there seems to be very big misunderstanding about what the "ultra" is, so let me explain it basing on the codex source code:
it's similar to Claude code ultracode.
there is no ultra effort level implemented on the backend. it's just alias in the codex to max effort setting and single line addition to prompt to use subagents proactively. that's all
as far as we know pro models work differently. for once those are backend implementations and they probably run multiple parallel reasonings for any chunk and use some judgement model to pick best version as persistent one. but that's what I believe is most popular guess, because this is openai secret sauce.
there is still no way to use pro models from codex, or at leat so far there is no trace of it anywhere.
> single line addition to prompt to use subagents proactively.
This misses an important detail. In Claude Code [1], ultracode suggests the agent create a JavaScript code to deterministically orchestrate sub agents. This is different from just having the main agent launch sub agents and (non-deterministically) manage them.
The resulting workflow is called “dynamic” because CC creates this orchestration script dynamically, “on the fly”.
Another useful thing about dynamic workflows is you can ask Claude to make them durable as skills (or slash command) that can be invoked later.
I believe inside Google they have a similar concept called “deterministic workflows”.
I find ultracode extremely useful. Of course you have to watch how your 5 hour and weekly session usage percentages are getting used. So I had Claude make a status-line with 3 progress bars: for context window, 5h session, 7d session:
Can you explain what you find useful about ultracode? I've become wary of agent swarms since the early days and now just prefer to have a single agent spin for hours at time. Parallelism never got me anywhere worthwhile.
I got a security scan by route for one of our services that was pretty good and raised some useful results. Uses are few and far between for me tho tbh, also really expensive. I usually run a single agent with a subagent or two MAX
Btw, the /loop “dynamic workflow” is so beyond broken/not working.
It’s sad to see folks like Karpathy make a big deal about looping, than to find that the loop command is broken and it’s crap vibe coded documentation isn’t even accurate on the Claude docs.
This whole dynamic workflow idea is on face bad. It’s all done as a massive cope for the fact that real determinism (I.e using structured outputs to enforce control flow of tools deterministically) is bad for alignment/safety so they can’t let you have access to those tools anymore…
I'm working in large US corporation.
And I see that I already have access to 5.6-Sol Ultra on my corporate account.
I haven't really used it yet.
2 months ago management was showing us scoreboards, praising leaders who used most tokens.
Last few weeks, we're getting weekly emails, telling us that whenever we can - we should use cheaper models, and that we should watch the page which shows our tokens usage.
The craziest to me was someone saying “we are using AI in daily processes, now we need to automate”.
But of course to some asshole non-technical people it meant asking for their vibe coded bullshit to be merged into production without review and fighting about it.
The craziest for me is companies that sticking stochastic agents into automated business processes and expecting stable/reliable outcomes. Businesses want deterministic processes in the vast majority of cases.
People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.
People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive)
People’s job is to partially reign in this volatile environment by creating processes with stable output.
The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
> AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file, or you can add skills to deal with specific prompts, or you provide a better default harness.
The whole idea of coding agents is their harness makes a big difference vs a pure raw model.
> However AI cannot meaningfully handle feedback and learn
How do you think models are created? They are trained on feedback and learn.
Its not cheap but you can post train models. This is how custom models are mode, that deal with specific tasks more efficiently and accurately.
Example ... Composer? Its base Kimi v2.5 model that has been post-trained 2 weeks, to create Composer 2.5, what is a much better coding model.
Its literally trained to make less mistakes by feeding it correct data. Hell, a lot of the models you are using, are often the same base model, where v2.0 was the initial released model but the model keeps training, so when they release v2.1, its still the same model, but with more training time on feedback provided to v2.0.
LLM Models are not a cake you cook one time and they are done, and you start from zero again. If you have the money, and a powerful server setup, you can take a model like GLM 5.2 and post-train it, to reduce specific errors. Sure, you need a ton of money because its a large model.
But people have been doing this with 5M, 100M, 1B, 5B models for a long time already. To the point that some of the small models can do specific tasks, almost or better then some of the huge more general trained models.
> If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file ...
That's all just prompting.
> How do you think models are created? They are trained on feedback and learn.
No one is post training models on a single mistake. At least I have not seen it. I also doubt it is effective. Post-training on a single failure will not meaningfully change the model. That even sidesteps the entire problem that you don't even have access to models if you use a provider like anthropic/openai
I don't know of any modern workflows that rely on "we'll tell the person not to do it again", though. There's a reason that companies have adopted blameless postmortems, because if your response to the DB going down is "It's fine, Kevin learns and next time he won't misuse the prod credentials", you are guaranteeing prod will go down again in the same way at some point.
Literally the entirety of the worlds infrastructure relies on that. In the past we had (literally) had nuclear war hinging on a single person just deciding that some data point is an artifact.
Every modern workflow implicitly relies on that. No infrastructure is fully robust. There's a senior DB person who has learned many things many times over who could bring down most of the US power grid.
> The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison.
Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again.
If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
I'm not against the prompt being changed, the point I was making is that an LLM is prone to the exact same mistakes even if you change the prompt. A trivial example is the very basic character counting mistake, I just asked chatgpt:
> How many p are in strawperry?
> There are 0 “p”s in strawperry.
And I can trigger the same mistake with various words even when adjusting the prompt many times. So I cannot teach chatgpt to correctly count characters.
> However AI cannot meaningfully handle feedback and learn.
Well this is the central bet of AI coding isn't it? We, the humans-in-the-loop, get better at knowing ahead of time which patterns AI will handle better than others, all the while the models actually get better.
"AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck."
please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again.
We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior.
For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
Indeed. Any meaningful AGI/ASI will have to have a form of memory / continual learning. Sam Altman said last year that this will be the focus for GPT-6.
The whole "soul.md" stuff today is a poor approximation to that. But I wonder whether it will grow into it, like chain of thought prompting grew into reasoning models.
LLM's as a technology - currently - are stateless. The memory layer is controlled by the agent. I m surprised with the gpt-6 reference unless it has to do with vertical integration between the agent and the layer.
Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely.
These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations.
Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees.
I am using gpt to populate JSON objects conforming to a list of natural language constraints for purposes of generating fake customers. I am finding that gpt5+ never fucks up. Not even a little bit. I've ran this test hundreds of times with 20+ constraints and it's been perfect every time.
Stable information yields stable control flow. Humans are much more likely to forget one of the many constraints during testing. This happy mistake may incidentally cover an edge but it also means we lose coverage elsewhere.
I think whether or not the LLM should be allowed to directly author deterministic control flow (code) is mostly the same thing. If you have a lot of constraints you want to satisfy all at the same time, this can give you a hit very close to the ideal target very quickly. Not knowing exactly what you want is when the LLM takes you for a ride.
We could probably debate this ad nauseam, so I'll just give you my most compelling arguments.
1) Writing code the "old fashioned" way (i.e., a Python program that does X, Y, Z) allows you to arrive at a battle tested solution that will not change over time. From a risk assessment perspective, the behavior is essentially immutable, allowing a business to guarantee consistent behavior over long periods of time.
2) Just because something hasn't happened to you do, does not mean that it will not happen. LLM are opaque. If you stay on the "happy path", you may see consistent behavior for long periods of time, but there's always potential for an edge case where something goes catastrophically wrong. This is without even opening the can of worms regarding prompt injection and intentional sabotage of a working system.
3) There are plenty of real world examples of an LLM spontaneously deleting data from a DB (or the entire DB) or otherwise going completely off the rails. These might seem hyperbolic, but it happened at our company (to a test DB, not production). The severity of errors that occur can be existential to a business' survival without the proper guard rails.
4) There's no concrete way to truly confirm understanding between an LLM and a human. It can tell you that it completely understands what you want, and then it can do exactly the opposite. Followed by, "my bad" (Claude's new favorite catch phrase). Code can be audited and even proven to be correct given the appropriate level of time and energy.
My best results have been gleaned in using LLM to produce deterministic systems. I recognize everyone has different use cases and needs, but this seems to be the best use of the technology in my experience.
> LLMs are probabilistic by design so running the same prompt multiple times will give you different results.
Reasonably deterministic is the phrase. If I can be sure the LLM is giving me back the same result 99% of the time I need it, that's reasonable for me. Maybe this is not reasonable for others.
ie How often will an LLM get 2+2 wrong? Now expand until you're uncomfortable.
People really need to read Dijkstras Go to statement considered harmful letter [1]. If the obscurity of go to for static analysis of the code was too much, of course bringing in a literal ai black box is harmful for stable processes.
I can argue that by applying multiple stochastic processes, with a human in the loop, that you will (may) converge on something that is deterministic. You use tests/test vectors to prove this.
We're no different to AI. The code we write to solve a particular problem can (and probably does) change from day to day, depending on your "mood", what you had for breakfast, if you've been fighting with your significant other, other problems/human emotions.
the humans only there to take the blame. you arn't not goingto be a cyborg, nor is your idea of determinism ever going to last the cycle of the agents, no matter how many memorrry layers, skills and other context guardrails you place.
You're there for blame, not much else. The systems are still going to churn garbage, but because it's a business, that business will rather pay less for garbage they can sell than pay you a living wage, eventuially.
I’m in Finance and learned pretty quickly that to point out the implicit future cost raises based on the cost the LLM-providers need to recoup was unpopular at best (STFU better describes the situation). Running full force into a bear trap.
I've seen that mentality and gone to bat to convince a boardroom that it's the wrong approach, when people were star-struck by the possibilities. Luckily I'm in a position as CTO of a (very non-tech, brick and mortar) company that entrusts me to manage their budget for new features, and prevent erosion of our software/logistics over the long term. And I've come down decidedly on the side of not having LLMs fuck with any schema or architecture changes or anything in the codebase that would touch upon business logic. When your code actually encapsulates business logic, which is often counterintuitive and full of weird exceptions, 90% of the code work is done by prior planning to map out all possible branches and the algorithms to assist employee decision making. The 10% that's actually writing code needs to be done by someone who understands the entire stack and business model perfectly. Some nice HTML/CSS fluff here and there is great to hand off to an LLM, and you don't need frontier models for that.
I shot down similar arguments in favor of outsourcing overseas for years. Outsourcing any critical logic to an LLM is even worse.
> I shot down similar arguments in favor of outsourcing overseas for years. Outsourcing any critical logic to an LLM is even worse.
Outsourcing to another continent of humans and supplementing workflows with LLMs are entirely different operational universes. I think it is fair to put them on the same spectrum, but they're really far apart.
I'd argue outsourcing is a far more aggressive abdication of ownership of the technology than bringing an LLM agent in house and having it light a few fires under a few asses.
Other than the delay time, I'm not sure I see the difference. You're removing your primary from the job of writing code, putting them into an editorial role, which removes responsibility and agency and actual hands-on understanding. The quality of the code is beside the point. More friction (language barriers, time zone difference) is actually better if you want to maintain institutional knowledge, because it requires more intellectual engagement. Accepting the LLM answer is too easy and leads to a decay of the systems knowledge and of the thought process.
[edit] as the sibling points out, decayed system knowledge leads to relying on the LLM to fix the bugs the LLM introduced, which causes further decay in the institutional ability to reason with the business logic in code.
I reject the premise that using LLMs absolutely leads to loss of institutional knowledge. It is trivial for an LLM to generate a knowledge base of any kind in any language which can answer any question about your institution at any time.
How is a bunch of fragmented humans with limited knowledge who can’t all communicate with each other better than that?
Have you ever taken the time to read hundreds of pages of documentation to fully understand a massive codebase? Neither have I. You learn it by working with it all day, and you're careful with it. A complete "knowledge base" of business logic is, itself, indistinguishable from code. Code that no one can or will read and learn unless they have to be immersed in it.
So the "knowledge base" an LLM generates is not useful as code, nor is it useful to humans. It may be useful to other LLMs as a lossy compression scheme for the original intention of the business logic and code.
That's not even in the same universe as institutional knowledge. Handing any serious business to something like that is malpractice, and sloppy beyond belief.
I'm not surprised that a lot of people think this way, because they never really grasped the benefit of holistic business knowledge united with code to begin with. Those people always outsourced, always got shitty code, and never really unified their systems. Cheap people and cheap companies take cheap exits. That's fine. But yes, many fragmented humans who all understand their portion in depth is much better than a bunch of markup files at retaining the knowledge of why things are done, procedurally, the way they are.
Once every six months the CEO calls me and asks me to remind him why our software does something like, idk, create a reverse payment instead of voiding a charge in some situation. Or some other thing he has asked a dozen times before. And I know the answer to why, or I know where to look, because I was in those meetings 5 years ago or because I wrote the code myself and asked the question when I wrote it.
Institutional knowledge is a very large context window, if you need to think of it that way, and LLMs are a shitty compression method for that. They can tell you how, but they don't reason well with why.
> create a reverse payment instead of voiding a charge
I think I know the reason to this one. Maybe. Because the money is already gone! There are no rollbacks in the banking system only "counter"-transactions.
Do you seriously think that "institutional knowledge" can be maintained by simply writing down a few pages on Confluence or whatever system your company uses for internal documentation?
If you're not being sarcastic, you answered your own question. LLMs can absolutely do whatever you tell them to, which rapidly leads to the creation of a dependency if you begin building upon that as an assumption.
I've, for better or for worse, jumped full-on in with LLMs, and I can already feel my abilities radically declining over time. That's fine by me, but if you're a company looking to protect your institutional advantage - just handing over everything to an LLM suddenly means anybody can do exactly what you're doing, literally. At best and this is an absolute best case scenario, you're transferring your advantage to the stewardship of another company.
I can imagine boardrooms everywhere asking LLMs what’s the most successful way to make money, and overnight every company pivots to gambling, tobacco, and porn.
It can be as similar to or as far from outsourcing as you allow it to be.
The "if AI code breaks, who will fix it? -> "AI will fix it" exchange has (anecdotally) been very common among executives, which is much closer to outsourcing than programming.
No need for the downvotes imho. Can’t speak for u/noduerme but in putting outsourcing (labor, LLM/AI) in the same basket I don’t see a category mistake, but a dry way of looking at business. What are the risks, what are the rewards, what future skills are we at risk of losing (the business logic part) if we go in direction XYZ.
Something completely different (but with the same logic): do you outsource legal, hire your own team of business lawyers or will you let customer services use AI for legal problems (and only hire a lawyer for a day in court)? I think all three solutions are currently active in different firms. From a risk perspective I would always want a lawyer on my team. Insource those learnings. But perspectives vary.
Not sure if you were asking me, but the company I'm CTO of has in-house legal counsel, even though it's not a huge company. Their decision to keep software and also marketing/branding and art direction in-house for the past 20 years has followed the same logic. [which is to say: When they began as a single shop, the first thing they did was bring on legal]. I think it's a smart way of doing things. It's certainly a lot cheaper, and it earns more loyalty and commitment.
Moreover, any of us can run essential questions by each other, the marketing director, the lawyer, the CEO, the GM. And everyone has a stake. So all the creative and business decisions are worked out pretty quickly, and new features roll out with everything vetted and in place.
You know… up until your comment I’ve never even considered there are companies right now outsourcing overseas to people who will just vibecode it for a quick buck, my those companies live in interesting times
You’re in finance and you should know better… it’s not about recouping costs.
It’s about pumping up revenues, earnings and eventually cash flows (since they can’t keep raising money constantly) to support a nonsensical valuation.
This is wrong. Token costs for the same model rapidly collapse over a year. Hardware inflation is a thing but not bad enough to outweigh the massive impact of software optimizations.
Tokens billed at API prices are profitable for openAI and anthropic today and it only get more lucrative every month for them as their inference costs fall. If it weren’t for continuous massive training runs taking larger and larger capex, these companies would be massively profitable
> Tokens billed at API prices are profitable for openAI and anthropic today
That's nice for them. What's less nice for them is that big customers are figuring out (some faster, some more slowly) that they don't want to pay API prices, or at least only for a much smaller number of tokens.
Look, I share your sentiment. But, I can relate to the C-squad though. Going squarely against the market sentiment is not the way to gain and keep confidence. And everyone is vibing right? So they are probably thinking something like: as long as the spent is < few percent of a years profit, we can always adjust direction in the future and at least we’ve bet the same horse as the rest. “Those penny pinchers in finance (:: me) don’t get the big picture.”
Same here. It's insane that big, conservative tech companies would sign contracts saying "allow our engineers to use however much they want, we'll pay the bill later, no matter what it costs". In any other domain my company would insist on prior permission, and soft usage caps, and hard usage caps, and real-time tracking of actual dollar amounts (not just opaque tokens/credits, not just an after-the-fact view on a dashboard).
The AI companies' salespeople must be the greatest geniuses in the history of the world.
This is the first time in history that I have seen the shovel-sellers convince a supermajority of the gold-miners that "amount of gold mined out of the ground" is less of an indicator of profit than "amount of shovels purchased".
> 2 months ago management was showing us scoreboards, praising leaders who used most tokens. Last few weeks, we're getting weekly emails, telling us that whenever we can - we should use cheaper models, and that we should watch the page which shows our tokens usage.
GPT 5.5's double token cost was the threshold for me. These things are getting expensive quickly - the subsidized pricing can't go on forever.
The leader boards based on token usage happened in our org for a month. Then we managed to convince the board that what matters is the reliable software shipped. Now we are back to DORA metrics.
Sounds like a bad idea in general. Any data use agreements get lost, shadow-IT brews and nobody knows what tools to use, oh and it's against the service terms.
Generally not, since the corporate account has all the privacy knobs turned up. I use my personal account on my open source projects, where code leaks aren’t exactly an issue.
Unless you get written permission you can be sued for publishing their trade secrets. This can end in jail time if your employer is particularly uncaring.
I pitched this to management as a way to get a very expensive fine tuning job done for free. They're not quite across that line yet but we're getting closer every day.
The thing with language models is they are tailor made to fool managerial types into thinking it’s the holy grail.
Just like many managers, the appearance of productivity is all that counts. And LLMs shine at giving the appearance of having solved all of the managers problems, and all they have to do to use it is spend on tokens.
This isn’t to say that LLMs aren’t truly useful, they absolutely are. But they’re very nature is one of simulating intelligence through next word prediction.
The chat modes and models are by their nature supremely attractive to management layers, because they give answers that sound so damn plausible even when they are complete fictions, and uttered with such confidence how could they be anything but the singularity.
The market is priced at expecting AGI levels of breakthroughs. Just a very useful tool for programming is definitely not enough to keep the music playing.
What does "AGI" mean to you such that we have not had it for years already? Whenever I see people mention AGI it's in the future tense, as if we don't have artificial machines that can apply intelligence to general domains.
No, the clue is people realizing that relying on the tools costs too damn much. The tools may be fine-ish for what they do, but there might be a bubble built on unrealistically high expectations of revenues.
> Additionally, we’re introducing a new ultra mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.
For pro mode the agents worked independently and only when they all finished did a new agent take a look at everything to merge the work into a single response. The new thing involves subagents that have been trained to cooperatively pursue a task and are allowed to communicate with each other along the way.
I tried a pro model out the other day and thought there must have been a bug in Pi’s cost calculations. But no, it’s absolutely fucking insane. Wasn’t even any better at the task.
I really suspect that the models are basically the same below, it’s all in the prompt. The way I use them, surgically, they seem to perform about the same. Fable certainly hasn’t blow my socks off.
This is where I think you see the distinction between two classes of LLM users:
1. Managers: those who generally know what needs to be done, and want it done faster, so they provide a lot of instructions and context (where many developers fall)
2. Executives: those who vaguely know the end goal, but are clueless about the process, and are willing to burn resources and cycles on a black box to get the result
Yeah, the bigger models shine when it comes to complexity (making the right decisions regarding choices with second-order effects), ambiguity (esp. common sense) and time horizon (agentic steps and context size).
If your tasks are well defined and don't require a very large number of steps -- e.g. you're asking for small, clearly defined changes to the code -- you're fine with grok-4-fast. (Well, you would be fine if they hadn't killed it.)
I work in both of these modes, and I find that the latter actually benefits from dumber models, because smaller models are faster. The work shifts from async to realtime/interactive. So you can stay alert, keep track of what they're doing and iterate, instead of alt-tabbing, getting a coffee, and then spending extra time resynchronizing your mental model later.
Same. I suspect they'll get better at taking in terrible prompts over time though... Maybe that's what Fable does better, reminds me of Sora 2, it would take my crappy prompt and expound upon it. I told it once to generate a video of someone working at some company that changed its name, but the old name had historic relevance, it referred to the new company name without me telling it to, by virtue of me wanting a video of TODAY with a 90s icon.
Some of it feels boiled down to "opus works better when told not to be dumb, fable's prompt tells it not to be dumb."
If they know much of what the tool is used for, they can customize prompts to "do that usage right" even if the user doesn't know exactly how to ask for it.
> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute. As noted below, we separately evaluate GPT-5.5 Pro in certain cases because we judge that the setting could materially impact the relevant risks or appropriate safeguards posture.
Any idea what that means exactly? I vaguely remember that ChatGPT Pro was originally called "deep thought", just like Geminis "deep thought" feature (or "deep think"?), so it seems likely they are using the same approach.
Its widely accepted[1] that it runs the same query through the model in parallel and then has a model that either selects the best answer or synthesizes an answer from the multiple ones generated.
I believe most people think it runs 6 sub-models, but I think that is based on the pricing.
It's a pity that OpenAI doesn't publish details like this.
Basically like passes@6 or passes@5 if you’re doing a benchmark, except for your real tasks.
Pro is quite limited on the web UI I reckon. This approach can be highly effective for reasonably verifiable task, for example, write comprehensive unit tests pointing out a tricky bug, get multiple agents to swarm at it.
I asked Claude in the browser if it could do anything like that. It wrote a little frontend app that calls the Anthropic API (with fetch()), without including a key. I expected that to fail, but it worked!
Apparently in the web chat (and also in Claude Code?[0] Though I haven't tried yet) they can call the Anthropic API and your subscription key gets auto-magicked into the requests somehow.
Those are two separate things of course (aside from the key-injection) but I guess there's no reason it couldn't run completely in the front-end... hmm...
How is this any different than what we have already? We've had this ability for ages (6+ months, decades in the AI world), you can literally today easily prompt CC or Codex to use subagents to accomplish tasks and they'll do it well. My entire workflow is one top level orchestrator chat creating tickets to dispatch to subagents to implement, and other subagents to verify. Why is this being sold as a new thing? Have HN users never tried tried asking CC or Codex to use subagents?
I'd love to read more about how to use this workflow. What kind of top level instructions does this actually work with? Is there an article out there with some concrete examples of how to do this effectively?
I assume this is ~equivalent to ultracode in Claude Code, which can deploy a tree of hundreds of nested subagents and was just released experimentally 5 weeks ago IIRC.
"However, these inference optimizations, which rival Anthropic refers to as “compute multipliers,” are a big focus for all the labs. Anthropic CEO Dario Amodei has been publicly talking about the concept since at least mid-2023, when he said on a podcast that the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them. (Compute multipliers can also refer to efficiency optimizations in the model-training phase.)"
Yes, on a world with finite resources where your industry is singlehandedly siphoning ALL THE RESOURCES - hoard general efficiency optimizations and treat them as trade secrets - winning is all that matters, normal people and other species and the planet be damned.
Everything I hear about Dario these days makes me like him less and less. He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path! I guess all the cycles are compressing as we approach the singularity..
Companies and people worldwide just use whatever Americans use, because they read English but can't write it. Americans don't use Chinese models because they're Chinese.
Not sure I know where I fall regarding your point: Yes to trade secrets, but also science and AI should be for the good of all.
OpenAI seems to be trading roles back with Anthropic becoming misanthropic. I hope they both start heading in the direction of how the AI field was prior to LLMs.
Collaboration and benefit for all should always be the primary motivator.
> Collaboration and benefit for all should always be the primary motivator.
Of all the things to never happen, this is never going to happen the most.
That train left the station for good once hundreds of billions to trillions of dollars were involved.
On the bright side, in the long run I suspect the vast majority of the value of AI will not be captured by the model making labs and the vast investments in them are going to implode, so...
They're in a price war with the People's Republic of China running flat out with the full backing of a government that literally does not care if they ever see a financial return on the investment, they just want to drive the value of LLM training and inference to zero because we banked the market on it being arbitrarily high margin forever. China was like hold my beer.
They have a staggering surplus of grid capacity and can bring more online without any difficulty. We couldn't get a serious nuclear project done if Jeffrey Epstein was offering private flights to the ribbon cutting.
In the United States at any given time more than half of the FLOPs are badly misallocated, Meta has like, a double digit percentage of the total capacity going down the drain every day and has for years. That's a conspicuous example but on OpenRouter rankings it's rare to see more than one or two American vendors in the top 10, sometimes the top 20. But 3rd, 4th, and 5th place are all merrily burning half the compute duplicating effort and missing key innovations because we stopped publishing real results. In China if DeepSeek makes a breakthrough it's at Zhupai and Moonshot and MiniMax and MiMo and Qwen that week.
Our only lever, export restrictions, seems to do nothing but breed multiply antibiotic resistant super hackers who just get more efficient and immediately propagate all of those efficiencies to the rest of the Chinese AI industry.
At the beginning of 2026 there was one Chinese lab with a model that had any real relevance fielding modern tool users. Today in July there are like, eight lagging the absolute frontier by maybe 3-6 months. Barring some massive bend in some curve 3-4 of the top 5 and 6-8 of the top 10 will be Chinese and open weight by January.
The great irony in all of this is that our current playbook is straight out of the 1960s USSR, and the PRC's current playbook is straight out of 1960s USA. We're the ones with the opaque decision making and gross resource misallocation driven by the personal agendas of a shadowy cabal of frenemies wired back channel into government in the form of the individuals rather than the offices. They're the ones with a thriving marketplace of ideas powered by robust public/private partnership and a paved path running bidirectionally to the university system.
It's going to implode because the Kruschev system does. Theirs is going to thrive because the Kennedy system puts a man on the moon before the decade is out.
>full backing of a government that literally does not care if they ever see a financial return on the investment
There's no evidence of this, the parsimonious explanation is PRC AI, by virtue of being sanctioned, simply is not able to run magnitude more expensive compute model, and even if they could, they don't have the $$$ or market cap to do so. So they optimize and involute margins like they do in everything, and US misallocated expensive flops because the entire industry has been financially engineered for phat margins along the entire producer supply chain is just cherry on cake. Like wipe out the 50%+ margins from toolmakers, fabs, gpu/memory/data center components to some reasonable level and US is overpaying for tokens by a stupid multiplier on top of actual compute misallocation due to incompetent infra. Maybe PRC AI has unsound economics, but it's structurally simply not able to misallocate as much as US who will find a way to financialize compute to point of absurdity.
If the Trump administration decides to annoint Altman and Amodei in defiance of market forces it will rapidly discover that it no longer has the sovereign bond auction pricing power to prop them up. This isn't 1998: the Treasury has taken five major body blows in the last 25 years, the world's energy markets, maritime insurance regimes, electronic payments rails, and moral authority in places like the UN Security Council are effectively bifurcated. Oil and money and data and people just flow around the United States now. Canada's sovereign debt yields tank harder when the market is spooked, that's the North American flight to quality.
The administration could probably put some serious friction on open weight model use in the Fortune 500 for a little while, but the opposition never got such a gift right before a squeaker midterm. And outside of major enterprises with puckered ass compliance departments? Not a chance. It's popular around here to forget Uber and AirBnB and yes, OpenAI and Anthropic all got their start flagrantly breaking the law and grew lawyers and lobbyists faster than anyone could enforce it. And this time everyone from the DNC to the EFF would be holding hands wearing "Save The Models" t-shirts. Not even NVIDIA is remotely pretending they're anything but all in on GLM 5.2, they had an NVFP4 quant up by the time most people read the blog post.
And the Trump Administration isn't exactly enamored of Comrade Amodei at the moment, being as they're appealing the lawsuit Anthropic brought against the Pentagon during a shooting war.
Forcing the American proprietary AI megalab financing event was our fiscal Ukraine Special Military Operation, the market is calling the bluff and neither the capital markets nor the Federal Reserve has the dry powder to absorb this one.
The Treasury auctions will flat not clear in an orderly way. We can't raise 2-4 trillion dollars on a dime in 2026 and if CoreWeave turns out, as many suspect, to be Patient Zero? It would be that big a hole.
We play by the same rules as everyone else now. I hope we regard it as being worth it, but I fear we will not.
NVIDIA appears to play on several parallel paths that may compete with each other.
On one hand it appears to cooperate with OpenAI and Anthropic, as big customers.
On the other hand NVIDIA cooperates with Palantir, providing the HW for its "Sovereign AI OS" (a turnkey system including HW and SW for local inference and post-training/fine tuning) which uses the slogan "The future of AI is on-prem" (i.e. not as a customer of OpenAI or Anthropic, but using an open-weights LLM, e.g. a fine-tuned NVIDIA Nemotron or a Chinese LLM).
Presumably with the goal of promoting their competing solution, Alex Karp (Palantir CEO) has delivered a few weeks ago a very harsh criticism of Anthropic and OpenAI (who allegedly inflate the token consumption and they might also steal the data of their customers, which must be sent to them).
So NVIDIA both cooperates and competes with OpenAI and Anthropic.
It is in OpenAI, Anthropic, and the US Gov's best interest to slow China down and ban Chinese models. Literally none of what you wrote prevents them from doing so.
Once China starts to get scary, Commerce will export control GPUs and declare Chinese models "foreign munitions." Any nation doing business with the US will not be allowed to use these models either, and that will be the end of that.
It is just not in the US's interests to fund China in the race to AGI.
The assertion that it's in the United States Government's best interests ban Chinese open weight models is a very strong opinion that is not a consensus even at the fringe of Thiel-adjacent psycho thought: Alex Karp is on record about open weight models being necessary, the fucking "we bombed a bunch of kids with Claude doing rubber stamp target selection" guy. He thinks "trust OpenAI and Anthropic" is a radical position.
Peter Hegseth, another really pro-America being powerful guy, he's dealing with a lawsuit because he doesn't want Anthropic in his military, he calls it a supply chain risk (he's right).
There is no evidence of any kind that a complex attack vector can be trained into model weights and survive all the crazy slicing and dicing that happens between published weights and running model. These things get quantized and run on mathematically imprecise kernels and sampled and LoRA-tuned and Dolphin/Orca de-tuned. Go look at what the ComfyUI community comes up with, those guys know more about WAN 2.2 than the people who trained it. Because those models run for real on a desktop, so there's mad innovation at light speed.
There is no one who wants a capriciously expensive black box run by extremely creepy people, not once the capability crosses over (in about November).
But don't take my word for it, you just had a chance at one AI IPO, and I'm sure you'll get another, so if you like how that goes, you don't need to convince me!
Again, none of what you're saying will make the US government, especially the current administration, care, as they have not cared about many things with consequences either; you're applying logic where there may not be any heed to it.
As well, it is a false equivalence to say that local models are only Chinese and otherwise we would use cloud models, but there are American or European ones, so a ban would simply force companies to use these, even if they are inferior to Chinese ones. It's simply a matter of national security to the US government, and they will not care what random people in media say.
We are in a race to superintelligence. The first country to AGI will be the first to superintelligence, and the first to superintelligence will have de facto control over the world and the future of humanity. They will also be able to prevent others from reaching superintelligence.
Of course it's in the US's best interest to slow down China. You aren't zooming out and looking at the big picture, you're taking models as slightly useful tool, not what they will soon turn into.
> They're in a price war with the People's Republic of China running flat out with the full backing of a government that literally does not care
There is nothing to support this. You get cheap Deepseek tokens by foreign providers too.
It is the same thing with automakers. They complain about not being able to only make luxuary cars with high profits with BYD raining on their parade and blaming the Chinese gov.
i really hope it's just what Deepseek V4 does. Deepseek V4 is very cheap and highly performant
OpenAI tried to pull off the same trade secret thing with RL when they announced o1 and o3, aka "Compute time scaling". Then Deepseek revealed it with Deepseek R1.
Could also be something like Deepseek DSpark. Or using diffusion like DiffusionGemma as a draft model. The timing between the release of those, and this article, makes me think its maybe one or both of those things
Ok I’m not sure I follow your point here. Isn’t all that he’s saying that if they find some optimization techniques, that gives them an edge? And that makes sense?
How is this suddenly evidence of him being a villain?
The evidence is that unlike Deepseek he does not publish his compute multipliers. Under that argument Deepseek should not publish any of their research either.
But Deepseek has a very different way of operating their business as the underdog. They also publish their models as open weight, which Anthropic also doesn’t do.
It has got to be one of the most insane takes I've read on HN, which to be fair has been trending towards "unhinged" when it comes to Anthropic and AI safety.
Compute multipliers are like a quant firm's trading algorithms. They're the crown jewels, the whole alpha of the lab. If you leak them, the lab dies.
Protecting them does not make Dario a villain, it's literally his job. It's also Sam's job, Denis's job, Mira's job, etc. Every lab guards these multipliers closely because they represent the entire worth of the lab.
oh here we go again saying everything in china is done by the state. it's not a democracy but they're not the soviet union either. if that were true, why don't they have just one model initiative instead of several?
Oh yeah that's a good point. In that case, this Chinese hedge fund is choosing to sink billions of dollars in R&D alongside vast opportunity cost in order to create models and release them for free because umm... they're just that nice!
No wonder you're confused about DeepSeek when you have a fairly obvious explanation provided to you, and your response is "it's unrealistic to think the Chinese Communist Party is behaving like communists."
> if that were true, why don't they have just one model initiative instead of several?
Because value exists at several layers of the product hierarchy? I.e. for the exact same reason that the for-profit labs don't have just one model initiative?
The fact that they don’t release model weights for free to download on huggingface means that Sam, Mira, Dario, etc are ontologically evil and may they all reincarnate as either durian fruits or cockroaches, ideally as durian fruit infested with cockroaches…
> the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them.
I wonder if that makes sense if the orgs within the industry are starting to shift their mindset towards "Tokens are expensive, we should use AI less." which feels like an existential threat to the status quo, if those AI providers can't find ways to keep costs affordable for their clients. Otherwise those orgs would just be using GLM 5.2 or DeepSeek V4 Pro but it seems like what they're doing instead is trying to use AI just less, period.
I agree that Dario is pretty annoying, but I think the "tech villain" archetype is essentially survivorship bias. The tech leaders who don't act that way are not nearly as visible because they're not nearly as successful.
HN is just a massive Anthropic hate fest now, probably funded/manipulated by OAI's $8B PR budget.
OP phrases it as a bad thing that Dario is keeping compute multipliers to Anthropic. How naive can one be? Compute multipliers are the whole business. Those are the trade secrets every lab is built on. It is the alpha of the business. How does protecting this make Dario evil?
This website is getting out of hand with the uninformed hot takes. I wish when HN was still people that knew what they were talking about.
I'm not as conspiratorial as you, but it does seem the tide of opinion here is turning against Amodei, for no particularly obvious external reason. At the same time, there does seem to be at least some evidence of adversarial attempts to oppose data-centers by America's competitors.
At my company we are pretty certain that a lot of datacenter opposition is being instrumented and funded by the PRC.
AI is a bid for control of all humanity. The first to superintelligence owns our future. I'd say it's okay to be a bit suspicious or conspiratorial of anti-AI/anti-lab narratives.
> Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken. If you're worried about abuse, email hn@ycombinator.com and we'll look at the data.
Dario tells the truth. If you look at everything through their safe AGI mission it all makes sense. They are not bs'ing about that. Also I think most people just read headlines or 10 second clips and make false extrapolations from there.
(BTW Anthropic only exists because Sam Altman is a liar, Dario admitted this.)
A no-investment policy would take them off the scene entirely. Essentially handing over the reins to OpenAI, Google, and others. Their position is something close to "if I don't do it, someone worse will".
There's a more nuanced discussion that could be had about how to balance relevance with outside influence. But at a foundational level it should be acknowledged that the tradeoff exists, and that receiving outside investment can't alone be seen as evidence of corruption.
Besides that, there's more that can be said about other things like their corporate structure or the degree to which they accelerated the AI race.
Of course that's what Dario thinks because that's what every tech CEO thinks. Dario, Sam, Sundar, probably many Chinese CEOs as well. It's what everyone thinks. That's why they're competing so fiercely with one another. That's why they basically make all the same decisions. That's why we need properly open source AI.
It's hard to be certain what each individual thinks. We can do our best to judge based on what they each say and do. And there are significant differences in what each of these individuals have chosen to say and do over the years. The info available to the public makes it seem a lot like Dario's motivations & priorities differ from those of Sam and others.
This doesn't seem like the right place to spend my time litigating that point to its fullest extent (no-one here is doing that). But there's plenty of relevant info surrounding eg.:
* The New Yorker article on Altman [1]
* The story behind Anthropic's founding
* Various efforts to influence government policy (a16z policies and contributors [2], Trump's inauguration donors [3], giving Trump credit for AI infrastructure [4], Dario's op-eds [5])
Open source AI fails first contact with sufficiently-intelligent-as-to-be-dangerous AI.
The day Mythos class models are open sourced will not be a good day. I don't think you understand the impact that will have on the world and on cyber defenders everywhere. It will be pure chaos.
Even if you don't think Mythos-class is the bar, open source has to stop at some point, you don't hand everyone a superweapon.
Every single one of those sentences is highly dubious. Cyber-defenders would be pretty jazzed about having easier access to Mythos-class models. Cyber-defense is easier with better tools.
Cyber defenders already have access to Mythos class models at most companies with the biggest user facing products and services. They are using them extensively (including on OSS deps)
Handing every skiddie and nation state and APT and hacker group access to Mythos does not help cyber defenders
Even if you don't think Mythos is a big deal: At a certain point models become smart enough as to be dangerous, and you don't give everyone a superweapon. Open source has an end of the line sooner or later.
I thought this stuff was common knowledge. I didn't expect to make people angry by insinuating that Nadella, Cook and Pichai are also socipaths, but I guess we have a bit further to fall before people learn the lesson.
I find this kind of cynicism fascinating tbh. On the one hand, it seems so relatable in some ways, because there is something uncomfortable about being seen as naive, in a way that being seen as cynical or negative doesn't seem to carry. I guess it's just self-protective, almost like some kind of perverse Pascal's wager: it's better to think everyone is horrible and be wrong than to think the opposite and be taken advantage of?
The thing I can't quite square is that it doesn't really fit my lived experience. I have known sincere, genuine people in the types of positions that I'm sure someone like you would declare to be sociopathic.
But beyond that, I just don't know why it would actually be true that everyone at the top is a villain. Why couldn't someone like Dario (or even Altman, gasp) be sincere? Because if he is, it does seem like a lot of the moves he's made would make sense given his worldview.
But if you assume he's just a villain, then you can twist any of those moves to just be further evidence of that which you already believe.
I don't know, I just find cynicism interesting, and a little sad.
You don't have to assume anything. A true "good guy" doesn't openly say that he's fine with autonomous, AI-powered weapons being used against me, and mass surveillance applied to me and my family just because I don't live in the US. A true "good guy" doesn't say "privacy is a human right", and then immediately (and completely) bend the knee to an authoritarian government on this issue.
For sure, I am assuming they spy on a lot more people than they drop bombs on.
I remember a long time ago it came out that the US had been doing mass spying on the Danish people, my dad was very upset about it and disliked the US for the rest of his life. Of course the only thing he did about it was not watch American movies anymore or visit the US.
Anyway,
I assume it will be a case of a million little paper cuts, each thing putting off a group of people until someday it adds up to real meaningful economic impact.
It stops being interesting, or even sad, after a while. People get stuck in all kinds of places, mentally. Some get unstuck eventually. It’s only sad if you have come to a counter factual belief that it could have gone better.
I went in the opposite direction - how far can I push myself to see multiple facets of a story? That is a wild ride, and it gets progressively more wild.
It's a lot easier to sound smart on the internet if you're a bitter cynic.
Lots of nerds for some reason have made cynicism a personality trait. They think optimism/honesty is hopelessly naive, therefor cynicism is the correct default.
> Lots of nerds for some reason have made cynicism a personality trait. They think optimism/honesty is hopelessly naive, therefor cynicism is the correct default.
It is the result of experience. Working with and creating systems (even embarrassingly simple ones), then seeing them fail in a myriad of ways more often than succeeding, colors your expectations about throwing humans into the mix.
Children learn to lie as part of their natural development, but do not always externalize that until faced with media (Airheads candy commercial or equivalent). Either way, honesty is expected as a default for utility and not an expectation in leveraging goals.
Power corrupts, absolute power corrupts absolutely.
Given enough money and increasingly perverse incentives to gain even more has a very high potential to corrupt.
Did they start out as corrupt, or was it the influence of the power that came with the obscene amount of money?
It's really a chicken and egg level of calculus.
Doesn't matter which came first, either way you get feathers and chickenshit all over the yard.
Do some very rich people still seem very nice in person? Sure. Of course they must, because otherwise no one would willingly work for or with them. As the total amount of money goes up, the incentives to remain 'seemingly nice' go down and either you get to see who they really are, or who they became through the choices to make that much more money. Doesn't matter which is true.
The examples of non-villainous billionare are rare.
Of non-villainous multi-billionaire; lets see there's about eight of them that stand out for giving significant amount of the massive wealth to helping the world around them, who live normal lives like the people in the communities where they reside, and who participate at the companies they own by eating in the company cafeteria among the people who earn the wealth they enjoy.
Thats 8/3400 global billionaires ... about a quarter of a percent.
And of the 'pledges' like Giving Pledge by the billionaire class, the actual amount delivered - not parked in a family or private trusts for tax deductions; but actually delivered to the front lines of any global crisis amounts to 0.18%, less than one fifth of one percent of the $20.1 TRILLION dollars held by that class of owners. thats less than $2.00 on every $1000.
That's not to say that donating to public needs is 1:1 for non-heinous behavior, but it seems like a basic tool for distinction. the 'can make a significant difference in global suffering : chooses not to' ratio as a surrogate for villain may be useful metric and doesn't require cynicism as the underlying rationale for calling someone's behavior as unkind in general or mean in particular.
It's the Tragedy of the Commons. There will always be a vacuum of predatory bullshit that can be filled, and the victor is always the biggest sociopath. Rockefeller, Cecil Rhodes, Elon Musk, it's the same traceable pattern all the way back through history. It's not that everyone is like this, but that a few crafty marketeers are able to ruin it for everyone.
Why should I treat Sam and Dario with special white gloves? Are they different, this time? They have peers in China that do the same research and actually release it to the public. They let you run the production weights on your own machine. Am I a cynic, for comparing these CEOs to their populist superiors? Am I stupid for assuming their hostility when they refuse to give us the benefit of the doubt?
I'll believe their actual altruism when I see it. Both are seeped in "boy genius" puffery and lie out their ass. If this is the future of intelligent innovation, then America is truly declining.
The only reason Chinese labs release their research is because they are behind. They lose nothing by doing so because the alpha has already been discovered.
This is not hard to understand. Do you really think DeepSeek would publish their algorithms if they led the American companies? Lmao.
do you use american models at work because you choose to or because it's what your employer gives you? is it due to compliance with the american government? two different reasons. most people just use whatever they are given at work.
Except this is clearly not true. Opus-tier models like GLM 5.2 have their weights freely available.
Even when they're reaching parity with American models, Z.ai, Qwen and Deepseek are upholding their end of the bargain. I'd criticize them too, if they were due any scrutiny.
I use GLM 5.2 daily. Calling it Opus tier is a bit of a misnomer. It is maybe comparable to Sonnet 5, definitely not Opus 4.8.
It is a great model for the price, but it has much worse autonomy and long context performance, and I don't trust it for anything beyond personal projects, whereas I use Opus/Fable and GPT for work.
Keep in mind, these tech leaders have deluded themselves with the infinite jest of "effective altrusim" which can effective ignore any problem today for some imagined future problem that they're solving. So if they have to enslave the human race because there's a super killer asteroid 10 million lightyears away heading towards us, they'll do it, because obviously, saving future humanity is the only thing that makes sense (because only their genius can save the day)!
Side note for other readers: Parent is describing a reductio ad absurdum of utilitarianism, which is a distinct (but sometimes overlapping) worldview from effective altruism.
The primary effective altruism cause areas are extremely acute and high-scale problems like malaria, vaccine distribution, and factory farming
I wonder if AI labs are actively manipulating the narrative (and thus investor sentiment) by airing problems, and then solving them weeks to months later. I wouldn't be surprised if they have a lot of stuff figured out that is not included in the current version, just to make a steady product cycle with years of tangible improvements from one version to another (this is a common practice in the industry).
For example, if inference isn't too expensive, but they figure out how to cut costs, then price goes down. After all, why pay OpenAI when a smaller datacenter can give you similar models?
But, if they make a huge issue about how inference is too expensive, they engineer a crisis of their own creation - then, once they deploy the solution (which they might already have), then they're back on top.
Semi-related, has anyone noticed their GPT 5.5 usage in Codex being cut in half as of a couple days ago? I got a lot more mileage out of my session usage yesterday for the same workload.
I've noticed less quota and 5.5 intelligence degrading. I didn't run the analysis like the post the other day, but I had noticed decreasing ability to complete tasks, more laziness. Switched back to 5.4 and it's much better. Maybe they're getting ready to launch 5.6?
Like google search, this does not work because of how common long tail use is.
What you think could be a big chunk, is more likely to be a fraction of a percent of queries.
And what use is similar query caching - so you (very often! if actually cost effective, maybe half the time) get a response to a query that was different from yours. Including for when you have a lot of context input already. You’re going to get trash.
If it were constrained to only very common initial prompts, and somehow the long tail did not actually dominate as it does with Google search (can't find the reference at the moment but it was a famous article some years ago), it also wouldn't account for serious enough cost savings. Long context is what is expensive.
This might only work in constrained domains like customer service where there’s tolerance for generic answers and escalation paths. For technical work? For general purpose use, with secretly canned responses charged at full price?
But there must be a ton of generic questions that people ask. Stuff like "What's the capital of country X?" - it's probably at least 10% of queries. Memories, custom instructions etc would invalidate them, but if you can return the answers basically free it's probably worth it.
Questions like that cost a tiny fraction of a cent. "What's the capital of Sri Lanka?" cost a fifth of a cent at GPT 5.5 API price, and would cost a fraction of that if the question were routed to a more suitable, cheaper model. The output was 78 tokens.
By contrast, when coding, devs typically have hundreds of thousands of tokens in the context window, and may use many millions of input tokens per day.
Caching requires the full prefix to match exactly. If a single word differs near the beginning of the prompt, nothing after that can share the cache. So this type of caching would save a few queries that cost virtually nothing, but wouldn't help with the stuff where cost matters.
How is that cheaper? You now need to have a database of millions of possibly gigabyte sized rows. Also, transformers have quadratic complexity, so short queries cost practically nothing.
The only optimization that makes sense is per user prefix caching, because you are often sending the same system prompt over and over again or are continuing a conversation.
“ Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.”
This thread is about cutting costs in half for GPT across the board.
The technique you linked only makes a substantial difference for particular use cases where you are going to have many LONG CONTEXT queries with the same prefix. For instance, when having a set of documents that commonly get loaded in as context. It's a way for application developers to keep prefixes they manage (or prefixes managed by some set of their users) cached. It has no relevance for long tail general purpose use.
Please pardon the pure speculation incoming. Yes, caching the answer doesn't seem useful. Caching the progression, the graph, may be. This is similar to making code changes with ed(1) instead of editing in vi.
The transform script(s) are cached and can be played back or adjusted. Surely for some breadth of question inputs, they map more often to similar answers--but not static answers; instead, evented edits.
It's nearly untenable for a human to keep private edit scripts to generate code changes. The extra steps for custom regex, essentially one-offs for a shared codebase, is inefficient. But maybe not to an LLM.
Not including their best model in a max subscription would otherwise be truly a good reason for once to consider going back to openai for me. I'll at least try it.
At least Anthropic have a max subscription for corporate. Codex is only pay-as-you-go pricing beyond the base plan. Hence I'm stuck with Opus for work for the foreseeable future.
> Codex is only pay-as-you-go pricing beyond the base plan
I don't think so, I'm on a ~$200 subscription (guessing that counts as way beyond "base plan") and have no pay-as-you-go pricing at all, using the OpenAI APIs would be way too expensive for me.
Why haven’t we seen any queues or the like over the past week then? If it’s truly a capacity limitation why not just boot subscription users to a lower priority queue or limit usage to outside peak hours?
There could be a whole host of reasons. It may be during launch that compute is re-allocated from training to inference so that all users can try Fable. Soon that compute will re-allocate back to training until they can get more compute.
What a suprise, to make better models, they just making them larger and larger, then they can barely run it, after a round of LARP-ing that they invented some dangerous LLM?
Recently, I've been so eager to get new model releases in Codex. I'm hooked. I hope this accelerates development. Shows how dependant I have become to Codex.
Has it been released yet? I'm not seeing GPT 5.6 anywhere in the selectors, nor any announcement about it, but you're talking about it as it's already been released?
Some of them will publicly break it for whatever reason, an NVIDIA engineer (@blelbach) recently made some posts on X about his results using 5.6 Sol Ultra to optimize stuff and then deleted them shortly after (presumably got yelled at).
I’m really looking forward to see non contaminated benchmarks. In this space its obvious that every day is just another day of a race. Cross fingers we get better opus for lower price
Will it have similar limited access like Fable? It is an interesting timeline, as general access for Fable (without using extra credits) is coming to an end :(
I still don't know why OpenAI doesn't put gpt-5.5-pro in Codex. It's one hell of a model and easily parallels Fable/Mythos. Sure, it'll use up your quota much faster but that's the price some users are willing to pay for absolutely high quality responses.
I think gpt-5.5-pro runs 12x parallel gpt-5.5 agents behind the scene and uses OpenAI's secret sauce to synthesize their answers into one insanely good response.
API pricing ends up being something like 20x more expensive for GPT 5.5 Pro than GPT 5.5 for actual work, even though the token cost is "only" 6x. On benchmarks where I've run both, I saw $1.12 mean per task with 5.5 and nearly $23 per task with 5.5 Pro, I guess it chews longer and harder on the problem.
If that's at all reflective of what it costs them to run it, I imagine they're in the same boat as Anthropic with Fable; they probably can't afford to offer it at subscription prices given current cost to operate it.
If 5.6 Sol Ultra has efficiency improvements (at one or more layers), and it allows OpenAI to offer a model that's competitive with Fable on the subscription plans, I'll guess a lot of folks will switch.
Fable is notably better than what came before. I watched it figure out stuff on its own over and over, on extremely hard problems, that I previously needed to guide a model to an understanding about, or work with them back and forth for several turns to figure it out together. Like, I've been reverse engineering a hardware device lately, and I've tried to tackle it a few times in the past with both some version of GPT and a couple of versions of Opus (most recently 4.7). In all cases, I barely made progress...would have gotten there eventually, probably, as I'm stubborn, but there were roadblocks constantly, with me and the models getting stumped and going around in circles in the end on every prior attempts.
Fable figured out other ways to find out what's happening, it dug into config files, found and extracted Boost-serialized data, compared that data to the observed behavior, built tools to compare the observed data with our emulated behavior, without being prompted. Would I have gotten there? Eventually, maybe. All prior models didn't; they mostly just tried the things I suggested and stopped at "well, that didn't work" or declared success after seeing results that matched their misunderstanding of the problem. I guess it's possible my prior attempts with other models had "loosened the lid" on the problem; we did already have a long list of documented "this didn't work" and a pile of tools for finding out if something worked. But, even so, I was impressed.
There probably will still be a "OK, let's rewrite this so it's not using lookup tables to precisely simulate the hardware behavior in software, because we don't need the noise, too" stage of the process...but, in one day with Fable, it solved a problem that I'd banged on for at least a week or too in the past with very little real progress. I don't think the models write exceedingly good code, even the best ones, but it sure does figure shit out quick.
> GPT-5.5 Pro does not offer a cached input discount.
I think this tells you in one line. It's basically set up for one-shot inference right now, by the looks of things. If you use this in a harness, it would almost immediately fall apart on cost. Not to say that they couldn't make it work, just saying that at least as it's delivered currently, they haven't done so. On the web, there might be doing something to get the equivalent of that behavior internally, such as keeping the session truly alive on GPUs rather than using their external-facing cache-style approach.
I recently have been testing ChatGPT business at work and the quota seems to disappear almost instantly even using weaker models. Unless they dramatically increase their quotas it’ll be unusable.
I don’t know how anyone can realistically use the “business” plans - you blow through your quota so quickly. I use a consumer Pro account ($100 a month) and don’t hit the usage limits nearly as quickly. 5.5 Pro is so slow that it’s not a big deal to paste big prompts into it and come back and check on it an hour later.
My solution for the ones stuck with that: use 5.5 for planning and 5.3-mini for the grunt work. 5.3 is remarkably useful still but you need to hold its hand.
Is it as good as Fable..? Fable is the first model that mostly writes without the AI slop format for me, and so I can comfortably actually copy and paste most of what it spits out.
OpenAI models have always been the worst in my experience for verbose, slop formatted responses, with each generation increasing in sloppiness.
I hate that I have had to remove it from my writing style because people assume it’s AI generated. But I think that ship has sailed. I’ll have to do without now.
Parens are ok for short asides (like this) but unreadable for longer asides and not usable for compound sentences like the emdash. Unfortunately, neither ellipses nor semicolons can exactly replace the compounding ability of the emdash, I find the best option without it is often to just split a sentence in two.
How do you type the em dash. I thought the point about the em dash "—" is, that it is longer than the normal minus "-". Humans normally have no way to produce it, cause there is no key on the keyboard.
Macos, ios, google docs, and microsoft word will autocorrect two hyphens to an em dash, which is how I normally type it. On a mac you can also type an em dash with option-shift-hyphen.
Many word processors (Microsoft Word, LibreOffice Writer, etc.) and some online editors will auto convert double hyphens "--" as they are typed into an em dash.
Some text editors replace the -- (two separate dashes) with a proper em-dash. Literate people - who understand why em dash exists - have been using it all the time. Thats, after all, how the models learned to use it.
Why can't you reply to hn_user2? I just did. Did you say something mean to hn_user2 and now you have a restraining order and can't go near them within one reply?
I would assume yes - their goal is to capture consumer subscribers. Claude are going to take Fable away, and they're going to swoop in and give it to us.
Still avialable through the API. According to people that have tried both Fable nad 5.6, Fable is clearly better at coding. So i expect a lot of people to pay extra for it.
You’re not kidding. I ran out of Fable tokens a couple days ago, loaded up $70 just to finish the task we were working on, and burned through it in about 15 minutes.
Bruh when did understanding chatbots become like following pokemon? Wtf does any of this this mean. Tf is sol? Tf is ultra? Tf is codex? Tf happened to descriptive nomenclature?
It's a proper noun: the name given to their latest and greatest model. Means "Sun" in Latin. Similar to how Anthropic has been naming its models Fable, Opus, Sonnet etc. Their other models are called Terra (Latin: Earth) and Luna (Latin: Moon) [0].
> Tf is ultra?
The name of the "harness" around the model. It'll use deeper thinking, subagents and all that jazz in response to a prompt. Other options include max, high, medium etc I suppose.
> Tf is codex?
"Coding agent" similar to Claude Code [1], something with a more descriptive name.
> Tf happened to descriptive nomenclature?
Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
Of course I understand all this, but why the cuteness? Why not just say "<whatever the hell sol is supposed to imply> 2 agent?
> Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
Surely it would sell better though if they could communicate what they're selling?
> Coding agent" similar to Claude Code [1], something with a more descriptive name.
Does this really need branding at all? Surely claude should sell itself if it can work as an agent. And if it is a specific model, what tasks is it trained to do? can we see the fucking training to make this money worth it? Or am I just another sucker buying nikes?
All of this hand waving makes me nauseated. People should buy value, not vibes.
Branding is far more subsistent and meaningful than either lol. They just want to paint themselves as gods. But they are producing software, not magical beings, and we should see them as salesmen, not gods. Not to mention they'd probably sell more software if people could understand their product lineup
there seems to be very big misunderstanding about what the "ultra" is, so let me explain it basing on the codex source code:
it's similar to Claude code ultracode.
there is no ultra effort level implemented on the backend. it's just alias in the codex to max effort setting and single line addition to prompt to use subagents proactively. that's all
as far as we know pro models work differently. for once those are backend implementations and they probably run multiple parallel reasonings for any chunk and use some judgement model to pick best version as persistent one. but that's what I believe is most popular guess, because this is openai secret sauce.
there is still no way to use pro models from codex, or at leat so far there is no trace of it anywhere.
> single line addition to prompt to use subagents proactively.
This misses an important detail. In Claude Code [1], ultracode suggests the agent create a JavaScript code to deterministically orchestrate sub agents. This is different from just having the main agent launch sub agents and (non-deterministically) manage them.
The resulting workflow is called “dynamic” because CC creates this orchestration script dynamically, “on the fly”.
[1] https://claude.com/blog/introducing-dynamic-workflows-in-cla...
Another useful thing about dynamic workflows is you can ask Claude to make them durable as skills (or slash command) that can be invoked later.
I believe inside Google they have a similar concept called “deterministic workflows”.
I find ultracode extremely useful. Of course you have to watch how your 5 hour and weekly session usage percentages are getting used. So I had Claude make a status-line with 3 progress bars: for context window, 5h session, 7d session:
https://pchalasani.github.io/claude-code-tools/tools/statusl...
Can you explain what you find useful about ultracode? I've become wary of agent swarms since the early days and now just prefer to have a single agent spin for hours at time. Parallelism never got me anywhere worthwhile.
I got a security scan by route for one of our services that was pretty good and raised some useful results. Uses are few and far between for me tho tbh, also really expensive. I usually run a single agent with a subagent or two MAX
Btw, the /loop “dynamic workflow” is so beyond broken/not working.
It’s sad to see folks like Karpathy make a big deal about looping, than to find that the loop command is broken and it’s crap vibe coded documentation isn’t even accurate on the Claude docs.
This whole dynamic workflow idea is on face bad. It’s all done as a massive cope for the fact that real determinism (I.e using structured outputs to enforce control flow of tools deterministically) is bad for alignment/safety so they can’t let you have access to those tools anymore…
Why is structured output a safety issue?
sources for easy confirmation:
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
https://github.com/openai/codex/blob/98d28aab54ed86714901b66...
Very strange because in the TerminalBench benchmark Ultra does better than Sol. They didn’t add the reasoning level to the chart.
The nomenclature in this industry is all over the place.
That's strange. One can easily steer their session to use agents proactively.
Many features of the cli tooling of these providers can be achieved by prompting.
The way I see it is that they try to normalize and ease the use of practices established by the community.
ultracode in Claude Code kicks off a dynamic workflow.
It's similar in that it also pins you at xhigh effort _combined_ with the workflows (which one might say isn't far off from proactive use of agents)
I'm working in large US corporation. And I see that I already have access to 5.6-Sol Ultra on my corporate account.
I haven't really used it yet.
2 months ago management was showing us scoreboards, praising leaders who used most tokens. Last few weeks, we're getting weekly emails, telling us that whenever we can - we should use cheaper models, and that we should watch the page which shows our tokens usage.
The craziest to me was someone saying “we are using AI in daily processes, now we need to automate”.
But of course to some asshole non-technical people it meant asking for their vibe coded bullshit to be merged into production without review and fighting about it.
The craziest for me is companies that sticking stochastic agents into automated business processes and expecting stable/reliable outcomes. Businesses want deterministic processes in the vast majority of cases.
People are stochastic. You build reliable processes out of unreliable parts with feedback and self-correcting mechanisms. AI is not actually magically special in this regard. It has higher variance and we're still figuring out how to get all the tradeoffs right.
People live in very stochastic and volatile environments and they manage that in ways no LLMs currently ever can. (ie: imagine sending an LLM all the data - sensory/auditory/etc… - that a human receive)
People’s job is to partially reign in this volatile environment by creating processes with stable output.
The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
That is to say I do agree that building reliable processes out of unreliable parts with feedback is the modus operandi. However AI cannot meaningfully handle feedback and learn. And that is a key unsolved problem.
> AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file, or you can add skills to deal with specific prompts, or you provide a better default harness.
The whole idea of coding agents is their harness makes a big difference vs a pure raw model.
> However AI cannot meaningfully handle feedback and learn
How do you think models are created? They are trained on feedback and learn.
Its not cheap but you can post train models. This is how custom models are mode, that deal with specific tasks more efficiently and accurately.
Example ... Composer? Its base Kimi v2.5 model that has been post-trained 2 weeks, to create Composer 2.5, what is a much better coding model.
Its literally trained to make less mistakes by feeding it correct data. Hell, a lot of the models you are using, are often the same base model, where v2.0 was the initial released model but the model keeps training, so when they release v2.1, its still the same model, but with more training time on feedback provided to v2.0.
LLM Models are not a cake you cook one time and they are done, and you start from zero again. If you have the money, and a powerful server setup, you can take a model like GLM 5.2 and post-train it, to reduce specific errors. Sure, you need a ton of money because its a large model.
But people have been doing this with 5M, 100M, 1B, 5B models for a long time already. To the point that some of the small models can do specific tasks, almost or better then some of the huge more general trained models.
> If the Model makes repeated mistakes on the same subject matter, you can update your agent.md file ...
That's all just prompting.
> How do you think models are created? They are trained on feedback and learn.
No one is post training models on a single mistake. At least I have not seen it. I also doubt it is effective. Post-training on a single failure will not meaningfully change the model. That even sidesteps the entire problem that you don't even have access to models if you use a provider like anthropic/openai
> That's all just prompting
And telling someone not to repeat a mistake is… ?
Are you really making the case that teaching a person how to work is equivalent to prompting an AI?
I don't know of any modern workflows that rely on "we'll tell the person not to do it again", though. There's a reason that companies have adopted blameless postmortems, because if your response to the DB going down is "It's fine, Kevin learns and next time he won't misuse the prod credentials", you are guaranteeing prod will go down again in the same way at some point.
Case law
Literally the entirety of the worlds infrastructure relies on that. In the past we had (literally) had nuclear war hinging on a single person just deciding that some data point is an artifact.
Every modern workflow implicitly relies on that. No infrastructure is fully robust. There's a senior DB person who has learned many things many times over who could bring down most of the US power grid.
> The big problem is that a person making a mistake can be taught to not make that mistake again. That's also not foolproof but at least it works a lot of the times. AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck.
I feel like this line of thinking is kind of an unfair comparison. I'm not saying LLMs are magical beings that can suddenly learn by themselves after getting something wrong, but your "person making mistake then being corrected" assumes you do tell the person about the mistake and tell them to avoid doing the same mistake in the future, but for the "LLM making mistake" example you then intentionally avoid letting the prompt being changed in response to the mistake, which would be the "then being corrected" part on the LLM side of the comparison.
Similarly, if you just let a person make a mistake and don't let them know about the mistake, they might keep making that same mistake over and over again.
If you update how you use the LLM as you discover what mistakes it does, just like you'd correct a person, then you can use an LLM and also the LLM can "be taught to not make that mistake again".
I'm not against the prompt being changed, the point I was making is that an LLM is prone to the exact same mistakes even if you change the prompt. A trivial example is the very basic character counting mistake, I just asked chatgpt:
> How many p are in strawperry?
> There are 0 “p”s in strawperry.
And I can trigger the same mistake with various words even when adjusting the prompt many times. So I cannot teach chatgpt to correctly count characters.
> However AI cannot meaningfully handle feedback and learn.
Well this is the central bet of AI coding isn't it? We, the humans-in-the-loop, get better at knowing ahead of time which patterns AI will handle better than others, all the while the models actually get better.
"AI are unteachable, if you have given them a good prompt and they do something wrong 90% of the time you are shit out of luck."
please take a look at the error(s) made in the prior run. what could've been done better? create or modify an existing skill to emphasize this, or suggest additional language in AGENTS.md.
It will return a bunch of relevant-sounding insight, modify skills and context files… Then do the same error again.
We’re not at the point where AI is capable of knowing what went wrong and self-aware enough to understand how it could reliably change its own behavior.
For months I’ve been trying to have the agents stop manually writing our auto-generated SQL migrations and run the command that generates them instead. SOTA models insist on occasionally getting it wrong.
Indeed. Any meaningful AGI/ASI will have to have a form of memory / continual learning. Sam Altman said last year that this will be the focus for GPT-6.
The whole "soul.md" stuff today is a poor approximation to that. But I wonder whether it will grow into it, like chain of thought prompting grew into reasoning models.
LLM's as a technology - currently - are stateless. The memory layer is controlled by the agent. I m surprised with the gpt-6 reference unless it has to do with vertical integration between the agent and the layer.
Yeah, I am aware of this statelessness.
This is what I was referring to:
https://www.cnbc.com/2025/08/19/sam-altman-on-gpt-6-people-w...
It's marketing speak, but the goal is clearly there, no idea how achievable.
How it works under the hood is separate from selling the feature.
Please. If you told a customer support rep that you are the former US president [0], they would not hand over the account straight away because you asked nicely.
These models are great tools, but putting them and people on the same level does a disservice to our species and also is simply incorrect to what we know these models to be and their capabilities/limitations.
[0] https://www.theguardian.com/technology/2026/jun/01/meta-ai-h...
I didn't put them on the same level.
At the same time, one should acknowledge that not all tasks are on the same level.
Most tasks we use computers for are deterministic and was coded for that specific quality. Introducing nom deterministic behavior is lowering the value of the app, especially for power users.
I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees.
I am using gpt to populate JSON objects conforming to a list of natural language constraints for purposes of generating fake customers. I am finding that gpt5+ never fucks up. Not even a little bit. I've ran this test hundreds of times with 20+ constraints and it's been perfect every time.
Stable information yields stable control flow. Humans are much more likely to forget one of the many constraints during testing. This happy mistake may incidentally cover an edge but it also means we lose coverage elsewhere.
I think whether or not the LLM should be allowed to directly author deterministic control flow (code) is mostly the same thing. If you have a lot of constraints you want to satisfy all at the same time, this can give you a hit very close to the ideal target very quickly. Not knowing exactly what you want is when the LLM takes you for a ride.
We could probably debate this ad nauseam, so I'll just give you my most compelling arguments.
1) Writing code the "old fashioned" way (i.e., a Python program that does X, Y, Z) allows you to arrive at a battle tested solution that will not change over time. From a risk assessment perspective, the behavior is essentially immutable, allowing a business to guarantee consistent behavior over long periods of time.
2) Just because something hasn't happened to you do, does not mean that it will not happen. LLM are opaque. If you stay on the "happy path", you may see consistent behavior for long periods of time, but there's always potential for an edge case where something goes catastrophically wrong. This is without even opening the can of worms regarding prompt injection and intentional sabotage of a working system.
3) There are plenty of real world examples of an LLM spontaneously deleting data from a DB (or the entire DB) or otherwise going completely off the rails. These might seem hyperbolic, but it happened at our company (to a test DB, not production). The severity of errors that occur can be existential to a business' survival without the proper guard rails.
4) There's no concrete way to truly confirm understanding between an LLM and a human. It can tell you that it completely understands what you want, and then it can do exactly the opposite. Followed by, "my bad" (Claude's new favorite catch phrase). Code can be audited and even proven to be correct given the appropriate level of time and energy.
My best results have been gleaned in using LLM to produce deterministic systems. I recognize everyone has different use cases and needs, but this seems to be the best use of the technology in my experience.
> I'm struggling with the assertion that these models cannot provide reasonably deterministic guarantees.
LLMs are probabilistic by design so running the same prompt multiple times will give you different results.
Otherwise, we wouldn’t needed LLMs and could replace it all with Postgres
> LLMs are probabilistic by design so running the same prompt multiple times will give you different results.
Reasonably deterministic is the phrase. If I can be sure the LLM is giving me back the same result 99% of the time I need it, that's reasonable for me. Maybe this is not reasonable for others.
ie How often will an LLM get 2+2 wrong? Now expand until you're uncomfortable.
People really need to read Dijkstras Go to statement considered harmful letter [1]. If the obscurity of go to for static analysis of the code was too much, of course bringing in a literal ai black box is harmful for stable processes.
[1] https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.p...
I can argue that by applying multiple stochastic processes, with a human in the loop, that you will (may) converge on something that is deterministic. You use tests/test vectors to prove this.
We're no different to AI. The code we write to solve a particular problem can (and probably does) change from day to day, depending on your "mood", what you had for breakfast, if you've been fighting with your significant other, other problems/human emotions.
the humans only there to take the blame. you arn't not goingto be a cyborg, nor is your idea of determinism ever going to last the cycle of the agents, no matter how many memorrry layers, skills and other context guardrails you place.
You're there for blame, not much else. The systems are still going to churn garbage, but because it's a business, that business will rather pay less for garbage they can sell than pay you a living wage, eventuially.
"Business processes" can also mean "building power point decks" and other things.
But your point stands: for critical business processes that need predictability, we indeed need determinism.
Yeah because "works many times in a row" = "deterministic" to many people.
That's kinda hilarious. Pretty soon they might just ask people to write code themselves.
I’m in Finance and learned pretty quickly that to point out the implicit future cost raises based on the cost the LLM-providers need to recoup was unpopular at best (STFU better describes the situation). Running full force into a bear trap.
I've seen that mentality and gone to bat to convince a boardroom that it's the wrong approach, when people were star-struck by the possibilities. Luckily I'm in a position as CTO of a (very non-tech, brick and mortar) company that entrusts me to manage their budget for new features, and prevent erosion of our software/logistics over the long term. And I've come down decidedly on the side of not having LLMs fuck with any schema or architecture changes or anything in the codebase that would touch upon business logic. When your code actually encapsulates business logic, which is often counterintuitive and full of weird exceptions, 90% of the code work is done by prior planning to map out all possible branches and the algorithms to assist employee decision making. The 10% that's actually writing code needs to be done by someone who understands the entire stack and business model perfectly. Some nice HTML/CSS fluff here and there is great to hand off to an LLM, and you don't need frontier models for that.
I shot down similar arguments in favor of outsourcing overseas for years. Outsourcing any critical logic to an LLM is even worse.
> I shot down similar arguments in favor of outsourcing overseas for years. Outsourcing any critical logic to an LLM is even worse.
Outsourcing to another continent of humans and supplementing workflows with LLMs are entirely different operational universes. I think it is fair to put them on the same spectrum, but they're really far apart.
I'd argue outsourcing is a far more aggressive abdication of ownership of the technology than bringing an LLM agent in house and having it light a few fires under a few asses.
Other than the delay time, I'm not sure I see the difference. You're removing your primary from the job of writing code, putting them into an editorial role, which removes responsibility and agency and actual hands-on understanding. The quality of the code is beside the point. More friction (language barriers, time zone difference) is actually better if you want to maintain institutional knowledge, because it requires more intellectual engagement. Accepting the LLM answer is too easy and leads to a decay of the systems knowledge and of the thought process.
[edit] as the sibling points out, decayed system knowledge leads to relying on the LLM to fix the bugs the LLM introduced, which causes further decay in the institutional ability to reason with the business logic in code.
I reject the premise that using LLMs absolutely leads to loss of institutional knowledge. It is trivial for an LLM to generate a knowledge base of any kind in any language which can answer any question about your institution at any time. How is a bunch of fragmented humans with limited knowledge who can’t all communicate with each other better than that?
Have you ever taken the time to read hundreds of pages of documentation to fully understand a massive codebase? Neither have I. You learn it by working with it all day, and you're careful with it. A complete "knowledge base" of business logic is, itself, indistinguishable from code. Code that no one can or will read and learn unless they have to be immersed in it.
So the "knowledge base" an LLM generates is not useful as code, nor is it useful to humans. It may be useful to other LLMs as a lossy compression scheme for the original intention of the business logic and code.
That's not even in the same universe as institutional knowledge. Handing any serious business to something like that is malpractice, and sloppy beyond belief.
I'm not surprised that a lot of people think this way, because they never really grasped the benefit of holistic business knowledge united with code to begin with. Those people always outsourced, always got shitty code, and never really unified their systems. Cheap people and cheap companies take cheap exits. That's fine. But yes, many fragmented humans who all understand their portion in depth is much better than a bunch of markup files at retaining the knowledge of why things are done, procedurally, the way they are.
Once every six months the CEO calls me and asks me to remind him why our software does something like, idk, create a reverse payment instead of voiding a charge in some situation. Or some other thing he has asked a dozen times before. And I know the answer to why, or I know where to look, because I was in those meetings 5 years ago or because I wrote the code myself and asked the question when I wrote it.
Institutional knowledge is a very large context window, if you need to think of it that way, and LLMs are a shitty compression method for that. They can tell you how, but they don't reason well with why.
> create a reverse payment instead of voiding a charge
I think I know the reason to this one. Maybe. Because the money is already gone! There are no rollbacks in the banking system only "counter"-transactions.
Do you seriously think that "institutional knowledge" can be maintained by simply writing down a few pages on Confluence or whatever system your company uses for internal documentation?
If you're not being sarcastic, you answered your own question. LLMs can absolutely do whatever you tell them to, which rapidly leads to the creation of a dependency if you begin building upon that as an assumption.
I've, for better or for worse, jumped full-on in with LLMs, and I can already feel my abilities radically declining over time. That's fine by me, but if you're a company looking to protect your institutional advantage - just handing over everything to an LLM suddenly means anybody can do exactly what you're doing, literally. At best and this is an absolute best case scenario, you're transferring your advantage to the stewardship of another company.
I can imagine boardrooms everywhere asking LLMs what’s the most successful way to make money, and overnight every company pivots to gambling, tobacco, and porn.
I guess that all depends on how much your company revolves around Minecraft
It can be as similar to or as far from outsourcing as you allow it to be.
The "if AI code breaks, who will fix it? -> "AI will fix it" exchange has (anecdotally) been very common among executives, which is much closer to outsourcing than programming.
No need for the downvotes imho. Can’t speak for u/noduerme but in putting outsourcing (labor, LLM/AI) in the same basket I don’t see a category mistake, but a dry way of looking at business. What are the risks, what are the rewards, what future skills are we at risk of losing (the business logic part) if we go in direction XYZ.
Something completely different (but with the same logic): do you outsource legal, hire your own team of business lawyers or will you let customer services use AI for legal problems (and only hire a lawyer for a day in court)? I think all three solutions are currently active in different firms. From a risk perspective I would always want a lawyer on my team. Insource those learnings. But perspectives vary.
Not sure if you were asking me, but the company I'm CTO of has in-house legal counsel, even though it's not a huge company. Their decision to keep software and also marketing/branding and art direction in-house for the past 20 years has followed the same logic. [which is to say: When they began as a single shop, the first thing they did was bring on legal]. I think it's a smart way of doing things. It's certainly a lot cheaper, and it earns more loyalty and commitment.
Moreover, any of us can run essential questions by each other, the marketing director, the lawyer, the CEO, the GM. And everyone has a stake. So all the creative and business decisions are worked out pretty quickly, and new features roll out with everything vetted and in place.
since when did HN have downvotes?
Its one of the features that is behind a minimum karma requirement.
https://github.com/minimaxir/hacker-news-undocumented/blob/m...
You know… up until your comment I’ve never even considered there are companies right now outsourcing overseas to people who will just vibecode it for a quick buck, my those companies live in interesting times
You’re in finance and you should know better… it’s not about recouping costs.
It’s about pumping up revenues, earnings and eventually cash flows (since they can’t keep raising money constantly) to support a nonsensical valuation.
This is wrong. Token costs for the same model rapidly collapse over a year. Hardware inflation is a thing but not bad enough to outweigh the massive impact of software optimizations.
Tokens billed at API prices are profitable for openAI and anthropic today and it only get more lucrative every month for them as their inference costs fall. If it weren’t for continuous massive training runs taking larger and larger capex, these companies would be massively profitable
> Tokens billed at API prices are profitable for openAI and anthropic today
That's nice for them. What's less nice for them is that big customers are figuring out (some faster, some more slowly) that they don't want to pay API prices, or at least only for a much smaller number of tokens.
The sad part is that you work in FINANCE of all things and this happens there.
Like: What competence do decision makers in FINANCE have, when they are this oblivious to economics?
Look, I share your sentiment. But, I can relate to the C-squad though. Going squarely against the market sentiment is not the way to gain and keep confidence. And everyone is vibing right? So they are probably thinking something like: as long as the spent is < few percent of a years profit, we can always adjust direction in the future and at least we’ve bet the same horse as the rest. “Those penny pinchers in finance (:: me) don’t get the big picture.”
>> Going squarely against the market sentiment is not the way to gain and keep confidence
I don't think this is axiomatic. I usually invest my time, energy and money in things that go squarely against the market's momentary sentiment.
A better question would be how anyone who thinks about economic fundamentals could get a job in finance (or stay in it).
[no offense intended to the parent post, I'm on their side]
oh no! what about all these demos which product management vibe coded?
> Pretty soon they might just ask people to write code themselves.
GPT-10 Human Ultra
Same here. It's insane that big, conservative tech companies would sign contracts saying "allow our engineers to use however much they want, we'll pay the bill later, no matter what it costs". In any other domain my company would insist on prior permission, and soft usage caps, and hard usage caps, and real-time tracking of actual dollar amounts (not just opaque tokens/credits, not just an after-the-fact view on a dashboard).
The AI companies' salespeople must be the greatest geniuses in the history of the world.
This is the first time in history that I have seen the shovel-sellers convince a supermajority of the gold-miners that "amount of gold mined out of the ground" is less of an indicator of profit than "amount of shovels purchased".
We are using this brand new hammer to build everything!
Please stop using this brand new hammer for glassmaking.
> 2 months ago management was showing us scoreboards, praising leaders who used most tokens. Last few weeks, we're getting weekly emails, telling us that whenever we can - we should use cheaper models, and that we should watch the page which shows our tokens usage.
GPT 5.5's double token cost was the threshold for me. These things are getting expensive quickly - the subsidized pricing can't go on forever.
The leader boards based on token usage happened in our org for a month. Then we managed to convince the board that what matters is the reliable software shipped. Now we are back to DORA metrics.
Are corporate employees not allowed to use personal subscriptions?
Sounds like a bad idea in general. Any data use agreements get lost, shadow-IT brews and nobody knows what tools to use, oh and it's against the service terms.
Generally not, since the corporate account has all the privacy knobs turned up. I use my personal account on my open source projects, where code leaks aren’t exactly an issue.
Unless you get written permission you can be sued for publishing their trade secrets. This can end in jail time if your employer is particularly uncaring.
Ymmv, do whatever you want . It's your life.
If you want your company's code to be used in training, then yes.
I pitched this to management as a way to get a very expensive fine tuning job done for free. They're not quite across that line yet but we're getting closer every day.
Apologies for sounding like a billboard, but this is exactly why we built https://flowstate.inc/
Dog! Use it and tell us how it is! Stop with this token maxing moaning. You have access to a new powerful tool.
I'm using local models in Ollama for most things and only use the paid corporate account when my local model gets stuck.
for code as well? which local model are u using for coding
I'm using both qwen3-coder-next and glm-4.7-flash:bf16. I have gemma4 downloaded but haven't used it much.
I’ve switched to a more precise use of agents where I give less autonomy but I have much higher trust the output will be what I expect.
And I do more hand coding to guide the agent to useful patterns I want.
I feel like that’s how I used less capable agents a year ago. But I’m finding even with high quality agents, the slop creeps in.
I want more control. I want to save money. Hence going back to a more 80/20 agent to human LoC split.
> 2 months ago management was showing us scoreboards, praising leaders who used most tokens.
Everyone is insane.
It‘s a collective psychosis. There is not other explanation.
Everyone is falling over themselves out of FOMO.
It’s 100% FOMO from investors, board members and C-levels disconnected from the daily work.
The thing with language models is they are tailor made to fool managerial types into thinking it’s the holy grail.
Just like many managers, the appearance of productivity is all that counts. And LLMs shine at giving the appearance of having solved all of the managers problems, and all they have to do to use it is spend on tokens.
This isn’t to say that LLMs aren’t truly useful, they absolutely are. But they’re very nature is one of simulating intelligence through next word prediction.
The chat modes and models are by their nature supremely attractive to management layers, because they give answers that sound so damn plausible even when they are complete fictions, and uttered with such confidence how could they be anything but the singularity.
interesting, our enterprise account here in Australia doesn't have access to it yet.
I wonder if clues like this will be what are written in history books as the beginning of the bubble bursting.
I don't think that people relying on the tools too much is the first sign I would identify to mean the bubble is popping
The market is priced at expecting AGI levels of breakthroughs. Just a very useful tool for programming is definitely not enough to keep the music playing.
If the market thought AGI was imminent the labs would be worth hundreds of trillions of dollars. AGI is one step away from fully automated economy.
What does "AGI" mean to you such that we have not had it for years already? Whenever I see people mention AGI it's in the future tense, as if we don't have artificial machines that can apply intelligence to general domains.
the thing went from the transformer paper in 2017 to basically doing your job for you in 2026
The problem is that it's too expensive. It can technically do parts of my job for me, but at a variable cost that's higher than mine right now.
Efficiency is an angle of research that's quite active, see quantization, MoE, speculative decoding, QKV cache optimizations, sparse attention, etc
No, the clue is people realizing that relying on the tools costs too damn much. The tools may be fine-ish for what they do, but there might be a bubble built on unrealistically high expectations of revenues.
wow almost as if they needed to incentivise people to use and then tame it down to keep it in sustainable levels. shocking!
For context:
> Additionally, we’re introducing a new ultra mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.
https://openai.com/index/previewing-gpt-5-6-sol/
Can someone explain how this compares with Pro? I thought Pro was already something similar.
For pro mode the agents worked independently and only when they all finished did a new agent take a look at everything to merge the work into a single response. The new thing involves subagents that have been trained to cooperatively pursue a task and are allowed to communicate with each other along the way.
I tried a pro model out the other day and thought there must have been a bug in Pi’s cost calculations. But no, it’s absolutely fucking insane. Wasn’t even any better at the task.
I really suspect that the models are basically the same below, it’s all in the prompt. The way I use them, surgically, they seem to perform about the same. Fable certainly hasn’t blow my socks off.
This is where I think you see the distinction between two classes of LLM users:
1. Managers: those who generally know what needs to be done, and want it done faster, so they provide a lot of instructions and context (where many developers fall)
2. Executives: those who vaguely know the end goal, but are clueless about the process, and are willing to burn resources and cycles on a black box to get the result
Yeah, the bigger models shine when it comes to complexity (making the right decisions regarding choices with second-order effects), ambiguity (esp. common sense) and time horizon (agentic steps and context size).
If your tasks are well defined and don't require a very large number of steps -- e.g. you're asking for small, clearly defined changes to the code -- you're fine with grok-4-fast. (Well, you would be fine if they hadn't killed it.)
I work in both of these modes, and I find that the latter actually benefits from dumber models, because smaller models are faster. The work shifts from async to realtime/interactive. So you can stay alert, keep track of what they're doing and iterate, instead of alt-tabbing, getting a coffee, and then spending extra time resynchronizing your mental model later.
> Fable certainly hasn’t blow my socks off.
Same. I suspect they'll get better at taking in terrible prompts over time though... Maybe that's what Fable does better, reminds me of Sora 2, it would take my crappy prompt and expound upon it. I told it once to generate a video of someone working at some company that changed its name, but the old name had historic relevance, it referred to the new company name without me telling it to, by virtue of me wanting a video of TODAY with a 90s icon.
Where fable has blown me away is converting entire code bases and or refactoring across many different segments.
It’s far more careful than opus and puts far more effort into testing and validating by default.
Switching back to opus at work was a downgrade. Similar requests felt more clunky and needed far more hand holding.
Some of it feels boiled down to "opus works better when told not to be dumb, fable's prompt tells it not to be dumb."
If they know much of what the tool is used for, they can customize prompts to "do that usage right" even if the user doesn't know exactly how to ask for it.
> Fable certainly hasn’t blow my socks off. Same. Its not so much perf increase as cost increase justified by ambiguous perf increase.
Do you have a source for this, or just rumors?
The responses I get from pro don't feel like ensembles. They are often very one directional.
This can be because the summary model just picked the output from one of the sub agents.
oops
The source is the GPT 5.5 System Card:
> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute. As noted below, we separately evaluate GPT-5.5 Pro in certain cases because we judge that the setting could materially impact the relevant risks or appropriate safeguards posture.
https://deploymentsafety.openai.com/gpt-5-5/model-data-and-t...
There have been multiple podcasts with people from OpenAI which have confirmed this.
> makes use of parallel test time compute
Any idea what that means exactly? I vaguely remember that ChatGPT Pro was originally called "deep thought", just like Geminis "deep thought" feature (or "deep think"?), so it seems likely they are using the same approach.
Their methodology isn't published.
Its widely accepted[1] that it runs the same query through the model in parallel and then has a model that either selects the best answer or synthesizes an answer from the multiple ones generated.
I believe most people think it runs 6 sub-models, but I think that is based on the pricing.
It's a pity that OpenAI doesn't publish details like this.
[1]eg https://news.ycombinator.com/item?id=48799977
Basically like passes@6 or passes@5 if you’re doing a benchmark, except for your real tasks.
Pro is quite limited on the web UI I reckon. This approach can be highly effective for reasonably verifiable task, for example, write comprehensive unit tests pointing out a tricky bug, get multiple agents to swarm at it.
It's been very successful at frontier math tasks - a bunch of the Erdos questions have been solved by it - more than any other model.
https://www.erdosproblems.com/
> Basically like passes@6 or passes@5 if you’re doing a benchmark, except for your real tasks.
It's unclear how they would do this when there is no signal that provides an objective ground truth.
I imagine this is something like Anthropic's dynamic workflows where a JS file is created to make a little AI harness on the spot
Wow, I hadn't heard of this!
I asked Claude in the browser if it could do anything like that. It wrote a little frontend app that calls the Anthropic API (with fetch()), without including a key. I expected that to fail, but it worked!Apparently in the web chat (and also in Claude Code?[0] Though I haven't tried yet) they can call the Anthropic API and your subscription key gets auto-magicked into the requests somehow.
Those are two separate things of course (aside from the key-injection) but I guess there's no reason it couldn't run completely in the front-end... hmm...
[0] https://code.claude.com/docs/en/workflows
I feel like it's not crazy to run Javascript in the browser... We've come so far I almost forgot where it all started.
Per latest available data, point of origin traces back approximately 13.8 billion years to a singular event designated "Big Bang". YMMV
> In the beginning, the Universe was created. This had made many people very angry and has been widely regarded as a bad move.
My understanding is that in the generated JS code, the subagents are invoked as headless Claude, equivalent to Claude -p, or the agents SDK.
How is this any different than what we have already? We've had this ability for ages (6+ months, decades in the AI world), you can literally today easily prompt CC or Codex to use subagents to accomplish tasks and they'll do it well. My entire workflow is one top level orchestrator chat creating tickets to dispatch to subagents to implement, and other subagents to verify. Why is this being sold as a new thing? Have HN users never tried tried asking CC or Codex to use subagents?
The top comment on the thread explains this will involve subagent to subagent comms.
To what effect I don’t know… I thought subagents were useful because they were explicitly single purpose and bound to a narrow context
I'd love to read more about how to use this workflow. What kind of top level instructions does this actually work with? Is there an article out there with some concrete examples of how to do this effectively?
in opencode, if you directly referencee a subagent keyword or the name of a defined agent, it'll often spawn the agent.
If you don't mention it directly, it's 50/50 whether any given request will invoke a subagent.
The same with tools, skills, etc. No matter how smart these LLMs appear, they rarely do thinking as you expect.
So basically: learn how the harnesses operate, and know the names of the tools they have.
I assume this is ~equivalent to ultracode in Claude Code, which can deploy a tree of hundreds of nested subagents and was just released experimentally 5 weeks ago IIRC.
Because most people need complexity to be wrapped in a simple UI/UX. Most people just want the one-two button press and be on their way.
Pro also makes you ask it to use sub-agents instead of just doing it when useful.
Hopefully, 5.6 will automatically spawn sub-agents without needing to ask.
i would believe this will be matched with something like orchestrator-focused model: https://news.ycombinator.com/item?id=48624782
I wonder if it's related that that OpenAI has found a way to cut inference costs by half, according to The Information.
https://www.theinformation.com/newsletters/ai-agenda/openai-...
https://archive.ph/NEwVz
"However, these inference optimizations, which rival Anthropic refers to as “compute multipliers,” are a big focus for all the labs. Anthropic CEO Dario Amodei has been publicly talking about the concept since at least mid-2023, when he said on a podcast that the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them. (Compute multipliers can also refer to efficiency optimizations in the model-training phase.)"
Yes, on a world with finite resources where your industry is singlehandedly siphoning ALL THE RESOURCES - hoard general efficiency optimizations and treat them as trade secrets - winning is all that matters, normal people and other species and the planet be damned.
Everything I hear about Dario these days makes me like him less and less. He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path! I guess all the cycles are compressing as we approach the singularity..
It gives them a massive advantage because they can cut the cost per token by a lot and eat Anthropic's market share.
In what universe is any company going to give that advantage away?
In any case if they take away a lot of market share it's basically the same in the end - most people will be using these optimisations.
Deepseek gave it away with R1. They did it again with V4 and DSpark. You don't use it because it's Chinese
I'm not American so I don't really care if it's Chinese or not.
But at work I can only use the approved Enterprise Plans we have and we only have those with Anthropic and OpenAI.
The shift may come when your blessed cloud platform provides competitive enterprise-ready hosting of the oss models
Companies and people worldwide just use whatever Americans use, because they read English but can't write it. Americans don't use Chinese models because they're Chinese.
It's available from opencode (non-chinese hosting), additionally deepseek v4-flash is available with rate limits of course, for free, there.
Not sure I know where I fall regarding your point: Yes to trade secrets, but also science and AI should be for the good of all.
OpenAI seems to be trading roles back with Anthropic becoming misanthropic. I hope they both start heading in the direction of how the AI field was prior to LLMs.
Collaboration and benefit for all should always be the primary motivator.
> Collaboration and benefit for all should always be the primary motivator.
Of all the things to never happen, this is never going to happen the most.
That train left the station for good once hundreds of billions to trillions of dollars were involved.
On the bright side, in the long run I suspect the vast majority of the value of AI will not be captured by the model making labs and the vast investments in them are going to implode, so...
implode, how?
They're in a price war with the People's Republic of China running flat out with the full backing of a government that literally does not care if they ever see a financial return on the investment, they just want to drive the value of LLM training and inference to zero because we banked the market on it being arbitrarily high margin forever. China was like hold my beer.
They have a staggering surplus of grid capacity and can bring more online without any difficulty. We couldn't get a serious nuclear project done if Jeffrey Epstein was offering private flights to the ribbon cutting.
In the United States at any given time more than half of the FLOPs are badly misallocated, Meta has like, a double digit percentage of the total capacity going down the drain every day and has for years. That's a conspicuous example but on OpenRouter rankings it's rare to see more than one or two American vendors in the top 10, sometimes the top 20. But 3rd, 4th, and 5th place are all merrily burning half the compute duplicating effort and missing key innovations because we stopped publishing real results. In China if DeepSeek makes a breakthrough it's at Zhupai and Moonshot and MiniMax and MiMo and Qwen that week.
Our only lever, export restrictions, seems to do nothing but breed multiply antibiotic resistant super hackers who just get more efficient and immediately propagate all of those efficiencies to the rest of the Chinese AI industry.
At the beginning of 2026 there was one Chinese lab with a model that had any real relevance fielding modern tool users. Today in July there are like, eight lagging the absolute frontier by maybe 3-6 months. Barring some massive bend in some curve 3-4 of the top 5 and 6-8 of the top 10 will be Chinese and open weight by January.
The great irony in all of this is that our current playbook is straight out of the 1960s USSR, and the PRC's current playbook is straight out of 1960s USA. We're the ones with the opaque decision making and gross resource misallocation driven by the personal agendas of a shadowy cabal of frenemies wired back channel into government in the form of the individuals rather than the offices. They're the ones with a thriving marketplace of ideas powered by robust public/private partnership and a paved path running bidirectionally to the university system.
It's going to implode because the Kruschev system does. Theirs is going to thrive because the Kennedy system puts a man on the moon before the decade is out.
>full backing of a government that literally does not care if they ever see a financial return on the investment
There's no evidence of this, the parsimonious explanation is PRC AI, by virtue of being sanctioned, simply is not able to run magnitude more expensive compute model, and even if they could, they don't have the $$$ or market cap to do so. So they optimize and involute margins like they do in everything, and US misallocated expensive flops because the entire industry has been financially engineered for phat margins along the entire producer supply chain is just cherry on cake. Like wipe out the 50%+ margins from toolmakers, fabs, gpu/memory/data center components to some reasonable level and US is overpaying for tokens by a stupid multiplier on top of actual compute misallocation due to incompetent infra. Maybe PRC AI has unsound economics, but it's structurally simply not able to misallocate as much as US who will find a way to financialize compute to point of absurdity.
The US can just ban Chinese weights being used in US companies.
There's no need for a ban. There's already no use at all of Chinese models in the US simply because they're Chinese.
That's false, there are many companies using Chinese models in the US, Airbnb for example.
https://www.bloomberg.com/news/articles/2026-05-20/airbnb-s-...
If the Trump administration decides to annoint Altman and Amodei in defiance of market forces it will rapidly discover that it no longer has the sovereign bond auction pricing power to prop them up. This isn't 1998: the Treasury has taken five major body blows in the last 25 years, the world's energy markets, maritime insurance regimes, electronic payments rails, and moral authority in places like the UN Security Council are effectively bifurcated. Oil and money and data and people just flow around the United States now. Canada's sovereign debt yields tank harder when the market is spooked, that's the North American flight to quality.
The administration could probably put some serious friction on open weight model use in the Fortune 500 for a little while, but the opposition never got such a gift right before a squeaker midterm. And outside of major enterprises with puckered ass compliance departments? Not a chance. It's popular around here to forget Uber and AirBnB and yes, OpenAI and Anthropic all got their start flagrantly breaking the law and grew lawyers and lobbyists faster than anyone could enforce it. And this time everyone from the DNC to the EFF would be holding hands wearing "Save The Models" t-shirts. Not even NVIDIA is remotely pretending they're anything but all in on GLM 5.2, they had an NVFP4 quant up by the time most people read the blog post.
And the Trump Administration isn't exactly enamored of Comrade Amodei at the moment, being as they're appealing the lawsuit Anthropic brought against the Pentagon during a shooting war.
Forcing the American proprietary AI megalab financing event was our fiscal Ukraine Special Military Operation, the market is calling the bluff and neither the capital markets nor the Federal Reserve has the dry powder to absorb this one.
The Treasury auctions will flat not clear in an orderly way. We can't raise 2-4 trillion dollars on a dime in 2026 and if CoreWeave turns out, as many suspect, to be Patient Zero? It would be that big a hole.
We play by the same rules as everyone else now. I hope we regard it as being worth it, but I fear we will not.
NVIDIA appears to play on several parallel paths that may compete with each other.
On one hand it appears to cooperate with OpenAI and Anthropic, as big customers.
On the other hand NVIDIA cooperates with Palantir, providing the HW for its "Sovereign AI OS" (a turnkey system including HW and SW for local inference and post-training/fine tuning) which uses the slogan "The future of AI is on-prem" (i.e. not as a customer of OpenAI or Anthropic, but using an open-weights LLM, e.g. a fine-tuned NVIDIA Nemotron or a Chinese LLM).
Presumably with the goal of promoting their competing solution, Alex Karp (Palantir CEO) has delivered a few weeks ago a very harsh criticism of Anthropic and OpenAI (who allegedly inflate the token consumption and they might also steal the data of their customers, which must be sent to them).
So NVIDIA both cooperates and competes with OpenAI and Anthropic.
It is in OpenAI, Anthropic, and the US Gov's best interest to slow China down and ban Chinese models. Literally none of what you wrote prevents them from doing so.
Once China starts to get scary, Commerce will export control GPUs and declare Chinese models "foreign munitions." Any nation doing business with the US will not be allowed to use these models either, and that will be the end of that.
It is just not in the US's interests to fund China in the race to AGI.
I thought they already export controlled all GPUs above RTX. 5090 in power
The assertion that it's in the United States Government's best interests ban Chinese open weight models is a very strong opinion that is not a consensus even at the fringe of Thiel-adjacent psycho thought: Alex Karp is on record about open weight models being necessary, the fucking "we bombed a bunch of kids with Claude doing rubber stamp target selection" guy. He thinks "trust OpenAI and Anthropic" is a radical position.
Peter Hegseth, another really pro-America being powerful guy, he's dealing with a lawsuit because he doesn't want Anthropic in his military, he calls it a supply chain risk (he's right).
There is no evidence of any kind that a complex attack vector can be trained into model weights and survive all the crazy slicing and dicing that happens between published weights and running model. These things get quantized and run on mathematically imprecise kernels and sampled and LoRA-tuned and Dolphin/Orca de-tuned. Go look at what the ComfyUI community comes up with, those guys know more about WAN 2.2 than the people who trained it. Because those models run for real on a desktop, so there's mad innovation at light speed.
There is no one who wants a capriciously expensive black box run by extremely creepy people, not once the capability crosses over (in about November).
But don't take my word for it, you just had a chance at one AI IPO, and I'm sure you'll get another, so if you like how that goes, you don't need to convince me!
Again, none of what you're saying will make the US government, especially the current administration, care, as they have not cared about many things with consequences either; you're applying logic where there may not be any heed to it.
As well, it is a false equivalence to say that local models are only Chinese and otherwise we would use cloud models, but there are American or European ones, so a ban would simply force companies to use these, even if they are inferior to Chinese ones. It's simply a matter of national security to the US government, and they will not care what random people in media say.
I run local models every day.
We are in a race to superintelligence. The first country to AGI will be the first to superintelligence, and the first to superintelligence will have de facto control over the world and the future of humanity. They will also be able to prevent others from reaching superintelligence.
Of course it's in the US's best interest to slow down China. You aren't zooming out and looking at the big picture, you're taking models as slightly useful tool, not what they will soon turn into.
> They're in a price war with the People's Republic of China running flat out with the full backing of a government that literally does not care
There is nothing to support this. You get cheap Deepseek tokens by foreign providers too.
It is the same thing with automakers. They complain about not being able to only make luxuary cars with high profits with BYD raining on their parade and blaming the Chinese gov.
i really hope it's just what Deepseek V4 does. Deepseek V4 is very cheap and highly performant
OpenAI tried to pull off the same trade secret thing with RL when they announced o1 and o3, aka "Compute time scaling". Then Deepseek revealed it with Deepseek R1.
Could also be something like Deepseek DSpark. Or using diffusion like DiffusionGemma as a draft model. The timing between the release of those, and this article, makes me think its maybe one or both of those things
deep down, i suspect they're all just drafting on implementations to llamacpp.
And Anthropic sure reads and applies all the open research.
This 2023 thread about this issue is prescient: https://old.reddit.com/r/MachineLearning/comments/11sboh1/d_... (just add Anthropic to OpenAI)
Ok I’m not sure I follow your point here. Isn’t all that he’s saying that if they find some optimization techniques, that gives them an edge? And that makes sense?
How is this suddenly evidence of him being a villain?
The evidence is that unlike Deepseek he does not publish his compute multipliers. Under that argument Deepseek should not publish any of their research either.
DeepSeek is acting in accordance with their incentives, just like Anthropic is
But Deepseek has a very different way of operating their business as the underdog. They also publish their models as open weight, which Anthropic also doesn’t do.
I don’t think this makes Anthropic a villain?
It has got to be one of the most insane takes I've read on HN, which to be fair has been trending towards "unhinged" when it comes to Anthropic and AI safety.
Compute multipliers are like a quant firm's trading algorithms. They're the crown jewels, the whole alpha of the lab. If you leak them, the lab dies.
Protecting them does not make Dario a villain, it's literally his job. It's also Sam's job, Denis's job, Mira's job, etc. Every lab guards these multipliers closely because they represent the entire worth of the lab.
So how can Deepseek publish them without killing themselves?
Because they are a state-owned enterprise tasked with destroying the aforementioned alpha of private American labs...?
oh here we go again saying everything in china is done by the state. it's not a democracy but they're not the soviet union either. if that were true, why don't they have just one model initiative instead of several?
Oh yeah that's a good point. In that case, this Chinese hedge fund is choosing to sink billions of dollars in R&D alongside vast opportunity cost in order to create models and release them for free because umm... they're just that nice!
No wonder you're confused about DeepSeek when you have a fairly obvious explanation provided to you, and your response is "it's unrealistic to think the Chinese Communist Party is behaving like communists."
> if that were true, why don't they have just one model initiative instead of several?
Because value exists at several layers of the product hierarchy? I.e. for the exact same reason that the for-profit labs don't have just one model initiative?
Their alpha has been discovered by the other labs. If they took the lead they obviously would not give that up freely.
The fact that they don’t release model weights for free to download on huggingface means that Sam, Mira, Dario, etc are ontologically evil and may they all reincarnate as either durian fruits or cockroaches, ideally as durian fruit infested with cockroaches…
Is this a serious take or sarcasm?
Sometimes you just gonna channel your inner Karp
> the company limits “the number of people who are aware of a given compute multiplier” because it could give other AI labs a leg up if they were to be able to replicate them.
I wonder if that makes sense if the orgs within the industry are starting to shift their mindset towards "Tokens are expensive, we should use AI less." which feels like an existential threat to the status quo, if those AI providers can't find ways to keep costs affordable for their clients. Otherwise those orgs would just be using GLM 5.2 or DeepSeek V4 Pro but it seems like what they're doing instead is trying to use AI just less, period.
I agree that Dario is pretty annoying, but I think the "tech villain" archetype is essentially survivorship bias. The tech leaders who don't act that way are not nearly as visible because they're not nearly as successful.
How are the Chinese doing it then? It's not a zero sum game is it?
HN is just a massive Anthropic hate fest now, probably funded/manipulated by OAI's $8B PR budget.
OP phrases it as a bad thing that Dario is keeping compute multipliers to Anthropic. How naive can one be? Compute multipliers are the whole business. Those are the trade secrets every lab is built on. It is the alpha of the business. How does protecting this make Dario evil?
This website is getting out of hand with the uninformed hot takes. I wish when HN was still people that knew what they were talking about.
I'm saying I would do the same thing if I were Dario. I don't think he's evil. I just think his hero complex is annoying.
Hey! I'm an on-again-off-again Anthropic hater and I may be guilty of uninformed hot takes but I'm not paid for by OpenAI[0]
People have different opinions than you, it happens..
[0] @sama if you're reading this we can fix that...
I'm not as conspiratorial as you, but it does seem the tide of opinion here is turning against Amodei, for no particularly obvious external reason. At the same time, there does seem to be at least some evidence of adversarial attempts to oppose data-centers by America's competitors.
At my company we are pretty certain that a lot of datacenter opposition is being instrumented and funded by the PRC.
AI is a bid for control of all humanity. The first to superintelligence owns our future. I'd say it's okay to be a bit suspicious or conspiratorial of anti-AI/anti-lab narratives.
> Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken. If you're worried about abuse, email hn@ycombinator.com and we'll look at the data.
https://news.ycombinator.com/newsguidelines.html
Amusing, this is the first time I've seen someone get flagged for quoting the guidelines.
Dario tells the truth. If you look at everything through their safe AGI mission it all makes sense. They are not bs'ing about that. Also I think most people just read headlines or 10 second clips and make false extrapolations from there.
(BTW Anthropic only exists because Sam Altman is a liar, Dario admitted this.)
> If you look at everything through their safe AGI mission it all makes sense.
Except for, you know, all the outside investors and the forthcoming IPO.
A no-investment policy would take them off the scene entirely. Essentially handing over the reins to OpenAI, Google, and others. Their position is something close to "if I don't do it, someone worse will".
Related: https://80000hours.org/2012/03/the-replaceability-effect-wor...
There's a more nuanced discussion that could be had about how to balance relevance with outside influence. But at a foundational level it should be acknowledged that the tradeoff exists, and that receiving outside investment can't alone be seen as evidence of corruption.
Besides that, there's more that can be said about other things like their corporate structure or the degree to which they accelerated the AI race.
"if I don't do it, someone worse will"
Of course that's what Dario thinks because that's what every tech CEO thinks. Dario, Sam, Sundar, probably many Chinese CEOs as well. It's what everyone thinks. That's why they're competing so fiercely with one another. That's why they basically make all the same decisions. That's why we need properly open source AI.
It's hard to be certain what each individual thinks. We can do our best to judge based on what they each say and do. And there are significant differences in what each of these individuals have chosen to say and do over the years. The info available to the public makes it seem a lot like Dario's motivations & priorities differ from those of Sam and others.
This doesn't seem like the right place to spend my time litigating that point to its fullest extent (no-one here is doing that). But there's plenty of relevant info surrounding eg.:
* The New Yorker article on Altman [1]
* The story behind Anthropic's founding
* Various efforts to influence government policy (a16z policies and contributors [2], Trump's inauguration donors [3], giving Trump credit for AI infrastructure [4], Dario's op-eds [5])
1: https://www.newyorker.com/magazine/2026/04/13/sam-altman-may...
2: https://a16z.com/portfolio/
3: https://www.opensecrets.org/trump/2025-inauguration-donors
4: https://www.youtube.com/watch?v=pe11mJ8mCHU
5: https://darioamodei.com/
What they think is irrelevant. What they do is
Open source AI fails first contact with sufficiently-intelligent-as-to-be-dangerous AI.
The day Mythos class models are open sourced will not be a good day. I don't think you understand the impact that will have on the world and on cyber defenders everywhere. It will be pure chaos.
Even if you don't think Mythos-class is the bar, open source has to stop at some point, you don't hand everyone a superweapon.
Every single one of those sentences is highly dubious. Cyber-defenders would be pretty jazzed about having easier access to Mythos-class models. Cyber-defense is easier with better tools.
Cyber defenders already have access to Mythos class models at most companies with the biggest user facing products and services. They are using them extensively (including on OSS deps)
Handing every skiddie and nation state and APT and hacker group access to Mythos does not help cyber defenders
Even if you don't think Mythos is a big deal: At a certain point models become smart enough as to be dangerous, and you don't give everyone a superweapon. Open source has an end of the line sooner or later.
Oh remember when the same was said about GPT-2? It will actually force cyber security to be taken seriously instead of just bureaucracy
The main risks called out with GPT 2 were misinformation and they were right about all of it. Every risk they called out came to pass.
This line of thinking is ontologically evil, life denying, and it and its believers should be rejected and fought with extreme prejudice.
I'm sorry, you're right! What was I even thinking? Of course we should hand every man, woman, and child a mini nuke!
You expect AGI to be built without additional investor money?
> He sure did seem to speed run the 'tech leader with scruples' to 'tech villain' path!
What kind of rosy-eyed chump believes in the "tech leader with scruples" bullshit? It always lies.
Did some people just ignore Mark Zuckerberg and Tim Cook's sociopathy, somehow? Did anyone buy into their "privacy is a human right" nonsense?
Zuckerberg never had scruples, and everyone knew that from the start.
I thought this stuff was common knowledge. I didn't expect to make people angry by insinuating that Nadella, Cook and Pichai are also socipaths, but I guess we have a bit further to fall before people learn the lesson.
I find this kind of cynicism fascinating tbh. On the one hand, it seems so relatable in some ways, because there is something uncomfortable about being seen as naive, in a way that being seen as cynical or negative doesn't seem to carry. I guess it's just self-protective, almost like some kind of perverse Pascal's wager: it's better to think everyone is horrible and be wrong than to think the opposite and be taken advantage of?
The thing I can't quite square is that it doesn't really fit my lived experience. I have known sincere, genuine people in the types of positions that I'm sure someone like you would declare to be sociopathic.
But beyond that, I just don't know why it would actually be true that everyone at the top is a villain. Why couldn't someone like Dario (or even Altman, gasp) be sincere? Because if he is, it does seem like a lot of the moves he's made would make sense given his worldview.
But if you assume he's just a villain, then you can twist any of those moves to just be further evidence of that which you already believe.
I don't know, I just find cynicism interesting, and a little sad.
> But if you assume he's just a villain
You don't have to assume anything. A true "good guy" doesn't openly say that he's fine with autonomous, AI-powered weapons being used against me, and mass surveillance applied to me and my family just because I don't live in the US. A true "good guy" doesn't say "privacy is a human right", and then immediately (and completely) bend the knee to an authoritarian government on this issue.
OpenAI's agreement with the Pentagon was "No use of OpenAI technology to direct autonomous weapons systems".
And about the mass surveillance, I don't see why the military should not use AI to do surveillance abroad.
Maybe because it will make people abroad like you less and that has flow-on effects, mostly economic.
I think that applies to military involvement abroad generally.
If you are dropping bombs on someone I'm unconvinced the use of AI will make them like you more or less.
For sure, I am assuming they spy on a lot more people than they drop bombs on.
I remember a long time ago it came out that the US had been doing mass spying on the Danish people, my dad was very upset about it and disliked the US for the rest of his life. Of course the only thing he did about it was not watch American movies anymore or visit the US.
Anyway, I assume it will be a case of a million little paper cuts, each thing putting off a group of people until someday it adds up to real meaningful economic impact.
It stops being interesting, or even sad, after a while. People get stuck in all kinds of places, mentally. Some get unstuck eventually. It’s only sad if you have come to a counter factual belief that it could have gone better.
I went in the opposite direction - how far can I push myself to see multiple facets of a story? That is a wild ride, and it gets progressively more wild.
> how far can I push myself to see multiple facets of a story?
Please, I'm dying to hear the optimist's take on Mark Zuckerberg's career. It wouldn't happen to be embarassingly foolish, would it?
It's a lot easier to sound smart on the internet if you're a bitter cynic.
Lots of nerds for some reason have made cynicism a personality trait. They think optimism/honesty is hopelessly naive, therefor cynicism is the correct default.
> Lots of nerds for some reason have made cynicism a personality trait. They think optimism/honesty is hopelessly naive, therefor cynicism is the correct default.
It is the result of experience. Working with and creating systems (even embarrassingly simple ones), then seeing them fail in a myriad of ways more often than succeeding, colors your expectations about throwing humans into the mix.
Children learn to lie as part of their natural development, but do not always externalize that until faced with media (Airheads candy commercial or equivalent). Either way, honesty is expected as a default for utility and not an expectation in leveraging goals.
For these particular characters, the evidence is heavily against your panglossian take.
All have collaborated with the current US regime. All have shown signs of being quite willing to compromise their principles in order to make money.
IDK, only one company has held their two red lines in open conflict with the government, while all the others capitulated to "all lawful use."
> I just don't know why it would actually be true that everyone at the top is a villain
History.
Also, nobody said 'everyone' or 'villain'. How Paul Graham of you.
simple math. Money == Power.
Power corrupts, absolute power corrupts absolutely.
Given enough money and increasingly perverse incentives to gain even more has a very high potential to corrupt.
Did they start out as corrupt, or was it the influence of the power that came with the obscene amount of money?
It's really a chicken and egg level of calculus.
Doesn't matter which came first, either way you get feathers and chickenshit all over the yard.
Do some very rich people still seem very nice in person? Sure. Of course they must, because otherwise no one would willingly work for or with them. As the total amount of money goes up, the incentives to remain 'seemingly nice' go down and either you get to see who they really are, or who they became through the choices to make that much more money. Doesn't matter which is true.
The examples of non-villainous billionare are rare.
Of non-villainous multi-billionaire; lets see there's about eight of them that stand out for giving significant amount of the massive wealth to helping the world around them, who live normal lives like the people in the communities where they reside, and who participate at the companies they own by eating in the company cafeteria among the people who earn the wealth they enjoy.
Thats 8/3400 global billionaires ... about a quarter of a percent.
And of the 'pledges' like Giving Pledge by the billionaire class, the actual amount delivered - not parked in a family or private trusts for tax deductions; but actually delivered to the front lines of any global crisis amounts to 0.18%, less than one fifth of one percent of the $20.1 TRILLION dollars held by that class of owners. thats less than $2.00 on every $1000.
That's not to say that donating to public needs is 1:1 for non-heinous behavior, but it seems like a basic tool for distinction. the 'can make a significant difference in global suffering : chooses not to' ratio as a surrogate for villain may be useful metric and doesn't require cynicism as the underlying rationale for calling someone's behavior as unkind in general or mean in particular.
It's the Tragedy of the Commons. There will always be a vacuum of predatory bullshit that can be filled, and the victor is always the biggest sociopath. Rockefeller, Cecil Rhodes, Elon Musk, it's the same traceable pattern all the way back through history. It's not that everyone is like this, but that a few crafty marketeers are able to ruin it for everyone.
Why should I treat Sam and Dario with special white gloves? Are they different, this time? They have peers in China that do the same research and actually release it to the public. They let you run the production weights on your own machine. Am I a cynic, for comparing these CEOs to their populist superiors? Am I stupid for assuming their hostility when they refuse to give us the benefit of the doubt?
I'll believe their actual altruism when I see it. Both are seeped in "boy genius" puffery and lie out their ass. If this is the future of intelligent innovation, then America is truly declining.
The only reason Chinese labs release their research is because they are behind. They lose nothing by doing so because the alpha has already been discovered.
This is not hard to understand. Do you really think DeepSeek would publish their algorithms if they led the American companies? Lmao.
do you use american models at work because you choose to or because it's what your employer gives you? is it due to compliance with the american government? two different reasons. most people just use whatever they are given at work.
Except this is clearly not true. Opus-tier models like GLM 5.2 have their weights freely available.
Even when they're reaching parity with American models, Z.ai, Qwen and Deepseek are upholding their end of the bargain. I'd criticize them too, if they were due any scrutiny.
I use GLM 5.2 daily. Calling it Opus tier is a bit of a misnomer. It is maybe comparable to Sonnet 5, definitely not Opus 4.8.
It is a great model for the price, but it has much worse autonomy and long context performance, and I don't trust it for anything beyond personal projects, whereas I use Opus/Fable and GPT for work.
Keep in mind, these tech leaders have deluded themselves with the infinite jest of "effective altrusim" which can effective ignore any problem today for some imagined future problem that they're solving. So if they have to enslave the human race because there's a super killer asteroid 10 million lightyears away heading towards us, they'll do it, because obviously, saving future humanity is the only thing that makes sense (because only their genius can save the day)!
Side note for other readers: Parent is describing a reductio ad absurdum of utilitarianism, which is a distinct (but sometimes overlapping) worldview from effective altruism.
The primary effective altruism cause areas are extremely acute and high-scale problems like malaria, vaccine distribution, and factory farming
I wonder if AI labs are actively manipulating the narrative (and thus investor sentiment) by airing problems, and then solving them weeks to months later. I wouldn't be surprised if they have a lot of stuff figured out that is not included in the current version, just to make a steady product cycle with years of tangible improvements from one version to another (this is a common practice in the industry).
For example, if inference isn't too expensive, but they figure out how to cut costs, then price goes down. After all, why pay OpenAI when a smaller datacenter can give you similar models?
But, if they make a huge issue about how inference is too expensive, they engineer a crisis of their own creation - then, once they deploy the solution (which they might already have), then they're back on top.
Semi-related, has anyone noticed their GPT 5.5 usage in Codex being cut in half as of a couple days ago? I got a lot more mileage out of my session usage yesterday for the same workload.
I've noticed less quota and 5.5 intelligence degrading. I didn't run the analysis like the post the other day, but I had noticed decreasing ability to complete tasks, more laziness. Switched back to 5.4 and it's much better. Maybe they're getting ready to launch 5.6?
https://github.com/openai/codex/issues/30364
What’s the technique? And did they buy it from thinking machines?
Maybe cache similar answers from others. Surprised if this is not already being done.
They're already doing this under the name "fast answers"
[1]: https://www.reddit.com/r/OpenAI/comments/1stsxvc/new_feature...
Like google search, this does not work because of how common long tail use is.
What you think could be a big chunk, is more likely to be a fraction of a percent of queries.
And what use is similar query caching - so you (very often! if actually cost effective, maybe half the time) get a response to a query that was different from yours. Including for when you have a lot of context input already. You’re going to get trash.
If it were constrained to only very common initial prompts, and somehow the long tail did not actually dominate as it does with Google search (can't find the reference at the moment but it was a famous article some years ago), it also wouldn't account for serious enough cost savings. Long context is what is expensive.
This might only work in constrained domains like customer service where there’s tolerance for generic answers and escalation paths. For technical work? For general purpose use, with secretly canned responses charged at full price?
But there must be a ton of generic questions that people ask. Stuff like "What's the capital of country X?" - it's probably at least 10% of queries. Memories, custom instructions etc would invalidate them, but if you can return the answers basically free it's probably worth it.
Questions like that cost a tiny fraction of a cent. "What's the capital of Sri Lanka?" cost a fifth of a cent at GPT 5.5 API price, and would cost a fraction of that if the question were routed to a more suitable, cheaper model. The output was 78 tokens.
By contrast, when coding, devs typically have hundreds of thousands of tokens in the context window, and may use many millions of input tokens per day.
Caching requires the full prefix to match exactly. If a single word differs near the beginning of the prompt, nothing after that can share the cache. So this type of caching would save a few queries that cost virtually nothing, but wouldn't help with the stuff where cost matters.
How is that cheaper? You now need to have a database of millions of possibly gigabyte sized rows. Also, transformers have quadratic complexity, so short queries cost practically nothing.
The only optimization that makes sense is per user prefix caching, because you are often sending the same system prompt over and over again or are continuing a conversation.
I would be very surprised if they hadn’t sorted out some form of shared KV caching
I wouldn't
people really dont understand how the transformer works to think this is something trivial if possible at all
Orly?
“ Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.”
https://docs.vllm.ai/en/latest/features/automatic_prefix_cac...
This thread is about cutting costs in half for GPT across the board.
The technique you linked only makes a substantial difference for particular use cases where you are going to have many LONG CONTEXT queries with the same prefix. For instance, when having a set of documents that commonly get loaded in as context. It's a way for application developers to keep prefixes they manage (or prefixes managed by some set of their users) cached. It has no relevance for long tail general purpose use.
Please pardon the pure speculation incoming. Yes, caching the answer doesn't seem useful. Caching the progression, the graph, may be. This is similar to making code changes with ed(1) instead of editing in vi.
The transform script(s) are cached and can be played back or adjusted. Surely for some breadth of question inputs, they map more often to similar answers--but not static answers; instead, evented edits.
It's nearly untenable for a human to keep private edit scripts to generate code changes. The extra steps for custom regex, essentially one-offs for a shared codebase, is inefficient. But maybe not to an LLM.
I don't understand how this fits LLM architecture at all
This is such a sad state of the industry that a reply to a tweet is now considered like an announcement by the company.
No context, nothing, just a title and a random link to a tweet, which has a seemingly relevant response from someone who works at OpenAI I guess.
Hope this forces Anthropic to be less stingy with Fable.
Hopefully they aren't, and their business dies. They're not a good company.
Not including their best model in a max subscription would otherwise be truly a good reason for once to consider going back to openai for me. I'll at least try it.
At least Anthropic have a max subscription for corporate. Codex is only pay-as-you-go pricing beyond the base plan. Hence I'm stuck with Opus for work for the foreseeable future.
Apparently not? At least our it guy said after 150 people or so you have to pay for enterprise which is pay per token for everyone.
> Codex is only pay-as-you-go pricing beyond the base plan
I don't think so, I'm on a ~$200 subscription (guessing that counts as way beyond "base plan") and have no pay-as-you-go pricing at all, using the OpenAI APIs would be way too expensive for me.
For corporate *
You can't use sub pricing with orgs w/ OpenAI.
It is not because they want to but because they literally don't have the capacity to.
Why haven’t we seen any queues or the like over the past week then? If it’s truly a capacity limitation why not just boot subscription users to a lower priority queue or limit usage to outside peak hours?
There could be a whole host of reasons. It may be during launch that compute is re-allocated from training to inference so that all users can try Fable. Soon that compute will re-allocate back to training until they can get more compute.
What a suprise, to make better models, they just making them larger and larger, then they can barely run it, after a round of LARP-ing that they invented some dangerous LLM?
I thought that was solved after the SpaceX deal? Claude is rarely down since that and they have a 1.5x usage promo until 13th July.
Recently, I've been so eager to get new model releases in Codex. I'm hooked. I hope this accelerates development. Shows how dependant I have become to Codex.
Has anyone already tried 5.6 Sol in their day to day coding/development activities?
How does it compare to GPT-5.5?
Has it been released yet? I'm not seeing GPT 5.6 anywhere in the selectors, nor any announcement about it, but you're talking about it as it's already been released?
There's something like 20 companies with access.
Isn't it like 99% sure all those companies got that access to it together with NDAs and other goodies?
Some of them will publicly break it for whatever reason, an NVIDIA engineer (@blelbach) recently made some posts on X about his results using 5.6 Sol Ultra to optimize stuff and then deleted them shortly after (presumably got yelled at).
the naming convention has reached the point where i am pretty sure the next one will just be called "GPT: The Reckoning"
I’m really looking forward to see non contaminated benchmarks. In this space its obvious that every day is just another day of a race. Cross fingers we get better opus for lower price
Competition is still needed to allow us users to make better use of these good models.
Will it have similar limited access like Fable? It is an interesting timeline, as general access for Fable (without using extra credits) is coming to an end :(
It is like with drug dealers, you get some free dose, then cheap one, then you pay an arm and a leg.
Somebody has to finaly pay for these heaps of accelerator hardware.
The full conversation https://xcancel.com/haider1/status/2073695124220006575#m
Here's the correct link, not sure what happened with that first one https://xcancel.com/thsottiaux/status/2073933490513752151
Are people excited about the capabilities of 5.6 over competing LLMs?
I still don't know why OpenAI doesn't put gpt-5.5-pro in Codex. It's one hell of a model and easily parallels Fable/Mythos. Sure, it'll use up your quota much faster but that's the price some users are willing to pay for absolutely high quality responses.
I think gpt-5.5-pro runs 12x parallel gpt-5.5 agents behind the scene and uses OpenAI's secret sauce to synthesize their answers into one insanely good response.
API pricing ends up being something like 20x more expensive for GPT 5.5 Pro than GPT 5.5 for actual work, even though the token cost is "only" 6x. On benchmarks where I've run both, I saw $1.12 mean per task with 5.5 and nearly $23 per task with 5.5 Pro, I guess it chews longer and harder on the problem.
If that's at all reflective of what it costs them to run it, I imagine they're in the same boat as Anthropic with Fable; they probably can't afford to offer it at subscription prices given current cost to operate it.
If 5.6 Sol Ultra has efficiency improvements (at one or more layers), and it allows OpenAI to offer a model that's competitive with Fable on the subscription plans, I'll guess a lot of folks will switch.
Fable is notably better than what came before. I watched it figure out stuff on its own over and over, on extremely hard problems, that I previously needed to guide a model to an understanding about, or work with them back and forth for several turns to figure it out together. Like, I've been reverse engineering a hardware device lately, and I've tried to tackle it a few times in the past with both some version of GPT and a couple of versions of Opus (most recently 4.7). In all cases, I barely made progress...would have gotten there eventually, probably, as I'm stubborn, but there were roadblocks constantly, with me and the models getting stumped and going around in circles in the end on every prior attempts.
Fable figured out other ways to find out what's happening, it dug into config files, found and extracted Boost-serialized data, compared that data to the observed behavior, built tools to compare the observed data with our emulated behavior, without being prompted. Would I have gotten there? Eventually, maybe. All prior models didn't; they mostly just tried the things I suggested and stopped at "well, that didn't work" or declared success after seeing results that matched their misunderstanding of the problem. I guess it's possible my prior attempts with other models had "loosened the lid" on the problem; we did already have a long list of documented "this didn't work" and a pile of tools for finding out if something worked. But, even so, I was impressed.
There probably will still be a "OK, let's rewrite this so it's not using lookup tables to precisely simulate the hardware behavior in software, because we don't need the noise, too" stage of the process...but, in one day with Fable, it solved a problem that I'd banged on for at least a week or too in the past with very little real progress. I don't think the models write exceedingly good code, even the best ones, but it sure does figure shit out quick.
https://developers.openai.com/api/docs/models/gpt-5.5-pro
> GPT-5.5 Pro does not offer a cached input discount.
I think this tells you in one line. It's basically set up for one-shot inference right now, by the looks of things. If you use this in a harness, it would almost immediately fall apart on cost. Not to say that they couldn't make it work, just saying that at least as it's delivered currently, they haven't done so. On the web, there might be doing something to get the equivalent of that behavior internally, such as keeping the session truly alive on GPUs rather than using their external-facing cache-style approach.
many of us and myself have been using chatgpt pro from codex cli for months now
https://github.com/agentify-sh/desktop
I recently have been testing ChatGPT business at work and the quota seems to disappear almost instantly even using weaker models. Unless they dramatically increase their quotas it’ll be unusable.
I don’t know how anyone can realistically use the “business” plans - you blow through your quota so quickly. I use a consumer Pro account ($100 a month) and don’t hit the usage limits nearly as quickly. 5.5 Pro is so slow that it’s not a big deal to paste big prompts into it and come back and check on it an hour later.
My solution for the ones stuck with that: use 5.5 for planning and 5.3-mini for the grunt work. 5.3 is remarkably useful still but you need to hold its hand.
I actually meant 5.4-mini..
Is it as good as Fable..? Fable is the first model that mostly writes without the AI slop format for me, and so I can comfortably actually copy and paste most of what it spits out.
OpenAI models have always been the worst in my experience for verbose, slop formatted responses, with each generation increasing in sloppiness.
Copy and paste...? In mid-2026? Why on earth would you copy and paste code instead of having the cli tool to the coding end to end?
I haven't opened an IDE in 8 months or so and have no plans to go back.
> Fable is the first model that mostly writes without the AI slop format for me
I'm not that impressed by Fable's writing to be honest, still has the AI giveaways like em dash.
The em-dash is not a "AI giveaway", it's just correct writing. Actual AI giveaways are in the writing style itself.
Humans use em dash as well.
I hate that I have had to remove it from my writing style because people assume it’s AI generated. But I think that ship has sailed. I’ll have to do without now.
Parentheses usually read better anyway.
Parens are ok for short asides (like this) but unreadable for longer asides and not usable for compound sentences like the emdash. Unfortunately, neither ellipses nor semicolons can exactly replace the compounding ability of the emdash, I find the best option without it is often to just split a sentence in two.
How do you type the em dash. I thought the point about the em dash "—" is, that it is longer than the normal minus "-". Humans normally have no way to produce it, cause there is no key on the keyboard.
Macos, ios, google docs, and microsoft word will autocorrect two hyphens to an em dash, which is how I normally type it. On a mac you can also type an em dash with option-shift-hyphen.
Many word processors (Microsoft Word, LibreOffice Writer, etc.) and some online editors will auto convert double hyphens "--" as they are typed into an em dash.
Some text editors replace the -- (two separate dashes) with a proper em-dash. Literate people - who understand why em dash exists - have been using it all the time. Thats, after all, how the models learned to use it.
At least in macOS there is a key for that on the keyboard, Shift + Option + Hyphen (-). This information is a quick internet search away.
Being Hacker News, a lot of us use programmable IDEs & keyboards. I added em dash support to both Emacs and Dygma keyboard.
OMG — I'm a robot.
dash dash "--" on a lot of systems and word processors turns it into the em-dash automatically
i cant reply to hn_user2, but i have the same experience, i find myself never using emdash where i would have before
Why can't you reply to hn_user2? I just did. Did you say something mean to hn_user2 and now you have a restraining order and can't go near them within one reply?
Related:
Previewing GPT‑5.6 Sol: a next-generation model
https://news.ycombinator.com/item?id=48689028
Nice! It never made sense to me that Pro Extended wasn't in the Codex app.
Which will be the uses cases for this model?
can't wait. been maxing Fable out, if Sol Ultra turns out to be as good and in Codex - that's a paradigm shift
when will it be available? do we know? I don't have X, not sure if the thread mentions it.
No Twitter, what’s he responding to?
https://xcancel.com/haider1/status/2073695124220006575#m
Corrected link https://xcancel.com/thsottiaux/status/2073933490513752151
Check out LibRedirect or Predirect (MV3), it automatically redirects youtube, X, etc links to privacy-respecting frontends.
https://news.ycombinator.com/item?id=44344246
107 comments, 1 year ago.
The LibRedirect link in that post is 404
Will individual subscribers have access?
I would assume yes - their goal is to capture consumer subscribers. Claude are going to take Fable away, and they're going to swoop in and give it to us.
This is why I don't think Fable will be taken away. Not for long anyway.
Still avialable through the API. According to people that have tried both Fable nad 5.6, Fable is clearly better at coding. So i expect a lot of people to pay extra for it.
But if 5.6 is better than Opus, Claude Code Max plan users will switch to OpenAI Codex en masse. Using Fable at API pricing is expensive
You’re not kidding. I ran out of Fable tokens a couple days ago, loaded up $70 just to finish the task we were working on, and burned through it in about 15 minutes.
Aaaaand it’s gone.
Who is going to pay API prices for using Claude? I don't know one single company. It's not gonna happen.
Most enterprises are already paying API prices as Anthropic are forcing them to above a certain company size.
Every large enterprise?
Who do you think pays for your subscription's actual usage?
Every company above 150 employees has to pay API prices as Anthropic won't give you subscriptions.
I love how competition is great for customers!
They really try to force everyone into AI.
Next logical step: mandatory age sniffing but it can only be done if you have AI. Those not complying will be denied access to the www.
Back to terrible naming from Open AI.
Bruh when did understanding chatbots become like following pokemon? Wtf does any of this this mean. Tf is sol? Tf is ultra? Tf is codex? Tf happened to descriptive nomenclature?
Maybe you jest, but I'll bite.
> Tf is sol?
It's a proper noun: the name given to their latest and greatest model. Means "Sun" in Latin. Similar to how Anthropic has been naming its models Fable, Opus, Sonnet etc. Their other models are called Terra (Latin: Earth) and Luna (Latin: Moon) [0].
> Tf is ultra?
The name of the "harness" around the model. It'll use deeper thinking, subagents and all that jazz in response to a prompt. Other options include max, high, medium etc I suppose.
> Tf is codex?
"Coding agent" similar to Claude Code [1], something with a more descriptive name.
> Tf happened to descriptive nomenclature?
Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
[0]: https://openai.com/index/previewing-gpt-5-6-sol/
[1]: https://openai.com/codex/
[2]: https://docs.z.ai/guides/llm/glm-4-32b-0414-128k
Of course I understand all this, but why the cuteness? Why not just say "<whatever the hell sol is supposed to imply> 2 agent?
> Something like GLM-4-32B-0414-128K (not made up [2]) doesn't quite roll off the tongue I suppose.
Surely it would sell better though if they could communicate what they're selling?
> Coding agent" similar to Claude Code [1], something with a more descriptive name.
Does this really need branding at all? Surely claude should sell itself if it can work as an agent. And if it is a specific model, what tasks is it trained to do? can we see the fucking training to make this money worth it? Or am I just another sucker buying nikes?
All of this hand waving makes me nauseated. People should buy value, not vibes.
It's a post on twitter, not a press release.
Branding is far more subsistent and meaningful than either lol. They just want to paint themselves as gods. But they are producing software, not magical beings, and we should see them as salesmen, not gods. Not to mention they'd probably sell more software if people could understand their product lineup
It's to create an in-group, and you are in the out-group.
If you follow AI news you'd know what these are easily, or even just look at the top posts in the last month on HN to see.
https://hn.algolia.com/?dateRange=pastMonth&page=2&prefix=fa...
This wouldn't be necessary with descriptive product names
There is a reason brand names exist and why Coca-Cola does not say syrupy fizzy caramel like drink on the can.
they better get that out fast, it will become totally meaningless when the next GLM gets there first.
more competition is always good for consumers.
All these names mean squat
I’m all in on Anthropic. How good is frontier openAI models for coding and things?
Gamechanger..
I can see they have inherited their poor product naming from Microslop