Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

The Second Worst $100B Investment in The History of AI?

We recorded 13 claims from this post. 2 were checked against evidence. 11 can’t be checked. 1 more is held. Read the post at its source.

Claims from this post (13)

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.

  1. Assessment: UnresolvedSaid

    Observation

    “Most of those who invested in driverless cars lost money, unless they invested early and cashed out, before patience began to run out.”

    Our reading: Most of those who invested in driverless cars lost money, unless they invested early and cashed out before patience began to run out.

    No horizon Evidence as of

    Limitation: The evidence covers selected investments, not a representative population.

  2. Assessment: Can’t be checkedSaid

    Recommendation or value judgment

    “By any reasonable account the worst investment in the history of AI has to be the over $100 billion that have been invested in “driverless” cars.”

    Our reading: By any reasonable account, the over $100 billion invested in driverless cars has to be the worst investment in the history of AI.

    No horizon

    Qualifier: by any reasonable account

  3. Assessment: UnresolvedSaid

    Observation

    “There may be a payoff someday, but thus far there has not been a lot.”

    Our reading: As of March 2024, investment in driverless cars has not produced much payoff.

    No horizon Evidence as of

    Limitation: The company examples are not a representative industry return calculation.

  4. Assessment: Can’t be checkedSaid

    Observation

    “The generality of Waymo’s approach is very much still in question, and I am not sure anyone else is still seriously in the full autonomy game.”

    Our reading: The generality of Waymo's approach (to conditions such as bad weather, poorly mapped roads and different driving patterns) is very much still in question, and Marcus is not sure anyone else is still seriously in the full autonomy game.

    No horizon

    Qualifier: I am not sure

  5. Assessment: Can’t be checkedSaid

    Forecast

    “But misery loves company, and the forlorn $100B may see company before the end of the decade.”

    Our reading: Before the end of the decade, another $100B AI investment (Microsoft/OpenAI's Stargate) may join driverless cars as a poor investment.

    No horizon

  6. Assessment: Can’t be checkedSaid

    Rhetoric

    “A $100B LLM is still an LLM.”

    Our reading: A $100B LLM is still an LLM.

    No horizon

  7. Assessment: Can’t be checkedSaid

    Forecast

    “Driverless cars were crippled by the endless long tail of outliers; LLMs may well pattern similarly, working in many cases but often troubled by the unexpected, even when scaled.”

    Our reading: LLMs, even when scaled, may well remain often troubled by unexpected cases, as driverless cars were by the long tail of outliers.

    No horizon

    Qualifier: even when scaled

  8. Assessment: Can’t be checkedSaid

    Forecast

    “Reasoning and planning in unexpected scenarios are still likely to be flawed.”

    Our reading: Even a $100B LLM will likely still have flawed reasoning and planning in unexpected scenarios.

    No horizon

    Qualifier: in unexpected scenarios

  9. Assessment: Can’t be checkedSaid

    Forecast

    “On many tasks, the results might be only slightly better than a $10B model or a $1B model, but the operating costs presumably would be much larger.”

    Our reading: On many tasks, a $100B LLM's results might be only slightly better than those of a $10B or $1B model, while its operating costs would presumably be much larger.

    No horizon

    Scope: on many tasks

  10. Assessment: Can’t be checkedSaid

    Forecast

    “On most problems, customers probably would not be willing to pay the required premium.”

    Our reading: On most problems, customers probably would not pay the premium required by a $100B LLM.

    No horizon

    Qualifier: on most problems

  11. Assessment: Can’t be checkedSaid

    Forecast

    “Presumably the operating idea here is that (a) $100B will equal AGI and (b) that customers will pay literally anything for AGI. I doubt either premise is correct.”

    Our reading: A $100B LLM investment is unlikely to produce AGI.

    No horizon

  12. Assessment: Can’t be checkedSaid

    Forecast

    “Presumably the operating idea here is that (a) $100B will equal AGI and (b) that customers will pay literally anything for AGI. I doubt either premise is correct.”

    Our reading: It is doubtful that customers would pay literally anything for AGI.

    No horizon

  13. Assessment: Can’t be checkedSaid

    Conditional forecast

    “If they are wrong, the net effect will be to delay AGI, because their project will siphon away talent from other approaches – just as it already has, but more so.”

    Our reading: If the Stargate premises are wrong, the project will delay AGI by siphoning talent away from other approaches.

    No horizon

    Only if: if the premises ($100B equals AGI; customers pay anything) are wrong

Held from this post (1)

These records did not pass every check, so nothing about them is published. Held is not a finding.