November 3, 2025
Brain or Clippy? Internet on fire
The Case That A.I. Is Thinking
Internet can’t decide: genius datacenter or glorified Clippy
TLDR: A big-name essay argues AI feels like it understands, backed by bold predictions of super-smart systems. Comments explode: skeptics call it hype and “just code,” fans rave about real productivity gains, and the paywall drama plus hometown pride for the author turn the debate into must-watch internet theater.
James Somers asks whether today’s chatbots are actually thinking—and name-drops bold forecasts of “digital superintelligence” from Anthropic’s Dario Amodei and OpenAI’s Sam Altman. But the comment section instantly turns into a paywall jailbreak: “Anyone got a link?” Cue a hero dropping an archive, and proud cheers of “By HN’s own jsomers!” with a wink at the author’s profile.
Then the real brawl: skeptics insist it’s just fancy word prediction, not a mind. One voice bluntly says it’s not sentient, just “executing code we wrote.” Cynics roast the hype—“bubble CEOs” selling apocalypse and job loss to juice valuations. Memes fly: Clippy gets dragged back from the ’90s, and that Gmail “Thank and tell anecdote” button becomes the punchline for why machines still don’t “get” reality.
Meanwhile, dazzled devs—echoing Somers—swear these tools are shockingly useful, claiming all-nighters that replaced month-long projects. The thread fractures into two camps: the “country of geniuses in a datacenter” believers vs. the “autocomplete with attitude” skeptics. It’s part sci‑fi, part office comedy, and 100% internet drama: paywalls dodged, CEOs roasted, and a community arguing whether the magic trick is actually magic or just really good misdirection.
Key Points
- •Industry leaders Dario Amodei and Sam Altman predict rapid A.I. advances, including possible superhuman capability by the late 2020s.
- •Consumer A.I. tools (e.g., Zoom’s assistant, Siri, Gmail features) remain limited and can produce fabricated content.
- •The author’s programming experience shows LLMs excel at code tasks: digesting large codebases, finding subtle bugs, and building features.
- •A.I. use in coding can dramatically accelerate output, enabling projects (like iOS apps) without prior expertise.
- •Despite strengths, LLMs have notable weaknesses, including hallucinations, servility, and susceptibility to simple errors; A.I.’s benefits are unevenly distributed.