A trillion dollars is a terrible thing to waste
We recorded 14 claims from this post. 4 were checked against evidence. 10 can’t be checked. 8 more are held. Read the post at its source.
Claims from this post (14)
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: Can’t be checkedSaid
Rhetoric
“But none of what Sutskever said should actually come as a surprise, especially not to readers of this Substack, or to anyone who followed me over the years.”
Our reading: None of what Sutskever said should actually come as a surprise, especially not to readers of Marcus's Substack or to anyone who has followed Marcus over the years.
No horizon
Scope: especially not to readers of this Substack or anyone who followed me
Assessment: SupportedSaid
Observation
“The unfairly dismissed Apple reasoning paper laid bare the generalization issues; another paper called “Is Chain-of-Thought Reasoning of LLMs Mirage? A Data Distribution Lens” put a further nail in the LLM reasoning and generalization coffin.”
Our reading: Research papers (Apple's reasoning paper and the chain-of-thought mirage paper) demonstrated LLM reasoning and generalization failures.
No horizon Evidence as of
Limitation: The result depends on reading the claim as empirical task failures rather than universal incapacity.
Assessment: Can’t be checkedSaid
Forecast
“So venture capitalists may continue to support LLM mania, at least for a while.”
Our reading: Venture capitalists may continue to support LLM mania, at least for a while.
No horizon
Qualifier: at least for a while
Assessment: UnresolvedSaid
Observation
“The first and most obvious answer is money, which I estimate, back of the envelope as (roughly) a trillion dollars, much of it on Nvidia chips and massive salaries.”
Our reading: Supposing for the sake of argument that LLMs have run their course and new ideas are needed, Marcus estimates, back of the envelope, the money cost of the field taking so long to recognize this at roughly a trillion dollars, much of it on Nvidia chips and massive salaries.
No horizon Evidence as of
Limitation: The spending boundary and starting date remain ambiguous.
Assessment: Can’t be checkedSaid
Rhetoric
“If the definition of insanity is doing the same thing over and over and expecting different results, trillion dollar investments in ever more expensive experiments aiming to reach AGI may be delusional to the highest degree.”
Our reading: Trillion-dollar investments in ever larger experiments aimed at AGI may be delusional.
No horizon
Assessment: SupportedSaid
Observation
“Each new bigger, more expensive model ekes out measurable improvements, but returns appear to be diminishing (that’s what Sutskever is saying about the Kaplan laws) and none of these experiments has solved core issues around hallucinations, generalization, planning and reasoning, as Sutskever too now recognizes.”
Our reading: No LLM scaling experiment has solved the core problems of hallucination, generalization, planning and reasoning.
No horizon Evidence as of
Limitation: 'Solved' has no stated threshold. Someone who counts IMO gold or big benchmark gains as solving reasoning would read the claim differently.
Assessment: SupportedSaid
Observation
“It has never worked.”
Our reading: Building ever larger LLMs has never succeeded in reaching AGI.
No horizon Evidence as of
Limitation: The conclusion depends on the ordinary public-demonstration reading of AGI.
Assessment: Can’t be checkedSaid
Forecast
“But it’s not just that a trillion dollars or more might go down the drain, but that there might be considerable collateral damage, to the rest of society, both economic and otherwise (e.g., in terms of how LLMs have undermined college education).”
Our reading: A trillion dollars or more of LLM investment might be lost, with considerable economic and other collateral damage to society.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“In the worst case, the impact of a deflated AI bubble could be immense.”
Our reading: In the worst case, the impact of a deflated AI bubble could be immense.
No horizon
Only if: if the AI bubble deflates
Assessment: Can’t be checkedSaid
Observation
“The whole thing looks incredibly fragile.”
Our reading: The AI investment-driven economy looks incredibly fragile.
No horizon
Assessment: Can’t be checkedSaid
Observation
“The sad part is that most of the reasons have been known – though not widely accepted – for a very long time. It all could have been avoided.”
Our reading: Most of the reasons to doubt LLMs have been known, though not widely accepted, for a very long time, and it all could have been avoided.
No horizon
Scope: most of the reasons
Assessment: Can’t be checkedSaid
Observation
“here we may have wasted a trillion dollars and several years to rediscover what cognitive science already knew.”
Our reading: The AI industry may have wasted a trillion dollars and several years rediscovering what cognitive science already knew.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“A trillion dollars is a terrible amount of money to have perhaps wasted. If the blast radius is wider it could be a lot more.”
Our reading: If the fallout spreads wider, the cost of the LLM bet could be much more than a trillion dollars.
No horizon
Only if: if the blast radius is wider
Assessment: Can’t be checkedSaid
Forecast
“It is all starting to feel like a tale straight out of Greek tragedy, an avoidable mixture of arrogance and power that just might wind up taking down the economy.”
Our reading: The AI boom just might end up taking down the economy.
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 read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: LLMs have undermined college education, given as an example of collateral damage to society.
- The two reviews reached different assessments. Our reading, not assessed: Essentially all of what Sutskever said was in Marcus's 2018 article 'Deep learning: A Critical Appraisal' and/or his 2022 'Deep learning is hitting a wall', which argued for neurosymbolic approaches to complement neural networks, for more innate constraints, and that the Kaplan scaling laws would eventually reach diminishing returns.
- The two reviews read the claim differently
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: Larger LLMs give measurable but apparently diminishing improvements.
- The criteria are ambiguous. Our reading, not assessed: To a first approximation, all the big tech companies, from OpenAI, Google, Meta, xAI and Anthropic to several Chinese companies, keep doing the same experiment over and over: building ever larger LLMs in hopes of reaching AGI.
- The two reviews read the claim differently