The debate pitting AI safety against AI opportunity presents a false choice. Historical parallels, like the railroad industry, show that safety regulations (e.g., standardized tracks, air brakes) were essential for enabling greater speed, reliability, and economic potential. Trustworthy AI will unlock greater opportunity.

Top AI labs like Anthropic publicly state that slowing down AI development would benefit society. However, they are caught in a strategic trap: a unilateral pause is unviable. Without a global agreement, any lab that pauses simply allows less cautious competitors to seize the lead, potentially making the ecosystem less safe.

A pause on training new, more capable AI models could paradoxically increase risk. It would halt progress at the few, relatively safety-conscious frontier labs, allowing less scrupulous competitors to catch up. Meanwhile, compute stockpiling would continue, making any subsequent capability leap even faster and more dangerous.

Delaying public model releases isn't a real solution for pacing AI development. The critical risk lies in a lab using its most advanced, unreleased models to accelerate internal R&D, potentially leading to a private "singularity." Meaningful pacing must limit the resources dedicated to this internal recursive loop.

AI offers incredible short-term benefits, from fixing daily problems to curing diseases. This immediate positive reinforcement makes it extremely difficult for society to acknowledge and address the simultaneous development of long-term, catastrophic risks, creating a classic devil's bargain.

Instead of only slowing down risky AI, a key strategy is to accelerate beneficial technologies like decision-making tools. This 'differential technology development' aims to equip humanity with better cognitive tools before the most dangerous AI capabilities emerge, improving our odds of a safe transition.

The default assumption is that slowing innovation is inherently bad. With a technology as potent as AI, a deliberate slowdown is a feature, providing critical time to understand the systems, manage disruptions, and build governance structures before irreversible consequences occur. A true halt is not the alternative.

Even if the market would eventually build decision-making tools, their impact is time-sensitive. Waiting for commercial rollout might mean they arrive after AGI, too late to help navigate the riskiest period. Therefore, philanthropic or impact-driven acceleration, even by a few months, is highly valuable.

The race for AI supremacy is governed by game theory. Any technology promising an advantage will be developed. If one nation slows down for safety, a rival will speed up to gain strategic dominance. Therefore, focusing on guardrails without sacrificing speed is the only viable path.