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This essay argues that superintelligence's greatest contribution won't be brilliant breakthroughs but rather handling the unglamorous institutional work needed to turn ideas into reality. The authors sketch two futures: one where AI thinks its way to progress with minimal physical resources, another where it spends most of its time managing the vast bureaucratic and logistical machinery required to execute increasingly complex experiments.
- Frontier intelligence and institutional intelligence are complements—genius designs the blueprint, but someone has to lay the stone. History shows this complementary work has grown more elaborate, not simpler, as projects scale from Galileo's telescope to the James Webb Space Telescope.
- A "civilization of depth" would see superintelligence optimize its way around resource constraints through pure reasoning, while a "civilization of width" would require ever-larger experiments and bureaucratic coordination, making execution work more valuable than ideation.
- Even if AI surpasses humans at both thinking and doing, humans may retain value through comparative advantage in frontier insights, creative diversity, and the simple fact that not all minds can be geniuses—some must be bureaucrats.
Alexandr Wang describes Muse, an AI system designed to act as a personal manager that translates vague wants into concrete action plans—making calls, sending emails, solving problems—so humans can focus on the wanting and dreaming part. The pitch is that unlocking everyone's agency to pursue their actual goals could reshape what humanity builds.
- Most people never articulate their dreams, let alone pursue them, because they don't know where to start and get stuck by obstacles before even saying the goal aloud.
- An AI agent as a "second mind" could handle the execution layer—logistics, coordination, problem-solving—leaving the human to provide only the initial desire.
- Humanity has been shaped by the tiny fraction of people who figured out how to realize their wants; giving everyone an agent to execute on their dreams could unlock potential from eight billion people instead of a few fanatics.
Google has all the data and services needed to build a personal AI assistant better than Meta's Muse—Gmail, Calendar, Search—but keeps shooting itself in the foot with competing products, slow launches, and confusing branding. Meanwhile Meta shipped a polished product that actually works.
- Google is launching Gemini Spark as its answer to Muse, but it's stuck in limited beta with poor marketing, while Meta aggressively rolled out Muse to mainstream users.
- Google has at least four overlapping personal AI products in development (Gemini Spark, CC, Daily Brief, AI Inbox) that should be consolidated into one, creating market confusion instead of momentum.
- Muse simply has better product design—a guided interface and personality that makes it intuitive to use, while Gemini Spark leaves users confused about what it even does.
Building something well and shipping it are opposite skills—perfectionism that makes you a good craftsperson makes you reluctant to release work. The only way forward is forcing yourself to publish despite thinking it's flawed, because volume and iteration beat polished isolation.
- Gifted programmers and writers often sabotage themselves by refusing to ship work that doesn't meet their standards, but imperfect shipped code beats perfect unshipped code every time.
- You can't predict what people will find valuable, so producing high volume of work yields better results than agonizing over individual pieces—your throwaway posts often outperform what you labored over.
- Shipping is a learnable skill that gets easier with practice; the more frequently you publish, the less perfectionism paralyzes you, and you stop being able to distinguish between work you felt good about and work that actually resonated.
This article lays out two guiding principles for PMMs: safeguard your unique skills (storytelling, judgment, strategic thinking, and stakeholder influence) and protect your cognitive abilities by using AI as a second step. It then offers a practical three-tier framework—execution, thinking, and scaling workflows—and advice on selecting high-impact use cases based on how you actually spend your time.
- Do the thinking yourself first, then use AI as a critic/amplifier—not the other way around—to avoid "AI brain rot" that erodes storytelling, strategic reasoning, and influencing skills
- Real insight still comes from live customer conversations and sales calls, not AI transcripts of them
- A three-tier framework works best: Level 1 (execution tasks like summaries/release notes) for low-risk time savings, Level 2 (thought partnership) for stress-testing messaging and strategy, Level 3 for high-stakes work still requiring full human oversight
- PMM's job is shifting from content producer to quality gatekeeper, strategist, and connector as AI floods the market with generated content