To me, the graph is more about price elasticity rather than the Jevons paradox.
Note that this is not the absolute number chart, but against the baseline of each model themselves.
So Luna price going to 10% -> 13x token usage, meaning dollar revenue is 1.3x regardless of the token usage/price. This is the real shape of the Jevons: 1.3x in this natural experiment.
A bit background info: Openrouter customers are super price sensitive. It’s understandable: they pay actual $ to make API calls. Meanwhile the API is unified, meaning switching model is just one line change (sure you need eval but not everyone is rigorous and many applications are truly not sensitive to model switching).
So what we have observed with Openrouter is that model traffic migration is the norm. Whenever there is a new model coming out with huge discount, usage shifts to it. Don’t take my word for it, see the latest traffic of Ox Alpha and also the MiMo v2.5 a few months ago.
Thus, both can be true: the exponential growth of the token usage will continue, and the revenue may just be growing much slower. And mind you Wall Street cares about $ not # of tokens.
When a natural experiment of slashing the pricing 10x gets 13x more usage in a highly fluent market, then my read is OpenAI does not really have much Jevons Paradox left to explore as of now, at least in the Luna tier. They are not getting much more revenue with the price lever.
My prediction: the end state by the end of the year is likely: 1) all flash level models are in a bloody race to the cost bottom, squeezed by Chinese models running on latest hardware. 2) all flagship level models are at $2.5 in $10 out, triple the usage right now, but frontier lab ARR growth slows down.
Continue to long things to price tokens, but relatively neutral on people selling the tokens.
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What happened when GPT 5.6 Terra and Luna were heavily discounted on OpenRouter?
Token usage exploded by 13.8x
Jevons Paradox = as technology makes the use of a resource more efficient, total consumption of that resource increases rather than decreases 🧵
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I name this "biological middleware" last year. Similar sentiment.
alright whoever came up with the term MEAT PROXY is insane
A universal truth:
own the substrate that compounds
I wrote about the "patent" period of frontier models a few days ago.
New data point is clear: for those "little brother" models, frontier labs are increasingly losing the pricing power, thanks to fierce competition.
This time around, GPT 5.6 Terra ($2/$12) is right below the Kimi K3 price ($3/$15).
The pricing dynamics will again be similar to big Pharma -- the premium drugs are premium, but the last gen and generics are cheap and competitive.
I won't be surprised that a natural next step for frontier labs is to open-weight those models one generation behind, because it would be easier to lock the ecosystem onto the harness + tax the inference providers than keeping up all the inference stack internally.
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GPT 5.6 的中档和低档模型都降价了,而且和我前文所写的“专利”药品一样:越是低档的价格降低越多。这次上线才三周时间就下调价格
无论是被迫还是主动选择:前沿实验室一定会持续使用这个策略 -- 即除最强模型之外(有定价权),在中档和低档模型上,不寻求很高的 margin. 目前开放权重模型也就是比最好的模型差半档的样子,因此为保障用户锁定在生态系统,就需要中档和低档模型的定价低于对应的开放权重模型。 Terra 的 $2/$12 就正好切在 Kimi K3 的 $3/$15 下方一点。
这样外推下去的话,一年内 OpenAI /Anthropic 把上一代的中档,低档模型开放权重我都不感到惊奇:为了继续让用户为最高档的模型付费,除了模型要保持领先外,生态也很重要。为了培养生态,把已经不能够赚取高额利润的模型开放出来给社区(继续有商业协议保护)是一个理性的选择。
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We are committed to pushing the model frontier across cost efficiency, capability, and speed.
Starting today, we are reducing prices for GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20% , and offering a faster option for GPT-5.6 Sol in the API.
Luna and Terra’s lower prices are reflected in how usage is counted in Codex and ChatGPT Work, so your usage goes further.
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You really don't need "software factory" or "loop engineering" to work effectively, or in this case burn $10K per month on a subscription of $200/month.
A few very simple tips:
* make every ticket ready to start -- pay attention to the grooming and design phase of any task -- ruthlessly delete tickets;
* set up a separate repo for the OS that's different than the code repo -- this repo stores working agreements, workflows, learnings, docs. Keep the code repo(s) clean and dry -- pure code and nothing else.
* always start working from the OS repo: OS + linear tickets == durable work context. Code is just a short-lived artifact
* if you actually need to use goals, write them down in the OS first and then issue them to agents.
* don't ever read code, but design a few "gates" your agent have to go through. For me: pixel gate for me to review UX, code review gate(automatic), release gate (automatic).
* Design your "squad" -- a group of agents together doing one task. One squad for one complete epic. Tickets should be filed at squad level, such as "finish onboarding v2", not at task level, like "add a button to onboarding".
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过去的 30 天其实没有用任何所谓的“软件工厂”/loop/graph,就是遵照一些基本的软件工程的原则,默默地就烧掉了 13B 的 tokens,折合 API 价格 $10110.
就是几个很普通的原则如上
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Two papers at #
SIGIR2026#: ROO and SilverTorch. Both were bets from 2021: fix the logging schema and better models will follow; a multi-funnel retrieval system reduces to one trainable layer.
The full story, five years later, and how to build a scaling law:
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Fun fact:
ARG SLOP SHOP is the exact anagram for LOOPS + GRAPHS.
Just saying.
Are we still talking loops or did we shift to graphs yet?
Just like what I said last week.
@claudeai Dear Claude,
Can you put this extension in a /loop every week?
Sincerely,
Every User of Claude
This honestly is why I build Noah to handle computer operations as opposed to just trust model writing bash.
You need a well designed and verifiable harness for bash.
Check out for fixing hard computer issues in minutes.
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I'm so angry... the OpenAI team is looking into it, but this feels like something that should happen with GPT-3.5.
Not a mid-2026 frontier model on the highest reasoning level.
Just made a quick post on r/MacOS about Noah -- an agent fixing your Mac issues in minutes.
This is the OSS version -- same harness as the SaaS version, bring your own key.
May I have some upvotes please. :)
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I asked GPT-live what's the square root of 1,258,963,547 fully expecting it would use a calculator as (I assume) it is out of distribution.
No, it just gave me the answer: 35,481.8763.
I then asked the log of it. Again correct.
Transformers are built differently.
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One should rationally assume a bug (of yourself-OS) before assuming astronomical bad luck.
As someone who actually homeschooled my three kids:
How fast you can make them learn the academic is not an important metric for children’s development, nor should be optimized via AI.
They pick up things quickly no matter what. AI tutoring has an alignment problem: it hits a very particular reward pathway that real world learning / truth seeking would not use — the instant reward and feedback loop that is effectively for passing tests but useless for long horizon tasks.
Structure and accountability throughout the day is the key. AI tutoring solves a sub problem, not the core of it.
Kids need challenging projects on top of all these iPad driven gimmicks.
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Hedge fund managers, venture capitalists, and tech executives are pulling their kids from public and private schools to enroll them in AI-tutoring startups that pay staff to stop calling themselves teachers and face no state requirement to report how well any of it works.
Alpha School, now in a dozen cities and expanding to nearly two dozen more this fall, charges $75,000 a year in San Francisco for two hours of AI tutoring followed by project workshops, using software that tracks how closely a child pays attention. Forge Prep in New Jersey charges up to $36,000 and promises graduates a $200,000 payout if they start a company instead of taking a job. Billionaire Bill Ackman has publicly backed Alpha. Neither school reports outcomes to any state, and Forge's own founder admits there is no data showing things are going well.
Alpha sells home-schooling software on top of tuition. Forge's startup fund assumes graduates will found companies rather than join a labor market its own AI curriculum is designed to help automate. Alpha's spokeswoman says its families are overwhelmingly finance, venture capital, and tech workers, meaning the industry building the AI disrupting entry-level jobs is now selling itself a private curriculum to land its own kids on the ownership side of that disruption.
Alpha's in-person staff voted to reject the word teacher, and its remote coaches monitoring the AI software are scattered across the globe. Stanford's Victor Lee says dropping the title diminishes the professionalism teaching requires. Stanford's Caroline Hoxby, who studies these programs rather than sells them, says there is negligible scientific evidence behind any of it.
My take: The same investor class funding AI tools built to gut entry-level jobs is now buying its own children a private exemption from those tools' consequences, for $75,000 a year.
Public schools absorb AI disruption unfunded and unreformed while this cohort builds itself an unaccountable alternative marketed as personalization. This is what class stratification looks like once it gets rebranded as coaching, guides, and life skills.
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@claudeai Dear Claude,
Can you put this extension in a /loop every week?
Sincerely,
Every User of Claude
Past Hello/你好/Bonjour, foreign language learning is pure tail risk management.
The longest 4 minutes of my life.
I believe the age of building AI native desktop apps is here.
As glorious as web is, many AI capabilities these days require a local runtime to fully interact with computers and applications.
I heard a lot of this new paradigm by building Noah. The key reflection can be summarized into one sentence: desktop app provides capabilities, backend provides intelligence and policy.
In this paradigm, each client release is almost boring and customers don’t even care about a version number, and you basically treat the desktop app as one of the many views of your SaaS.
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The only thing I type into my terminal these days is
> claude --dangerously-skip-permissions
Future humans may treat these as their first words.
Truly a beautiful moment where I am not sure if I should embrace more of the “Intelligent Design” theory or less. 🤣
Jokes aside Fable 5 seems to be very good at sequencing and self verification, which are key aspects of delivering long horizon tasks.
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So I gave Fable 5 the watchmaker benchmark: a full Swiss lever movement in Three.js. Real gear ratios (18,000 bph), working escapement, breathing hairspring — and the hands tell actual time. It verified its own work with vision, in a loop, until done. ⌚
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