Nobel Prizes and The AI Hype Hall of Fame
We recorded 7 claims from this post. 5 were checked against evidence. 2 can’t be checked. 7 more are held. Read the post at its source.
Claims from this post (7)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: SupportedSaid
“Musk’s robotaxi promise hasn’t fared much better.”
What we tested: Elon Musk's 2019 promise of over a million Tesla robotaxis has largely failed to come true.
What happened: Tesla’s signed January disclosure still described launching the business and fully autonomous operation as future developments. This supports the inference that the million-robotaxi promise for 2020 remained substantially unfulfilled. (Tesla, Inc.; copy hosted by AnnualReports, )
Assessment: Can’t be checkedSaid
“Per the 80:20 rule I described a few days ago, it’s easy to make demos; 80% of driving, 80% of some scientific problem, etc. Getting the last 20% is often really really hard.”
What we tested: In AI it is easy to build demos covering 80% of a task such as driving or a scientific problem, but getting the last 20% is often very hard.
Assessment: SupportedSaid
“Even in the best work, like Google’s new AI co-scientist, humans scientists are still choosing the research goals.”
What we tested: Even in the best AI-for-science work, such as Google's AI co-scientist, human scientists still choose the research goals.
What happened: Google's launch description makes scientist-supplied objectives the system's input and describes experts selecting its evaluation goals. AI-generated hypotheses and research plans therefore operated within human-chosen objectives. (Google Research, )
Assessment: UnresolvedSaid
“That is not going to magically change next year, at a time when AI still can’t reliably solving river-crossing problems.”
What we tested: In 2026, AI will still not be choosing its own research goals in place of human scientists.
Assessment: UnresolvedSaid
“That is not going to magically change next year, at a time when AI still can’t reliably solving river-crossing problems.”
What we tested: Current AI cannot reliably solve river-crossing puzzles.
Assessment: Can’t be checkedSaid
“Nobel-calibre science is a lot harder than driving, which we still have not reliably solved”
What we tested: Nobel-calibre science is much harder than driving, and AI still has not reliably solved driving.
Assessment: UnresolvedSaid
“Nobel-calibre science requires sound logic, reliable causal reasoning, and originality, and none of those are firmly in the grasp of current AI.”
What we tested: Current AI does not firmly have the sound logic, reliable causal reasoning, and originality that Nobel-calibre science requires.
Held from this post (7)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The criteria are ambiguous. Our reading, not assessed: As far as Marcus knows, in the nine years since Hinton's 2016 prediction, AI has not replaced a single radiologist.
- The record does not meet the current rules. Our reading, not assessed: Human scientists will get a lot of leverage from AI, as they did from databases and calculators.
- The record does not meet the current rules. Our reading, not assessed: It is highly unlikely that AI systems will conceive of and carry out Nobel-caliber work on their own in the next few years.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: Marcus thinks DOGE and its allies will use predictions of AI surpassing Nobel laureates to justify cuts to scientific institutions.
- The record does not meet the current rules. Our reading, not assessed: AI built in 2026 or 2027 will be valuable to future Nobel laureates.