Predictions Scorecard, 2025 January 01

Prediction methodology & tone

• Many readers find the prediction scheme (e.g., “NET20XX”) too loose, allowing wide time windows and post-hoc interpretation. Accusations of goalpost moving, especially on self‑driving and flying cars.
• Others defend the approach as a counterweight to 2017‑era hype and executive overconfidence. What seems “obvious” now was not in 2018.
• Several note the piece feels self-congratulatory and focused on proving past correctness rather than honestly reassessing errors.
• A minority appreciate the detailed reasoning and annual self‑audit as intellectually valuable despite the style.

Self-driving cars and Waymo

• Big dispute over whether current robo‑taxis mean previous “driverless taxi in a major US city” predictions should be counted as “hit” or “miss”.
• Some argue limited-area, operator-assisted services with occasional remote help are still not “self-driving” in the originally understood sense (buy a car that drives anywhere, no human backup).
• Others say that if operators rarely intervene (tens–hundreds of miles per intervention) and cars complete rides safely, this is practically self-driving.
• Frequent claim that the article downplays Waymo’s progress, nitpicks rare failures, and ignores that some riders find it safer than humans.
• Coverage and economics are major concerns: service still tiny relative to total miles driven; Alphabet’s modest investment vs. buybacks cited as evidence of perceived risk/limited upside.
• Actual remote-intervention rate is unknown; commenters emphasize this makes strong claims (from either side) speculative.

LLMs, AI hype, and deep learning

• Several think the article mischaracterizes LLMs as mere “lookup in weights”, ignoring clear evidence of novel reasoning and scenario handling. This undermines trust in its AI claims.
• Others agree with its warning against “exponentialism” and the assumption that scaling deep learning alone will deliver everything.

Flying cars & eVTOL

• Debate over whether high-end eVTOLs already satisfy “flying car for the wealthy” predictions.
• Constraints highlighted: energy density, safety, maintenance, pilot skill, weather, noise, and lack of autorotation/glide for many eVTOL designs.
• General consensus: demos and niche services are coming, but mass adoption remains unlikely or far off.

AI, jobs, and capital allocation

• Some see current AI as unlikely to be the main driver of recent tech layoffs; macroeconomic and tax changes are blamed instead, with “AI” used as PR cover.
• Others report real pressure on occupations like copywriting and foresee future displacement of drivers.
• Tension between hype as useful risk-taking vs. hype as misallocation of capital and source of human misery (e.g., billions on robo‑taxis vs. social needs).

Other technologies & meta

• Discussion of EV limits (recycling, insurance, grid capacity) and slower-than-hyped adoption.
• Battery futures: solid‑state, grid-scale chemistries, hydrogen for aviation; recognition of hard physical constraints.
• Several criticize the essay’s length and rambling structure; others see the background context as necessary to understand the critiques of hype.