Wisdom emerges from the contrast of diverse viewpoints. If future generations are educated by a few dominant AI models, they will all learn from the same worldview. This intellectual monoculture could stifle the fringe thinking and unique perspectives that have historically driven breakthroughs.

The AI safety community acknowledges it lacks all the ideas needed to ensure a safe transition to AGI. This creates an imperative to fund 'neglected approaches'—unconventional, creative, and sometimes 'weird' research that falls outside the current mainstream paradigms but may hold the key to novel solutions.

While mitigating catastrophic AI risks is critical, the argument for safety can be used to justify placing powerful AI exclusively in the hands of a few actors. This centralization, intended to prevent misuse, simultaneously creates the monopolistic conditions for the Intelligence Curse to take hold.

Even powerful AI CEOs feel helpless, asking 'what can I do?' Aza Raskin argues this highlights a fundamental truth: agency for systemic problems like AI is not an individual attribute. It emerges from collective action. The question isn't 'what can I do?' but 'what can we do?'

The tech industry's tendency to seek a single, "one-shot" solution like AGI is framed as a dangerous laziness. This mindset avoids the hard, messy work of building diverse, localized, and incremental solutions, which represents a more practical and safer path for progress.

While AI massively increases an individual's ability to create, it also reduces the need for large, collaborative teams. This creates a paradox where greater individual agency could lead to less inclusion and a more atomized workforce, concentrating "meaningful" work among a smaller group of empowered individuals.

AI labs underinvest in theoretical alignment research because their culture is built on the rapid, rewarding feedback loops of empirical work. This 'dopamine hit' from seeing empirics work well creates a strong bias against the slower, more abstract work required to solve long-term safety.

Human intelligence leaped forward when language enabled horizontal scaling (collaboration). Current AI development is focused on vertical scaling (creating bigger 'individual genius' models). The next frontier is distributed AI that can share intent, knowledge, and innovation, mimicking humanity's cognitive evolution.

Instead of the "move fast and break things" ethos, AI safety should be modeled after complex, collaborative efforts like the global cooperation that fixed the ozone layer or Toyota's safety culture. These approaches prioritize systemic checks, collaboration, and distributed skills over individual genius.