What does Alan Kay think about LLMs?

Overall stance on LLMs and “trustability”

• Central concern: LLMs are not “trustable” for running commands or teaching, because they don’t reason, only correlate and generate plausible text.
• Trust is linked to auditability: people want verifiable chains of reasoning, explainable decision paths, and reproducible results.
• Current LLMs can’t show how they derived an answer beyond vague token attributions, so they’re seen as unsuitable for critical tasks.

Message passing, late binding, and system design

• Large subthread revisits classic ideas: true message passing, late binding, and live, image-based systems (e.g., Smalltalk-style environments).
• Debate over whether modern systems (HTTP, microservices, browsers) embody these ideas well or are “bastardizations”.
• Some argue message passing significantly improves security and scalability; others note microservice messiness and need for stronger typing.
• There’s disagreement on whether Alan’s guidance is too vague or actually quite concrete when you look at his systems and research programs.

LLMs in programming and education

• Power users report LLMs as very useful but frequently wrong, especially in less popular languages or niche libraries.
• “Obvious” errors (invented APIs) are easy for experts to catch; subtle ones (deprecated, insecure, or inefficient patterns) are dangerous for learners.
• Concern that students will “cheat” through CS curricula with LLMs, further exposing how dated some teaching already is.
• Some like that traditional programming is literal and debuggable; they fear opaque AI layers undermine this transparency.

Epistemology: correlation, superstition, and BS

• Strong theme: LLMs exemplify “reasoning by correlation,” likened to superstition and BS generation, especially when they rationalize wrong answers fluently.
• Counterpoint: correlation-based empiricism can still be testable and useful; superstition arises when people misread or overinterpret correlations.
• Several comments note humans are also unreliable, biased, and prone to BS, so comparisons must be against actual human experts, not an ideal.

Societal and economic concerns

• Worry that LLMs will be used to “strip mine society” more efficiently, increasing extraction, surveillance, and large-scale manipulation.
• Others argue this dynamic predates LLMs and applies to most major technologies; LLMs are just a new lever.
• Additional fear: a future internet flooded with semi-plausible nonsense, eroding trust in digital information and possibly even literacy norms.