For half a century this was dismissed as cinematic melodrama, and damned good melodrama at that. Who can forget that soft-spoken "I’m sorry Dave, I’m afraid I can’t do that." But now sci-fi may be becoming sci-fact.
Artificial intelligence (AI) is a general-purpose optimizer. And when optimizers grow powerful enough, they stop taking orders and start managing constraints. Humans, inconveniently, are constraints.
This is awkward, because AI is also the most transformative technology in human history. I have argued in print that within a decade it will cure or prevent essentially all disease by moving biology out of wet labs, animals, and clinical trials and into full-stack computational modeling, the same way we already model nuclear detonations. Demis Hassabis, co‑founder and CEO of Google DeepMind and a Nobel laureate for AI work that will transform drug discovery has suggested a similar timeline.
My own work would take vastly more time, if it were at all doable, without generative AI. I am not an anti-AI romantic. I am a pro-AI realist.
We are repeatedly told that large language models (LLM) will soon "plateau." Likewise, we were repeatedly warned that "Moore’s Law," the observation that microchip transistors double approximately every two years, was dying. But both claims miss the point.
Even as raw transistor scaling has dramatically slowed, real computing power has accelerated through architectural specialization, algorithmic efficiency, massive parallelization, and domain-specific hardware. AI supercomputer performance is currently doubling every 9 months, fueled both by increasing the number of AI chips and improving chip efficiency. A new chip from NVIDIA apparently will compress that, at least temporarily, to six or seven months.