The traditional academic career path of becoming a replica of one's Principal Investigator (PI) is largely obsolete. A PhD provides a broad skill set in critical thinking and data management applicable across many sectors. Young researchers should focus on the big problems they want to solve, not just replicating a disappearing job.

AIs excel at exploring millions of problems at a surface level (breadth), a scale humans cannot match. Human experts provide the depth needed to tackle the difficult "islands" AIs identify. Science must shift from its current depth-focused model to one that first uses AI to map entire fields and clear away low-hanging fruit.

Scientific progress requires more than just papers that lead to tenure. It also needs tool-building, software development, and connecting disparate ideas. These activities are valuable for science but often undervalued by academic incentive structures, creating an opportunity for new institutions to fill the gap.

AI's true power in science isn't autonomous discovery, but process compression. It acts as an expert guide, allowing motivated individuals to navigate complex fields like drug discovery and assemble workflows that once required multiple specialized teams, blurring the line between professional research and individual effort.

While independent research is often glamorized, a more effective strategy is to 'not write alone.' Instead of relying on self-improvement hacks to overcome solo work challenges, it is often better to collaborate with people whose skills complement your weaknesses, creating a more productive system.

A single AI agent attempting multiple complex tasks produces mediocre results. The more effective paradigm is creating a team of specialized agents, each dedicated to a single task, mimicking a human team structure and avoiding context overload.

Professionalizing science creates competent specialists but stifles genius. It enforces a narrow, risk-averse culture that raises average quality (the floor) but prevents the polymathic, weird explorations that lead to breakthroughs (the ceiling).

The frenzied competition for the few thousand elite AI scientists has created a culture of constant job-hopping for higher pay, akin to a sports transfer season. This instability is slowing down major scientific progress, as significant breakthroughs require dedicated teams working together for extended periods, a rarity in the current environment.

The solution to massive problems isn't a lone genius but collaborative effort. Working together prevents reinventing the wheel, allocates resources effectively, and creates leverage where the outcome is greater than the sum of its parts. Unity invites disproportionate success.