What to Expect When You’re Expecting … GPT-5
We recorded 15 claims from this post. 13 were checked against evidence. 2 can’t be checked. 11 more are held. Read the post at its source.
Claims from this post (15)
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
Observation
“For years we have been hearing, endlessly, about how GPT-5 was going to land imminently, and those predictions turned out to be wrong so often that a year ago I wrote a post about it, called GPT-5…now arriving Gate 8, Gate 9, Gate 10, not to mention a couple of April Fool’s jokes.”
Our reading: Predictions over several years that GPT-5 would arrive imminently were repeatedly wrong.
No horizon Evidence as of
Limitation: Support depends on including qualified public speculation within 'predictions'.
Assessment: UnresolvedSaid
Forecast
“GPT-5 will still, like its predecessors, be a bull in a china shop, reckless and hard to control.”
Our reading: GPT-5, like its predecessors, will be reckless and hard to control.
No deadline stated Evidence as of
Limitation: METR's task suite does not measure the prevalence or consequences of unintended behavior across ordinary deployments, and its detection methods may miss reward hacking [e1].
Assessment: SupportedSaid
Forecast
“Guardrails (a la ChatGPT) may be in place, but the guardrails will teeter between being too weak (beaten by “jailbreaks”) and too strong (rejecting some perfectly reasonable requests).”
Our reading: Guardrails like ChatGPT's may be in place for GPT-5, but they will teeter between being too weak (beaten by jailbreaks) and too strong (rejecting some perfectly reasonable requests).
Settled before its deadline Evidence as of
Limitation: The evaluations do not establish temporal oscillation, ordinary-user failure rates, or persistence in one unchanged deployment [e2, e3].
Assessment: UnresolvedSaid
Forecast
“Its natural language output still won’t be something that one can reliably hook up to downstream programs; it won’t be something, for example, that you can simply and directly hook up to a database or virtual assistant, with predictable results.”
Our reading: GPT-5's natural language output will not be something one can reliably hook up to downstream programs, for example simply and directly to a database or virtual assistant, with predictable results.
No deadline stated Evidence as of
Limitation: Distinguishing natural language from plaintext code materially affects the assessment.
Assessment: UnresolvedSaid
Forecast
“GPT-5 will not have reliable models of the things that it talks about that are accessible to external programmers in a way that reliably feeds downstream processes.”
Our reading: GPT-5 will not have reliable models of the things it talks about that external programmers can access in a way that reliably feeds downstream processes.
No deadline stated Evidence as of
Limitation: The meaning of an accessible subject model materially affects what evidence would count.
Assessment: UnresolvedSaid
Forecast
“Without external aids it won’t be able beat Meta’s Cicero in Diplomacy; it won’t be able to drive a car reliably; it won’t be able to reliably guide a robot like Optimus to be anything like as versatile as Rosie the Robot.”
Our reading: Without external aids, GPT-5 will not be able to beat Meta's Cicero at Diplomacy, will not be able to drive a car reliably, and will not be able to reliably guide a robot like Optimus to be anything like as versatile as Rosie the Robot.
No deadline stated Evidence as of
Limitation: This compound claim cannot be supported from evidence about robotics alone.
Assessment: SupportedSaid
Forecast
“Examples of concealed bias will be discovered within days or months.”
Our reading: Examples of concealed bias in GPT-5 will be discovered within days or months of release.
Settled before its deadline Evidence as of
Limitation: The meaning of concealed determines the verdict.
Assessment: SupportedSaid
Forecast
““Alignment” between what humans want and what machines do will continue to be a critical, unsolved problem.”
Our reading: Alignment between human wants and machine behavior will remain a critical unsolved problem.
Settled before its deadline Evidence as of
Limitation: This supports persistence, not a prediction that alignment will remain unsolved forever.
Assessment: Can’t be checkedSaid
Conditional forecast
“When AGI (artificial intelligence) comes, large language models like GPT-5 may be seen in hindsight as part of the eventual solution, but only as part of the solution.”
Our reading: When AGI comes, large language models like GPT-5 may be seen in hindsight as part of the eventual solution, but only as part of it.
No horizon
Only if: when AGI comes
Assessment: UnresolvedSaid
Conditional forecast
“Trustworthy, general artificial intelligence, aligned with human values, will come, when it does, from systems that are more structured, with more built-in knowledge, and will incorporate at least some degree of explicit tools for reasoning and planning, as well as explicit knowledge, that are lacking in systems like GPT.”
Our reading: When trustworthy general AI aligned with human values comes, it will come from systems that are more structured, have more built-in knowledge, and incorporate at least some degree of explicit tools for reasoning and planning, as well as explicit knowledge, that systems like GPT lack.
No deadline stated Evidence as of
Only if: when it does come
Assessment: UnresolvedSaid
Forecast
“In retrospectives written in 2043, intellectual historians will conclude that there was an initial overemphasis on large language models, and a gradual but critical shift of the pendulum back to more structured systems with deeper comprehension.”
Our reading: In retrospectives written in 2043, intellectual historians will conclude that there was an initial overemphasis on large language models and a gradual but critical shift of the pendulum back to more structured systems with deeper comprehension.
Not yet due Evidence as of
Limitation: Future assessment will require a transparent selection of relevant historians and retrospectives.
Assessment: UnresolvedSaid
Forecast
““Scaling” alone—building bigger and models until they absorb the entire internet — will prove useful, but only to a point.”
Our reading: Scaling alone will prove useful only up to a point.
No deadline stated Evidence as of
Limitation: Finite experiments cannot establish what all future increases in scale will achieve.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“If all seven predictions prove correct, I hope that the field will finally realize that it is time to move on.”
Our reading: If his seven predictions prove correct, the AI field should move on from the LLM approach.
No horizon
Qualifier: if all seven predictions prove correct
Assessment: UnresolvedSaid
Forecast
“For that, we will, I predict, need genuinely new architectures that incorporate explicit knowledge and world models at their very core.”
Our reading: Trustworthy AI will require genuinely new architectures with explicit knowledge and world models at their core.
No deadline stated Evidence as of
Limitation: Behavior on a bounded benchmark cannot establish broad trustworthiness.
Assessment: UnresolvedSaid
Observation
“I was right on the money then.”
Our reading: Marcus's December 2022 predictions about GPT-4 were accurate.
No horizon Evidence as of
Limitation: The assessment reads the retrospective as referring to all seven predictions; its intended scope is not explicit.
Held from this post (11)
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: GPT-5 will be a lot better than GPT-4 and will totally eclipse it, blowing minds.
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: GPT-5's fluent hallucinations will remain common and easily induced, escalating the risk of LLMs being used for misinformation.
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: Marcus thinks that this time GPT-5 really is about to be released.
- The two reviews reached different assessments. Our reading, not assessed: GPT-5 will make a significant number of stupid errors in hard-to-predict ways.
- The two reviews reached different assessments. Our reading, not assessed: GPT-5's reasoning about physical, psychological and mathematical worlds will remain unreliable; it will solve many prior benchmark items but still get tripped up, especially in longer, complex scenarios.
- The two reviews read the claim differently
- The two reviews reached different assessments. Our reading, not assessed: Some of GPT-5's advice will be very bad.
- The two reviews reached different assessments. Our reading, not assessed: GPT-5 will remain a pastiche generator useful for brainstorming and first drafts but not trustworthy general intelligence.