ASI (Artificial Superintelligence)
- Definition
- A theoretical AI that dramatically surpasses the best human minds in every field. ASI remains speculative, but its possibility shapes long-term safety research and existential-risk debates.
- Why it matters
- ASI may be speculative, but the policies and investments shaped by its possibility are very real. Governments are drafting legislation, labs are building safety infrastructure, and billions in capital are being allocated based on ASI timelines. Even if ASI never arrives, the pursuit of it has already created the most powerful narrow AI systems in history. Business leaders do not need to believe in ASI to take it seriously; they need to understand that the people controlling billions in AI investment do believe in it, and that belief drives the research agenda, talent markets, and regulatory landscape that shapes every AI product on the market.
- In practice
- Ilya Sutskever left OpenAI in 2024 to co-found Safe Superintelligence Inc. (SSI), a company focused exclusively on building safe superintelligence. SSI raised $1B at a $5B valuation before shipping any product, signaling investor appetite for the superintelligence thesis. Leopold Aschenbrenner's 'Situational Awareness' essay, published after his departure from OpenAI, argued that superintelligence could arrive by 2027 and triggered widespread debate about compute scaling trajectories. Whether or not that timeline holds, it is actively shaping how governments and corporations plan.
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Related terms
AGI (Artificial General Intelligence)
A hypothetical AI system that matches or exceeds human-level reasoning across every cognitive domain. No AGI exists today, but the race to build one is driving hundreds of billions in investment.
Alignment
The challenge of making an AI system's goals and behaviors match human intentions and values. Misalignment risk grows as models become more capable, making this a top priority for safety teams.
AI safety
The interdisciplinary field focused on ensuring AI systems behave as intended and do not cause unintended harm. Encompasses alignment research, red teaming, content filtering, and policy advocacy.
Scaling laws
Empirical relationships showing that model performance improves predictably as you increase data, compute, and parameters. Scaling laws are why labs are pouring billions into ever-larger training runs.
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