API

CrowdGoesWildWoooo

Seems like what they planned to do was allowing traders which probably has no background in software engineering to cook up their own vibecoded software but after careful consideration, the juice isn’t worth the squeeze. Also they are paying API which definitely makes it significantly less worth it.

SolomonKings7

I mean, frontier models are the best for prototyping. Big companies can create their internal AI via open-source one. Smaller one are relied on frontiers, but you they can always adjust API-token usage.

Embarrassed-Bank2835

I’ve built a few tools over time, mostly to reduce decision fatigue rather than add more signals. The most useful ones were pretty boring: trade journals, setup trackers, premarket dashboards, and scripts that automatically calculate things like win rate, expectancy, MFE/MAE, time-of-day performance, and rule violations. The less useful ones were usually the ones trying to be too clever. Biggest challenge was reliability. A tool can look great in testing and then become useless if the data feed is stale, an API changes, timestamps don’t line up, or one bad input silently breaks the logic. If I were starting over, I’d build around one specific pain point and keep the first version ugly but dependable. I’d also integrate existing APIs/tools where possible instead of rebuilding basic infrastructure from scratch. The stuff I still use is the stuff that saves me from repeating manual work or helps me review decisions objectively. If a dashboard just gives me more things to stare at during a trade, it usually makes me worse, not better.

Cannabis-Attorney

To address the big picture - in the .com era, software followed hardware. The early winners were the hardware companies (think CSCO) and then the software winners followed (GOOG, FB, etc) once the hardware infrastructure existed to enable the software winners to become winners. The same is playing out here. The early winners are the hardware companies (chips, networking, power, cooling) and software will follow. DOCN is building a moat of software middleware - the "orchestration" framework to build AI solutions by connecting inferencing, model selection, agentic actions, and "old school" cloud (database/storage/containers) in a way that is very easy for non-fortune 1000 and native AI companies to manage. These companies will have  core developer teams for their primary product but not a large (or any) team to manage "IT", so the ability for DOCN to say to them "here is a dashboard and single endpoint API - knock yourselves out" is very compelling. Additionally, DOCN is optimizing their platform (this is where it is actually good to own the hardware) so that their token speed and price optimization are competitive advantages for their customers. As we move into "the future" whoever can deliver the right tokens at the best price has a compelling business.

Zuitsdg

Of course work is covering for my enterprise job subscriptions and tokens. Maintaining, fixing, and developing a few applications. (But velocity is insane, basically doing weeks/month of projects in mere days) However, I am using a lot more personally/privately. At work, I am developing just regular 2020s enterprise applications - it’s slow as fuck for AI times. Privately, I am developing 2027 enterprise applications: basically running 10 Opus/Sol level agents 24/7 for over have a year already :D (also sometimes using it for freelance jobs on the side, and doing smaller side projects) $500 is my Anthropic share - I am also using Codex Max a lot, looking at an Moonshot AI sub for Kimi, and having API Keys for basically all AI providers (most because of OpenClaw in Q4, but I deprecated mine) My bottleneck has become CI runners for checks and harnesses. I have 50 runners privately at the moment, but it’s still the bottleneck :D basically merging PullRequest every 5-10 minutes. (And yes, it’s quite secure, I fix vulnerabilities hours or days before my enterprise security departments inform us) Coding is the main part, as I am an IT professional, but I am also running a few AI applications at work and privately for other professions. (Specialized fields, cutting hours of work into minutes) And for reference: we had to scale down LLMs, e.g. only using Luna or older GPT mini models to match human performance, as the higher models were way above human level in those task, but you don’t want those LLMs finding all the issues of your companies :D it’s enough to get a few improvements. Smaller side projects: some personal design/asset creation, did some music & soundeffects for my liking. (And yeah, I am also a musician, performed a lot last decade, produced an album and what not, but also like some AI ones. Couldn’t link one unfortunately) We still have a few years to a decade, but I am certain, we can do basically all work with digitalization, robots or AIs in the medium-term future. Will be an interesting decade to come)

mehmilani

I haven't used Claude for algorithmic or agent trading, so I can't offer any insight. I only used it to build my portfolio reporting tool using my brokerage account's API key, so it generates these kind of nice reports.

Zuitsdg

Of course work is covering for my enterprise job subscriptions and tokens. Maintaining, fixing, and developing a few applications. (But velocity is insane, basically doing weeks/month of projects in mere days) However, I am using a lot more personally/privately. At work, I am developing just regular 2020s enterprise applications - it’s slow as fuck for AI times. Privately, I am developing 2027 enterprise applications: basically running 10 Opus/Sol level agents 24/7 for over have a year already :D (also sometimes using it for freelance jobs on the side, and doing smaller side projects) $500 is my Anthropic share - I am also using Codex Max a lot, looking at an Moonshot AI sub for Kimi, and having API Keys for basically all AI providers (most because of OpenClaw in Q4, but I deprecated mine) My bottleneck has become CI runners for checks and harnesses. I have 50 runners privately at the moment, but it’s still the bottleneck :D basically merging PullRequest every 5-10 minutes. (And yes, it’s quite secure, I fix vulnerabilities hours or days before my enterprise security departments inform us) Coding is the main part, as I am an IT professional, but I am also running a few AI applications at work and privately for other professions. (Specialized fields, cutting hours of work into minutes) And for reference: we had to scale down LLMs, e.g. only using Luna or older GPT mini models to match human performance, as the higher models were way above human level in those task, but you don’t want those LLMs finding all the issues of your companies :D it’s enough to get a few improvements. Smaller side projects: some personal design/asset creation, did some music & soundeffects for my liking. (And yeah, I am also a musician, performed a lot last decade, produced an album and what not, but also like some AI ones. Music of the month for me: https://youtu.be/3TNpOD6bov8 ) We still have a few years to a decade, but I am certain, we can do basically all work with digitalization, robots or AIs in the medium-term future. Will be an interesting decade to come)

henryfw

I'm trying to use DCF with Gemini Argon on high. It thik # My Honest Answer: What Is a Reasonable Valuation? You have to separate **two numbers**: 1. **If your scenario were already guaranteed (**`OpenAI dead + China locked out`**):** * **$400B to $550B** is a very reasonable, disciplined valuation (weighted between Conservative and Central Duopoly, since `AWS` and `MSFT` will aggressively push their hosted open-source models to keep Anthropic from getting too much leverage — and **\~$200B–$280B** if you still refuse to pay for post-2036 terminal value). 2. **Reasonable valuation** ***today*** **in the real world (where OpenAI is still alive and open-source keeps improving):** * Give your "OpenAI dies + clean Western duopoly" scenario a generous **30% probability** (`~\$475B` midpoint) and our competitive/commoditizing base world a **70% probability** (`~\$105B` midpoint including a modest commodity terminal value): 0.30×$475B+0.70×$105B≈$216B0.30 \\times \\$475\\text{B} + 0.70 \\times \\$105\\text{B} \\approx \\$216\\text{B}0.30×$475B+0.70×$105B≈$216B * **Reasonable intrinsic fair value today:** **\~$200B to $250B**. * **Attractive buy price (with a real margin of safety):** **\~$125B to $175B**. Look at what that tells you about **$965B**: at $965B, you are paying the **Stretch Bull Duopoly price (**`\$985B`**) upfront today** — meaning if OpenAI literally collapses tomorrow, China gets banned globally, *and* Anthropic grows to $470B in revenue, **you barely make your 10% return, and if anything less happens, you lose money** # Then why did Series H print at $965B? Two structural reasons explain how private investors signed off on $965B without needing common stock to be worth $1T on a conservative DCF: 1. **Preferred seniority & strategic "round-tripping" (Sections 1 & 3):** * Private round investors bought **preferred stock** (sitting inside **$136.3B** of senior liquidation preferences that get paid back before common in a weak exit), not unshielded common equity. * Strategic cloud and chip backers simultaneously locked Anthropic into **$895.4B of infrastructure commitments** (Section 3). If a cloud/silicon supplier invests $10B at a lofty $965B headline mark while collecting $100B+ of high-margin cloud/chip contracts back from Anthropic, **the supplier profits on the commercial contract even if the equity mark gets cut in half**. Public common-stock buyers get zero supplier rebates. 2. **Venture call-option pricing ("AGI replaces white-collar labor"), not DCF pricing:** At \~$1T, buyers are not pricing an API or SaaS company — they are buying a 10%–15% probability call option that autonomous agents capture a multi-trillion-dollar slice of global payroll *and* that 2–3 frontier labs form a permanent oligopoly with pricing power.

Papierz_Safe

open weights still need massive GPU allocation and someone to maintain the routing logic though, so non-tech enterprises usually just end up paying the API markup anyway

Maxdiegeileauster

I hate this take, NO ENTERPRISE IS PAYING SUBSCRIPTIONS THEY ARE TO GET THE AVERAGE JOE OR SMALL BUSINESS HOOKED. At my company we pay ridiculous API prices to anthropic where they have 90% operating margins (as per their S-1).

Minimum_Thought_5361

OpenRouter measures traffic passing through its own API, not Anthropic's entire business. OpenRouter also shows open source models like Deepseek, GLM and Qwen are growing at faster rates than Anthropic. Ramp is a far broader dataset than OpenRouter. Their growth has slowed significantly. [https://ramp.com/data/ai-index?metric=token-spend&detail=maker&mode=volume](https://ramp.com/data/ai-index?metric=token-spend&detail=maker&mode=volume)

RationalBeliever

I have been building my testing system every week day for the last 2 years. I use the highest Claude subscription. Your prompting depends on your strategies, data API, broker API, etc.

atuladomicielo

API price is not the same as cost. They have 70%+ margins on API pricing. 

ThatOtherOneReddit

The issue for corporations who are the big people's main money maker is if you actually have a mid-size business that would reach 50%+ utilization equivalent on a cluster that is worth \~$100k is that you can buy a cluster to run those huge open models and get rid of $1k-3k a DAY API bills. The thing pays for itself literally in less than a year and you can just repurpose the equipment to the newest best model as you want. That's the danger to the big dogs. Also a 5090 has the GPU horse power to run a couple hundred Gig model at a few tokens/s but doesn't have the vram. If you could parallelize it across 2-4 for the $10k they were worth a year ago you could get buy ins at pretty low rates. If GPU & Ram prices even drop to what they were a year ago the appeal of Open Source is such a dominant cost comparison. Most companies need document processing and unstructured -> structured text processing. Agentically they need very small things also. With the capabilities we have now it will take years to integrate the AI capabilities we have and honestly most companies don't NEED more than that. Being a super math genius doesn't help 95% of business. The issue the cost to reach the scale these companies need is just beyond what the market is willing or wants to pay to reach it.

antei_ku

Inference is cheap. You can already run very competent quants on consumer PCs. Most companies not doing cutting edge research/development could sustain most of their usage on-prem if AI companies weren’t monopolizing supply just so you rely on their cloud API.. Just like before AI you only rented cloud compute for the massive storage, crazy specs or need the enterprise overhead

kurtcop101

There's plenty of data if you look. Also, it's obvious enough due to models of comparable size and the price gaps and how caching tokens work. I'm not gonna go through all the numbers, but subscriptions were calculated to be a 39% profit margin. API margins are astronomical.

Maxdiegeileauster

no, enterprise pays API rates. Enterprises aren't allowed on subs, only small businesses.

SpeedflyChris

So the trap they are in is this: Option 1: Continue spending enormous amounts that they can't afford on training ever-larger models, hoping that enterprise clients are always willing to pay a 2500+% premium over technology from a few months ago. Option 2: Cut spending on training, get forced to compete exclusively on price, go bankrupt due to spending commitments. They already know that the market is increasingly price-sensitive. Why do you think the latest flagship models have become cheaper? Why do you think there's such a big focus on launching cheaper versions of high end models (eg GPT6.1 Sol and Terra, both actually somewhat price-competitive)? The technology is great. I use it every day in my work, hell I have Qwen 3.8 Flash Next and Claude running on my laptop right now. Given that Anthropic let you burn $8000+ worth of API credits per month on a $200/month sub (losing a huge amount of money in the process of course) it makes sense, but for the enterprise customers who actually pay the bills, the cheaper models are already close enough that you rarely actually need super expensive models.

DonaldDeeeez

Yes China (who notoriously never steals American tech) said "We're powerful and don't need your AI" and we believe that??? Every financial incentive is for us to go full steam ahead with AI and you don't think this money would lead to pushing false naritives so others can make trillions??? Frontier labs are taking steps to stop distillation despite the gobs of money they make from those Chinese API calls. Ok ok I hear you. We can't let China get ahead. So make AI a private government project. We advance our tech and outpace China. Yeahhh that'll never happen lol. Too much money to be made by the frontier labs.

SpeedflyChris

You seem to think I don't rate the technology. I do, I just understand that the incredibly high priced API usage from enterprise customers that Anthropic et al are banking on does have some price sensitivity, and when the models that cost 1-3% as much are only a few months behind in capabilities, that's not encouraging for the investment thesis that they have any sort of moat at all.

SpeedflyChris

> What I don't know is if Anthropic is picking up any kind of margin on a customer like me or actively subsidizing me They're actively subsidising you. Try asking for the API-equivalent cost of the work you've done based on the log files (I think this only works if you're running in local folders, not sure if it works for cloud sessions). I got a couple of months of the top max plan to do a bunch of personal projects with it, and in the month where I used almost my entire allowance every week the api-equivalent cost was >$8000.

roughscenes

The current frontier models are significantly better the current open weight models, even when served via API at full model weight, never mind the stuff you could actually host locally even if you had four DGX sparks. Straight coding it may be fine, but the 'thinking' around knowledge work is far apart. Perhaps not for long, though.

Original-Poet1825

Inference is already profitable for Anthropic. They make their money via API. It’s the training costs that make it hard to be profitable. Probably they will just stop releasing models so fast, expand into other spaces beyond SWE, optimize inference further and increase costs. They have a lot of levers to play with tbh

SpeedflyChris

Yeah exactly. You can see it happening already as well. Why would I pay Anthropic $20-50 per million output tokens for jobs that open weight models can do for $0.08-$0.50 per million? That's why Openrouter traffic is now mostly cheaper Chinese models. If people aren't paying astronomical amounts for API access, suddenly those trillions in spending commitments become a little hard to afford.

SpeedflyChris

You're working on the assumption that all of these wildly unprofitable, hugely indebted companies will continue to pour ever larger amounts of cash into the buildout. I don't think that's the case. I think that it's more likely that, as smaller models increasingly reach the point of "good enough" for most home and business users, the price paid for frontier-level inference will drop even further than it already has (you can see this happening already with how much cheap models like Deepseek V4.1 Flash have taken over API demand), in the process hitting revenue for a lot of heavily-indebted labs and killing the economics of spending untold billions funded by high-yield debt on training larger and larger models

vadbv

On Opus 5.5 running out of tokens is an impressive task, unless you are just telling it to work endlessly on its own. I’m paying 90$ per month and have agents running 5 softwares Monday through Sunday and still only reach about 60% at most. How can that be considered expensive when one developer costs 100k per year? Mind you this developer is also doing marketing and finances for me…And subsidized relates to API vs subscription, but the API route seems highly unlikely as the business model for the long term.

bzl33

because they use API pricing. If you got a standard $100 or $200/mo plan it'd be more usage than this.

AnApexBread

AI is heavily saturated. AI start up companies are a dime a dozen. I personally know 4 people who left their company to stand up an AI company that is nothing more than building a fancy wrapper around something like Claude to sell as a solution to a company. Those companies are good to get massacred soon because they're entirely reliant on the major AI companies and whatever price they set the API keys at.

dirtyshits

Because agents rely on backend API’s that can be cutoff of access or charger. They already are charging AI companies for api access and every other company the past 2 decades plus. This is part of their revenue right now. Ads are a different beast though. Their whole goal is to keep you in their ecosystem so they can serve those ads. At worst they are still making money from their competitors using their search. The average user won’t be using agents and go with the path of least resistance. If your phone is android, you use YouTube, gmail, Chromebooks, and any other one of platforms they have you locked in.

chill8989

My company pays for Github copilot which uses an ai credit model. You basically pay API prices. Most devs on our team spends 200-300$ in credits every month. Unfortunately Github doesn't  Companies today are profitable serving deepseek and qwen and they're almost frontier. If anthropic and openAI 3x their prices a lot of people will switch to these cheaper models. 

noiserr

Yes, but instruction set is mostly just "API" at this point since each CPU has its own internal micro ops. And most CPUs do employ some sort of a mix between RISC and CISC internally. Which is why the ISA differences are overstated. It's more about the actual architecture of the CPU independent of the ISA.

Hacking_the_Gibson

Google will charge AI labs to hit their search API, easy.

CodResponsible8482

I know and I really use AI a lot, I work with making apps that talk through API’s to automate company processes with accounting systems and other forms of databases. Sometimes I stop and think about how many people that has to find new ways to make a living. And wonder how this will affect societies purchasing power in the future.

Lynorisa

It is impossible to block scraping. Even paid official reddit API "scraping" is a one-time payment that get uploaded to HuggingFace or Arctic Shift, or private startup/corporate circles. Tech companies have no qualms with pirating, stealing, or ignoring TOS. I would like to remind everyone that very early on after ChatGPT's release, it had an issue where if you typed some usernames from r/counting it would spew nonsense numbers. You don't need to be a data scientist to know there absolutely no reason to train an LLM on r/counting The only reason they were training on it was because they were training on literally everything. It cost them essentially nothing scrape and store the entirety of reddit. Reddit only rolled out hurdles to prevent years after, around the time they struck the deal with Google, way too late.

Echo-Possible

Not true. It’s reported as such by people who don’t understand the business. The revenue concentration in big tech is driven by cloud hyperscalars who host their models behind their own API services. Amazon Bedrock, Google Vertex AI, Azure Foundry. So tokens generated by Anthropic models hosted on those clouds are reported as spend by those companies. It’s not internal spend. It’s spend by those cloud hyperscalar’s enterprise customers. The clouds get revenue share for hosting and serving Anthropicms models for their customers. Hope you learned something today. Please stop spreading misinformation.

zjz

you're either talking about old API apps or devvit apps, neither of which are employee-made.

Phukovsky

Canadian here looking for API-based platforms. What do you mean 'only option for Canadians'?

beeArebel

so umm guys how's that 100k truth social API that we all invested in

Acceptable-Future-66

Their revenue has grown massively during a period when they’ve dominated but they still haven’t made a profit. And they have absolutely no moat. I am a data centre developer and a Claude code max user, I don’t want this to be a bubble, and I love the tech, but the numbers and analysis of their business model speak for themselves. It’s not about just having a high revenue, Anthropic is going to end up with like $1trn of capex commitments and it has to keep spending forever to stay ahead of the game. Critically, they can’t keep raising prices because there’s competition everywhere. Anthropic reportedly recently dropped API pricing. As with most people you’re confusing how great and useful AI is with the financial health of the key companies at the beginning of the boom.

Lunar_Excursion

it was never a long shot. if you follow the data, tirz had overtaken sema earlier in the year. this was the backbone of the investment. the reliable revenue that would hold the stock up regardless of what happens with Foundayo. now the deal with Trump was a twist, but a good one. it made it so that Zep and Wegovy were the same price. that's like a Honda Civic or Lexus, which one you want for the same price? the data above is validation of that very thesis. LLY gets 7/10 from Bridge. the added volume more than compensates for the price cut. the ATTAIN-1 drop was the alpha. the drop was too severe for the results if you looked further into the data. in addition, reta data was due this year but all the unofficial data was already well known by the results of the peptide bros if you followed that. that's more alpha. and lastly, they had a wild card in elora. the Phase 2 results were a nice surprise and validation of the pipeline. this led to the recent EloraTZP results and again, more alpha. Even at ATH $1400+, there is still alpha. people think orfo is a flop. they even missed Q3 expectations on revenue, BUT it's only the first inning... the first big catalyst is FDA approval for T2D. i think they will undercut Ozempic pill (formerly Rybelsus) on price/rebate and take over this market. i think they also transition jardiance and DPP4 users to Foundayo. there is a boatload of evidence the results are FAR superior. if weight loss is a story of self pay, then T2D is a story of reimbursement/rebates. if you know, you know. secondly is EU approval. it's just a matter of weeks away. Wegovy pill is API constraint because they have only launched in a couple of EU countries. they cannot supply all of them. Foundayo can and will. Lastly, they will launch in India and China. the small molecule margins allow aggressive pricing and in lower GDP markets they can scale very quickly. Wegovy pill has no such advantage. and then the ultimate ace up their sleeve. Foundayo will form the base of their "oral tirz". LLY is working on an oral small molecule GIP agonist. their patent is very mature with preclinical data, etc. they will combine it with orfo and have an oral version of their uber blockbuster drug just when patent protection is nearing an end. they also have mature small molecule GCG and small molecule amylin patents. they also have an oral "reta" peptide going to phase 2 just in case as well... i would also like to mention that they have very mature patents on a long acting "reta" as well. they will have an Innovation Day on Dec 7th and we may get some info, perhaps, on these preclinical assets that frankly will be blockbusters when tirz gets long in the tooth. so for me, i trust my DD and i size my position in accordance to the work i put in and the confidence i have in leadership, and stay attuned to execution. i usually roll my position, taking profits on the way up, and averaging on the way down so long as there is no change in fundamentals/thesis breakers. i let the data guide me... so far LLY has given me WSB sized tendies.... it was never a long shot...

Haunting_Ad_6021

Yes. I use Webulls API to semi automate my testing

Zing79

Except for one rather big quiet problem. These roll outs, all these companies are doing, are HEAVILY subsidized. These AI companies aren’t doing anything new. It’s the Uber model. Token cost isn’t anywhere near where it would actually will be. The rug pull on this will be generationally bad. Because these companies won’t undue the AI roll out even thought it’ll cost them more then the human employees they replaced with it. I’ve done openAI API usage. Which is factors more expensive than ChatGPT. And even those API tokens are subsidized.

jekpopulous2

Problem is that they can't jack the price when open-weight models are as good / cheap as they are. People sub to Claude, GPT, Grok, etc... because 95% of the tokens are subsidized. If any of these frontier models tried charging their subscribers anything remotely close to the API pricing everyone would leave overnight. Nobody is going to pay 50x more for Grok than Deepseek.

rudolf_de_Leeuw

On the channel point, a weaker take rate through the hyperscalers is more than a margin detail, because Amazon and Google are both investors and compute suppliers, so the same partners sit on the revenue side and the cost side. Before extrapolating the growth I'd want to see how much revenue comes direct through the API versus through cloud marketplaces, since those two streams probably carry very different margins.

NyaCat1333

They make the vast majority of their money through direct API usage from enterprise and big businesses and not people subbing for $20 a month. They currently have around 85% gross margins on these. Of course still deeply in the red because of unlimited commitments and new model research where most of their money goes towards. That 85% margin also tells you how little the actual subscriptions are actually subsidized. When they give you $2000 or so worth of credits for subbing for $200, they don't make a minus $1800.

CBFball

Now imagine what gets paid via API my man

Original-Poet1825

that will work until the claude code system prompt forces you to use their models with this option being available on API only. Then no individual will be able to afford it anyway given how expensive the API costs are right now

Sensitive-Talk9616

I am not an expert on this, but is ChatGPT actually indexing the web and creating the database necessary to perform the search? Or are they just using an API to pull search results from Google, Bing, or other search providers? In the days of human users, Google was able to use ads to monetize their search function. You claim that ChatGPT is now competing on providing search results. But, fundamentally, they still have to get them from someone. How much space does Google have in increasing the API costs or partner program costs? All the arguments I read are about the issues with serving search results or the monetization of them. But is the actual search function becoming obsolete, the indexing of the web Google is spending billions on? If the core product is still just as in-demand as ever, then Google should be able to keep increasing prices. If, in the future, most search users are agents, but the demand is still growing and AI service providers are happily paying for the search service, why would that be an issue for Google?

Kevcky

I think there's a pretty clear gap between your assumptions about enterprise data (&AI) and the reality of implementing it. You're explaining theoretical benefits to someone who's already buildingand testing these solutions. Back to Excel: reliable AI querying requires proper data ingestion, modelling and a semantic layer. Once you've built all that, most recurring business questions are already covered by standardized reporting. Yet people still export data to Excel, not because they lack answers, but because its practical use cases are enormous: it's literally a swiss army knife and no, 95% of them don't involve complex code heavy excels they supposedly wouldn't understand. Data warehouses didn't kill Excel. Neither did BI platforms or self-service analytics. They made data more accessible, but Excel remained the go-to tool for working with it afterwards. **I can easily see people interrogating and iterating over data through an LLM, only to export the results to Excel to actually work with them.** That's not Excel being replaced; that's another (much more expensive) way of feeding and wrangling data into it. We're already using AI where it matters to accelerate reporting development with Codex into Power BI and CoCo in Snowflake. We've also run PoCs with AI agents on Snowflake's semantic layer. Even with those foundations, AI lacks much of the operational context behind the data. In our testing, more complex questions without explicit guidance regularly produced incomplete, misleading or outright incorrect analyses. Giving directors unrestricted access can easily create more validation work than it eliminates. Trust me. The real acceleration we're actually seeing (and paying for) is in data preparation, modelling and report development, with analysts guiding and validating the AI. That's why I don't buy your prediction. AI is not eliminating its purpose. And to close on cost. Yours truly actually has to manage the Data & AI budget of our company and I think you're seriously underestimating the cost dimension both in terms of token spent and data warehouse compute. If you really want to leverage AI's full potential, with stronger models, API integrations, agents operating in sandboxed environments and iterative analysis, you're paying for actual token consumption and compute. A chat window in ChatGPT or Claude barely scratches the surface of what's possible. Every iteration consumes resources, potentially triggering additional database compute, while existing reports already answer most recurring questions. Multiply that by hundreds users freely interrogating data, and the economics become very different from the frictionless experience you're describing. I'll leave it at that. Hopefully this provided some useful perspective on the realities of enterprise data architecture and AI for anybody that cared to read it.

babayaga2026

I think it is directly related to the 'AI' Old Dips: Narrative-driven (DeepSeek, headlines). Earnings held up, dip bought. ​Current Sell-Off: Balance-sheet driven. Hyperscaler CapEx ($650B+) is shifting to debt while enterprise revenue stays flat. ​The Math: 3–5 year GPU depreciation schedules mean massive drag on earnings if API monetization doesn't catch up fast. ​Where we are: Post-Euphoria, early Complacency. Rallies are supply-heavy until free cash flow turns positive again.

skilliard7

I'm a software engineer too, but the cost per useful unit of work has massively come down the past few months. Back in July/August, I would spend like $500 a month coding with an AI agent because API prices were really high and you needed frontier models to do a good enough job for the complex applications I worked on. Nowadays, I spend less than $100 a month on AI coding because 6.1 Sol is dirt cheap and better than the best models from July.

blood__drunk

Flawed inference. Open router is for people who need to pay API rates and want access to cheap models. If you're using Anthropic then you've not got much benefit in going to open router apart from some edge cases.

Bartocity

Yeah but are you using Opus5.5 or fable to make slides? And would you pay Anthropic API costs to avoid making slides? Or are you just using copilot? Like me. (Fuck making slides)

ZapsTheCan

Can you tell me more about using other LLMs? I just use GPT. If that goes away, what is my "good enough" options? Install local PC possible? What is openrouter? Is that only for API devs?

beefycarrot93

To play the devils advocate here, Search as a whole is under pressure from AI which is a less profitable business, more competition and provides less ads (although they are planning to increase ads here too). The Cloud growth is impressive, but its important to understand what that growth consists of. Q3 2025 --> Q3 2026 103% cloud growth: * Traditional cloud services (VM's, Databases, Compute etc) - 54% * AI Compute Infrastructure - 29% * Chip leasing (Their own TPU system) - 10% * AI Software and API monetization - 7% The traditional cloud service growth is still great at 54 %, but a very big chunk of that this year is because of AI readiness, they transition infrastructure this year to be able to deploy ai models on infrastructure later. I'm not gonna say google don't look good with the price , but i would say its hard to project growth and value of the company. Cloud growth is not cloud growth, its partly cloud which is a high gross margin money machine and part AI which is not. Google is not valued only because of growth, but for gross profit margins too, that seems to be under pressure all across the here.

robotlasagna

I think you are misunderstanding. The model distillation API is used to take a general model and hyper focus it for only a specific task. This allows the model to be run on small hardware locally. This is the next phase of the AI proliferation where you have small devices running these models doing real work.

robotlasagna

I think only Anthropic or OpenAI possess the technical talent to do the next big thing: The understanding of reduction/specialization of these models into smaller hardware that fits into robots. Everyone else is just copying the frontier models which is the commoditization aspect. As an investor my thesis is that the real money comes from what they do next using their model distillation API which for both companies is proprietary.

BumbleSlob

>  It's typical techbro-speak delivered with hyperbole and confidence. Kind of a dick thing to say but whatever. The setup I am running is ridiculously good for $10k investment which is about 3.5mo of a developers API spend at my job.  It doesn’t take a top economist to realize these companies are extremely vulnerable.

BumbleSlob

^ extremely stupid take because the model I have now is the worst model ill ever have.  Also, I’ve already spent equivalent of 5k of what Opus API cost would cost me and the hardware itself has appreciated about 5k in total so I’ve already broken even in about 3 months so I disagree with your assessment. 

OvertaxedOne

The agent/harness layer is definitely the moat. I've used 3 different models today (for different purposes) all from different companies. The model is basically ephemeral at this point, just pick from a list, it'll probably be fine unless you're doing something really specialized. The harness layer, however, is very, very sticky. You could take all 3 models away from me tomorrow and I'd hardly care, just move to different/new ones in 5 minutes. Take away my harness layer?? Those are fighting words! We work with companies all the time setting up and deploying AI solutions, some local, some cloud. The biggest thing we give them is "DO NOT build to one API, and DO NOT use any harnesses that lock you into a specific model/provider". Those are the layers of the stack that very quickly get you stuck into 10X token spend if you go with something proprietary.

squatrackcurling

My esteemed colleagues, their gross margin on API is 90% and 50% on consumer products so they’re getting paid alright. Unemployed dumbasses posting brainrot memes from behind Wendy’s dumpsters is a free advertising for them, not an intended source of revenue.

evangelism2

AI will never get to the point, at least in reasonable businesses that are doing it correctly, that it will supersede the cost of developers. I can say this with 100% confidence as somebody who already leverages these tools right now. Even if I used them at full-fat enterprise API costs, it does not cost as much as my salary and it absolutely, at the minimum, doubles my productivity

AlanzAlda

Claude isn't as great as it seems on the surface, when you spend a lot of time with it. There's essentially no most there, cursor agents code better (with with grok), and openai codex is better at zero shot app development. Claude is also slow as hell. I can have codex take 4-5 rounds of actions, each doing more than Claude, in the same time it takes Claude to do one round. At the API level, Anthropic may lead the synthetic benchmarks, but it also costs like $10 to solve problems that open source models can solve almost as well for like $0.10. You can argue that this is actually a highly inflated margin and people will pay it anyway... But people are actually curtailing their use of it and switching to open router. The more you know...

admidral

And that’s one reason why you sign ZDR agreements with these companies or preferably that while using it on a third party like Bedrock. I bet that dude just has a 200 dollar plan or something not an enterprise API agreement. The DoD and those like that are what’s going to keep them in check

Dumb_Nuts

As another example, I'm on a 2-person investment team. I've automated so much of my workflow on process items while just sipping tokens. Coming from a larger org before where I had a team of operations/backoffice folks to handle docs, I was mildly annoyed with the work. I've hooked up to our reporting software API and automated 90% of the work. Would have needed to hire someone at $100k+ before to handle it, or pay our software vendor $20k to do the same. I built this on slow Fridays and a couple hours here and there on the weekends over a few months. That's ignoring how intertwined it's become with our actual investing process on data gathering, research, modeling, and internal materials. I'm not a programmer but understand how to structure automations and software from some hobby projects over the years. But in 2 years I imagine just about anyone will be able to one-shot extremely functional applications. I think there's a lot of people without access to the fully powered agents and models. My wife is F500, but not software and not given full access. Her job could be so much easier and it's only a matter of time.

MaybeLiterally

OP, being pro AI on 90% of Reddit is only going to result in downvotes. I'll agree with you, mostly because people are missing some key components of what makes Anthropic valuable: The things that AI will create for them. Bio is going to be big: [https://www.anthropic.com/research/agents-in-biology](https://www.anthropic.com/research/agents-in-biology) The idea here is Anthropic would patent the discoveries and license them for a cost. Labs will be here as well: [https://www.anthropic.com/news/introducing-anthropic-labs](https://www.anthropic.com/news/introducing-anthropic-labs) So don't think of it as JUST the model, there are 4 other major US companies that are making frontier models, the real way to make money is create the frontier model, use it internally for a number of months to produce discoveries while you're adjusting it for public use, and then when internally there is a new more capable model, release the previous one and continue to make discoveries. OpenAI is doing this as well. Don't get me wrong, the API, along with the public harness will be the main breadwinners, but keep an eye on how they're using the intelligence to create IP that they can make money on. Imagine if Anthropic managed to create a new and novel human insulin for diabetes, and since the cost to discovery was lower, they're able to sell it for much cheaper than current treatments. The value there is astronomical. Look at the bigger picture, and the potential opportunities. I will concede that the possibilities are theoretical, and we will need to see if these discoveries actually happen, but investors aren't looking at what it's doing NOW to make an investment decision, because you'd be right it's hard to justify a 1.5T valuation. Investors are looking at the next 5-10 years of potential. THAT, for many, justifies at least a 1.5T valuation. You might not think so, and that's fine. These stocks aren't for everyone.

tothe69thpower

Max 20x is a loss leader. It is only available to the public and not enterprise. Its only purpose is to juice up public visibility and hype around the company. All the money that Anthropic and OpenAI is actually making on inference is via the API (enterprise). But even in big tech nobody has dumb shit like "I have 20 agents running" because unless there's actual ROI they're gonna cut your limits. If I had 20 agents running loops 24/7 I would be burning hundreds of bucks a day in API cost, for what? Product management doesn't even make decisions fast enough to even make having 20 agents possible, let alone necessary.

Beetin

>For example the Max20 plan on Anthropic is $200/mo. If you pay through API tokens for the same limits, it costs about $4000. I mean, The premise isn't wrong (they are definitely losing money on compute), but believing Anthropic when they give you two numbers and saying you are getting a deal is true, is like going to a sushi restaurant and seeing '8 rolls costs $30 dollars, or buy all you can eat sushi for 80 dollars, and thinking that means they are losing tons of money on the AYCE if you eat enough.

Youknowimtheman

Which is funny because we're still in the "sell it below cost" phase. The real costs are an order of magnitude or more what subscriptions currently cost to be profitable and still be able to train new models. For example the Max20 plan on Anthropic is $200/mo. If you pay through API tokens for the same limits, it costs about $4000.

Outrageous-Risk-5747

The trouble isn't that anthropics software offering isn't good. It certainly is. The question is, do you think as a company it would survive given the insane amount of capital spending it needs to do just for its models to stay competitive? In my book, using any API other than those of the big cloud providers is complete harakiri

Minimum_Thought_5361

API price is the true price and it accounts for majority of their revenue. Subscriptions are heavily subsidised and will have to be hiked or heavily nerfed in the future.

DaFookCares

Most of Anthropic's revenue comes from 1% of their customers who happen to be start ups using their API's and operating at a loss, on investor money, trying to gain a market foothold. So, there's your point made.

-Crash_Override-

We run both GCP and AWS at work, and have used Azure extensively. Im hitting bedrock and vertex for API calls. If by 'easy to intergrate' you actually mean ''this is what we have at our disposal' those are two very different things. I've done plenty of cross platform work, GCP > AWS, Snowflake > GCP, etc and it is all equally as easy. So, yeah, youre exposing yourself as an amateur.

h3Xx

I work at a company where people would spend 1k dollars per day on tokens on Claude and anthropic made us premium partners. We have to use API only due to compliance reasons with ZDR and US-only traffic and Anthropic charges even more for these options. Today, we are using DeepSeek v4.1 flash at a fraction of the cost and much faster speeds. I can literally run 20 agents at the same time with a smaller price than haiku and on par intelligence of Opus. This is why I think openAI and anthropic are delulu with their evaluation. If they want to be competitive with the inference models they would either have to lower the price substantially which would slash their revenue or convince the public the models are malicious and ban them so they charge you at a premium. also we had a case where we were being hacked and anthropic were saying you have to pay more to become a cyber partner or we cannot help you.

Slow_Yogurtcloset388

Mainstream programming itself may no longer be a job except at the most experienced level. programming is just a means to end, to solve a problem. I am an engineer for hardware, and claude can easily outcode the software side of our products. It can take our documentation, look at our API more and map out and reverse engineer, rebuild our software. Its smaller more self contained stacks but a $200 claude subscription can replace a software engineer. The programmer often sits at the far side of what your product is trying to solve, because his job is not to use to product. I don't need to write the code, I need to understand the problem my product is trying to solve and work towards the solution while claude fixes it. Will tons of companies become dependent on this? Yes absolutely, just like tons of companies are dependent on email and excel.

demaraje

10T ahahhahaha. So it's going to become as big as Microsoft, Amazon and Alphabet PUT TOGETHER, by selling overpriced API tokens to businesses that ... do what? Bake cakes? I swear. I don't know if LLMs fried everybody's brain or you guys were so technically illiterate from the start

jack6245

Because it's setup on an existing cloud platform already not its own API so it inherits a lot of the organization setup, permissions and quotas from that

ehhhhprobablynot

AI is slowing killing the internet. Google itself sacrificed ad revenue with AI summarization because the risk of becoming irrelevant was greater. People have zero incentive to create new web content when AI just scrapes it and summarizes it before anyone ever visits their webpage, whether that’s Google or ChatGPT. Eventually there will be hardly any new human-generated content on the internet and AI will have nothing new to learn from. Reddit will likely one day be the largest depository of evergreen human-generated content on the internet. AI companies will have to pay for that content after they shut off the API and force people to log in. There will be short-term pain, but it does have a lot going for it in the long run.

skilliard7

It's not charity, but companies also aren't scrutinizing AI spend or doing a cost benefit analysis. Engineers were being evaluated on how many tokens they used, which led to a lot of engineers recklessly burning tokens to inflate rankings. Eventually that was discouraged, but AI budgets are still so high that cost isn't even being considered, so engineers just use the shiniest model available because they aren't being encouraged to cut costs, just to ship code. Claude models cost 4-5x as much per unit of worked perform than competing models. At the same time, cost per unit of work is rapidly coming down on competing models. Back in July, I was capable of spending $400-500 a month on API costs using cheaper OpenAI models(GPT 5.5) to get my work done. Had my employer given me enough budget for me to use Opus or Fable, I could've easily spent $2k a month. Costs have come down drastically on AI coding since then due to greater token efficiency, less mistakes (so less iteration), and lower price per token. I now spend under $100 per month using GPT 6.1 Sol. I have a coworker that was even able to Switch from Opus to GPT 6 Luna, and spends like $2 a month. I think revenue growth is likely to slow down considerably in the coming quarters. Anthropic's success will depend on a new wide-scale application for their models other than coding and agents.

alberto_467

>at 50% of the cost of Opus 5.5 Which cost are you referring to? The API cost or an estimate based on the subscription? Because the API price is wild but the subscription is not bad value

jack6245

We do use Gemini API in our product just because it's, cheaper, easy to integrate and has a lot of choices. For actual work though we dont

Lighthouse_seek

>pay $20/month for Claude You might be mistaken in how anthropic makes money. Almost all of it is from enterprise. That's where the real money is because if you are a software company and you need Claude code for your engineers, you need to pay API rates for Claude code, which is very expensive. In fact Claude subscriptions mainly exist as a gateway to get people to want Claude code at their company. Only 10% of revenue comes from subscriptions but they take up 40% of computing power Incidentally, Gemini is also weakest in coding. We'll see if it changes with Gemini 4 carbon, but I suspect a lot of companies will just stick with openAI/anthropic

98Saman

Even if you annualize Anthropic's latest quarter to account for growth, Google Cloud alone just did $30.7B in a single quarter ($122B+ run rate) growing 103% YoY, and actually generated $11.1B in operating profit instead of a net loss. And if Anthropic really does become a $10T company, Google owns around 14% of them and rents them the TPUs anyway, which only makes the case for GOOGL at $3.5T even stronger. Saying consumer doesn't matter in AI is wild. Selling enterprise API tokens is a race to the bottom where labs have to cut prices 30% to 40% every few months just to stay competitive. Google is already crushing enterprise with a $561B Cloud backlog, but owning the consumer ecosystem (Gemini just hit 1B users alongside Search, Android, and YouTube) is where high-margin ad and shopping revenue comes from without fighting an API price war. Also, comparing a frontier AI lab to a traditional SaaS company completely ignores unit economics. Traditional SaaS has 80%+ gross margins because serving normal software costs pennies. Running frontier AI models burns massive compute on every single prompt, which is why Anthropic's gross margins in the S-1 are around 40% and why they had to sign over $500B in non-cancelable compute commitments. When compute is literally your product, renting it means your cloud landlords (Google and AWS) capture the margin.

Teiktos

Who cares about consumers? They make the big bucks with companies that pay API prices.

Backrus

Yeah, and if China does both cheap memory (constant rumours about breakthroughs), and Huawei gets green light to start selling their cards worldwide, NVDA is cooked. Ofc not in the US because it's a crony oligopoly, but the rest of the world will have a viable alternative. Even if CUDA ecosystem is superior, Chinese lab proved that you can train and ship frontier models on Huawei chips. LLM knowledge has already pretty much converge and you need a really specific tasks to be able to tell the difference between models. So if everything is the same, I'm going with cheap Chinese API. Especially post IPOs when subsidizing subs will be against shareholders' interests.

FuckTheStateofOhio

There are diminishing returns on using conversational data for training. The time for reddit to capitalize on this was 5-10 years ago, when their API was free.

Street-Corporation

Anyone can just scrape Reddit unfortunately, the API is a great piece of software but anyone can build it now.

Beautiful-Onion-8925

1600 shares at 142 avg cost basis. Holding because core thesis is still in tact 8-10B annual revenue over the next 3-5 years with market cap over $100B They can get there a number of ways - most likely being 7-8B in ad revenue with $8-10 ARPU plus 1-2B in data deal Or if LLm citations do continue to chew into ad revenue then data money will have to scale more aggressively Right now is tough because there is no data deal but I think it’s safe to say a new deal will happen and at a much higher valuation than $60M per year from each LLM (likely closer to $150-200M per LLm or more) The main driver for the sell off today is DAUs stalling and that is 💯 because LLMs are citing Reddit data without user going directly to API There’s zero world that LLMs will continue to cite Reddit data at the rate they are without paying closer to market value for it- $8 ARPU over the next 3-5 years

pink_ego_box

>It's data >They can sell it for billions as training data for AI models!! >They sign an API agreement with Google for peanuts ($60 mil) >Data gets crawled for free by all the other AI models >Market cap $25B Ok regard that looks like deep value and a lot of alpha there

Exact_Entrepreneur66

LOL mango actually going ahead with truth social API https://www.reuters.com/business/finance/bloomberg-two-trading-firms-agree-pay-trumps-truth-social-faster-access-2026-10-29/

Last_Musician1579

When they made their API calls wildly expensive, this is what I hoped for lol

Fifteen_inches

anyone with two brain cells to run together could see how API sellers and AI scrapers are opposing and not cooperative. AI is built on theft and the bubble pops when the data holders want the AI scrapers to pay for it.

DefinitionOfAsleep

The weird thing is that they were talking about restricting access to accounts not logged in (and IIRC newly made accounts). Which is clearly aimed at restricting access to AI models training for free off their data, to force more companies to pay for API access.

StrikingMajor8488

> Did you just ignore the stuff about Kimi K3.1's reduced cyber capabilities? I did. The talks about "export restrictions in China" have been going on for literally months now. One single release with some shortcomings does not retroactively change the landscape. You ignored the question where I asked what exactly the restrictions right now at this very moment are imposed. > Except the model is at the moment only accessable with a paid API. A frontier model which has just been released and is not given all away to the global public accross the world for free with all the bells and whistles? Is there any chance you would expect the same or even a fraction of that from Anthropic or any of it's american competitors? > The open weight lobotomized release is on November 4th. What are the parts which have been "lobotomized" (other than the bits with reduced cybersecurity I reckon)? Do you have inside knowledge on the matter, something leaked publicy or is it one of those "trust me bro" instances? If you cannot use the cut own free and/or open models from China for your mission-critical cybersecurity or enterprise level tasks, then this discussion was never about you or your work or your perspectives. > Yeah thats where the money is. Which is the seguey into my last point I will make here, which harkens back to my first post to that other poster: **"AI will operate at a loss until they've succeeded"** using the Amazon and Uber model would mean the people are left with no other options and get compelled to use only the offerings of companies that extinguish competition and then raises prices once they become a monopoly/duopoly. China is offering the world options here that wouldn't allow Anthropic, OpenAI, the "free market"^TM AI companies to establish one-sided dominance all over the world, where all other competition has been snuffed out. Can't pump those IPO/stock numbers up if others don't believe your company is the next coming of mecha-jesus and the well and truly final manifestation of the AI-Gods.

SpeedflyChris

Is that Argon being less efficient, or Claude/Codex subscriptions being hugely unprofitable? I have a 20x plan for Claude that I got to do a bunch of personal projects, the API equivalent cost of all my usage last month exceeded $8000. A single 5 hour session limit on that plan will let you burn more than $500 worth of usage. Having tried out Argon on Openrouter it used fewer tokens and at a lower API cost than Opus would have been (actually came in cheaper than Sonnet on the tasks I tested), it's just that Google clearly aren't as willing to throw giant bundles of investor cash into a big fire all day.

Inprobamur

All the models use the same API standard.

contrary-by-nature

Honestly, I feel like it's motivated by simple P/L concerns. AI summaries scrape reddit. Disabling this makes them pay the API fees. Then humans read the AI summaries, clicking the inevitable reddit link inside, and then come read like one page on the website which costs them a small amount of money they don't make back from ads or whatever else.

Kalkaline

The writing was on the wall with the API issues and "new" Reddit. They had a good formula and started botching it long before they went public. 

Echo-Possible

A huge chunk of both Claude and OpenAI tokens actually pass through Azure, AWS, and GCP. They have revenue share because the cloud hyperscalars host every model behind an API. Enterprises trust a model hosted by the Big 3 over OpenAI and Anthropic who may or may not steal your business by RL post training on data aggregated from your prompts. The value of the Big 3 clouds is the same for AI computing as it is for traditional cloud computing.

Submitten

That’s exactly what it means. How can you index it if you can’t read it. They’d need an agreement or use API. Which isn’t usually what they want because google doesn’t want to serve up links to content that people can’t access.