Meta’s Muse Shopped So Hard Stripe Raised the Limits -- AI Brief October 23

Good day %%first_name%%. Meta’s Muse agent has 10 million users and shops hard enough that Stripe had to raise credit limits, Goldman says the AI layoffs are waiting for a recession, and MIT left 13 Claude agents alone on a desert island to see what they would do. They played.

Meta’s Muse Shopping Outran Its Credit Limits Fortune

* What happened: On Meta’s October 21 earnings call, Mark Zuckerberg said more than 10 million people in the US and Canada have set up Muse, its personal AI agent, since it opened on September 8. He called that pace more than twice where Threads started. About a quarter of them use it daily, shopping is the most common use, and the average purchase is over $200.

* Why it matters: Muse can browse the web, send emails and book travel for you, and shopping is what people trusted it with first. Demand was high enough that Meta worked with Stripe to raise credit limits in the first week, which Zuckerberg said were “above what any of us expected.” Muse stays free for consumers unless they want more tokens or more advanced models, and Meta plans to charge businesses a fee when Muse helps complete a sale.

* What everyone’s saying: The bill is the other headline. Fortune reports Meta spent almost $35 billion on capital expenditures in the quarter, posted negative free cash flow of $2.1 billion, and expects $135 billion to $145 billion for the year, and Zuckerberg called Muse “very compute-intensive to run.” Yesterday we covered Anthropic’s Claude Duet, the other agent launch in this race, and CNBC has the full earnings call rundown.

* My read between the lines: The number to watch is not 10 million, it is the cut: Zuckerberg said Meta will “get a percent of the value that we are helping to create for people.” CFO Susan Li described a “Personal Agent Protocol,” being developed with partners including Shopify and Walmart, where your agent talks to a store’s agent instead of browsing its website, and even she conceded adoption among businesses “will likely be uneven.” Retailers hearing that pitch can fairly wonder who ends up owning the customer.

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OpenAI’s Fast Decisions API Goes Public OpenAI and TechCrunch

* What happened: On Thursday, October 22, OpenAI made its Decisions API generally available. Built on GPT-6 Luna, it handles questions with a fixed set of allowed answers: you give it a question and the options, and it returns a probability, a choice or a score, up to 10 times faster than Luna through the standard Responses API. Since the beta it added prompt caching, US and EU data residency, and up to 400 questions per request.

* Why it matters: Apps and agents make lots of tiny decisions, like where to route a request or whether a command looks risky, and waiting on a big model for each one adds up. Datadog said it saw 40x faster p95 latency scoring the risk of agent-generated commands, and Speak said its time to next action fell from 1.4 seconds to 0.13. It costs $0.10 per million input tokens, with no charge for output tokens.

* What everyone’s saying: TechCrunch frames it as OpenAI’s answer to TypeSafe’s Jev, the model that created the category of fast, cheap “decision” models. OpenAI’s API product lead Nikunj Handa said his colleagues saw Jev and thought, “wow, we have like a million use cases for this.” TypeSafe is still the most queried on OpenRouter, while OpenAI’s model ranks eighth and fourteenth on two public indices.

* My read between the lines: Handa described the product as “just plain old Luna,” reconfigured for speed, which tells you how quickly a big lab can match a startup’s category. TechCrunch also notes it is unclear what makes a good decision model beyond “it works for you,” so ignore the leaderboards and run your own workload before you rip out anything.

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Goldman: AI Layoffs Are Waiting for a Recession Fortune

* What happened: In a note published Thursday, Goldman Sachs economist Joseph Briggs estimated that if AI lifts productivity by 15% in the US and other developed economies, up to 9% of workers could be “displaced during the AI transition,” with slower hiring weighing on employment by another 3%. That is roughly one in 11 workers.

* Why it matters: The finding is about timing. Briggs wrote that automation-relat