With industry dominating large-scale compute, academia's function is no longer to train the biggest models. Instead, its value lies in pursuing unconventional, high-risk research in areas like new algorithms, architectures, and theoretical underpinnings that commercial labs, focused on scaling, might overlook.

A proposed alternative to the PhD is the "science house," a small, apprentice-based collective. Scientists would live and work together, free from academic incentives like tenure and journal publishing, and release their findings directly to the internet.

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.

Nonprofits occupy a unique space. While academia pursues discovery and industry seeks revenue, nonprofits can fund "infrastructure" projects like large, open-access datasets. These efforts accelerate the entire ecosystem, a goal neither academia nor industry is incentivized to pursue alone.

With industry dominating large-scale model training, academia’s comparative advantage has shifted. Its focus should be on exploring high-risk, unconventional concepts like new algorithms and hardware-aligned architectures that commercial labs, focused on near-term ROI, cannot prioritize.

With industry dominating large-scale model training, academic labs can no longer compete on compute. Their new strategic advantage lies in pursuing unconventional, high-risk ideas, new algorithms, and theoretical underpinnings that large commercial labs might overlook.

Traditional academic promotion criteria, which prioritize publications, disincentivize clinicians from pursuing innovation. Dr. Power argues that for universities to truly support medical invention, they must update their standards to grant patents and industry consulting equivalent academic weight to research papers.

Instead of funding small, incremental research grants, CZI's philanthropic strategy focuses on developing expensive, long-term tools like AI models and imaging platforms. This provides leverage to the entire scientific community, accelerating the pace of the whole field.

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).