The AI industry is hitting data limits for training massive, general-purpose models. The next wave of progress will likely come from creating highly specialized models for specific domains, similar to DeepMind's AlphaFold, which can achieve superhuman performance on narrow tasks.

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.

Instead of building a single, monolithic AGI, the "Comprehensive AI Services" model suggests safety comes from creating a buffered ecosystem of specialized AIs. These agents can be superhuman within their domain (e.g., protein folding) but are fundamentally limited, preventing runaway, uncontrollable intelligence.

Microsoft’s approach to superintelligence isn't a single, all-knowing AGI. Instead, the strategy is to develop hyper-competent AI in specific verticals like medicine. This deliberate narrowing of domain is not just a development strategy but a core safety principle to ensure control.

The current approach of building generalist models like ChatGPT, containing all human knowledge, is inherently unsafe. A safer paradigm involves creating specialized AIs for specific tasks (e.g., translation) that lack dangerous capabilities like bioweapon design, similar to how a Pentagon janitor is denied access to nuclear codes for security.

The path to AGI won't be uniform. Instead, we'll see 'jagged superintelligence,' where models achieve superhuman capabilities in specific verticals with high verifiability, such as coding, finance, and scientific research. These specialized peaks of excellence will appear long before a generalized intelligence is achieved.

Huang redefines "superintelligence" not as a single, all-knowing AGI, but as specialized systems that vastly outperform humans at a specific task. Citing self-driving cars and protein synthesis as examples, he asserts that we have already crossed the threshold into the era of superintelligent AI in narrow, practical applications.

The pursuit of AGI is misguided. The real value of AI lies in creating reliable, interpretable, and scalable software systems that solve specific problems, much like traditional engineering. The goal should be "Artificial Programmable Intelligence" (API), not AGI.

The focus on AGI can obscure more immediate threats. Even narrowly capable AI tools pose existential risks. For example, an AI that only excels at biotechnology research could make it easy for malicious actors to develop dangerous pathogens, regardless of its general intelligence.