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.

Eclipse Ventures founder Lior Susan shares a quote from Sam Altman that flips a long-held venture assumption on its head. The massive compute and talent costs for foundational AI models mean that software—specifically AI—has become more capital-intensive than traditional hardware businesses, altering investment theses.

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.

Fei-Fei Li expresses concern that the influx of commercial capital into AI isn't just creating pressure, but an "imbalanced resourcing" of academia. This starves universities of the compute and talent needed to pursue open, foundational science, potentially stifling the next wave of innovation that commercial labs build upon.

During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.

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.

Ilya Sutskever argues the 'age of scaling' is ending. Further progress towards AGI won't come from just making current models bigger. The new frontier is fundamental research to discover novel paradigms and bend the scaling curve, a strategy his company SSI is pursuing.

The rapid evolution of AI means traditional private equity M&A timelines are too slow. PE firms and their portfolio companies must now behave more like venture capitalists, acquiring earlier-stage, riskier AI companies to secure necessary technology before it becomes unaffordable or obsolete.

A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.