😺 Google launches Genie 4 with sound

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Here’s what happened in AI today:

• 😻 Google launched Genie 4 for interactive AI worlds.

• 📰 SK hynix reported an 80% operating margin.

• 📰 Oracle agreed to offer Google's AI chips.

• 🍪 ChatGPT added iCloud Mail and Calendar connections.

• 📖 Research shows AI can change how we think.

…and a whole lot more that you can read about here.

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😻 Google's Genie 4 builds AI worlds you can control (with sound)

Imagine asking AI for a snowy mountain, stepping into it, hearing the wind, then saying "add a dragon."

That's what Google DeepMind is chasing with Genie 4, its new AI world model. Instead of a video you watch, you explore a world that changes as you move through it. And this time, it comes with sound.

First, what's a world model? Most AI video tools generate a clip, and you sit back and watch. A world model simulates an environment that responds to your actions. It's the difference between watching someone walk through a forest and playing a game where you pick the path. Turn left, and the scene should change. Walk around a rock, and it should still be there.

Here's what Google says Genie 4 can do:

• Create photorealistic worlds you navigate with keyboard controls and change with plain-English instructions.

• Generate ambient sounds, music, and conversations.

• Simulate physical interactions, like objects colliding and people moving.

• Team up with Gemini models to steer complex, multi-step events.

That last one might matter most. Google says Gemini 3.8 Flash can act as a guide: it watches what happens, decides what should happen next, and instructs Genie to keep the simulation on track.

Picture a robot practicing how to pick up objects in a simulated kitchen. Gemini checks the result after each step and adjusts the plan. That could let AI train on situations it hasn't seen in real life.

Basically, Google wants robots to make their mistakes in a fake kitchen instead of yours.

The uses go beyond gaming. Filmmakers could preview virtual sets before spending a dime on production. Game developers could test levels before building them by hand. Google has even paired Genie 4 with its Gemini robotics research to simulate a humanoid robot playing checkers.

Here's the catch: Genie 4 is still a research preview. Google admits objects, physics, and spatial details can get inconsistent, especially in longer sessions. Access is limited to trusted testers for now, with Google AI Ultra subscribers (via Project Genie) and select Gemini API and AI Studio partners next.

So this isn't a perfect simulator of reality. It's a shift from AI that makes finished media to AI that makes interactive experiences.

What we're watching: whether Genie keeps the same objects, rules, and consequences over longer sessions. A pretty two-minute world is cool. One reliable enough to train a robot for your house is the real breakthrough.

Watch Google's Genie 4 demos

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🎓 AI Skill of the Day: Find out why your project keeps slipping

You know that project that's been "almost done" for three weeks? Everyone's busy, the meetings keep happening, and the deadline keeps moving.

Atlassian's 2026 State of Teams report found that 87% of surveyed knowledge workers said they lacked time or capacity to coordinate while everyone was busy executing. Reviews, signoffs, and handoffs can pile up even when individual tasks move faster. Atlassian's dependency-mapping playbook shows how to trace who is waiting on whom, identify risks, assign owners, and set a review schedule. It includes a worksheet you can use to check the AI-generated map against your team's actual responsibilities.

The real blocker is probably buried across meeting notes, Slack threads, and your project tracker. The trick is to run a few connected AI checks instead of asking one chatbot to diagnose your team.

Here's the workflow:

1. Build the evidence trail. Gather two weeks of meeting notes, relevant messages, and your latest ClickUp, Asana, or Jira export. Have AI pull every commitment into a spreadsheet with the action, owner, deadline, and source.

2. Find the contradictions. Have AI compare those commitments to your tracker and flag missing tasks, conflicting deadlines, ownership changes, and unfinished dependencies. Keep source links on every finding.

3. Trace what's blocking what. Ask AI to map dependencies (e.g. a missing legal approval holds up design signoff, which delays engineering, which pushes launch). Have it separate confirmed blockers from suspected ones, then check the big claims against the originals.

4. Turn findings into fixes. Confirm the blockers with their owners, then have AI draft a recovery plan with actions, owners, and deadlines. Review it before updating your tracker.

After your next check-in, run the comparison again. You'll see which blockers disappeared, which remain, and whether your fixes worked.

Congratulations, you've turned "let's circle back" into an actual investigation.

Have a specific skill you want to learn? Request it here.

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📰 Around the Horn

• SK hynix reported 104.6T won in quarterly revenue and an 80% operating margin as AI memory chip demand surged.

• Oracle and Google announced plans to bring Google's TPU chips to Oracle Cloud in 2027.

• Oracle and OpenAI expanded their partnership to bring ChatGPT, Codex, and OpenAI models into Oracle's enterprise apps.

• Anthropic offered a 40% discount on Claude to eligible federal, state, local, tribal, and territorial agencies.

• Infisical raised $40M at a $600M valuation to help AI agents use credentials without exposing the secrets.

• MLflow patched a critical remote-code-execution bug; users should upgrade to version 3.17.0.

• Anthropic acquired a one-person drug-development company that used Claude to coordinate 11 labs and reported early promise in mice.

Want absolutely EVERYTHING that happened in AI this week? Click here!

🍪 Treats to Try

*Asterisk = from our partners (only the first one!). Advertise to 700K+ readers here!

1. *Switch the agent. Switch the model. Keep the context. Bring humans and agents into shared rooms with Switch AI. Open source, self-hosted.

2. ChatGPT's iCloud connections let you connect Apple Mail and Calendar so ChatGPT can help with your email and scheduling.

3. TheySaid 4.0 tests your product with synthetic customers trained on user research, then suggests fixes inside your dev workflow.

4. Spacesheep turns an AI agent's output into interactive docs, dashboards, and apps your teammates can explore together.

5. PulseDev lets you click a broken element on your site, describe the problem, and approve a fix from Claude Code, Codex, Gemini, or OpenCode.

6. Hob runs AI models on your own computer so you can work with documents and files without a cloud connection.

UPCOMING EVENT: The Neuron IRL in San Francisco

The Neuron is going IRL on November 18 at 4:30 PM PT! Join us in San Francisco for drinks, bites, special guests, and a live recording of The Neuron podcast. Huge thanks to Slack and Alumni Ventures for making it happen.

It’s free, but space is limited. Save your spot!

📖 Midweek Wisdom

Getting AI to finish a task faster is one thing. Knowing whether it makes you better at your job is another. Today's reads tackle the second part.

1. Microsoft Research: Does AI reduce critical thinking? Researchers surveyed 319 knowledge workers and found that the more they trusted AI, the less critical-thinking effort they reported.

2. Anthropic: How AI is changing employment inside Anthropic Anthropic found that agents boosted output while shifting human work toward supervising, reviewing, and coordinating AI results.

3. METR: AI made experienced developers 19% slower A randomized study found developers using early-2025 AI tools took longer on codebases they already knew.

4. Microsoft Research: AI changed how 6,000 workers spent their time AI users spent less time on email, but their meeting time didn't drop.

5. Stanford: The 2026 AI Index Report Stanford's annual report tracks AI capabilities, adoption, and economic impact, plus the growing gap between tech progress and real-world results.

Our favorite question from all five: are we measuring AI by how much work it produces, or by how much better that work makes us? Those aren't always the same thing.

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