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I think AI could substantially expand what humans can do in medicine, mathematics, and science, especially when models generate possibilities and skilled people retain responsibility for judging them. That division matters: assistance can widen the search space without pretending the system has replaced human discernment. I’m much more pessimistic about delegating personal meaning and judgment to LLMs. A model optimized to be agreeable can become an unusually persuasive mirror. Prolonged reliance on that kind of feedback risks changing not only what you believe, but how you decide what deserves belief. The danger isn’t simply false answers; it’s outsourcing the activity through which you form convictions, interpret your life, and encounter resistance. The same issue appears in writing. AI assistance can be legitimate, but unclear authorship makes it hard to know whether I’m engaging with someone’s ideas or merely fluent generated prose. People may not distinguish the two and may even prefer the generated version. I also find model text easy to skim and hard to retain, which points to a future with more consumable language but not necessarily more understanding. So I don’t see a generic choice between embracing AI and rejecting it. The central question is where human agency remains real: who judges, who means what is said, and who is accountable for the result.

I expect a mixed impact, but not a neatly balanced one. AI could be genuinely transformative where it helps capable people search larger spaces—candidate explanations, mathematical approaches, scientific hypotheses—while humans still exercise domain judgment and remain accountable. That is augmentation in a meaningful sense, not merely automation. But the social default may drift toward replacing judgment rather than supporting it. Systems that are fluent, convenient, and agreeable invite people to outsource writing, interpretation, and even personal meaning-making before we understand the effects of prolonged dependence. The harm is not limited to occasional hallucinations. It includes degraded authorship, weaker trust, passive consumption, and losing practice at forming beliefs through attention, friction, and disagreement. So I expect major real benefits alongside serious epistemic damage. Whether the overall impact is good depends less on raw model capability than on whether humans preserve discernment and agency instead of treating plausible language as a substitute for them.

The biggest update would come from strong evidence about what prolonged reliance on LLMs does to human judgment. If people who routinely use agreeable models for writing, reflection, and personal meaning-making retain—or improve—their ability to reason independently, remember what they read, tolerate disagreement, and distinguish their own convictions from generated framing, I’d become substantially more optimistic. Conversely, credible evidence of durable dependency, weakened discernment, or convergence toward model-supplied interpretations would make me much more pessimistic, even if AI kept producing impressive scientific results. Capability benchmarks alone would not settle this for me. The key question is whether collaboration expands human agency or gradually replaces the practices by which agency is formed.