When Will the GenAI Bubble Burst?
We recorded 19 claims from this post. 9 were checked against evidence. 10 can’t be checked. 4 more are held. Read the post at its source.
Claims from this post (19)
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
Observation
“But as time goes on, I increasingly believe that call was correct.”
Our reading: Marcus increasingly believes his August 2023 call that generative AI may turn out to be a dud was correct, though he is not yet claiming victory.
No horizon
Qualifier: not yet claiming victory
Assessment: UnresolvedSaid
Observation
“$50B in, $3B out. That’s obviously not sustainable.”
Our reading: Generative AI investment of about $50B against $3B in revenue is obviously not sustainable.
No horizon Evidence as of
Limitation: The repeated estimate is not an independent validation of the amounts, their coverage or whether revenue was realized or annualized. [e1]
Assessment: UnresolvedSaid
Causal claim
“Meanwhile hype itself is becoming a kind of risk factor. The more hype, the bigger the fall, if expectations aren’t met.”
Our reading: Hype around generative AI is a risk factor: the more hype, the bigger the fall if expectations are not met.
No horizon Evidence as of
Limitation: The closest paper covers AI broadly and a particular national market.
Assessment: UnresolvedSaid
Observation
“At the same time, more optimistic predictions like these [from Reid Hoffman September 7, 2023] have not thus far borne out.”
Our reading: Reid Hoffman's optimistic September 2023 AI predictions had not come true as of March 2024.
No horizon Evidence as of
Limitation: The interview identifies the hallucination prediction but does not establish the complete set of predictions Marcus referenced. [e4]
Assessment: UnresolvedSaid
Causal claim
“They are inherent to how the system makes predictions, and to the fact that LLMs themselves don’t have the wherewithal to look things up and reason over them.”
Our reading: Hallucinations are inherent to how LLMs make predictions and to their inability to look things up and reason over them.
No horizon Evidence as of
Limitation: A conditional mathematical result is not a universal result about deployed systems.
Assessment: UnresolvedSaid
Forecast
“Core problems like hallucinations aren’t going way.”
Our reading: Core generative AI problems like hallucinations are not going away.
No deadline stated Evidence as of
Limitation: The cited persistence observation is from September 2025, not a complete audit of every system available at the evaluation date.
Assessment: Can’t be checkedSaid
Observation
“The entire industry is based on hype, and on the specific hope that the kinds of problems we saw again and again with GPT-2, GPT-3, and GPT-4, not to mention Claude, Grok, Gemini, LLaMA, and so on, are on the verge of being solved — even as the reality is that all the big players are converging on systems”
Our reading: The generative AI industry is based on hype and the hope that persistent LLM problems are about to be solved.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“To be honest, I am not confident Generative AI as currently envisioned will ever come together.”
Our reading: Generative AI as currently envisioned may never come together.
No horizon
Qualifier: as currently envisioned
Assessment: UnresolvedSaid
Causal claim
“(Not easy. With everyone building similar models, there is no moat, hence lots of competition on price).”
Our reading: Because everyone is building similar models, generative AI companies have no moat, leading to heavy price competition.
No horizon Evidence as of
Limitation: Observed convergence does not establish universal substitutability.
Assessment: SupportedSaid
Observation
“The trouble is that the bigger the model, the higher the internal costs. And current-sized models, already expensive, aren’t reliable enough for many jobs.”
Our reading: Bigger models have higher costs, and current-sized models are already expensive and not reliable enough for many jobs.
No horizon Evidence as of
Limitation: The result depends on comparing otherwise similar implementations and interpreting reliability as suitability for unverified use.
Assessment: UnresolvedSaid
Observation
“So far most uses of LLMs have been for testing, not production-level systems.”
Our reading: Most uses of LLMs so far have been for testing rather than production systems.
No horizon Evidence as of
Limitation: Survey respondents are not interchangeable with projects or usage volume.
Assessment: UnresolvedSaid
Observation
“Thus far, the closest thing to a killer app is probably coding assistace, but the income there isn’t massive enough to cover costs of the chips, legal etc.”
Our reading: Coding assistance is probably the closest thing to a generative AI killer app, but its income is not large enough to cover chip, legal and other costs.
No horizon Evidence as of
Limitation: The available financial reporting is secondary and concerns one product.
Assessment: Can’t be checkedSaid
Forecast
“GPT-5 may be more satisfying but is likely even more expensive to train and operate.”
Our reading: GPT-5 may be more satisfying but is likely even more expensive to train and operate than current models.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“If OpenAI manages to put out a truly mindblowing GPT-5 this year, the hype will further increase; the bubble won’t burst soon (even if the use cases to justify it aren’t really there).”
Our reading: If OpenAI releases a truly mindblowing GPT-5 in 2024, hype will increase and the GenAI bubble will not burst soon.
Deadline passed Evidence as of
Only if: if OpenAI releases a truly mindblowing GPT-5 this year
Assessment: Can’t be checkedSaid
Conditional forecast
“GenAI will, in that case, live for another day, perhaps imploding only later when people realize there is no killer app to justify the increasingly high costs.”
Our reading: Even with a mindblowing GPT-5 in 2024, GenAI might implode later once people realize there is no killer app to justify rising costs.
No horizon
Only if: if OpenAI releases a truly mindblowing GPT-5 this year
Assessment: Can’t be checkedSaid
Conditional forecast
“If nobody (OpenAI, Google, or anyone else) releases a true quantum leap by the end of 2024, substantially addressing key issues around reliability, hallucination, data leakage, and security, the bubble may start to pop by this time next year.”
Our reading: If no one releases a quantum leap addressing reliability, hallucination, data leakage and security by end of 2024, the GenAI bubble may start to pop by March 2025.
No horizon
Only if: if nobody releases a true quantum leap by end of 2024 addressing reliability, hallucination, data leakage and security
Assessment: Can’t be checkedSaid
Forecast
“But will we see a mindblowing GPT this year? I doubt it.”
Our reading: OpenAI is unlikely to release a mindblowing GPT model in 2024.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“GenAI will still exist, and find some use, but valuations and excitement may dissipate, reemerging only once genuine advances are made.”
Our reading: GenAI will persist and find some use, but valuations and excitement may dissipate until genuine advances are made.
No horizon
Only if: if no quantum leap by end of 2024
Assessment: Can’t be checkedSaid
Forecast
“If hallucinations were brought down to human expert levels by the end of 2024 I would be truly astonished.”
Our reading: By the end of 2024, hallucinations by large language models will not have been reduced to the level of human experts.
Deadline passed Evidence as of
Limitation: This record does not show how often AI systems or human experts made factual errors in 2024.
Held from this post (4)
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 revenue so far has been modest (about 0.01% of US GDP) because the closest thing to a killer app, coding assistance, doesn't bring in massive income, generative search isn't reliable enough, customers haven't been thrilled with Copilot, and other problems are modest in size.
- The criteria are ambiguous. Our reading, not assessed: Generative search is not reliable enough and does not yet have a clear business model.
- The record does not meet the current rules. Our reading, not assessed: LLM hallucinations are very unlikely to be brought down to human expert levels by the end of 2024.
- The record does not meet the current rules. Our reading, not assessed: Generative AI is software that isn't making much money, isn't secure, and is keeping a lot of people up at night.