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Today's five/N°31 · Thursday, November 5, 2026/Filed 03:49 ET

AI news today: Voters reject AI data centers, Meta buys Conductor

The data center backlash got a vote count, AI budgets got a ceiling, and the agent economy got a receipt from Uber.

The five · ranked by consequence

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  1. Voters reject AI data centers in 40 of 41 ballot measures

    Voters sided against data center development in all but one of 41 local ballot measures on Tuesday, CNBC reports, and 56% told its poll data centers shaped their vote. The buildout's biggest risk is now local, and it just won.

    Why it matters Every model you call runs in a building someone had to approve. If Ohio and Texas slow approvals or make operators pay their own grid costs, new capacity arrives later and costs more, and some of that lands in what you pay per token. Plan on compute staying tight through 2027.

    SRCCNBC

  2. Meta buys Conductor and shuts its cloud on Nov. 20

    Conductor, the app that runs several coding agents at once, is joining Meta, and its cloud goes read-only Nov. 13 and shuts Nov. 20. If you use it, your export deadline is two weeks away.

    Why it matters If your team runs Claude Code or Codex through Conductor Cloud, you have until Nov. 13 to keep working there and until Nov. 20 to get your data out. Export this week, then pick a replacement that doesn't hold your work in someone else's cloud.

    SRCConductor

  3. Apple trims engineers' AI budgets to $200 a day

    Apple cut some engineers' daily AI spending caps from $300 to $200 last month and may go to $100 for some teams, Bloomberg reports. Unlimited AI at work is ending, and the per-seat budget is the new normal.

    Why it matters If your company pays for your coding agents, a number like Apple's is coming to your team. The people who keep their tools are the ones who already know which tasks need a top model and which run fine on a cheap one. Learn that split now.

    SRC9to5Mac (citing Bloomberg)

  4. Anthropic's FlashProve proves which model answered you

    Anthropic and university researchers released FlashProve, an open-source system that cryptographically proves a 400B-parameter model produced a given token, at about 72 times the time of the prefill. It's slow and narrow, and it's a working answer to "did they swap my model?"

    Why it matters When you pay for a frontier model through any API, you take the provider's word that you got it. FlashProve shows that word can be checked with math on an ordinary CPU. It won't touch your bill this year, but it gives buyers and regulators something concrete to ask providers for.

    SRCAnthropic

  5. Uber says AI agents booked just 4 rides in its busiest week

    AI agents ordered four Uber rides and about 500 Uber Eats orders during Halloween week, out of roughly 320 million trips and orders, Uber said on its earnings call. Agentic shopping is a demo, not yet a channel.

    Why it matters If you're building for agents that buy things on people's behalf, this is the hardest usage number yet from a company every agent wants to plug into. Build for it, but don't budget as if the traffic is here. Uber's CEO says it's very early.

    SRCTechCrunch

Prompt of the day

Find the AI spend you can cut without losing quality

› Apple is reportedly cutting some engineers' daily AI budgets and pushing cheaper models. Do the audit before someone does it for you.

I'll paste a list of the AI tasks I or my team ran this week. For each one I'll give: what the task was, which model or tool I used, roughly how often I do it, and how much the output quality mattered (low, medium, high).

[paste your list here]

For each task:
1. Say whether it needs a top-tier model, a mid-tier model, or a small fast model, and give a one-line reason.
2. Flag tasks where I'm likely paying for reasoning I don't use (formatting, extraction, short rewrites, classification).
3. Flag tasks where a cheaper model is a false economy because a mistake costs more than the tokens.
4. Suggest one way to cut the input I send for each repeated task (shorter context, reusable instructions, summaries instead of full documents).

Then give me a table: task, current tier, recommended tier, expected savings (small, medium, large), risk (low, medium, high). End with the three changes I should make first. Don't invent prices. If you need a number you don't have, ask me for it.

Works in any AI chat. In Magai, run it against several models at once.

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