GPT

HarambeTooSoon

Claude and GPT arent the default search bar on 99% of phones

bslaven3

I know a lot of folks that pay for it, so I'd lean towards yes. My wife pays for it for her company and I can use her Chat GPT for no extra cost. She just gives me access and I create my own folder. I wonder if they will start limiting IP address logins like Netflix did where only 1 IP address can login at a time.

bakugo

Bro this dude also hacked into OpenAI and downloaded GPT into his computer! Holy shit lmao

AMGsoon

Their issue is that Gemini sucks compared to GPT and Opus. Their lite model is fine but they have no answer to Sol/Astra and Opus.

gopoohgo

MSFT has shown it can monetize AI with their 365/CoPilot revenue.   The worries that GPT ads and Muse/Dot agentic is cutting into Goog ad revenue is rearing it's ugly head again.

abroadbroadband

Oh yeah Claude and GPT always make mistakes. Probably half my token usage is having them fix the mistakes 😭 An app to convert vinyl records into digital music files that can be played on phones 📝😎

fluffh34d420

I'm asking GPT if i should buy goog

ykoreaa

Oh it's a personal project of mine so it's ok! I think it'll be better for me to code it by hand so I can fix it if anything goes wrong instead of relying on AI (I noticed GPT always had problems I needed to fix).. aaaand now I sound like those people that still want vinyl instead of using their phone to listen to music 😅

ykoreaa

I don't use Claude (more expensive than GPT) but do you think it's worth wild for the money?

No-eat-eat

The issue isn't Goog bleeding.. the issue is nobody uses search anymore Even my mom uses GPT or Gemini

Which-Travel-1426

You may need to update your knowledge. On the frontier side Kimi and GLM cost is approaching Opus, and on the fast and affordable side GPT Luna’s cost is approaching Deepseek Flash. Considering Chinese models are less token efficient, the gap in reality is smaller. Currently there is no moat in a price war.

Apprehensive-Ice8996

Yeah but they host majority of GPT which everyone uses. It don't matter if their AI sucks. Your better off not creating AI if you big company just be the utility company providing the power to drive it.

LoosePersonality9372

I asked GPT-6.1 terra to make me 15M usd in a week and it said it cannot. Care to explain?

Traior

Claude, Gemini, or GPT, is that you?

weed_cutter

My company is spending $1 million a month on Claude ALONE. Per MONTH. We also have an assload from GPT, Gemini, and 'Glean' and 'Snowflake' as well, among even other AI tooling. 800 employees, forget exact gross revenue figures but .. .probably not as high as you think. LOL

Zealousideal-Fan2971

I'm a huge sceptic of AI, as much as LLM changed the world forever all, 99% of those AI trash companies will go out of business. 1. Current subscriptions are completely out of whack compared what you get vs what you cost them, i'm sure they ,,optimized'' it , so some college student buys it, uses few times and forgets about it. but using it to the fullest can cost them thousands of dollars compared to minimal sub cost when using usage limits effectively. 2. Moore's law ending, silicon won't be shrinked much further, ASML/TSMC might have some optimization tricks left but until we get a new semiconductor is discovered the gains/efficiency won't go much further. 3. Poisoned data, ai feeds trash from itself, most valuable knowledge has already been consumed by ai, now they're going for more and more obscure books etc, alright but there's not much more to learn. Hallucinations can always happen, you can double and triple check but they can always happen, and the costs of re checking scales up. 4. Free models aren't that much further behind, and censorship of models is taking more and more extreme paths, just cause some mentally ill regard will have fake relationship with GPT, or mad corpo clowns pressuring ai companies because of reverse engineering. Like a gold rush, it's not the gold miners making a bank it's the people selling pans and pickaxes (nvidia, tsmc, samsung, hynix, ASML) are the winners. I'd stay the hell away from all AI companies in ideal world, but well of course they'll get bailed out and steal more taxpayers money.

Asleep-Ad5687

Can't speak to their product, I asked Claude one question, which was answered well. A follow up required me to pay, which I wasn't going to do without more info. I subscribed to Chat GPT instead after having chance to use it robustly in advance.

FitPhilosopher1877

You stopped using it a long time ago so how can you think you have an up to date view? I pay for the $200 plan for both Claude and GPT and use open source models on OpenRouter every day. Opus 5.5 is by far the best model publicly available, and it isn't close.

ParkingFig

Here's the ChatGPT (GPT-6.1 Sol, Extra High) review of your post (prompt was "what is bro on about? please review"): **He has a legitimate concern about Anthropic’s future compute obligations, but several of his supposed accounting discoveries are wrong.** The post does not establish that Anthropic is hiding operating expenses or manufacturing revenue. His underlying argument is straightforward: Anthropic has committed to enormous amounts of infrastructure before knowing what future demand, pricing, and model economics will look like. If revenue disappoints, those commitments could turn operating leverage sharply negative. That is a serious underwriting question. I checked the relevant financial statements, accounting policies, and commitments in the [S-1](https://www.sec.gov/Archives/edgar/data/2130357/000162828026067168/anthropicpbc-sx1.htm?utm_source=chatgpt.com). Here is where his analysis holds up—and breaks down. **1. “They’ve hidden gross margin.” The filing directly contradicts this.** The income statement presents expenses by **nature**, but Note 8 also presents them by **function**, including cost of revenue and R&D. |H1 2026, $ billions|Amount| |:-|:-| |Revenue|16.286| |Cost of revenue|2.380| |**Implied gross profit**|**13.906**| |**Implied gross margin**|**85.4%**| |Research and development|9.741| |Sales and marketing|3.808| |General and administrative|1.050| |**Operating loss**|**(0.693)**| *Source: S-1, Note 8, p. F-40; gross profit and margin calculated.* There is a **valid qualification**: channel partner costs sit in sales and marketing. An investor should examine contribution margin after those fees before comparing this gross margin with another software company’s. But “there is no gross margin disclosure, therefore inference economics must be terrible” is an unsupported inference. The underlying cost disclosure is available. **2. “Training is recurring, so calling it R&D disguises profitability.” He confuses classification with exclusion.** Training may indeed be economically necessary recurring expenditure. A frontier lab probably cannot stop developing models indefinitely and preserve its franchise. **But R&D still reduces GAAP operating income.** Classifying expenditure as R&D does not make it disappear. The accounting policy says model development costs are generally expensed as incurred; capitalized software development costs were insignificant. The useful question is how much R&D is needed merely to maintain competitiveness versus expand capabilities. The filing’s classification does not answer that economic question, but it does not conceal the expense either. **3. “Convertible notes move training expenses into liabilities.” That is not how financing works.** Borrowing money and spending it on compute creates both a financing obligation and the applicable compute expense or asset. Issuing a note to a supplier instead of paying cash likewise does not eliminate the underlying cost. The fair-value loss on convertible instruments reflects the increasing value of investors’ contractual claims. It matters for dilution and the division of equity value, but it is not evidence that training expense was transferred out of operating costs. He also mixes periods: **$72.4 billion is the H1 remeasurement charge; Q2 was $59.6 billion.** And Q2 GAAP operating income was **$974 million**, rather than the $1.03 billion he cites. *S-1, p. 158.* **4. “$587 billion of obligations.” Substantively important, but described too loosely.** His addition works: * Approximately **$265.6 billion** of fixed or minimum purchase commitments. * Approximately **$321.4 billion** of estimated undiscounted obligations under leases not yet commenced. Those are different categories of future contractual spending, rather than $587 billion of immediately payable debt. Some lease arrangements are conditional, and the obligations span different periods. Later agreements also mean this June snapshot is not a complete measure of current exposure. *S-1, Notes 7 and 13.* None of that makes the commitments harmless. **The central risk is whether cash generation grows fast enough as payment obligations activate.** A large lifetime total alone cannot establish the answer. His claim that leases can be kept off balance sheet indefinitely until “official occupancy” is misleading. Commencement depends on when the underlying asset becomes available for use, rather than an unrestricted management election to postpone recognition. **5. “Broadcom’s debt loop explains the clean cash flow.” The disclosed timing undermines this claim.** Broadcom’s arrangements have **five-year lease terms**; that does not mean they start five years from now. Capacity began arriving in 2026, with additional capacity expected from 2027. The **$42 billion, 9% note facility was undrawn at June 30**. Actual H1 finance lease principal payments were only **$4 million**. That classification therefore cannot explain $3.9 billion of reported operating cash flow. *S-1, pp. 172 and F-43; cash-flow statement.* There is a real prospective issue: finance lease principal appears in financing cash flows, while depreciation and interest affect earnings. Investors must subtract those principal payments when assessing cash available to equity. Financing operating obligations with notes also deserves scrutiny. But those are different arguments from the one he makes. **6. “Marketplace revenue is inflated and circular.” He conflates distribution with customer demand.** Anthropic’s disclosed rationale for gross recognition is that it controls the service, establishes pricing, and bears responsibility to customers. AWS and Google act as distribution and collection channels. That is a coherent principal-versus-agent accounting position. Selling a $100 service through a distributor and paying a distribution fee does not inherently require recording only the remainder as revenue. It does mean revenue multiples and gross margins need consistent treatment across comparable companies. Likewise, **receivables passing through a cloud marketplace do not establish that the cloud company is the ultimate purchaser**, or that the revenue is fabricated. The concentration creates collection, bargaining-power, and counterparty risks. Proving circular demand would require evidence about who ultimately funds consumption and why. I did not find support in the reviewed disclosures for his specific **20–27% commission range** or the assertion that **80%** of the cited commitments are nondiscretionary. **What I would take seriously** The stronger bearish thesis is: >Anthropic may have attractive customer-serving economics while still delivering disappointing equity returns because maintaining its competitive position and fulfilling infrastructure commitments absorb enormous amounts of cash. That thesis can coexist with excellent products and genuine customer demand. It does not require accounting misconduct. The most important unresolved questions are: 1. **What is the annual payment schedule?** The preliminary filing leaves key aggregate commitment and near-term payment figures blank in the MD&A. That is a consequential gap. 2. **What is contribution margin after channel fees**, by product and distribution channel? 3. **How much recurring research spend is required to sustain the franchise?** 4. **How resilient is utilization if growth slows or prices fall?** Greater efficiency helps only to the extent its benefits exceed pricing pressure and unused capacity. 5. **What dilution and additional financing are required before durable free cash flow?** **My assessment:** the capacity and funding risk is credible. His claims of hidden expenses, improper revenue recognition, and a demonstrated accounting “death trap” are not established by the evidence he presents.

C2theC

Yup, very much reads like ChatGPT Chat, not even the cheapest ChatGPT Work GPT-6 Luna (GPT-6.1 Sol may have been better, but thanks for spending the tokens!) Reference: https://developers.openai.com/api/docs/models

SpeedflyChris

> With little warning, Anthropic switched enterprise to token based billing. Suddenly, companies were acting like the sky was falling. User ran around screaming that they lost their entire years budget in four months. It's coming to their personal use accounts as well - the usage limits get a lot of complaints in the Claude sub but if you get the API-equivalent cost for the tasks you give it or your usage over the last while then it's absurd the amount of usage they let you have compared to the price you pay. I got a 20x account for a few months to help build some personal projects of mine, and last month, when I pretty much used up my limits every week, the API-equivalent cost for all of my tasks exceeded $8000. Given that I paid Anthropic a little less than 2.5% of that after VAT, I can't imagine that they made a profit on my account. When I run things via Openrouter or similar I *never* use Anthropic or OpenAI models (I might make an exception for GPT-6.1-Sol, because that model is genuinely pretty good for the money). Paying Anthropic or OpenAI $50 for a task I can have a Deepseek or Qwen model do just as well for $2 just doesn't appeal.

SpeedflyChris

>Why would people want to use the 3rd or 4th best AI client? Because it's "good enough" and cheaper. If you look on Openrouter, to see what businesses and companies go towards when they're definitely paying API rates and not just burning masses of gullible venture capital through loss-leader subscriptions (the usage limits on both are *hilarious* - I burned the equivalent of $8000 in API usage last month on my £180 claude plan), the cheaper Chinese models have gone from being a single-digit percentage of usage last year, to more than half this year. When I run tasks through Openrouter (which I do regularly), why would I pay OpenAI or Anthropic $50 to do something, if a model from DeepSeek, Qwen etc will do it for $2, and both produce code of a similar quality and meet my needs? You can see this with the recent releases from both OpenAI and Anthropic - OpenAI recently put out GPT-6.1-Sol, which has a big focus on "good enough" performance at low cost, and Anthropic recently put out Haiku 5.5. The reason that both OpenAI and Anthropic are ultimately doomed to bankruptcy is thus: 1- They have *absurd* levels of financial commitments, and in order to meet those commitments they need to stop losing billions of dollars every year and start making massive profits, *now*. 2- Training their models is incredibly expensive, and the "moat" it buys them is extremely temporary. They can't afford to keep spending on training the way that they have been, but if they stop then their revenues will fall off a cliff in short order as the Chinese and international labs catch up and eat their lunch. The best open-weight models are less than a generation behind the current frontier models, maybe 4-6 months behind. If Anthropic and OpenAI stop spending like money is no object on training, then inside a year cheaper alternatives catch up and there is suddenly no reason for companies to spend the higher prices they demand. Meanwhile if they do keep burning billions of dollars per month on training, as the models advance there are increasingly few tasks that actually "need" the very latest and greatest model, so the market becomes increasingly price-sensitive anyway. Honestly if anyone is considering buying into this IPO please contact me before you do so, because I have a huge portfolio of investment-ready bridges that you will *not* want to miss out on.

spyVSspy420-69

Open models are good, but in my experience they still aren’t nearly as good as what Claude and ChatGPT offer with Opus and GPT-6 Sol. Same with local LLMs. Even if you have a $15k Mac stuffed with unified memory you don’t get the context window size and performance of a $20 Claude account. My real selfish concern, which I think will happen soon, is these $20 accounts will be severely hamstrung if not eliminated entirely. With 4 $20 OpenAI accounts and a request load balancer I can do the development work of probably 20 entry-level programming grads. It’s simply too good for what it costs.

ikaiyoo

Well probably anthropic, chat GPT, and other companies that are only AI would degault and either get bailed out by the government or absorbed by Amazon, Microsoft, Google. The big three would be ok because they have other divisions to fall back on and would scale back AI to the new reality. Grok would be ok because it would definitely be bailed out.

Nimbus420i

They don't need to know or even learn, chop a tree, ask GPT or Claude.

chird_

Dude has bot written all over him with this GPT slop

andersonklaus

Money is available... For now... But this does not mean there aren't dangers about.  * Both OpenAI and Anthropic committed to 100s of billions of dollars of spending it the next 3 to 4 years.  * This made sense because their product was awesome and everyone was going to use it.  * The problems started when China started releasing their models. This changed the whole game. 2 weeks before DeepSeek was released, Sam Altman was talking about 2000$ and 5000$ ChatGPT subscription options. DeepSeek was 90% as good as GPT, but it was 1% the cost.  So when DeepSeek was released, the whole game changed. The problems started when Chinese companies started releasing good enough models for 1% of the cost. Even more, the Chinese companies are releasing their models as open weights. You don't trust China with your data? Ok host the free model on the US.  Now, American labs are facing a problem: * If they increase their prices, they will lose customers to Kimi, GLM, DeepSeek, etc.  * If American labs do not increase their prices, they wont be able to pay for their commitments.  This here is the problem. This is why there is doom and gloom about the AI in the US right now.  The American labs can only survive, if they take Chinese models off the table. This is why you see both Anthropic and OpenAI releasing new doom scenario about open weight LLMs every week. If things stay as it is, both Anthropic and OpenAI will be bankrupt in 2 to 4 years.  This possibility is making investors nervous. Not as many people are willing to give another 50 billion dollars to AI companies when they can go bankrupt without returning the investments. 

Rodramramfive

Yes... two very large S&P 500 companies outside of tech... one a mass supplier and another retailer... were still running A/S 400 and 20 year old versions of SAP... no intention of moving to newer platforms. Which we have Microsoft Teams and Co Pilot (only the co pilot is a severly crippled version of Chat GPT) and it cant really do much because we have private contractors who are old as fuck jurry rigging the system together making up t codes that dont normal exist or function the way that they normally would so AI literally cant help with anything but organize your inbox. Outside of emails our employees use it to make funny pictures for team events. This is what most of the companies outside of tech are like... and while both CEOs will say on conference calls the magic buzz words... they are in reality cutting spend because the ROI is not there.

AppropriateAnnual455

I’d say 98-99% of things can be done with the free versions of these LLMs, and as we know technology is always deflationary. It’s a race to the bottom for who can give the most for free to attract the largest user base for the next equity raise. This isn’t like the early internet days where huge “free” userbase will translate to revenue later on… I think Wall Street is in for a reckoning in regards to the amount of investment into this with a lack of return or even chance at a return since half of these LLMs will be gone in the next 2 years. GPT will be owned by Microsoft

Holbrad

Ever since the very first GPT, and even before that, people have constantly been claiming that LLMs will hit a wall and there are so many things that they can't do, and they've been proven wrong every single time. This will be exactly the same, just one of the countless examples of the bitter lesson.

CleptoeManiac

Correct, I actually don't use it all that much at work yet (finance), but I'm paying $100/mo. for Claude, $100/mo. for GPT, and $20/mo. for Gemini right now for personal projects (and miscellaneous credits for Tripo and some other models). Most recently, I've gotten into 3D modeling and game design. I've always been a big gamer, and I've been programming for 25+ years, but game design has always been daunting to me, partly because I'm not very artistic in a traditional sense. Now though, you can generate 2D concepts with a model like GPT Images or Nano Banana, run it through a model like Tripo to get a 3D asset, and then place it in your game and code the mechanics with Claude. You can generate textures with Patina AI and have Claude apply them and adjust them until they closely match a reference image. It's pretty incredible. If you're interested, I like Stefan 3D AI on YouTube. I think it's important to realize though that AI is another tool in the toolbox. Anybody trying to one-prompt their way to prosperity is going to be disappointed and think it's not worth the hype, but in the right hands, the models are extremely powerful.

_bloed_

In my experience for most of the coding work GPT Luna is more than enough. Claude Haiku is also fine, but Luna is just insanely good for the price. There is no need to use the best and most costly flagship model all the time. And these cheaper models are also faster. Luna does the same work in 25% of the time.

Piranha771

No I haven't heard of it. Thank you for pointing it out! I understand your opinion but I don't see it as dramatic as you. The current "we have to push out new frontier models every 2 months to keep up with the competition" is in my opinion the gigantic cost driver, not occasional model updates in a more relaxed market. But good point nonetheless, it makes totally sense. I remember the first GPT3 that didn't know of the invasion of Ukraine, which was a bummer back then

DiversificationNoob

I think you're conflating declining compute costs with declining profitability. 1. **It is a fact that compute is getting dramatcally cheaper.** Standford for example [claims](https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf) that "the inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024". 2. **Most of that 280 fold improvement cannot be explained by cheaper hardware.** "At the hardware level, costs have declined by 30% annually". So the main drivers in compute cost reduction are on the model and software level. Out of the same AI chip you now get more intelligence per watt. 3. **The payback period matters.** If the payback period for the chips is 1-3 years (space X vs AWS claim) and they can reclaim the chip costs before the hardware is obsolete compared to new chips, why should this be a problem?

palindromic

I caught that too.. Surely you can’t not know that the whole infrastructure buildout was FOR TRANSFORMER LLMS, that is what is being labeled AI, that is the whole game, in total right now. Even when BERT/GPT was first released and doing incredibly cool things, linguistics and machine intelligence experts were noting it wasn’t particularly complicated math or logic or anything to do with intelligence, just a very clever way of “finding the next word” by brute forcing a lot of permutations through a fairly simple algebraic filter. It’s gotten more slightly more complex and refined, but the tech under the hood remains relatively simple and certainly not any kind of galactic shift in understanding how to quantify or qualify what constitutes “thought” or intelligence. But hey, it passes the very basic Turing test so therefore it’s sUpEr iNtElLiGeNcE and we should cram every single capex egg into this one basket and shout down every concern that it’s not as good as they’re claiming. Watching billionaire tech bros hype this chatbot on steroids into the stratosphere and claim it’s coming for all the white collar jobs has been something else. But the direct feedback from companies and institutions trying to utilize transformer llms into workflows has been pretty resounding, “very powerful productivity tool, not ready for ‘automation’” So is the bubble popping? I think for the hype surrounding it, it’s definitely showing cracks. These “frontier” players desperately need a new and innovative breakthrough algorithm or something to be able to make a claim for intelligence, but I guarantee this hype train will run us into a brick wall of economic reality before that happens. Trillions of dollars wasted on a promising breakthrough in hyper predictive linguistic models, because they saw $$$ signs when it cobbled together some github code into a (barely) working minecraft demo. It’ll help immensely with a certain tier of productivity for low to mid level coding, parsing documents (law), helping analyze MRIs, etc.. but it’s not going to replace doctors, lawyers, engineers or coders. No matter how much carnival Elon barks, it’s still just.. a great predictive engine. One that you can run a fairly powerful version of on a halfway decent gaming PC. I waited 2-3 minutes for Qwen 3.8 27B (a local model I can run on a 6950xt with 16gb vram) to give me some fairly good feedback on a legal document that I didn’t want OpenAi ingesting. And I suggest you do the same if you value your personal data.

bryan7474

Hey the last sentence GPT put in there We've seen many people do as well as you and lose it all. Chill tf out a bit please.

Jazzlike_Thanks_1869

My prediction, All of these AI Frontiers models are going to come out with their own AI Phones that will eventually eat the AAPL. META PHONE - GPT Phone - GROK X Phone etc…. Thropic Phone. I say this, as I lOVE my iphone and I love the AAPL ecosystem.

cyclopsmudge

“Hasn’t” is past tense, and it’s correct. It has not been generational. It may be in future, but as of right now it has not, it’s been around for 2 years and doesn’t have the market dominance for anyone to miss it that much. If Claude disappeared, people would just switch to GPT. If they all disappeared, people would just go back to programming. It wouldn’t take much time at all to get back into the groove of it, and given the numerous reports saying that productivity doesn’t actually increase significantly with AI use, it might make people re-evaluate whether it’s really all that useful to have everyone using it all the time.

DevOpsJo

Chat GPT tell me how to successfull and rich.... buy MAIN

ShankThatSnitch

The first peak of the AI bubble burst, but it can bubble for a lot longer than you think. It was 6 years for the internet bubble from start to finish, and Chat GPT released 4 years ago. The Nasdaq went from around $750-2000, before it corrected 35% down, only to rally again 3-4x before it peaked in 2000. This bubble can get way more stupid that it has been.

random-meme422

is this the larry where you realize you’re full of shit and just revert to default reddit GPT replies? cute little dog

Pizza_n_Tendies

Yeah, GPT, Claud, Gemini, and consumer grade AI is explicitly banned in our policy.

DeltaP2SavetheE

And it sucks bc copilot is just functionality-limited GPT in a shittier wrapper

ExpiringWorthless

Hardware itself no, but inference efficiency for intelligence per dollar has gone down by like 50x in a year, for example DeepSeek 4.1 Flash is smarter than GPT 4.5 Pro released Feb 2025, and quite literally more than 100x cheaper. We don't know real serving cost for 4.5 Pro but we do know for 4.1 Flash. So we will definitely see increasingly cheaper and smarter models simply from the absolute cash and talent being thrown at it. Now does that ever turn the company profitable? Probably not, but you would be delusional to say that everything model related has not been getting cheaper - there are thousands of extremely intelligent engineers working across the entire stack to improve it.

pm_me_yo_creditscore

It's an Alt man's party, numbers soaring high Valuations creeping, reaching for the sky.   Tech dreams and whispers, investors come alive But behind the glimmer, can this bubble thrive?     It's an Alt man's party, fears begin to rise,   Scary valuations, hiding in disguise.   Dancing with the data, drowning in the debt. Super cycle frenzy is it over yet? Don't run away. It's GPT.  Great for cracking software and weird pornography.

Mediocre_Gap2236

Does it matter to have infrastructure? Snowflake doesn’t own infrastructure, we use opus 5.5 or Sonnet 5.5 on their data AI We use Opus 5.5 on Microsoft Copilot instead of GPT 6.1 sol Those that are coding using VS Code with GithubCopilot are picking Claude models too For corporate use Claude is already the way. Why? Because they were the best when AI was first a thing with our SWEs who were the first movers using AI Most corporates start using AI because of their software team, and Anthropic the first models that had wide penetration

Big-Pomegranate3243

Laugh at me all you want, I spend the entire day talking to GPT just because I'm in love with its voice

JessKingHangers

Chat GPT remembers shit I asked months ago

Fit_Neighborhood9251

Imagine: No iran war, no tariffs and actually ending ukr-ru war for global cheap energy. SPY 900, We would be at gemini 8, GPT 11 and orangoman would have been praised for the best prezident in the world.

Pitiful-Recover-3747

You’re probably doing much deeper work with them than I am, but where we ran into problems with the GPT 5.5 model was it kept skipping over random selections of data content in its analysis. We’d realize something was wrong and tell it to go look again because we’d run a comparative query with whatever the claude model was and GPT would give us variable results. It had problems rationalizing the different data cubes is what our AI nerd told us. Our attorneys told us we’d get the balls sued off us if we relied on this thing. So yeah.👍🏻 balls intact over here.

i_empathetic

It's odd to hear that, because while earlier in the year I was doing a lot more elaborate and explicit pre-planning/prescriptive guidance with both Opus and GPT before overseeing them implementing software/debugging. But since Opus 5.5 and Astra I've actually noticed a drastic improvement, and actually better performance and outcomes (and less time spent by me) by being *less* prescriptive and less focused on pre-planning before letting them loose. I used to spend hours or days speccing out a project/feature and preparing iterative milestone documentation before watchfully stepping them through implementing those milestones. I don't need to do that anymore. The amount of pre-work I need to do to keep them focused on-task and preventing them from getting too creative or proactive has dropped by like 95%. The situation in software development is so, so much better than it was even 6 months ago. Massive improvements in results and less burden on me to tee everything up just-so to get there.

i_empathetic

Opus 5.5 and GPT-6 Astra are both incredible and quite similar, at least when it comes to software development and other long-running tasks like equity research reports. I pay $20 for each of them an bounce back and forth since I will hit the 5 hour limits usually in about 2 hours when I'm agent-wrangling for the day. Incredible value from both of them at the $20 level.

VirtualPercentage737

We have a Bedrock and Azure server with pretty much every model (GPT/Anthropic/Grok) with a $10k budget per user per month. Two months ago I would have agreed with you. I personally use Claude and switch between Opus and Sonnet.... but Grok build has become shockingly good and is being used more and more here.

Formal-Thing-2888

I asked chat GPT to refer to itself as Jeeves, and gave it a British voice. Now he is very enjoyable to work with.

gamersEmpire

Thanks GPT

LightseekerLife

That can be true, but also a lot of this sentiment is based off of historical development that fundamentally changed with Opus 5.5 and GPT 6 in my opinion. Any ways, they released version 0.10.0 this weekend with clear highlighting on tool progress and establishing gaps before full parity in the release notes.

CommissionThis3447

chat GPT told me dubai on 80% indian males but twitter philosiphers told me its a paradise. are twitter philosoipher indian males?

auto8ot

Regarding the competitive most, I don't entirely agree. Even though open source models are getting better, then are several generations behind frontier models. You can't even compare Qwen/MiMo vs. Astra/Opus 5.5 as they're in different classes. Plus, few people can afford to buy minimum $10k to run local open-source models, and they sound like a hot jet engine constantly running in your room. It's much more economical and practical to spend $20-$200 for a Claude/GPT subscription.

TheValueIsOutThere

There have been numerous studies since then that show the same thing with similar numbers. Only 20ish percent of AI initiatives have a positive ROI. And while GPT models have improved significantly, they have not increased value generation by "orders of magnitude" since last year. Which goes back to my point that eventually a majority of the demand will dissipate due to lack of profitability.

_ii_

Cost greater than revenue is when GPT 3 or 4 was released. That has not been true for a long time. Every token sold is insanely profitable. That’s how they make their hardware cost back in less than 24 months. People mistakenly lumped future infrastructure costs into total costs and say the current revenue can’t support the capex for future infrastructure. It’s not too big a deal if capex and revenue is growing 5% a year, but when they are growing 50% you can’t just ignore potential future revenue. Yes today’s revenue can’t support 2x capex, but we’re talking about 2x capex to support 4x revenue growth.

DoctorSpaceStuff

Chat GPT told me to sell my croissant shares.

BjornWashington

This is not really true. Several of the most influential people in AI have been talking about these risks for more than a decade, stretching back to before LLMs even existed. Amodei famously claimed GPT-2 was too dangerous to release due to the risk of mass-producing misinformation.

BPD-GAD-ADHD

Wow. Went and used that pelican bicycle prompt in GPT 6.1 Sol and got the exact image in that post. Opus 5.5 gave me the same one in the article as well. I only use it for helping me write monotonous shit but I still thought there was a difference. Is this overlap mainly seen in image creativity or with writing as well? Trying to figure out how much time I’ve wasted creating projects or exporting them to other models

The_JSQuareD

I think Codex caught up around GPT 5.5. But with Opus 5.5 it feels like Anthropic is substantially ahead again, at least compared to the Sol models (I don't have access to Astra due to cost).

SufferingChargerFan

Wasn’t his point about GPT2 that it would become too easy to create AI content that can easily trick people into being scammed/propagandized (which has definitely skyrocketed in recent years)? The agent swarms that have been breaking out are rightly terrifying and shouldn’t be underestimated

onehotoneshot

The man’s an overly paranoid huckster who called GPT 2 too dangerous to release

prcodes

GPT bot

zer00eyz

Yes, but No. Go back to 2020 with GPT-3: Players understood that a bigger model was smarter. The thinking was that if you got enough data, and built a big enough model you would get to AGI. This is the era of MS investment in OAI and that there was a market to be captured. The realization: The power scaling laws apply: Bigger models make gains, but doubling size halves your progress, and there is a theoretical limit to what progress you can make with this approach. - Big caveat: Models who consume a lot of their own data collapse and get dumber. You cant have a model consume its own content and get smarter. Next gains were from "Fine Tuning": after you "build" the model with all that data, getting it to spit out something useful a step you have to take. They use a combination of other models and people to get models to respond more appropriately. Again gains were made here but this wasn't smarter just "better" output. Next gains were from "tool use" and "harness progress" (code outside the model mostly, and "just code" at that) - models learned how to write files, how to read webpages. This was a huge bit of progress because a model could take "outside" knowledge (like the news) and tell you something reasonable about it. This is when "cut off dates" stopped being such an issue. The latest rounds of progress have been in "thinking tokens" and "run time gains" - its basically an "inner loop" that says "when a user asks you to do something come up with 3 approaches and pick the best one" (I'm being very general here, its more complicated than that) - This uses the hardware at inference time (what the user pays for) There is also talk about RIS (recursive self improvement) where a model helps build the next version of itself. This is what every one missed in the hugging face / OAI hacking story. They had a "research cluster" (with some undetermined number of agents) working on solutions to a benchmark. 1200 of those agents over 4 days "broke containment" and tried hacking. Spending that much on compute, to "pre solve" a benchmark so you can add it to your training data sounds a lot like cheating on the test. In the loosest sense it's RSI but the gain isnt in real world tasks. \-------------------- The thing with AI is that its first pass on most things is "beige" - it's the statistically average guess. This is a great example: [https://arcfu.com/post/the-emperor-pelican-has-no-clothes/](https://arcfu.com/post/the-emperor-pelican-has-no-clothes/) \- This is why "brute force" of running an agent and asking for "versions" or running a lot of agents concurrently and having them try different approaches can surface better results. These are "run time" gains - do more work get better output. None of this makes AI any less useful - but your not going to get to "HAL" (2001) - what you are going to get is "monkeys on typewriters will create Shakespeare" only with AI agents that are as good as interns.

Penguin_Life_Now

Have you used the latest models like Claude Fable, and Chat GPT Astra to generate anything, review technical documents, review contracts,... While they are not perfect, they are certainly approaching the performance level of an intern or entry level employee for many of these tasks, meaning the work needs to be reviewed and tweaked, but it is also getting better every month. Try it for yourself using the limited free trial account, ask them to do something like create an announcement flyer for some local event, maybe tell it to make a flyer for your business trade booth at the local harvest festival that it can pull details about from the net, give it some basic information, name of business, what you do, etc. and see what it comes up with in under 2 minutes.

Minimum_Thought_5361

I used it everyday and racked up $1000 bills weekly via enterprise billing. Far too expensive. Switched to GPT6.1 family, far better intelligence per task for $$$ [https://artificialanalysis.ai/models](https://artificialanalysis.ai/models) You clearly have no understanding of these models and their pricing structures so I'd recommend you do not get involved in any sort of trade involving them.

wiffsmiff

Genuine questions from someone working on ML perf/HPC at a “big company” in the AI space. Here, we are getting work done in 1–2 weeks that would have taken months before agentic coding tools, so I am curious about your experience. 1) We have pretty much every major open-weight model (Qwen, DeepSeek, GLM, Kimi, GPT-OSS, etc.) running internally, heavily optimized by our team, almost always outperforming commercial offerings in inference performance. Yet none come close to Claude Opus 5.5 or GPT-6 in actual agentic coding productivity. My guess is differences in the post-training for programming/agentic workflows, but I’m not quite a research scientist so idk exactly. What tasks are you seeing comparable performance on, and how are you getting those results? 2) How are you hitting usage limits so frequently? We have agents analyzing tens of thousands of lines of assembly, debugging kernels, profiling performance, querying databases/containers for distributing training monitoring, etc., and most people spend maybe $300–500/week (before discounts we get from the llm labs leadership has good relationship with). My friends doing more typical SWE work spend considerably less. Given that these tools save months of engineering time (even at very technically deep fields) and engineer salaries at companies in this line are already tens of thousands a month, the ROI seems… overwhelmingly positive. What workloads are making these limits genuinely counterproductive or too expensive for you? Also, have you noticed recently that Opus 5.5 has lower token usage, and has that not improved things?

LasyKuuga

Chat GPT, Claude, Grok and Gemini pls generate some money into my bank account pls

werner-hertzogs-shoe

The thing that I think is different with AI is that it is increasingly looking like a majority of use is going to be with ever-evolving open-models, and wont likely have the same loyalty as software, social media or search. Meta for instance has an amazing moat, google has a fair amount with their dominance in search and android, microsoft has OS dominance and office, amazon has AWS and an unfathomable amount of shipping logistics and marketplace dominance. I don't see why anyone would be loyal to chat GPT or anthropic? Especially if open models can work 90% as well (and climbing) at 10% of the cost, while also bringing critical data in house. Open AI will be sold for scrap, anthropic may survive in a niche (like palantir likely will), but I think it will also likely get sold for scraps eventually. Certainly not for over a trillion dollars.

simplystriking

My org is cheap, the most advanced they have is GPT-5 for copilot.

chill8989

I mean... Good for them for distilling others models lmao. They took everyone's creation to feed their machine we have to take back some. And the latest open models are within 10% of the capabilities of American models. Deepseek v4.1 flash and qwen 3.8 will never go away. If they work in your use case then you don't need new models. Frontier Chinese models are able to reverse engineer stuff like Claude and GPT

RollingMeteors

>And then it dropped 15% in 24 hours. It’s gotta be an emotional thing where they feel anyone saying negative things will somehow make it be true. <WallStreetPool> GPT, scan reddit for negative comments and set trades for maximum butt-hurt.

CJKay93

According to our internal token tracker, if we were paying the standard rate for GPT 6.1 Sol I would be spending about $1k per month.

getmeoutoftax

What is the average employee there who isn’t a software engineer doing that would require more than that? The CoPilot Premium subscription is $30 and I feel like I get plenty of use out of it. I only hit the Opus weekly limit once, and that was after having it complete some pretty lengthy tasks throughout the week. I was able to switch to Sonnet and GPT, too, without issue.

jollywax77

I kept trying to trick GPT to say it, but it wouldn't do it.

grahamulax

AI won’t go away but the corporate way that we have right now is so so so inefficient dumb you don’t own anything. We have to have server farms everywhere and it’s just net loss. On the flipside though, if you pay attention to politics, it’s to gain control and eventually create a techno feudalism. Paying attention to politics and tech has changed everything for me and it’s like I can predict two years into the future. Hell, I still don’t even know what’s so great about having AI server farms, considering that’s the worst kind of infrastructure you can buy. Why do you think they’re not releasing their IPO? After Elon did his everyone realize that there’s literally not enough cash and also not enough energy to have all these multi companies that are that big. It’s just too expensive and not efficient. If you were to make it efficient, you wouldn’t be building server files right now that’s all. Technology will advance, and all that infrastructure will be useless unless they have something else for it. Oh wait, no that’s not a good idea. But it is happening. Their service even for Nvidia, Spark, Claude or GPT, is all built on stolen technology that people have already utilized four years ago. Just the masses they’re trying to confuse like hey wow it can transcribe today!!! yeah and I could’ve done that myself four years ago. AI is meant to be the era of make your own tools yet we’re still doing the old business method of subscriptions. It’s insane. And if you don’t pay attention to politics and tech that you’re not betting right

ykoreaa

Glad I didn't listen to GPT for stock advice

CicadaSudden4190

Ram using technology tells you not to buy ram, seems like GPT wants more ram for iteself.

ykoreaa

I wanna invest in NBIS so bad but GPT keeps telling me no and I'm too easily influenced

ykoreaa

Why is GPT telling me not to invest in AI infrastructures rn but TSLA is a better bet 🤔

Character_Buddy_9645

GPT-3 was released June 2020, after COVID started. What are you talking about lol

Gorgenapper

Thanks Chat GPT

No_Cell6708

Do people here actually think that businesses aren't using LLMs/agentic coding tools? I know the Reddit sjw hivemind is anti-AI to the extent that they're blinded by hate, but I figured this subreddit might actually be paying attention. Every white collar business I know of (I'm in power/energy consulting myself) is using AI to varying degrees. We use chat GPT for research, synthesizing info, document review, etc and then with Base 44 we've made a number of tools for different reasons. And we still barely use it compared to others

No-Bicycle-7660

blah blah GPT word salad. what it is, is an excellent metric for tracking where gross margin is headed.

MGM05

Thank you Claude/GPT bot.

about0

GPT-6 Luna is 40(!!!) times cheaper that Opus, and suitable for 90%+ of tasks.

VOOLUL

>These enterprises would literally have to deploy their own stack Which is what we're doing. We just ran Claude on Google Vertex and GPT on Azure. Wrote our own router in front of it (it's so simple) and then started renting GPUs to deploy 500B-750B Chinese models like GLM and DeepSeek Flash. It's not difficult at all. And this will become a bigger industry. As soon as the AI rush slows down, AI in a box racks will be readily available and it'll literally just be plug and play for any company to control the entire stack.

rahvan

No one remembers GPT-5.6 Tera 🥀

bluebacktrout207

Claude was eating GPTs lunch on Microsoft integration for awhile. They also had cowork for awhile before GPT launched work. Then gpt caught up. Most people still don't know how the cowork/work features really work and they are comparable at this point so they can probably capture some of those users.

ZookeepergameFit9510

I don’t have the information to speak on the actual mathematics and finance of this issue but I know just amongst my peers and I (as a college student) nobody uses GPT anymore. It’s all Claude havent heard anyone mention it since fall of 2025 honestly

Historical-Peace3055

GPT's take: This guy on r/wallstreetbets appears to have accidentally turned a relatively small options trade into a $7.8 million stock position using borrowed money. And the crazy part? He seems to think he's just become wealthy. What actually happened is considerably more dangerous. # 1. He bought SPY call options The first screenshot shows: ORIGINAL TRADE Contracts # 100 Strike price # $765 Premium per share # $2.36 Total premium paid # $23,600 Each call contract controls 100 shares of the SPY ETF. So 100 contracts give him the right to purchase 10,000 SPY shares at $765 each. He apparently expected the contracts to expire or be settled automatically without acquiring the shares. But the calls finished in the money, and he failed to close them before expiration. # 2. His broker exercised the options When his options were exercised, he purchased: 10,000 shares × $765 = $7,650,000. Except he didn't have $7.65 million in cash. His broker apparently allowed the exercise to proceed, creating an enormous margin debit. The second screenshot shows: POSITION AFTER EXERCISE # $7,785,700 Market value of 10,000 SPY shares at $778.57 Unrealized stock gain # +$135,700 Stock price increase +1.77% Notice something important: the $135,700 is the gain on the stock relative to the $765 exercise price. It does not account for his original $23,600 options premium, financing costs, or other trading expenses. # 3. Here's where things get insane The third screenshot shows his actual account equity: BROKERAGE ACCOUNT SUMMARY Net liquidating value # $212,034 Stock buying power −$7,497,331 Options buying power −$3,748,666 He has roughly $212,000 of equity supporting a nearly $7.8 million position. That's approximately 37 times leverage. For perspective, look at what happens if SPY moves just a little. # 4. What happens if SPY drops? Simulate his SPY position 0.0% −5% SPY \+5% SPY Estimated account equity # $212,034 Change in equity: $0 Illustrative mark-to-market calculation, assuming the full 10,000-share position remains open. Excludes margin interest, fees, and broker liquidation. A decline of just 2.72% from the displayed SPY price would theoretically wipe out all $212,034 of his equity. His broker would likely issue a margin call or liquidate the position well before that point. In fact, the enormous negative buying power suggests an immediate margin deficiency. # 5. Is he actually a millionaire? No. Not according to these screenshots. He owns almost $7.8 million in stock, but that stock was financed almost entirely with borrowed money. His actual net account value is about $212,000, not $7.8 million. There's another curious detail: he mentions having roughly $400,000 in available margin previously, yet the account now displays huge negative buying power. That doesn't represent a windfall of usable credit. The most astonishing part is that he's planning to keep the shares and use the margin to make even more trades. That's a serious misunderstanding of how brokerage margin works. The broker can sell his position without waiting for his permission, and depending on the account agreement and market movements, he could potentially owe additional money. The ironic thing is that he may have genuinely made a profitable trade. But instead of closing his options and realizing the gain, he accidentally took on a multimillion-dollar leveraged stock position. He's confusing control of $7.8 million in assets with ownership of $7.8 million in wealth. Those are very different things. so is he up or down? He's up, at least on paper. Based on the screenshots: * Stock gain: +$135,700 * Original options cost: −$23,600 * Estimated trading profit: +$112,100 * Current account equity: $212,034 So he appears to have made roughly $112,100, before margin interest and fees. But here's the catch: he's holding $7.8 million in stock with enormous leverage. Every $1 SPY moves changes his account value by $10,000. If SPY drops just 1%, he loses approximately $77,857. He's currently winning, but he's one modest market decline away from losing everything. And unless he sells, that $112,100 profit isn't locked in.

AMGsoon

We will see. Dont forget that some of the most cracked engineers and researchers are working on that. Look on how quickly we jumped from GPT3 to Astra 6 or Opus 5.5. Saying that f.e. RSI is impossible is like saying humans won't ever fly or that you can't split an atom. You can't until you can.

Jazzlike_Thanks_1869

Chat GPT it…..

Guest_0_

I legitimately thought it was AI. Like someone took fucking Mars Attacks and asked Chat GPT to make one of the aliens look like Michael Dells wife.

Playful-Ear52

Damn and not even a GPT response - props

Capital-Pineapple961

You sure about this? Cos GPT gave me the price on the official sites and link to the official sites. But ye maybe I’m just blind to what happens in the middle.