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The AI consciousness debate can turn into a debate about who controls the future. Part 1 of 3. Next: the line Sam Altman draws.

Part 3 of 3: Customer work changed the hiring story. Alexandr Wang describes how automotive and autonomous-driving projects brought momentum and helped grow the team. Final part of the series.

Part 3 of 3: Thousands of simulated robots train in parallel. The demo then evaluates the policy before deploying it to the real MicroDuck. Final part of the series.

Part 2 of 3: A recruiting coffee is not the same as a recruit. Alexandr Wang describes the pitches that went nowhere, and why people who knew him were different. Next: how customer momentum helped hiring.

Part 2 of 3: Before a robot can learn, it needs a world to learn in. The demo builds objects, materials, physical properties and a simulation. Next: training, testing and the real robot.

Part 1 of 3: Why did Alexandr Wang hire people he knew from school? They trusted him. This series follows the early team, failed recruiting coffees and the momentum that helped hiring. Next: why those coffees went nowhere.

Part 1 of 3: Ask an agent to build an agent. Jensen’s opening line, then the whole journey: plan, simulate, train, deploy. Next: how the simulated world gets built.

More jobs in ten years? Daron Acemoglu disagrees with the prediction. Which side of this debate makes the stronger case? Source: 60 Minutes / @60minutes. No endorsement implied.

The speed of the transition matters, not just what AI can do. Daron Acemoglu explains why he sees this shift differently. Source: 60 Minutes / @60minutes. No endorsement implied.

New jobs can exist without replacing every job lost. Daron Acemoglu challenges the AI-training-jobs argument. What do you think? Source: 60 Minutes / @60minutes. No endorsement implied.

What I’m doing while all my agents work.

Most teams are somewhere between one-off AI help and connected workflows. Where do you fall on this ladder today? Reply with your number (1-7) and the one thing stopping you from moving up a step.

two new models are now available in agentic workers: • GPT-6 Astra for tool-heavy work, browsing, and multi-step action • Claude Fable 5.1 for long files, deep research, and document creation choose the model that fits the job.

With todays AI Outage we also saw a directly correlation to Github PRs being opened.

14 hours of prompting agents.

The math is simple. Agents cost less and do more.

Same hours. 10x output. That's what agents do.

Scale without hiring. That's the agent way.

One person. One army of agents. That's the whole playbook.