Turns out Generative AI was a scam
We recorded 8 claims from this post. 6 were checked against evidence. 2 can’t be checked. 8 more are held. Read the post at its source.
Claims from this post (8)
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: UnresolvedSaid
Observation
“Turns out it wasn’t, as Shira Ovide just reported:”
Our reading: Generative AI was not in fact contributing half of US GDP growth, contrary to the White House AI advisor's claim.
No horizon Evidence as of
Limitation: This does not validate the advisor's half-of-growth assertion.
Assessment: SupportedSaid
Observation
“They often produce workslop.”
Our reading: Large language models often produce low-quality 'workslop'.
No horizon Evidence as of
Limitation: Self-reported experience is not an independently graded output sample.
Assessment: UnresolvedSaid
Causal claim
“Generative AI has its uses, but it hardly surprising that most business have struggled to find return on their investment, given the massive reliability issues that continue to plaguage it.”
Our reading: Most businesses have struggled to get return on generative AI investment because of its ongoing massive reliability issues.
No horizon Evidence as of
Limitation: NANDA's interviews, conference survey and public-initiative review do not establish prevalence among all generative-AI investors [e1].
Assessment: UnresolvedSaid
Observation
“I don’t know that Generative AI was literally a scam, but the people selling it have tried to sell it as if it were tantamount to artificial general intelligence, when it’s not.”
Our reading: Generative AI vendors have marketed it as tantamount to AGI, but it is not AGI.
No horizon Evidence as of
Limitation: Altman's quotation comes from a third-party transcript and does not establish what all vendors promised [e1].
Assessment: SupportedSaid
Observation
“Although there are some practical use cases, such as coding, it is an inherently unreliable technology.”
Our reading: Generative AI has some practical uses such as coding but is an inherently unreliable technology.
No horizon Evidence as of
Limitation: Coding utility is supported here by self-reports; the experiment does not establish gains across coding tasks [e1, e3].
Assessment: SupportedSaid
Causal claim
“It is leading some people into serious mental health issues.”
Our reading: Generative AI is leading some people into serious mental health issues.
No horizon Evidence as of
Limitation: One clinical case does not establish population prevalence or quantify causal risk [e1].
Assessment: Can’t be checkedSaid
Conditional forecast
“And it may well lay waste to our economy, once banks and investors who bought the hype start to fall.”
Our reading: Generative AI may well lay waste to the economy once banks and investors who bought the hype start to fail.
No horizon
Only if: once banks and investors who bought the hype start to fall
Assessment: Can’t be checkedSaid
Forecast
“When all is said and done, my best guess is that generative AI will have done significantly more harm to society than good.”
Our reading: Marcus's best guess is that generative AI will ultimately have done significantly more harm to society than good.
No horizon
Held from this post (8)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews reached different assessments. Our reading, not assessed: Generative AI is ripping apart the educational system and the information ecosphere and is flooding the zone with nonconsensual deepfake porn.
- The record does not meet the current rules. Our reading, not assessed: Generative AI has been inherently unreliable from the start and none of the problems Marcus warned about in the last five years has been properly solved.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: Generative AI is threatening the environment through data centers built on too much speculation.
- The two reviews reached different assessments. Our reading, not assessed: A recent survey, the Remote Labor Index, found that large language models could do only 2.5% of human tasks, and Marcus says that figure is a massive overestimate because everything requiring physical labor was excluded.
- The record does not meet the current rules. Our reading, not assessed: Large language models still hallucinate, make boneheaded errors, and lack a proper concept of reality.
- The two reviews read the claim differently