The AI money and infrastructure story is turning sharp. Reddit is walling off its content as search traffic collapses, Alphabet shed hundreds of billions in value over AI spending, and data centres have become a live election issue in the US. On the product side, specialised models and silent-speech hardware show where the next wave of useful tools is heading.

IN THIS ISSUE · 5 SECTIONS

 1  Major AI News: Reddit walls off its content, Alphabet loses $500bn in market value, data centres on the ballot.

 2  Fun AI Topics: silent-speech glasses, a nine-minute copy for AirPods, a haiku from orbit.

 3  AI Tools: Timestral, Granola Voice Typing, Riverside Agents, ElevenLabs SFX v3, Phonic-1.

 4  Expert Prompt: turn a task you keep redoing into a reusable internal app.

 5  Trending: ads aimed at AI agents, Prime Video's AI-made series, specialised models.

LEAD STORY

Reddit is moving toward a primarily logged-in platform, starting with tests outside the US, so most posts will eventually need an account to view. The shift follows a fall of more than 1 million search referrals per week in October, as AI-generated summaries replace the clicks Reddit once got from Google. US daily users dropped from 53.2 million to 50.7 million in the quarter, yet revenue still rose 53% to $895 million. The company is betting that a smaller, loyal, logged-in base is worth more to advertisers than drive-by search traffic.

Why it matters: It is one of the clearest signs that AI search is breaking the referral model the open web was built on, forcing publishers to own their audiences directly.

ALSO TODAY

Alphabet shares fell more than 13%, erasing over $500 billion in market value after disappointing third-quarter results. The company admitted that AI Mode search earns less advertising revenue than conventional search, with Search growth slowing from 17% to 12% despite queries hitting a five-year high. Google also committed to more than $1 trillion in future infrastructure spending. The sell-off was part of a wider market wobble, with the Nasdaq 100 shedding over $1 trillion on AI-spending concerns and S&P Global moving Meta's outlook to negative.

Why it matters: Investors are starting to question whether huge AI infrastructure bills will pay off, and the doubt is hitting even the strongest balance sheets.

ALSO TODAY

About 20 US communities are voting on data centre bans or pauses during the midterms, as the industry faces growing local resistance. Some Democratic candidates are calling for a federal freeze on new facilities, and a Democratic House could create major obstacles for AI infrastructure plans. OpenAI reportedly plans to rebrand the sites as "AI compute campuses" after the election and will emphasise jobs, while the White House has pushed developers to supply their own power. President Trump has suggested developers also pay local residents a dividend.

Why it matters: Power, land and community consent are becoming the real constraints on AI growth, and a rebrand is unlikely to settle public anger.

A startup called Subvocal has built glasses that let you dictate text by silently mouthing words. They send radar signals through the wearer's face, detect tiny muscle movements, and convert them into text at up to 200 words per minute. You can dictate, prompt AI, or message other wearers without speaking aloud. The demo drew more than 11 million views over a weekend.

Why it is interesting: It points to a future where you control AI in public without saying anything out loud.

While Subvocal raised $1.3 million for its glasses, developer Joe Blau rebuilt the basic concept for existing AirPods Pro in nine minutes with a single prompt and released it free on GitHub. His app, subvox, reads the AirPods' motion sensors to detect head movements made while silently mouthing words, then matches them against trained templates. It uses plain pattern matching rather than an AI model.

Why it is interesting: It shows how quickly a splashy idea can be replicated, and why an idea alone is no longer much of a moat.

Google's Project Suncatcher satellite answered its first AI prompt from orbit, with the open Gemma 4 model writing a haiku about a blue marble below and starlight feeding its core. The poem reached Earth as the satellite passed over Svalbard, Norway, and the chief executive called it a hello world milestone. Power is the catch: the craft can run only one of its four chips at a time, in bursts of about 20 minutes. Two more powerful satellites planned for 2027 will test whether laser links can let a fleet share a single workload.

Why it is interesting: Orbital data centres could one day avoid the local fights now slowing ground-based sites.

[FORECASTING] Timestral - A family of small foundation models for forecasting numerical data like sales and energy use, with zero-shot predictions and no retraining. mistral.ai (launch post)

[DICTATION] Granola Voice Typing - Dictation that works in any app and uses meeting context to spell names and jargon correctly. granola.ai (product page)

[PODCASTS] Riverside Agents - Agents that prepare recording sessions, edit finished podcast episodes, and draft promotional posts. riverside.com (product page)

[AUDIO] ElevenLabs SFX v3 - Generates layered sound effects from a single prompt, such as footsteps in rain with thunder. elevenlabs.io (launch post)

[VOICE] Phonic-1 - A voice model that can switch among more than 50 languages within the same call. phonic.ai (company site)

Context: Use this to turn a recurring task you keep redoing into a reusable internal app, using a capable model that can create files and run code. Enable file creation and cloud code execution first, then upload a real example of your task.

Here is a real example of a task I repeat often: [upload a CSV, brief, form, checklist, or sample output]. Build me an interactive app that accepts the inputs I need, processes them the way this example shows, validates and flags any missing data, and gives me copy and export options for the result. Walk through the full workflow with my real data so I can enter inputs, run the task, then review, edit and export the output. Then help me fix any edge cases and adjust fields or buttons.

Example use case: A marketing coordinator uploads a sample monthly report CSV and gets a small app that formats and exports the report in seconds, then shares it so colleagues can run the same process without rebuilding it.

Publishers are starting to show ads directly to AI agents, serving machine-readable pages with labelled sponsored blocks that human readers never see. Google has added ads to AI Mode follow-up queries and expanded to more countries, over 100,000 advertisers have reportedly used ChatGPT ads, and OpenAI is testing separate labelled sponsored chats. Anthropic is keeping Claude ad-free through subscriptions, though it can still encounter sponsored web content. The risk is verification debt, where one agent repeats a sponsored claim without its label and others treat it as independent research.

Why it is important: As assistants make more decisions for us, knowing which information is paid for becomes essential to trusting their answers.

Source: Google

Prime Video's Castle Walls is billed as the first end-to-end AI-created show from a major streamer, an 11th-century epic of two 17-minute episodes made with generative video tools. It tests whether AI can cut production time and cost without alienating viewers. One critic called it a Vaseline-smeared mess and compared the human characters to melting wax statues. The verdict was that telling an AI to make it cinematic is not enough to create watchable television.

Why it is important: It is a real test of whether AI-generated video can meet audience standards, and the early answer is not yet.

A growing category of task-specific foundation models is challenging the habit of reaching for a large language model for everything. Alongside Timestral for forecasting, TabPFN predicts values for tabular data, Kumo Relational works across linked database tables, and decision models turn application state into typed choices. The idea is to combine a reasoning model for planning with specialised models for forecasts, risk scores and decisions, while plain code enforces the rules.

Why it is important: This could lower development costs and make AI applications more reliable than relying on one general model for every step.

That's it for today's Daily Pulse. Forward this to one person who wants to stay ahead of AI. See you in the next one. - Nicolas