7/6/2026 at 2:44:26 AM
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.
by throw394042
7/6/2026 at 7:47:07 AM
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.
by whstl
7/6/2026 at 7:50:22 AM
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.by tmountain
7/6/2026 at 8:05:23 AM
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.by barrkel
7/6/2026 at 12:56:43 PM
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.
by csomar
7/6/2026 at 10:14:07 AM
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.
by rowanG077
7/6/2026 at 11:07:50 AM
> 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.
by benjiro29
7/6/2026 at 1:44:33 PM
> 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
by rowanG077
7/7/2026 at 5:29:35 AM
> That's all just prompting.>> Joe, how many times have I asked you to pay attention to commas. Come on, "let's eat grandma".
by throw1234567891
7/6/2026 at 2:58:17 PM
> That's all just promptingAnd telling someone not to repeat a mistake is… ?
by brookst
7/6/2026 at 3:27:05 PM
Are you really making the case that teaching a person how to work is equivalent to prompting an AI?by rowanG077
7/6/2026 at 7:52:47 PM
Yes. Tell me why it’s different without using circular reasoning?by brookst
7/6/2026 at 9:18:41 PM
They're different mechanisms.Teaching modifies the learner. Prompting doesn't modify the model. It provides additional context that influences a single inference. A person who has learned something can apply it years later without being reminded. An LLM generally cannot unless the knowledge is incorporated into the model itself or provided again.
by rowanG077
7/6/2026 at 10:31:21 PM
Right, but "provided again" is what SKILL.md or whatever else are for.The value of LLMs is that they're stateless. With sufficiently detailed documentation and a well-bounded task, they are quite useful.
by SR2Z
7/7/2026 at 6:59:49 AM
I don't think it's generally thought of an advantage that LLMs are stateless. In deployment it's nice, it would be much better if the model could continually learn.I'm not claiming LLMs are not useful, they most certainly are.
by rowanG077
7/6/2026 at 11:14:23 AM
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.by karahime
7/6/2026 at 12:28:10 PM
Case lawby nprateem
7/6/2026 at 1:41:47 PM
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.by rowanG077
7/6/2026 at 1:27:53 PM
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.by catigula
7/6/2026 at 10:44:13 AM
> 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".
by embedding-shape
7/6/2026 at 5:09:20 PM
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.
by rowanG077
7/7/2026 at 7:19:49 AM
> the point I was making is that an LLM is prone to the exact same mistakes even if you change the promptThat'd be strange, what specific models?
Your example of "changing the prompt" doesn't actually tell the model what to avoid though, you're just rephrasing the question? Again you're not doing an equal comparison for the human, because in that example I bet you'd tell the human what they messed up and how to do it better, why not include that in the prompt when you compare these two?
by embedding-shape
7/7/2026 at 8:25:30 PM
> That'd be strange, what specific models?Fable. (and other claude ones)
I have a clojure/babashka environment that I work in. My agents/claude files specifically explain this and how to work with it and what to use etc. Fable decided it didn't want to use bb/clj and ran python to do cli work in one project.
So I update the files in all projects to specify that python is strictly off limits/not to be used/never to run it/etc and created a preToolUse hook -- because I learned that just because an agents file says something doesn't mean it gets followed. Hence the hooks, fool me once ...
Later, I start work in another project with the hooks and updated files saying to use the established bb/cli tools and to absolutely never run/use python etc. What does it do on the first task? Tries to run python, gets hit with the preToolUse hook error and replies with something like -- oops my bad I tried to use python even though the instructions told me not too. Every once in awhile in new tasks I still see the hook error at some point but it doesn't apologize like it did before it just switches to proper tools after it can't run what it wants even though it's in the instructions and could have been avoided.
by 1659447091
7/6/2026 at 10:32:08 AM
"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.
by rahidz
7/6/2026 at 11:29:24 AM
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.
by drdexebtjl
7/7/2026 at 5:28:34 AM
> 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.You can do the same to an LLM with the same outcome.
by throw1234567891
7/6/2026 at 10:54:30 AM
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.
by bananaflag
7/6/2026 at 11:10:03 AM
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.by Iolaum
7/6/2026 at 12:22:30 PM
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.
by bananaflag
7/6/2026 at 2:34:47 PM
How it works under the hood is separate from selling the feature.by fragmede
7/6/2026 at 2:53:56 PM
> 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.
by genidoi
7/6/2026 at 9:00:39 AM
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...
by Topfi
7/6/2026 at 9:51:28 AM
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.
by barrkel
7/6/2026 at 10:48:50 AM
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.by skydhash
7/6/2026 at 11:19:49 PM
I don't think this is true. Computers are still computers, and code executes (mostly) deterministically. What computers struggle with is making sense of unclear inputs, precisely because of their determinism. OCR, speech-to-text, and computer vision all proved extremely limited when tackled purely deterministically. The whole "ML revolution" before LLMs was about recognizing that messy, noisy data can be interpreted with some accuracy using statistical methods. LLMs are the continuation of that: they are even harder to measure and completely impossible to prove, but they bring capabilities we have been unable to achieve any other way for decades. Should they be used to handle deterministic logic? No, obviously not - but they do enable computer systems to start working in contexts where they either couldn't at all before, or were just bad at them.by klibertp
7/6/2026 at 9:00:00 AM
[dead]by gmerc
7/6/2026 at 8:18:17 AM
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.
by bob1029
7/6/2026 at 9:43:21 AM
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.
by tmountain
7/6/2026 at 9:53:05 AM
> 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
by alfiedotwtf
7/6/2026 at 1:54:10 PM
> 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.
by Supermancho
7/6/2026 at 9:01:24 AM
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...
by desterothx
7/6/2026 at 11:45:49 AM
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.
by sscaryterry
7/6/2026 at 11:59:27 AM
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.
by cyanydeez
7/6/2026 at 10:57:59 PM
I agree that we as humans are there to be held accountable. I can foresee a future where the "tech" guys will be blamed, whether or not those people are the software developers/engineers/programmers or the tech bros in Silicon Valley.Converging on a solution via an LLM is possible. The code, well, it wouldn't necessarily be the way a human would write it, better or not...
by sscaryterry
7/6/2026 at 10:16:27 AM
Yeah because "works many times in a row" = "deterministic" to many people.by johnisgood
7/6/2026 at 11:31:10 AM
"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.
by whstl
7/6/2026 at 7:32:04 PM
“Asshole non-technical people”You mean the people creating actual business value in every industry where software isn’t the product? Speak not unless spoken to, Mr. cost-center!
by RazorBucksICO
7/6/2026 at 10:41:24 PM
Without the "some" qualifier my quote totally loses meaning.In the same message you managed to twist my words, lack reading comprehension, jump to conclusions and do a personal attack.
Since you're new here, I recommend reading this: https://news.ycombinator.com/newsguidelines.html , especially this part: "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."
by whstl
7/7/2026 at 1:01:13 AM
I think they were making a joke. In the future, you might consider the advice you are giving as well as giving it.by djsjajah
7/7/2026 at 7:37:20 AM
If that’s the case, I will wait for their own answer.by whstl
7/6/2026 at 6:32:30 AM
That's kinda hilarious. Pretty soon they might just ask people to write code themselves.by noduerme
7/6/2026 at 6:55:08 AM
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.by wjnc
7/6/2026 at 7:33:48 AM
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.
by noduerme
7/6/2026 at 7:53:35 AM
> 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.
by bob1029
7/6/2026 at 8:07:50 AM
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.
by noduerme
7/6/2026 at 9:10:40 AM
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?by someguyiguess
7/6/2026 at 9:36:22 AM
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?by rglullis
7/6/2026 at 2:20:26 PM
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.
by noduerme
7/6/2026 at 3:24:09 PM
> create a reverse payment instead of voiding a chargeI 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.
by Xmd5a
7/6/2026 at 9:21:47 AM
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.
by somenameforme
7/6/2026 at 10:10:05 AM
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.by alfiedotwtf
7/6/2026 at 10:07:37 AM
I guess that all depends on how much your company revolves around Minecraftby alfiedotwtf
7/6/2026 at 8:01:13 AM
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.
by jddj
7/6/2026 at 8:25:49 AM
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.
by wjnc
7/6/2026 at 8:47:50 AM
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.
by noduerme
7/6/2026 at 9:19:59 AM
since when did HN have downvotes?by mdgld
7/6/2026 at 9:31:15 AM
Its one of the features that is behind a minimum karma requirement.https://github.com/minimaxir/hacker-news-undocumented/blob/m...
by kdbg
7/6/2026 at 10:03:57 AM
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 timesby alfiedotwtf
7/6/2026 at 12:01:39 PM
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.
by eiifr1
7/6/2026 at 12:37:45 PM
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
by Der_Einzige
7/6/2026 at 4:23:39 PM
> Tokens billed at API prices are profitable for openAI and anthropic todayThat'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.
by derdi
7/6/2026 at 7:13:07 AM
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?
by OtomotO
7/6/2026 at 8:30:56 AM
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.”by wjnc
7/6/2026 at 8:54:27 AM
>> Going squarely against the market sentiment is not the way to gain and keep confidenceI 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.
by noduerme
7/6/2026 at 7:52:13 AM
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]
by noduerme
7/6/2026 at 6:39:24 AM
oh no! what about all these demos which product management vibe coded?by imhoguy
7/6/2026 at 7:58:24 AM
> Pretty soon they might just ask people to write code themselves.GPT-10 Human Ultra
by re-thc
7/6/2026 at 9:24:53 AM
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.
by throwawayHBKFC
7/6/2026 at 11:53:00 AM
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".by HappMacDonald
7/6/2026 at 8:13:03 AM
We are using this brand new hammer to build everything!Please stop using this brand new hammer for glassmaking.
by abraxas_
7/6/2026 at 1:25:15 PM
> 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.
by JeremyNT
7/6/2026 at 5:40:38 AM
Are corporate employees not allowed to use personal subscriptions?by dprkh
7/6/2026 at 6:14:15 AM
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.by bulder
7/6/2026 at 5:44:51 AM
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.by pkulak
7/6/2026 at 7:14:00 AM
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.
by ffsm8
7/6/2026 at 6:45:48 AM
If you want your company's code to be used in training, then yes.by bberrry
7/6/2026 at 8:21:33 AM
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.by bob1029
7/6/2026 at 8:26:44 AM
Dog! Use it and tell us how it is! Stop with this token maxing moaning. You have access to a new powerful tool.by websap
7/6/2026 at 11:39:03 AM
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.by bobkb
7/6/2026 at 11:28:06 AM
Apologies for sounding like a billboard, but this is exactly why we built https://flowstate.inc/by whh
7/6/2026 at 7:17:51 AM
I'm using local models in Ollama for most things and only use the paid corporate account when my local model gets stuck.by Shorel
7/6/2026 at 12:53:31 PM
for code as well? which local model are u using for codingby ob12er
7/6/2026 at 4:21:41 PM
I'm using both qwen3-coder-next and glm-4.7-flash:bf16. I have gemma4 downloaded but haven't used it much.by Shorel
7/6/2026 at 5:37:03 AM
> 2 months ago management was showing us scoreboards, praising leaders who used most tokens.Everyone is insane.
by solumunus
7/6/2026 at 6:10:36 AM
It‘s a collective psychosis. There is not other explanation.by camillomiller
7/6/2026 at 6:32:07 AM
Everyone is falling over themselves out of FOMO.by elric
7/6/2026 at 7:48:33 AM
It’s 100% FOMO from investors, board members and C-levels disconnected from the daily work.by whstl
7/6/2026 at 1:08:45 PM
[dead]by rozal
7/6/2026 at 6:53:02 AM
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.
by y1n0
7/6/2026 at 1:08:04 PM
[dead]by rozal
7/6/2026 at 12:54:33 PM
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.
by softwaredoug
7/6/2026 at 6:33:26 AM
interesting, our enterprise account here in Australia doesn't have access to it yet.by senectus1
7/6/2026 at 5:14:41 AM
I wonder if clues like this will be what are written in history books as the beginning of the bubble bursting.by VeninVidiaVicii
7/6/2026 at 5:28:31 AM
I don't think that people relying on the tools too much is the first sign I would identify to mean the bubble is poppingby make3
7/6/2026 at 5:37:03 AM
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.by jvuygbbkuurx
7/6/2026 at 12:55:38 PM
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.by Zambyte
7/6/2026 at 7:54:46 AM
the thing went from the transformer paper in 2017 to basically doing your job for you in 2026by make3
7/6/2026 at 9:54:50 AM
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.by drw85
7/6/2026 at 2:46:13 PM
Efficiency is an angle of research that's quite active, see quantization, MoE, speculative decoding, QKV cache optimizations, sparse attention, etcby make3
7/6/2026 at 5:08:02 PM
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.by HDThoreaun
7/7/2026 at 7:07:05 AM
AGI means that eventually there is just one profitable company left; but it is not clear at this point which one it will be. Also it's worth noting that the current market capitalization for the technology industry just in the US is around a third of a hundred trillion dollars (Which would be explained by the possibility of AGI emerging from another country/continent)by icepush
7/6/2026 at 9:20:11 AM
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.by derdi
7/6/2026 at 5:27:38 AM
[dead]by make3
7/6/2026 at 8:28:23 AM
wow almost as if they needed to incentivise people to use and then tame it down to keep it in sustainable levels. shocking!by simianwords