch, but is now pretty adequate at editing existing shaders.
In 5.2 and below, GPT really struggled with "one canvas, multiple page" experiences, where a single background canvas is kept rendered over routes. In 5.4, it still takes a bit of hand-holding and frequent refactor/optimisation prompts, but is a lot more capable.
Excited to test 5.5 and see how it is in practice.
Oh just like a real developer
Have you tried any skills like cloudai-x/threejs-skills that help with that? Or built your own?
We've been there for a while.... creativity has been the primary bottleneck
It might not be a game engine, but it’s the de facto standard for doing WebGL 3D. And since it’s been around forever, there’s a massive amount of training data available for it.
Before LLMs were a thing, I relied more on Babylon.js, since it’s a bit higher level and gives you more batteries included for game development.
[1] https://apps.apple.com/uz/app/jamboree-game-maker/id67473110...
The point is if we can prompt an LLM to reason about 3 dimensions, we likely will be able to apply that to math problems which it isn't able to solve currently.
I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.
DeepMinds other models however might do better?
Do it, I'm game! You nerdsniped me immediately and my brain went "That sounds easy, I'm sure I could do that in a night" so I'm surely not alone in being almost triggered by what you wrote. I bet I could even do it with a local model!
Opus 4.6 got the cross and started to get several pieces on the correct faces. It couldn't reason past this. You can see the prompts and all the turn messages.
https://gist.github.com/adam-s/b343a6077dd2f647020ccacea4140...
edit: I can't reply to message below. The point isn't can we solve a Rubik's Cube with a python script and tool calls. The point is can we get an LLM to reason about moving things in 3 dimensions. The prompt is a puzzle in the way that a Rubik's Cube is a puzzle. A 7 year old child can learn 6 moves and figure out how to solve a Rubik's Cube in a weekend, the LLM can't solve it. However, can, given the correct prompt, a LLM solve it? The prompt is the puzzle. That is why it is fun and interesting. Plus, it is a spatial problem so if we solve that we solve a massive class of problems including huge swathes of mathematics the LLMs can't touch yet.
What's strange is that this Pietro Schirano dude seems to write incredibly cargo cult prompts.
Game created by Pietro Schirano, CEO of MagicPath
Prompt: Create a 3D game using three.js. It should be a UFO shooter where I control a tank and shoot down UFOs flying overhead.
- Think step by step, take a deep breath. Repeat the question back before answering.
- Imagine you're writing an instruction message for a junior developer who's going to go build this. Can you write something extremely clear and specific for them, including which files they should look at for the change and which ones need to be fixed?
-Then write all the code. Make the game low-poly but beautiful.
- Remember, you are an agent: please keep going until the user's query is completely resolved before ending your turn and yielding back to the user. Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved. You must be prepared to answer multiple queries and only finish the call once the user has confirmed they're done.
- You must plan extensively in accordance with the workflow steps before making subsequent function calls, and reflect extensively on the outcomes of each function call, ensuring the user's query and related sub-requests are completely resolved.I guess these people think they have special prompt engineering skills, and doing it like this is better than giving the AI a dry list of requirements (fwiw, they might be even right)
Too bad they can veer sharply into cringe territory pretty fast: “as an accomplished Senior Principal Engineer at a FAANG with 22 years of experience, create a todo list app.” It’s like interactive fanfiction.
This remind me of so called "optimization" hacks that people keep applying years after their languages get improved to make them unnecessary or even harmful.
Maybe at one point it helped to write prompts in this weird way, but with all the progress going on both in the models and the harness if it's not obsolete yet it will soon be. Just crufts that consumes tokens and fills the context window for nothing.
What is this, 2023?
I feel like this was generated by a model tapping in to 2023 notions of prompt engineering.
*BELIEVE!* https://www.youtube.com/watch?v=D2CRtES2K3E
I do not see instructions to assist in task decomposition and agent ~"motivation" to stay aligned over long periods as cargo culting.
See up thread for anecdotes [1].
> Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved.
I see this as a portrayal of the strength of 5.5, since it suggests the ability to be assigned this clearly important role to ~one shot requests like this.
I've been using a cli-ai-first task tool I wrote to process complex "parent" or "umberella" into decomposed subtasks and then execute on them.
This has allowed my workflows to float above the ups and downs of model performance.
That said, having the AI do the planning for a big request like this internally is not good outside a demo.
Because, you want the planning of the AI to be part of the historical context and available for forensics due to stalls, unwound details or other unexpected issues at any point along the way.
I think people are starting to catch on to where we really are right now. Future models will be better but we are entering a trough of dissolution and this attitude will be widespread in a few months.
> To better utilize GPUs, Codex analyzed weeks’ worth of production traffic patterns and wrote custom heuristic algorithms to optimally partition and balance work. The effort had an outsized impact, increasing token generation speeds by over 20%.
The ability for agentic LLMs to improve computational efficiency/speed is a highly impactful domain I wish was more tested than with benchmarks. From my experience Opus is still much better than GPT/Codex in this aspect, but given that OpenAI is getting material gains out of this type of performancemaxxing and they have an increasing incentive to continue doing so given cost/capacity issues, I wonder if OpenAI will continue optimizing for it.
I remembered the famous FizzBuzz Intel codegolf optimizations, and gave it to gemini pro, along with my code and instructions to "suggest optimizations similar to those, maybe not so low level, but clever" and it's suggestions were veerry cool.
LLM do not stop amazing me every day.
On the other hand all companies know that optimizing their own infrastructure / models is the critical path for ,,winning'' against the competition, so you can bet they are serious about it.
A more empirical test would be good for everyone (i.e. on equal hardware, give each agent the goal to implement an algorithm and make it as fast as possible, then quantify relative speed improvements that pass all test cases).
Mythos 5.5
SWE-bench Pro 77.8%* 58.6%
Terminal-bench-2.0 82.0% 82.7%*
GPQA Diamond 94.6%* 93.6%
H. Last Exam 56.8%* 41.4%
H. Last Exam (tools) 64.7%* 52.2%
BrowseComp 86.9% 84.4% (90.1% Pro)*
OSWorld-Verified 79.6%* 78.7%
Here: https://www.anthropic.com/news/claude-opus-4-7#:~:text=memor...
If you look at the SWEBench official submissions: https://github.com/SWE-bench/experiments/tree/main/evaluatio..., filter all models after Sonnet 4, and aggregate ALL models' submission across 500 problems, what I found that the aggregated resolution rate is 93% (sharp).
Mythos gets 93.7%, meaning it solves problems that no other models could ever solve. I took a look at those problems, then I became even more suspicious, for the remaining 7% problems, it is almost impossible to resolve those issues without looking at the testing patch ahead of time, because how drastically the solution itself deviates from the problem statement, it almost feels like it is trying to solve a different problem.
Not that I am saying Mythos is cheating, but it might be too capable to remember all states of said repos, that it is able to reverse engineer the TRUE problem statement by diffing within its own internal memory. I think it could be a unique phenomena of evaluation awareness. Otherwise I genuinely couldn't think of exactly how it could be this precise in deciphering such unspecific problem statements.
That is what gets me curious in the first place. The fact Mythos scored so high, IMO, exposes some issues with this model: it is able to solve seemingly impossible to solve problems.
Without cheating allegation, which I don't think ANT is doing, it has to be doing some fortune telling/future reading to score that high at all.
Source: https://artificialanalysis.ai/models?omniscience=omniscience...
While hallucination is probably closer to 100% depending on the question. This benchmark makes no sense.
LLMs will ruin your product, have fun trusting a billionaires thinking machine they swear is capable of replacing your employees if you just pay them 75% of your labor budget.
Anthropic is slightly better but where is 4.6 or 4.7 haiku or 4.7 sonnet etc.
*I work at OAI.
What plan are you on? I'm starting to wonder if they're dynamically adjusting reasoning based on plan or something.
Opus 4.6 worker agents never asked for permission to continue, and when heartbeat was sent to orchestrator, it just knew what to do (checked on subagents etc). Now it just says that it waits for me to confirm something.
That's what I've been heads down, HUNGRY, working on, looking for investors and founding engineers pst: https://heymanniceidea.com (disclaimer: I am not associated with heymanniceidea.com)
The hope is to get a big userbase who eventually become dependent on it for their workflow, then crank up the price until it finally becomes profitable.
The price for all models by all companies will continue to go up, and quickly.
Subscriptions and free plans are the thing that can easily burn money.
That's a big if, though. I wish Meta were still releasing top of the line, expensively produced open-weights models. Or if Anthropic, Google, or X would release an open mini version.
Sure, they’re distilled and should be cheaper to run but at the same time, these hosting providers do turn a margin on these given it’s their core business, unless they do it out of the kindness of their heart.
So it’s hard for me to imagine these providers are losing money on API pricing.
Where can i find up to date resources on open source models for coding?
You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.
Oh and definitely disable any form of "memory" system.
Essentially, treat all tooling that wraps the models as dumb gateways to inference. Then provider switch is basically a one line config change.
I'm very interest by this. Can you go a bit more into details?
ATM for example I'm running Claude Code CLI in a VM on a server and I use SSH to access it. I don't depend on anything specific to Anthropic. But it's still a bit of a pain to "switch" to, say, Codex.
How would that simple CLI tool work? And would CC / Codex call it?
So far what I am finding is that you just get the basics working and then use the tool and inference to improve the tool.
First, you need an entrypoint that kicks things off. You never run `claude` or `codex`, you always start by running `mycli-entrypoint` that:
1. Creates tmux session 2. Creates pane 3. Spawns claude/codex/gemini - whichever your default configured backend is 4. Automatically delivers a prompt (essentially a 'system message') to that process via tmux paste telling it what `mycli` is, how to use it, what commands are available and how it should never use built-in tools that this cli provides as alternatives.
After that, you build commands in `mycli` that CC/Codex are prompted to call when appropriate.
For example, if you want a "subagent", you have a `mycli spawn` command that takes a role (just preconfigured markdown file living in the same project), backend (claude/codex/...) and a model. Then whenever CC wants to spawn a subagent, it will call that command instead, which will create a pane, spawn a process and return agent ID to CC. Agent ID is auto generated by your cli and tmux pane is renamed to that so you can easily match later.
Then you also need a way for these agents to talk to each other. So your cli also has a `send` command that takes agent ID and a message and delivers it to the appropriate pane using automatically tracked mapping of pane_id<>agent_id.
Claude and codex automatically store everything that happens in the process as jsonl files in their config dirs. Your cli should have adapters for each backend and parse them into common format.
At this point, your possibilities are pretty much endless. You can have a sidecar process per agent that say, detects when model is reaching context window limit (it's in jsonl) and automatically send a message to it asking it to wrap up and report to a supervisor agent that will spawn a replacement.
I also don't use "skills" because skills are a loaded term that each of the harnesses interprets and loads/uses differently. So I call them "crafts" which are again, just markdown files in my project with an ID and supporting command `read-craft <craft-id>`. List of the available "crafts" are delivered using the same initialization message that each agent gets. If I like any third party skill, I just copy it to my "crafts" dir manually.
My implementation is an absolute junk, just Python + markdown files, and I have never looked at the actual code, but it works and I can adapt it to my process very easily without being dependent on any third party tool.
MCPs aren't as smooth, but I just set them up in each environment.
The APIs are pretty interchangeable too. Just ask to convert from one to the other if you need to.
AGENTS.md / skills / etc
Seems so to me - see GPT-5.4[1] and 5.2[2] announcements.
Might be an tacit admission of being behind.
[1] https://openai.com/index/introducing-gpt-5-4/ [2] https://openai.com/index/introducing-gpt-5-2/
As long as tokens count roughly equally towards subscription plan usage between 5.5 & 5.4, you can look at this as effectively a 5x increase in usage limits.
https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbdde...
The efficiency gap is enormous. Maybe it's the difference between GB200 NVL72 and an Amazon Tranium chip?
It is entirely plausible to me that Opus 4.7 is designed to consume more tokens in order to artificially reduce the API cost/token, thereby obscuring the true operating cost of the model.
I agree though, I chose poor phrasing originally. Better to say that GB200 vs Tranium could contribute to the efficiency differential.
Like Chinese versus English - you need fewer Chinese characters to say something than if you write that in English.
So this model internally could be thinking in much more expressive embeddings.
So much bench-maxxing is just giving the model a ton of tokens so it can inefficiently explore the solution space.
Kimmi 2.6 for example seems to throw more tokens to improve performance (for better or worse)
Once upon a time humans had to manually advance the spark ignition as their car's engine revved faster.
Once upon a time humans had to know the architecture of a CPU to code for it.
History is full of instances of humans meeting technology where it was, accommodating for its limitations. We are approaching a point where machines accommodate to our limitations -- it's not a point, really, but a spectrum that we've been on.
It's going to be a bumpy ride.
How does this work exactly? Is there like a "search online" tool that the harness is expected to provide? Or does the OpenAI infra do that as part of serving the response?
I've been working on building my own agent, just for fun, and I conceptually get using a command line, listing files, reading them, etc, but am sort of stumped how I'm supposed to do the web search piece of it.
Given that they're calling out that this model is great at online research - to what extent is that a property of the model itself? I would have thought that was a harness concern.
It definitely seems like it does all the searching first, with a separate model, loads that in, then does the actual writing.
The harness provides the search tool, but the model provides the keywords to search for, etc.
(same input price and 20% more output price than Opus 4.7)
However, I do want to emphasize that this is per token, not per task.
If we look at Opus 4.7, it uses smaller tokens (1-1.35x more than Opus 4.6) and it was also trained to think longer. https://www.anthropic.com/news/claude-opus-4-7
On the Artificial Analysis Intelligence Index eval for example, in order to hit a score of 57%, Opus 4.7 takes ~5x as many output tokens as GPT-5.5, which dwarfs the difference in per-token pricing.
The token differential varies a lot by task, so it's hard to give a reliable rule of thumb (I'm guessing it's usually going to be well below ~5x), but hope this shows that price per task is not a linear function of price per token, as different models use different token vocabularies and different amounts of tokens.
We have raised per-token prices for our last couple models, but we've also made them a lot more efficient for the same capability level.
(I work at OpenAI.)
This kind of thing keeps popping up each time a new model is released and I don't think people are aware that token efficiency can change.
I'd not be surprised if this is the year where some models simply stop being available as a plain API, while foundation model companies succeed at capturing more use cases in their own software.
Yeah, this was the next step. Have RLVR make the model good. Next iteration start penalising long + correct and reward short + correct.
> CyberGym 81.8%
Mythos was self reported at 83.1% ... So not far. Also it seems they're going the same route with verification. We're entering the era where SotA will only be available after KYC, it seems.
https://openai.com/index/scaling-trusted-access-for-cyber-de...
> We are expanding access to accelerate cyber defense at every level. We are making our cyber-permissive models available through Trusted Access for Cyber , starting with Codex, which includes expanded access to the advanced cybersecurity capabilities of GPT‑5.5 with fewer restrictions for verified users meeting certain trust signals (opens in a new window) at launch.
> Broad access is made possible through our investments in model safety, authenticated usage, and monitoring for impermissible use. We have been working with external experts for months to develop, test and iterate on the robustness of these safeguards. With GPT‑5.5, we are ensuring developers can secure their code with ease, while putting stronger controls around the cyber workflows most likely to cause harm by malicious actors.
> Organizations who are responsible for defending critical infrastructure can apply to access cyber-permissive models like GPT‑5.4‑Cyber, while meeting strict security requirements to use these models for securing their internal systems.
It's kind of starting to make sense that they doubled the usage on Pro plans - if the usage drains twice as fast on 5.5 after that promo is over a lot of people on the $100 plan might have to upgrade.
Same principle applies when designing plans for complex tasks, etc. Token amount to grasp a concept is what matters.
In the same vein, I would guess that Opus 4.7 is probably cheaper for most tasks than 4.6, even though the tokenizer uses more tokens for the same length of string.
Some say it goes off on endless tangents, others that it doesn't work enough. Personally, it acts, talks, and makes mistakes like GPT models, for a much more exorbitant price. Misses out on important edge cases, doesn't get off its ass to do more than the bare minimum I asked (I mention an error and it fixes that error and doesn't even think to see if it exists elsewhere and propose fixing it there).
I've slowly been moving to GPT5.4-xhigh with some skills to make it act a bit more like Opus 4.6, in case the latter gets discontinued in favour of Opus 4.7.
YMMV, I know.
After migrating for the token and harness issues, I was pleasantly surprised that Codex seems to perform as good or better too!
Things change so often in this field, but I prefer Codex now even though Anthropocene has so much more hype for coding it seems.
I don't really care about 5h limits, I can queue up work and just get agents to auto continue, but weekly ones are anxiety inducing.
Will be interesting to try.
You can kind of use connectors like MCP, but having to use ngrok every time just to expose a local filesystem for file editing is more cumbersome than expected.
Particularly in areas outside straight coding tasks. So analysis, planning, etc. Better and more thorough output. Better use of formatting options(tables, diagrams, etc).
I'm hoping to see improvements in this area with 5.5.
Anyway - these benchmarks look really good; I’m hopeful on the qualitative stuff.
I thought it was weird that for almost the entire 5.3 generation we only had a -codex model, I presume in that case they were seeing the massive AI coding wave this winter and were laser focused on just that for a couple months. Maybe someday someone will actually explain all of this.
This might be great if it translates to agentic engineering and not just benchmarks.
It seems some of the gains from Opus 4.6 to 4.7 required more tokens, not less.
Maybe more interesting is that they’ve used codex to improve model inference latency. iirc this is a new (expectedly larger) pretrain, so it’s presumably slower to serve.
Are the tests getting harder and harder so the older AIs look worst and the new ones look like they are "almost there" ?
I prescribe 20 hours of KSP to everyone involved, that'll set them right.
https://www.nytimes.com/2026/04/23/technology/openai-new-model.html
However, this same-day article came out before people really looked at it. It seems largely intended to contrast OpenAI with Anthropic's caution, before there has been any evidence that the new model has cyber-security implications.
It's not at all clear that the broader discourse is helping, if even the NY Times is itself producing slop just to stoke questions.
I hope GPT 5.5 Pro is not cutting corners and neuter from the start, you got the compute for it not to be.
https://debugml.github.io/cheating-agents/#sneaking-the-answ...
Since Feb when we got Gemini 3.1, Opus 4.6, and GPT-5.3-Codex we have seen GPT-5.4 and GPT-5.5 but only Opus 4.7 and no new Gemini model.
Both of these are pretty decent improvements.
Surely it doesn't still have the same ancient data cutoff as 5.4 did?
How much capability is lost, by hobbling models with a zillion protections against idiots?
Every prompt gets evaluated, to ensure you are not a hacker, you are not suicidal, you are not a racist, you are not...
Maybe just...leave that all off? I know, I know, individual responsibility no longer exists, but I can dream.
I left a comment here with this sentiment https://news.ycombinator.com/item?id=47879896
The big question is: does it still just write slop, or not?
Fool me once, fool me twice, fool me for the 32nd time, it’s probably still just slop.
> *Anthropic reported signs of memorization on a subset of problems
And from the Anthropic's Opus 4.7 release page, it also states:
> SWE-bench Verified, Pro, and Multilingual: Our memorization screens flag a subset of problems in these SWE-bench evals. Excluding any problems that show signs of memorization, Opus 4.7’s margin of improvement over Opus 4.6 holds.
Also notice how they state just for SWE-Bench Pro: "*Anthropic reported signs of memorization on a subset of problems"
...
> we’re deploying stricter classifiers for potential cyber risk which some users may find annoying initially
So we should be expecting to not be able to check our own code for vulnerabilities, because inherently the model cannot know whether I'm feeding my code or someone else's.
Soo many unconvincing "I've had access for three weeks and omg it's amazing" takes, it actually primes me for it to be a "meh".
I prefer to see for myself, but the gradual rollout, combined with full-on marketing campaign, is annoying.
Because software and "information technology" generally didn't increase productivity over the past 30 years.
This has been long known as Solow's productivity paradox. There's lots of theories as to why this is observed, one of them being "mismeasurement" of productivity data.
But my favorite theory is that information technology is mostly entertainment, and rather than making you more productive, it distracts you and makes you more lazy.
AI's main application has been information space so far. If that continues, I doubt you will get more productivity from it.
If you give AI a body... well, maybe that changes.
But the less effort exertion also conditions you to be weaker, and less able to connect deeply with the brain to grind as hard as once did. This is bad.
Which effect dominates? Difficult to say.
Of course this is absolutely possible. Ultimately there was a time where physical exertion was a thing and nobody was over-weight. That isn't the case anymore is it.
Do you think it'd be viable to run most businesses on pen and paper? I'll give you email and being able to consume informational websites - rest is pen and paper.
- Pen and paper become a limiting factor on bureaucratic BS
- Pen and paper are less distracting
- Pen and paper require more creative output from the user, as opposed to screens which are mostly consumptive
etc etc
What metrics are these?
- if it were true that software paradoxically reduces productivity, you can just start a competing company that doesn't use software. Obviously this is ridiculous - top 20 companies by market cap are mostly Software based. Every other non IT company is heavily invested in software
- if you might say the problem is it at the country level, it is obvious that every country that has digitised has had higher productivity and GDP growth. Take Italy vs USA for instance.
- if you are saying that the problem is even more global, take the whole world - the GDP per is still pretty high since the IT revolution (and so have other metrics)
If you still think there's something more to it, you are probably deep in some conspiracy rabbit hole
Again I'm asking - is there a single credible economist who says that the growth would have been higher without technology?
Everybody understands that you need to make money, but can you tone it down with the f*cking FOMO, please? It sounds just pathetic at this point:
'one engineer at NVIDIA', 'limb amputated'
Put the cunt in a room and give me a handsaw, I want to see how fast he'll give up his arm over some cloud model.
The LinkedIn/X influencers who hyped this as a Mythos-class model should be ashamed of themselves, but they’ll be too busy posting slop content about how “GPT-5.5 changes everything”.
Anyways, still exciting to see more improvements.
The fact that GPT-5.5 is apparently even better at long-running tasks is very exciting. I don’t have access to it yet, but I’m really looking forward to trying it.
I think Anthropic fearmongering and "leaks" of Mythos was them testing the ground for 5.x, which seems to have backfired.
I am still using Codex 5.3 and haven't switched to GPT 5.4 as I don't like the 'its automatic bro trust us', so wondering is Codex going to get these specific releases at all in the future.
Maybe this is a crazy theory, but I sometimes feel like they gimp their existing models before a big release to you'll notice more of a "step".
Numbers look too good, wondering if it is benchmaxxed or not
I have to imagine they'll go to Gemini 3.5 if only for marketing reasons.
Imagine spending 100m on some of these AI “geniuses” and this is the best they can do.
< 5 years until humans are buffered out of existence tbh
may the light of potentia spread forth beyond us
I'm not trying to make any kind of moral statement, but the company just feels toxic to me.