kevinfcassidyfla

25-year Instructional Designer / Developer creating custom learning that gets real performance results.

[kevinfcassidyfla @Design4Training]

Is AI already racing towards the bottom?

https://t.co/FjDM2yZUcN

[Hedgie @HedgieMarkets]

🦔Tech companies that pushed employees to maximize AI usage are now realizing the math does not work. Microsoft, Meta, and Amazon all set internal targets that pressured workers to use AI tokens aggressively to hit productivity scores. The problem is agentic AI burns up to 1,000 times more tokens per task than a standard LLM query because it loops through multiple steps and self-checks.
OpenClaw's creator Peter Steinberger said his team spent $1.3 million on OpenAI tokens in a single month. Nvidia CEO Jensen Huang told his engineers they should be consuming AI tokens worth at least half their annual salary every year. The behavior has its own name now, "tokenmaxxing."

My Take
The cost trajectory works backwards from how the labs sold it. Per-token prices have fallen, but the number of tokens each task consumes has climbed faster, and the all-in spend keeps going up release after release. Agentic AI is the worst offender because the model talks to itself, second-guesses itself, and runs the same logic three times before landing on an answer. Goodhart's Law also shows up clearly here. When AI usage became the performance review metric, employees started using AI to inflate the metric, not because the task needed AI.

OpenAI and Anthropic are losing roughly $2 for every $1 of revenue, and the only way the math fixes itself is by raising prices or capping consumption per enterprise contract. Both moves slow the revenue growth the labs need to show on the IPO roadshow. Goldman Sachs and the underwriters know this, which is why SpaceX's S-1 came out before OpenAI's. Whichever AI lab files first gets the cleaner narrative, and whoever files second has to explain why their largest enterprise customers just started rolling back token consumption. The companies pushing tokenmaxxing internally are now the same companies signaling cost pressure externally, and that contradiction is going to show up in earnings the moment these labs start reporting publicly.

Hedgie����

[Hedgie @HedgieMarkets]

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.

My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.

This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.

Hedgie🤗

[kevinfcassidyfla @Design4Training]

@TBLightning any chance Oliver Bjorkstrand is a healthy scratch tonight? Asking for a friend. #NHLTradeDeadline #NHL #GoBolts

[kevinfcassidyfla @Design4Training]

@spittinchiclets @KnucklesNilan30 Would have been next level move but if Sabourin goes uppercut he doesn't see it coming.

[kevinfcassidyfla @Design4Training]

@spittinchiclets Smarter fight by WIFI. If you are going to stand and chuck, tuck the chin in the chest, look at the feet and throw. Protect the chin. @KnucklesNilan30 would be proud.

[kevinfcassidyfla @Design4Training]

@Spaced_Practice Hi Chris! I have been AI certified since 2005. Allen Interactions that is! :-) I would love to see some information on if generative AI could support any part of SAM, A-Z, and keeping the process integrity at the same time. All the best!

[kevinfcassidyfla @Design4Training]

@elearning Great demo session, David! I love and have used #articulate #storyline360 freeform tool for years now. Any experiential interface you can dream up, freeform can handle it! Courses need never be linear unless you desire it. #elearning
https://t.co/TaRP33j4uq

[kevinfcassidyfla @Design4Training]

@Spaced_Practice Hi Chris. I agree. In terms of good ID interface, it's what matters to the learner, not just an algorithm. I fear people may get enamored with how fast it can perceivably build text and next, but that's not what good ID's do. So we will see.

[kevinfcassidyfla @Design4Training]

@Spaced_Practice you and dad have been my gold standard for why we do eLearning. Chat GPT. Is there a place in "F" of CCAF for it? Dad always said if bad training is accepted, then we have a conundrum. Is ChatGPT a symptom of "good enough"? #ChatGPT

[kevinfcassidyfla @Design4Training]

@NicolePapaPhD Make sure your neighbor's name is not "Murphy". I have a few around here in Tampa, Florida. Oh, they love their power tools here! :-)