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.

Simile's founder views academia as a vehicle for breadth, where researchers explore many parallel theses. He started a company because it is a 'machine for depth research,' enabling a focused team to pool resources and relentlessly pursue a single, ambitious vision needed to bring a complex product to market.

AI research involves exploring a dependency graph where ideas may fail (stochastic). This contrasts with software engineering's more deterministic path. Success requires "research taste"—an intuition for navigating this uncertainty, a skill often honed in PhD programs.

As large, commercially-focused AI labs shift resources from fundamental research to product development, a vacuum is created. This opens a critical window for universities, the open-source community, and independent researchers to pioneer the next generation of non-obvious AI breakthroughs.

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.

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.

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.

Pure, curiosity-driven research into quantum physics over a century ago, with no immediate application in sight, became the foundation for today's multi-billion dollar industries like lasers, computer chips, and medical imaging. This shows the immense, unpredictable ROI of basic science.

A critical mindset shift from academia to startups is embracing the "killer experiment." Academics may fear an experiment that disproves a long-held hypothesis. In contrast, biotech startups, with finite capital, must run these experiments early to either validate or kill a program, efficiently allocating resources to viable projects.