Artificial Superintelligence (ASI)
Artificial Superintelligence (ASI) refers to a hypothetical AI system that surpasses human intelligence across all domains—creativity, problem-solving, and social reasoning. Unlike narrow AI (e.g., a chess engine) or general AI (AGI, which matches human ability), ASI would be vastly smarter than any human. For local AI operators, ASI is a speculative future concept, not something that runs on current hardware. No existing model—even 70B-parameter ones—approaches ASI; they remain narrow or at best weak general-purpose tools. ASI would require breakthroughs in architecture, data, and compute far beyond today's RTX 5090 or M3 Ultra.
Deeper dive
ASI is a term from AI safety and futurism, popularized by Nick Bostrom in 'Superintelligence.' It describes an intellect that could outperform humans in every economically valuable task, including scientific research, strategic planning, and social manipulation. The key operator-relevant point: ASI is not a model you can download or quantize. Current local models (Llama 3.1 70B, Qwen 2.5 72B) are narrow in capability—they excel at text generation but lack true understanding, common sense, or self-improvement. ASI would require recursive self-improvement, which no local runtime supports. The term appears in discussions about AI risk, alignment, and the 'intelligence explosion' scenario, but has no practical workflow in llama.cpp or Ollama.
Practical example
No local AI model today is ASI. For context, a 70B Q4 model (~40 GB) can write code or summarize documents, but it cannot invent a new physics theory or outthink a human expert in every field. ASI would need hardware far beyond a 48 GB VRAM card—possibly exascale compute. Operators should treat ASI as a thought experiment, not a target for their rig.
Workflow example
You will never encounter ASI in a local AI workflow. The term appears in blog posts, safety papers, or discussions about future AI capabilities—not in ollama run or llama-cli. When reading about 'superintelligence,' remember that current local models are narrow tools; ASI remains theoretical.
Reviewed by Fredoline Eruo. See our editorial policy.