One reason I used it was that I wasn't locked into a single provider and switching them was as easy as changing a drop-down. Small feature? Sonnet or GPT5.4/mini? Large changes? Opus. And why not see how good Raptor Mini does this one refactor?
It also helped build an intuition of what wach model could do and which parts it was weaker at because you could try them almost side by side, especially if one model's output wasn't great.
That said, these were all side projects so nothing truly consequential. Otoh, you might leave some extra perf on the table but I found the models worked quite with the Copilot harness.
Yeah, this is a very useful abstraction layer. The entire concept of separating the model creator from the model runner is good for competition and is customer friendly. Which means they likely hate the concept and want to kill it.
Gosh, imagine getting to do that with your TV/Streaming subscription. Getting to pay one fee to access some set number of hours per month from any of the providers.
The problem is I can't afford the tokens! Even on my $10/mo plan, running either 100 opus, or 300 sonnet agent runs would cost hundreds of dollars - well above my budget!
27x for Opus is genuinely shocking. at that point you're not paying for convenience anymore, you're just paying a GitHub tax. OpenRouter or direct API makes way more sense unless you're really glued to the IDE integration.
Does it effectively bypass regional restrictions for you, so you can use something like the Claude API from unsupported regions such as Hong Kong, or does it still enforce the official providers' geo-restrictions?
OpenRouter is great for budget control, but as they are indirect APIs, your experience with cached tokens may vary, eventually costing much more than in direct depending on the providers.
You can pay with crypto though, which seems to be convenient for people under sanctions or with limited access, or if you are in low-tax jurisdiction (e.g. HK)
That said I think few people using openrouter are actually being selective about providers.
It took half a day to get my opencode setup, was not friendly. A lot of manually cross referencing model and providers. I was actually mainly optimizing for relatively fast providers. It all is super fragile and I'm sure half out of date; I have no idea if these picks are still fast, no promises they are still the same price (pretty terrifying honestly).
I'm mostly on coding plans so it doesn't super affect me. But man is it a bother to maintain.
What's annoying is that it's obvious. In the case of GPT 5.5, if Copilot is going to charge 7.5x what GPT 5.4 costs while OpenAI themselves via the API/Codex only charges 2x of what GPT 5.4 costs, that will immediately raise an eyebrow.
To anybody who's been watching the tech sector with a critical eye for pretty much any period from the late 90s and onward, this is just the enshittification process. For most of OpenAI's existence it's been obvious, to me, that investors were burning insane levels of capital to build the market, and now that folks are locked in, you're seeing higher fees, ads, etc. Yet again, the user is the product; the investors want to siphon your data, attention and once you're hooked, money. And for companies like Microsoft and Apple, those hooks can dig deep.
If you paid attention to the power requirements and amount of hardware being put into data centers, you should have realized that it cost them an order of magnitude more than you were being charged. To rework your analogy: they hooked you, now they're gonna see if they can reel you in.
They can only reel you in if its worth it. I still can code.
And while i do not spend 200$ privat, in my startup we discussed this and our current mental model is, that instead of hiring someone new, we prefer to have more money for tokens.
This is easier for us and has a bigger benefit. The cost of a new / first employee is very high, a 200$ subscription is not. Upgrading that to lets say 400 or 800$ is still alot easier and if i can run multiply and better agents with that money, lets goooo.
I'm looking at education -- teachers and students, not terribly tech savvy, are being mandated to use these tools. And then comes the rug-pull. It was worth it, but now it's outside of their budget. Poorer schools / students can't stay at the cutting edge; richer schools / students can.
“Enshitification” is just when unsustainable subsidies end?
Another reason to hate that word.
From a different perspective, you were granted an incredible gift from the companies who let you use their product on their dime. Hopefully you made the most of it when you had the opportunity.
No, it's much more than that. It starts with unsustainable subsidies, as Uber undermined the taxi industry with a ludicrous burn rate. And then, once everybody's hooked to the point that they can't imagine life without the product, you raise costs. And you iterate: raising costs, lowering quality, selling data, increasing addictiveness. Until everybody wants to get rid of it, hates every aspect of it, but is still hooked to the core product. I'm personally not using these tools, not using uber or Meta products. But I'm still using some Google products and it's hard to extricate them from my life now that I'm using them.
Let's call it for what it is dumping. Dumping things on market below cost of production. This should not have ever been allowed. RnD costs I can accept somehow. But in this case the interference should have always been billed for the real costs that it took to produce and pay off the capex.
Just be aware OpenRouter charges a 5.5% fee, I didn’t know until recently. I like the product, and I think the fee is fair, but if you want the absolute best pricing then go direct.
But with open router you can always just use the latest model. If you're committed to eg Claude opus then you're better off going directly to anthropic for sure, but if not, varying other models may be fine too, depending on use case and be massively cheaper. Eg new deep seek model with same mio context window or Kimi k2.6 with 270k context window for subagents which implement
>but if not, varying other models may be fine too, depending on use case and be massively cheaper
Do inference providers have standardized endpoints, or at least endpoints compatible with claude code? Otherwise to pay 5.5% on all your tokens just so it's slightly easier to swap providers (ie. changing a few urls?)
There's nothing trivial about getting a Google API key. Openrouter removes that stress from my life. And I can route requests to providers above a certain TPS threshold. And much more.
I had copilot mainly so I could write issues and throw agents at it, while I went off and did other things. Has been great for contained spot work.
At this point, I'll go ahead and leave it expire, and then consolidate between Codex and JetBrains AI. Especially since Xcode supports Codex with a first-party integration.
I thought I was smart for buying the annual plan after I graduated and lost my student plan and then GitHub taking away my Copilot Pro I got for free for being a author of a popular OSS project. Turns out I'm being punished for making that year commitment to them. I like to think I'm only a moderate user of GHCP so this is just terrible for me. I'm honestly thinking about cancelling and switching to alternatives while also looking at investing in a local LLM setup.
I understand it like : the 10 usd is for handling the business record, maybe also the harness, I get a few coins to kick tires, but to use it for anything real it’s pay as you go by the tokens list price.
They do for any new plan.
Those multipliers are only for people that paid annually. After their subscription ends they'll go into token based pricing like the rest of people.
One theory of the play of SpaceX might do if everyone migrates to query-based billing:
Provide cheap and unlimited access to Grok for programmers (hence the Cursor partnership/purchase for distribution).
-> This would drag massive revenue right before the IPO announcement, like if the company is super growing
-> At a loss, but don't worry, we need these funds to build the biggest datacenter of the universe.
This announcement would create enough momentum to increase valuation, and because of the merge of his companies, would save his X/Twitter investors from a tragedy.
-> Would also be a great service to Cursor investors and so, who are stuck with their VSCode fork
Which in turn owns Twitter. SpaceX is now a social media company in addition to a rocket company.
One theory I think Matt Levine posited, is that SpaceX will go public with dual-class stock that gives Elon control even with a minority ownership stake, and will subsequently buy Tesla, which doesn't have dual class stock, making SpaceX the singular "Elon Musk company", with him having operational control despite being public.
Lots of us have noticed that usage limits for Claude have been nerfed in recent weeks/months.
If anything, these new multipliers are more transparent than anything OpenAI or Anthropic have communicated regarding actual costs and give us a more realistic understanding of what it's costing these providers.
The fact that we were able to get such a substantial amount of usage for $20/$100/$200 a month was never meant to last and to think otherwise was perhaps a bit naive.
This feels like a strategy from the ZIRP era of tech growth where companies burned investor capital and gave away their products and services for free (or subsidized them heavily) in order to prioritize user acquisition initially. Then once they'd gained enough traction and stickiness they'd then implement a monetization strategy to capitalize on said user base.
However, inference costs for entirely good enough models are likely to keep declining in the future. We're probably hitting diminishing returns on model size and training. The new generations aren't quantum leaps anymore, and newer generations of open source models like DeepSeek are likely to start getting good enough.
There's going to be a limit to how much they can raise prices, because someone can always build out a datacenter and fill it up with open source DeepSeek inference and undercut your prices by 10x while still making a very good ROI--and that's a business model right there. Right now I'm sure there's a lot of people who will protest that they couldn't do their jobs with lesser models, but as time goes on that will get less and less. Already right now the consumers who are using AI for writing presentations, cooking recipe generation and ELI5 answers for common things, aren't going to be missing much from a lesser model. That'll actually only start to get cheaper over time.
Also for business needs, as AI inference costs escalate there comes a point where businesses rediscover human intelligence again, and start hiring/training people to do more work to use lesser models--if that is more productive in the end than shelling out large amounts of cash for inference on the latest models. [Although given how much companies waste on AWS, there's a lot of tolerance for overspending in corporations...]
Yups... Mythos is the smallest possible leap. Not a standard model generation advance, not even a version point advance. Just the smallest possible quanta of a change. We are absolutely hitting a plateau any day now. Any day. Any time. Any second now. Yup. Right now! Surely!
Yeah. AI progress is insanely fast if you compare it to anything else. Where else is a one year old technology already hopelessly outdated? 10 years ago is basically stone age.
I am continually tripped out by the fact when I was 16, I didn't have a 'smartphone' beyond a Windows Mobile 6 phone that had no internet on it.
Now, I have this high-resolution shiny object that can near instantaneously get any information I want along with _streaming HD video to it_ *anywhere*.
15 years even feels like a stone age. I can't fathom what it has to feel like people in their 60s and 70s.
I'm not quite 60, but it's always interesting to me that I feel quite the opposite of this. When I was 16, I didn't have a computer, didn't have a phone, had never used the Internet, but when I think of how life has changed, it's frankly not much. I woke up this morning, scooped my cats' litter boxes, took out some trash, made myself breakfast, ate that, read some news while eating, then lifted weights in my garage, had some work meetings, wrote up some instructions per a customer request from Friday, and am about to go drive to the lake to go do a 9 mile longboard loop.
That's very close to a normal day in 1996. The biggest difference is I read the news on my phone instead of a physical newspaper. The news was not any more interesting or informative because of that. I guess I can also still do the loop reasonably well, but I'm a lot slower than I was in 1996 when I was a cross-country state champion.
My parents are closing in on 70 and I guess I can't speak for them, but I'm at least aware of the daily routines of their lives, too. Walk the dog, do housework, DIY building projects, visit kids and grankids. Seems much the same, too, with the biggest difference being they're now teaching my sister's sons to play baseball rather than me, but shit, one of her sons even looks like exactly the same way I looked when I was 7! The more things change, the more they stay the same.
Depends on where you live. My dad is almost 80, grew up in a very rural area, and when he was 16 they'd just gotten indoor plumbing. Up until he was 14, his school was a one-room school house with no heating other than a wood stove. If you were the first kid to arrive for the day, it was your job to get the fire going in winter months.
Housework meant no laundry machine, no dishwasher, and possibly no vacuum cleaner. That means hand washing everything, and beating rugs with sticks and brushes to get the dust off of them.
The early lives of my grandparents (in their 90s) are so fascinatingly different to that of mine. But even by the time my parents were growing up in the 60s, life was not so different in the west. The real differentiators in living standards - energy, household appliances and cooking, modes of transport - were more or less figured out then. By the time my parents were young adults in the early 80s, so many of the aspects of "modern life" had been figured out.
I look at the life my kids live, and it's not so different to my childhood. The toys are similar, their housing is similar. Probably the biggest difference is the availability of content on demand rather than much more fixed TV schedules.
The big difference in the last 30 years hasn't so much been in the kind of middle class life you can live, but the number of people who live that kind of life. In the 90s 40% of people globally were living in extreme poverty. Now its under 10%. The kinds of lives the middle class live in China and Vietnam are closer to those of Europeans today, when even 30 years ago most people in those countries were living much closer to the way your dad grew up.
I wonder if AI will result in a step change of living standards? Perhaps along with robotics we'll finally get to do nothing at all at home? I'm not convinced it'll be quick though. Maybe another 30 years.
If your parents are closing in on 70, I would have expected you to be closer to not quite 50 than not quite 60.
I am just over 50 myself and I agree with your points. Technology has changed but life is largely very similar to wear it was in the 90s. At least day to day. Attitudes are way worse now.
I always wonder the views of older people. My parents are very technology forward and have been my entire life so it is difficult to gauge how different life is compared to when they were growing up.
It's easy to hear "Oh well I only had 640kb of memory and typed programs out of a magazine I got in the mail!" and see as distinct from having 'unlimited' resources and the internet.
Your insight is good ("The biggest difference is I read the news on my phone instead of a physical newspaper") that life sort of stays the same but the modality changes. People still go to the store like they did in the mid-1800s but now it is by car.
I wonder what our "industrial revolution" will be where the previous generation lived (ie: out in the country on a farm) totally different lives to the current (ie: in the city in a factory). Maybe when space travel and multi-planetary living is normalized?
> It's easy to hear "Oh well I only had 640kb of memory and typed programs out of a magazine I got in the mail!"
Since I was there (young, but there), I want to point out that this crosses three eras which all felt quite different:
1978: typed programs in from a magazine or loaded from a cassette (16kB, TRS-80)
1983: loaded programs from a floppy (64kB, Apple ][ and C64 etc)
1988: loaded programs from a hard disk (640kB, IBM PC and Mac).
Exact years vary but these eras were only about 5 years each. Nobody had a floppy in 1978 but almost computer user did by 1983; nobody had a hard drive in 1983 but almost everyone did by 1988.
To some degree this already happened with the move from the industrial city to suburbanization and then re-urbanization. In particular one of the most notable recent developments is that urban waterways are now pretty desirable places to be with parks and recreation; in most industrializing cities the waterfront was actively avoided because the industrial use made it polluted, smelly etc.
General agree... I still do the things (mid-50's) I used to do when I was a teenager with no computer, no phone.
But - now they are easier - I can read books on an e-ink screen and pretty much instantly find what I want to read next. I get my news on a phone. I used to watch TV/movies broadcast or on tape rentals. Now, I have just about everything I could ever want available - without ADs... those were such a time-waster.
What has changed is that I have access to MORE information than my local (or school) libraries could ever provide - in a variety of more accessible formats. Whatever tools I need to get "work done", I can find a myriad of free and open-source options.
But - the overall days and household family routines are the same - now, instead of reading a paper book while waiting to pickup my kids (or other family members) "back-in-the-day", I can read my device, or connect with my DIY communities online on my phone - or learn something new. I don't have to schedule life around major broadcast events, I can easily do many tasks while I am "out-and-about".
I mean let's be realistic - all that we know about the "mythical" Mythos is the carefully curated and release stuff by the Anthropic's PR team. Is it really a huge leap they are making it to be? I doubt it. In fact I bet if it was indeed that powerful and dangerous, as they imply, they'd find a way to release it immediately, devastate OpenAI and DeepSeek and secure a leading position in the market. Why is it not happening? I suspect because Dario is again at it, peddling his bullshit.
They've been like that for a while actually, I think at least since the big hype around ChatGPT 4.5 (or was it 5?) and that underwhelming, lukewarm, oversanitised presentation by Altman and his team.
And at some point even frontier model costs will hopefully come down (if there is still a meaningful difference between closed and open source models at that point) as all of the compute that's being built out right now comes online.
> because someone can always build out a datacenter and fill it up with open source DeepSeek inference and undercut your prices by 10x while still making a very good ROI-
Not sure how it all works out. Currently trillion dollar companies can't make a native app for platforms. Everything is just JS/Electron because economics does not work for them.
And here companies can make GW data center running very expensive GPUs for 1/10th of current prices. Sound little fanciful to me.
The price you pay for anthropic must include the price of training new and better models which is incredibly costly. If you use the models someone else already spend money to develop you don’t need to pay this price.
But the prices haven't been going up by multiples of 6 for the past few years. Things are actually changing now. I don't think it's over, but in the short term, it's going to be considerably more expensive.
They will smooth up the spike. Or be subtle and transform the existing quota so that they run out more quickly. Calling it caching, compression, optimisation, of course for the sacred benefit of the users.
My read is that the bubble as burst internally (angels, seeds, VCs, and even corporate got a grasp of the inflated promise). It will take while for the actual bubble to implode.
If/when it gets to the point where it can replace a skilled worker, the service can be sold for close to the same price as that skilled labour. But the AI can run 24/7, reliably, and scale up/down at a moments notice.
There's not going to be much competition to drive prices down, the barriers to entry are already huge. There'll likely to be one clear winner, becoming a near-monopoly, or maybe we'll get a duopoly at best.
I do. "Commoditize your complement". Want to sell lots of silicon? Give away good local models to run on that silicon.
Even if SOTA models in the cloud are a few percentage points better, most work can be routed to local models most of the time. That leaves the cloud providers fighting over the most computationally intensive tasks. In the long term, I think models are going to be local-first.
(Unless providers can figure out a network effect that local models can't replicate).
> In the long term, I think models are going to be local-first.
Why? There's an inherent efficiency advantage to scale, while the only real advantage for local models (privacy/secrecy) hasn't proven convincing for broader IT either.
Local first models aren't just more private than the API vendors, they also have the advantages of fixed cost, lower latency, and better stability - local models don't get nerfed/"updated" in the background like chatgpt does.
Maybe in a world where these AI companies behaved with some semblance of ethics and user-friendliness they would be on even ground, but for anyone paying attention local models are obviously the future.
That's a silly reason. For non-agent use cases what kind of utilization are you going to average on your own GPU, 5-10%? And that's without batching.
Even with overhead and scaling for peak use and a large profit margin, any company with an ounce of competition will be vastly cheaper than self-hosting. And for models you can run yourself, there will be plenty of competition.
The models I could reasonably run at home aren't experiencing big price hikes, as far as I'm aware.
Price is a reason to escape many proprietary models, but not so much a reason to self host. Buying an expensive GPU mostly for AI purposes is not likely to save money unless you load it all day long.
It's foolish not to care about privacy especially as a company. You know how it prevents you from emailing yourself your tax documents? Meanwhile thousands of employees are sending literal design docs, software, product goals, etc to several ai third partys. Not only is that insane, the companies they are sending it too intend too and openly admit to scanning the data, make software products themselves, and intend to create models that can produce their products automatically.
The reason local models hasn't caught on is several fold. It's marketing to say your company follows the latest trend, and there's an inherent pressure to keep AI companies afloat so the economy doesn't entirely collapse. The other is, it wasn't until the last month that these models have caught up to frontier models. They just did, and they are more efficient and don't require a team of 500 to deploy.
Why on earth would that happen when everything else is moving into the cloud to tie it to ever-escalating subscription fees and prevent piracy?
Even with gaming, where running high-end 3D games in the cloud seems like madness and inevitably degrades the quality of the experience, they won't stop trying.
"There's not going to be much competition to drive prices down, the barriers to entry are already huge. There'll likely to be one clear winner, becoming a near-monopoly, or maybe we'll get a duopoly at best."
Based on what exactly? So far every time OpenAI, Anthropic or whatever has released a new top performing model, competitors have caught up quickly. Open source models have greatly improved as well.
I expect AI to be just like cloud computing in general - AWS, Azure, GCP being the main providers, with dozens of smaller competitors offering similar services as well.
Right now China is flexing the future in my opinion. Smaller, widely available, frontier models for pennies on the dollar.
I think the future of ai will be breakthroughs that let it run on commodity hardware, and the average person will not be paying for it from the cloud unless they want to be surveilled or are stuck on older hardware.
Right now I am running about what was a frontier model 1-2 years ago on a junk machine. Some people are running what was a frontier model 4 months ago on PCs and laptops that cost 5,000. In a year I think the landscape will be even better.
Yes, a lot of people (not me). Why? Well because that was the whole value proposition of these companies, relentlessly pushed by their PR and most of the media- rememmber it was something something Pocket PhDs, massive unemployment etc?
Dunno, if in this day and age you are making inference more expensive, more scarce, you are honestly moving in the wrong direction and DeepSeek and others will gladly take your lunch.
> The hardware to run deepseek is still incredibly expensive.
Deepseek API pricing is very low compared to Anthropic/OpenAI API pricing.
For many, the 300% difference in pricing may be difficult to justify, if the quality difference is very small. And there will be many tasks where the most expensive/the best model, is not needed. Currently many people end up using Opus 4.7/GPT 5.5 for many tasks without thinking about it.
It's not really about that. China is eating the US's lunch when it comes to ai. Don't get me wrong opus is the strongest model out there today, but that's the us's only advantage right now. Deepseek,qwen,kimi, etc all have fundamental research making the models smaller, more efficient, scalable, etc. in the US the plan is to buy all the hardware, write legislature, embargo other countries, keep models and research closed, so people cannot innovate for the next two to five years.
Unlike the us chinas focus is on research and sustainable building. China also has really good infrastructure for energy, etc. it is also to their advantage to drop 5 billion instead of 2 trillion and beat the us while turning a profit.
Chinas focus in ai is less flashy and because they are the biggest manufacturing super power in the world right now, it directly feeds their economy. They aren't looking for applications or to replace thought workers with slop bots, they have natural needs for this technology. Us manufacturers can't compete so they have to keep companies from selling their goods there see byd. China sees it as commoditizing their complement, the us is risking its entire economy and it's environment and resources, kind of scary.
Near zero probability of that. The model is more efficient and the company who trained it did not blunder trillions of dollars to do so. China has better electricity infrastructure than the US too, so the likelihood they can scale out before the US ever could is high. Long term deepseek, Alibaba, etc hold the most cards for sustainable AI even despite the attempted Nvidia embargo
I am not shilling China, this is just what is happening right now.
I think the Chinese government works differently than the US government. I think China has been subsidizing their electricity grid for decades and leading the world on sustainable electricity namely solar. While the us has let their infrastructure rot and laughed at government inefficiencies for about half that time. The US has data centers running on gas right now while waging wars blowing up gas infrastructure world wide. It would be comical if it wasn't an environmental disaster. Most of them have no hopes at even getting enough power in well established areas short term.
I realize what I am saying may come off as propaganda because the US holds net negative views on China so here are some links.
I think because openai spent so much money upfront showing how it was possible to do this and laid out a product roadmap China got to get on board much cheaper and easier. I see no reason to not believe any of these companies when they say they didn't squander tons of money to do what they did because I don't know how openai has even spent all the money they have it's actually ridiculous to think about.
With this kind of pricing (sonnet 4.6 has 9x multiplier, previously 1x) it begs the question why use Copilot to begin with.
You could easily just buy the tokens directly and have a lot more choice as well.
GitHub has the full power of Azure with their hosted models but it's not being passed to consumers.
It also helped build an intuition of what wach model could do and which parts it was weaker at because you could try them almost side by side, especially if one model's output wasn't great.
That said, these were all side projects so nothing truly consequential. Otoh, you might leave some extra perf on the table but I found the models worked quite with the Copilot harness.
Gosh, imagine getting to do that with your TV/Streaming subscription. Getting to pay one fee to access some set number of hours per month from any of the providers.
Seems like folks would be better off with OpenRouter instead.
Does it effectively bypass regional restrictions for you, so you can use something like the Claude API from unsupported regions such as Hong Kong, or does it still enforce the official providers' geo-restrictions?
You can pay with crypto though, which seems to be convenient for people under sanctions or with limited access, or if you are in low-tax jurisdiction (e.g. HK)
That said I think few people using openrouter are actually being selective about providers.
It took half a day to get my opencode setup, was not friendly. A lot of manually cross referencing model and providers. I was actually mainly optimizing for relatively fast providers. It all is super fragile and I'm sure half out of date; I have no idea if these picks are still fast, no promises they are still the same price (pretty terrifying honestly).
I'm mostly on coding plans so it doesn't super affect me. But man is it a bother to maintain.
And while i do not spend 200$ privat, in my startup we discussed this and our current mental model is, that instead of hiring someone new, we prefer to have more money for tokens.
This is easier for us and has a bigger benefit. The cost of a new / first employee is very high, a 200$ subscription is not. Upgrading that to lets say 400 or 800$ is still alot easier and if i can run multiply and better agents with that money, lets goooo.
And Gemma 4 and other open models can easily be hosted even for schools.
Another reason to hate that word.
From a different perspective, you were granted an incredible gift from the companies who let you use their product on their dime. Hopefully you made the most of it when you had the opportunity.
Okay then this AI stuff isn’t an example of that even under your definition.
I've been wanting to get off MS more generally and this is good motivation. Will be playing round with OR this week.
Do inference providers have standardized endpoints, or at least endpoints compatible with claude code? Otherwise to pay 5.5% on all your tokens just so it's slightly easier to swap providers (ie. changing a few urls?)
Yep, you can plug deepseek/kimi/minimax into claude code just fine. Or run everything through another harness like opencode instead.
Apple still charges 30%. 5.5 seems pretty reasonable. /shrug I dunno.
Don't you still need to handle tokens with them? Also that's trivial.
> billing
Yes but you'd be paying for billing anyway.
> reliability
They increase reliability?
> middleware
Which you wouldn't need if you paid directly.
I'm not saying they shouldn't get 5.5%, but that list is mostly non-convincing.
> Apple still charges 30%.
3 of the 30 is for billing, with the rest mostly being gatekeeping with a fake justification on top.
For models that have multiple providers, they automatically route your requests to a different provider if one of them goes down.
A single point of access with a single key for all of these things is a worthwhile convenience.
Still worth it IMO to be able to switch from Provider A to Provider B if Provider A is having a bad day.
I had copilot mainly so I could write issues and throw agents at it, while I went off and did other things. Has been great for contained spot work.
At this point, I'll go ahead and leave it expire, and then consolidate between Codex and JetBrains AI. Especially since Xcode supports Codex with a first-party integration.
The only model I even used on Copilot was Sonnet and now its got a ridiculous multiplier.
At this point they might as well just charge per Million tokens like every other provider instead of having a subscription.
Pretty sure that's what they will eventually do
Provide cheap and unlimited access to Grok for programmers (hence the Cursor partnership/purchase for distribution).
-> This would drag massive revenue right before the IPO announcement, like if the company is super growing
-> At a loss, but don't worry, we need these funds to build the biggest datacenter of the universe.
This announcement would create enough momentum to increase valuation, and because of the merge of his companies, would save his X/Twitter investors from a tragedy.
-> Would also be a great service to Cursor investors and so, who are stuck with their VSCode fork
One theory I think Matt Levine posited, is that SpaceX will go public with dual-class stock that gives Elon control even with a minority ownership stake, and will subsequently buy Tesla, which doesn't have dual class stock, making SpaceX the singular "Elon Musk company", with him having operational control despite being public.
But they can't buy curser before their IPO so thats that?
Perhaps they have to much compute because Musk overpromised and Twittergroq doesn't need that much compute after he nerved the porn stuff?
If anything, these new multipliers are more transparent than anything OpenAI or Anthropic have communicated regarding actual costs and give us a more realistic understanding of what it's costing these providers.
The fact that we were able to get such a substantial amount of usage for $20/$100/$200 a month was never meant to last and to think otherwise was perhaps a bit naive.
This feels like a strategy from the ZIRP era of tech growth where companies burned investor capital and gave away their products and services for free (or subsidized them heavily) in order to prioritize user acquisition initially. Then once they'd gained enough traction and stickiness they'd then implement a monetization strategy to capitalize on said user base.
There's going to be a limit to how much they can raise prices, because someone can always build out a datacenter and fill it up with open source DeepSeek inference and undercut your prices by 10x while still making a very good ROI--and that's a business model right there. Right now I'm sure there's a lot of people who will protest that they couldn't do their jobs with lesser models, but as time goes on that will get less and less. Already right now the consumers who are using AI for writing presentations, cooking recipe generation and ELI5 answers for common things, aren't going to be missing much from a lesser model. That'll actually only start to get cheaper over time.
Also for business needs, as AI inference costs escalate there comes a point where businesses rediscover human intelligence again, and start hiring/training people to do more work to use lesser models--if that is more productive in the end than shelling out large amounts of cash for inference on the latest models. [Although given how much companies waste on AWS, there's a lot of tolerance for overspending in corporations...]
Now, I have this high-resolution shiny object that can near instantaneously get any information I want along with _streaming HD video to it_ *anywhere*.
15 years even feels like a stone age. I can't fathom what it has to feel like people in their 60s and 70s.
That's very close to a normal day in 1996. The biggest difference is I read the news on my phone instead of a physical newspaper. The news was not any more interesting or informative because of that. I guess I can also still do the loop reasonably well, but I'm a lot slower than I was in 1996 when I was a cross-country state champion.
My parents are closing in on 70 and I guess I can't speak for them, but I'm at least aware of the daily routines of their lives, too. Walk the dog, do housework, DIY building projects, visit kids and grankids. Seems much the same, too, with the biggest difference being they're now teaching my sister's sons to play baseball rather than me, but shit, one of her sons even looks like exactly the same way I looked when I was 7! The more things change, the more they stay the same.
Housework meant no laundry machine, no dishwasher, and possibly no vacuum cleaner. That means hand washing everything, and beating rugs with sticks and brushes to get the dust off of them.
I look at the life my kids live, and it's not so different to my childhood. The toys are similar, their housing is similar. Probably the biggest difference is the availability of content on demand rather than much more fixed TV schedules.
The big difference in the last 30 years hasn't so much been in the kind of middle class life you can live, but the number of people who live that kind of life. In the 90s 40% of people globally were living in extreme poverty. Now its under 10%. The kinds of lives the middle class live in China and Vietnam are closer to those of Europeans today, when even 30 years ago most people in those countries were living much closer to the way your dad grew up.
I wonder if AI will result in a step change of living standards? Perhaps along with robotics we'll finally get to do nothing at all at home? I'm not convinced it'll be quick though. Maybe another 30 years.
I am just over 50 myself and I agree with your points. Technology has changed but life is largely very similar to wear it was in the 90s. At least day to day. Attitudes are way worse now.
I always wonder the views of older people. My parents are very technology forward and have been my entire life so it is difficult to gauge how different life is compared to when they were growing up.
It's easy to hear "Oh well I only had 640kb of memory and typed programs out of a magazine I got in the mail!" and see as distinct from having 'unlimited' resources and the internet.
Your insight is good ("The biggest difference is I read the news on my phone instead of a physical newspaper") that life sort of stays the same but the modality changes. People still go to the store like they did in the mid-1800s but now it is by car.
I wonder what our "industrial revolution" will be where the previous generation lived (ie: out in the country on a farm) totally different lives to the current (ie: in the city in a factory). Maybe when space travel and multi-planetary living is normalized?
Since I was there (young, but there), I want to point out that this crosses three eras which all felt quite different:
Exact years vary but these eras were only about 5 years each. Nobody had a floppy in 1978 but almost computer user did by 1983; nobody had a hard drive in 1983 but almost everyone did by 1988.But - now they are easier - I can read books on an e-ink screen and pretty much instantly find what I want to read next. I get my news on a phone. I used to watch TV/movies broadcast or on tape rentals. Now, I have just about everything I could ever want available - without ADs... those were such a time-waster.
What has changed is that I have access to MORE information than my local (or school) libraries could ever provide - in a variety of more accessible formats. Whatever tools I need to get "work done", I can find a myriad of free and open-source options.
But - the overall days and household family routines are the same - now, instead of reading a paper book while waiting to pickup my kids (or other family members) "back-in-the-day", I can read my device, or connect with my DIY communities online on my phone - or learn something new. I don't have to schedule life around major broadcast events, I can easily do many tasks while I am "out-and-about".
Friction has been reduced.
And at some point even frontier model costs will hopefully come down (if there is still a meaningful difference between closed and open source models at that point) as all of the compute that's being built out right now comes online.
Not sure how it all works out. Currently trillion dollar companies can't make a native app for platforms. Everything is just JS/Electron because economics does not work for them.
And here companies can make GW data center running very expensive GPUs for 1/10th of current prices. Sound little fanciful to me.
It has been years now, of cash injections, investors can't keep feeding the beast forever.
That would be, even is, the smart thing to do.
In other words.
The bubble has burst. You're just in denial.
If/when it gets to the point where it can replace a skilled worker, the service can be sold for close to the same price as that skilled labour. But the AI can run 24/7, reliably, and scale up/down at a moments notice.
There's not going to be much competition to drive prices down, the barriers to entry are already huge. There'll likely to be one clear winner, becoming a near-monopoly, or maybe we'll get a duopoly at best.
Even if SOTA models in the cloud are a few percentage points better, most work can be routed to local models most of the time. That leaves the cloud providers fighting over the most computationally intensive tasks. In the long term, I think models are going to be local-first.
(Unless providers can figure out a network effect that local models can't replicate).
Why? There's an inherent efficiency advantage to scale, while the only real advantage for local models (privacy/secrecy) hasn't proven convincing for broader IT either.
Maybe in a world where these AI companies behaved with some semblance of ethics and user-friendliness they would be on even ground, but for anyone paying attention local models are obviously the future.
Even with overhead and scaling for peak use and a large profit margin, any company with an ounce of competition will be vastly cheaper than self-hosting. And for models you can run yourself, there will be plenty of competition.
Price is a reason to escape many proprietary models, but not so much a reason to self host. Buying an expensive GPU mostly for AI purposes is not likely to save money unless you load it all day long.
Because of nonexistent regulation. Just wait for it…
The legal situation in for example the EU is crystal clear, only that it will take some time to go though all court instances.
The reason local models hasn't caught on is several fold. It's marketing to say your company follows the latest trend, and there's an inherent pressure to keep AI companies afloat so the economy doesn't entirely collapse. The other is, it wasn't until the last month that these models have caught up to frontier models. They just did, and they are more efficient and don't require a team of 500 to deploy.
Why on earth would that happen when everything else is moving into the cloud to tie it to ever-escalating subscription fees and prevent piracy?
Even with gaming, where running high-end 3D games in the cloud seems like madness and inevitably degrades the quality of the experience, they won't stop trying.
Based on what exactly? So far every time OpenAI, Anthropic or whatever has released a new top performing model, competitors have caught up quickly. Open source models have greatly improved as well.
I expect AI to be just like cloud computing in general - AWS, Azure, GCP being the main providers, with dozens of smaller competitors offering similar services as well.
I think the future of ai will be breakthroughs that let it run on commodity hardware, and the average person will not be paying for it from the cloud unless they want to be surveilled or are stuck on older hardware.
Right now I am running about what was a frontier model 1-2 years ago on a junk machine. Some people are running what was a frontier model 4 months ago on PCs and laptops that cost 5,000. In a year I think the landscape will be even better.
Yes, a lot of people (not me). Why? Well because that was the whole value proposition of these companies, relentlessly pushed by their PR and most of the media- rememmber it was something something Pocket PhDs, massive unemployment etc?
Considering most of the cost of producing a model is the upfront cost rather than the running one, I kinda still do.
The point was never to produce 4 frontier models per company a year.
Deepseek API pricing is very low compared to Anthropic/OpenAI API pricing.
For many, the 300% difference in pricing may be difficult to justify, if the quality difference is very small. And there will be many tasks where the most expensive/the best model, is not needed. Currently many people end up using Opus 4.7/GPT 5.5 for many tasks without thinking about it.
Unlike the us chinas focus is on research and sustainable building. China also has really good infrastructure for energy, etc. it is also to their advantage to drop 5 billion instead of 2 trillion and beat the us while turning a profit.
Chinas focus in ai is less flashy and because they are the biggest manufacturing super power in the world right now, it directly feeds their economy. They aren't looking for applications or to replace thought workers with slop bots, they have natural needs for this technology. Us manufacturers can't compete so they have to keep companies from selling their goods there see byd. China sees it as commoditizing their complement, the us is risking its entire economy and it's environment and resources, kind of scary.
I am not shilling China, this is just what is happening right now.
I think the Chinese government works differently than the US government. I think China has been subsidizing their electricity grid for decades and leading the world on sustainable electricity namely solar. While the us has let their infrastructure rot and laughed at government inefficiencies for about half that time. The US has data centers running on gas right now while waging wars blowing up gas infrastructure world wide. It would be comical if it wasn't an environmental disaster. Most of them have no hopes at even getting enough power in well established areas short term.
I realize what I am saying may come off as propaganda because the US holds net negative views on China so here are some links.
https://www.technologyreview.com/2025/07/10/1119941/china-en...
https://www.wired.com/story/data-centers-are-driving-a-us-ga...
I think because openai spent so much money upfront showing how it was possible to do this and laid out a product roadmap China got to get on board much cheaper and easier. I see no reason to not believe any of these companies when they say they didn't squander tons of money to do what they did because I don't know how openai has even spent all the money they have it's actually ridiculous to think about.
https://the-decoder.com/openai-adds-111-billion-to-its-cash-...