Your Attention Is AI's New Bottleneck

This week, Every’s Frontier team, which tests new models and ways of working inside our own company, published what CEO Dan Shipper calls the most important formula in AI: it measures how much a model learns per unit of your attention—and shows how to maximize it. Also from the Frontier: why teams will keep paying for shared software when anyone can vibe code their own, and the professional video editing suite Kieran Klaassen built for himself in three days with Fable 5.2.—Grant Harvey

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What’s going on

• A bad week for Nvidia, in more ways than one. On Tuesday, investor Marcelo P. Lima claimed that Anthropic’s Mythos model (Fable with fewer guardrails, for Anthropic’s inner circle) took just three days to determine how to run itself on AMD chips. That threatens Nvidia’s deepest moat: CUDA, the software that has kept AI developers building on Nvidia hardware for nearly two decades. And then there’s the turmoil surrounding Jensen Huang: his ties to an alleged Chinese spy and multiple affairs, culminating in his resignation on Thursday, with CFO Colette Kress taking over as interim CEO. Our take: The Mythos claim is unverified, but the logic holds: The better models get at rewriting code for new hardware, the less any one chipmaker’s software can lock you in. Watch Nvidia’s earnings next Tuesday.
• AI is measurably speeding up AI research. The independent evaluator Model Evaluation & Threat Research (METR) concluded that Anthropic’s models are improving about twice as fast as they did from March 2024 to October 2025, mostly because Anthropic uses its own AI. The authors of AI 2027, a forecast in which an unchecked AI race ends badly unless labs slow down, say that puts us five months ahead of their timeline. Our take: Two weeks ago, we wrote that every model has a six-month shelf life now. METR’s estimate covers the past year, so that shelf life may already be closer to three months.
• Do this: Have AI build the machine. Common Tools cofounder Alex Komoroske’s advice: Don’t ask AI to win Factorio, a game where you win by automating factory lines—ask it to build a system that wins. Mike Taylor explains why: It’s “better and cheaper to get AI to build a system that solves a problem rather than solving the problem directly.” Next time you hand a model a task you’ll repeat, ask it to write the tool that does it instead.
• Do this: Set Ultracode and effort separately. In Claude Code, Ultracode coordinates AI agents to tackle complex tasks, while the separate effort setting controls how much reasoning the model does. Starting Ultracode from your phone bumps effort to “extra.”

A virtual summit for the new, new CFO

The era of the builder CFO is here. They are vibe coding deal desks, wrangling MCPs to deliver never-before-seen automations, setting up agent swarms to triage invoices, and imagining other inventive ways to methodically deploy agentic intelligence across finance workflows.

Traceable by Chargebee is a virtual event dedicated to documenting this shift. Join CFOs, controllers, finance engineers, and founders as they share field notes on the rise of the AI-native finance org. Featuring leaders from Public.com, Campfire, Sphere, Every, and other frontier teams.

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