How o3 and Grok 4 Accidentally Vindicated Neurosymbolic AI
We recorded 36 claims from this post. 10 were checked against evidence. 26 can’t be checked. 7 more are held. Read the post at its source.
Claims from this post (36)
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
“In my view, neither neural networks nor classical AI can really stand on their own.”
Our reading: In Marcus's view, neither neural networks nor classical symbolic AI can really stand on their own.
No horizon
Qualifier: in my view
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“We must find ways to bring them together.”
Our reading: The field must find ways to combine neural networks and symbolic AI.
No horizon
Assessment: Can’t be checkedSaid
Observation
“After a thirty-year journey, I believe that neurosymbolic AI’s moment has finally arrived, in part from an unlikely place.”
Our reading: Neurosymbolic AI's moment has arrived as of mid-2025.
No horizon
Assessment: SupportedSaid
Observation
“A quarter century later, hallucinations remain ubiquitous. Within a neural network substrate, the problem still has not been solved.”
Our reading: Hallucinations remain ubiquitous and unsolved in neural-network-based systems.
No horizon Evidence as of
Limitation: Both sources are developer disclosures [e1, e2].
Assessment: Can’t be checkedSaid
Causal claim
“Hinton’s long-time hostility against any role at all for symbols has, in my judgement, cost the field dearly.”
Our reading: Hinton's hostility to symbolic approaches has substantially harmed AI research.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“Many other important ideas have likely also yet to be discovered, precisely because the Hinton path has distracted immense resources from other ideas, fostering an intellectual monoculture that, in the words of Emily Bender, has been “sucking the oxygen from the room.””
Our reading: The dominance of the pure neural-network path has likely kept important ideas undiscovered by diverting resources into a monoculture.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“AlphaFold, AlphaProof, and AlphaGeometry are all successful neurosymbolic models. We would likely have more of such innovation already, if Hinton had not so insistently shaped the modern landscape in such a narrow-minded way.”
Our reading: AlphaFold, AlphaProof and AlphaGeometry are successful neurosymbolic models, and there would likely be more such innovation had Hinton not narrowed the field.
No horizon
Qualifier: if Hinton had not shaped the landscape narrowly
Assessment: Can’t be checkedSaid
Causal claim
“We are drowning in a sea of mediocre prose precisely because LLMs, the backbone of virtually all current sytems, lack the underlying representational wherewithal to do better.”
Our reading: LLMs produce mediocre prose because they lack the representational capacity to do better.
No horizon
Assessment: UnresolvedSaid
Observation
“Yet nearly all funding has been aimed simply at making them larger.”
Our reading: Nearly all AI funding has gone to making LLMs larger.
No horizon Evidence as of
Limitation: The selected annual period cannot settle an unspecified cumulative historical claim.
Assessment: SupportedSaid
Observation
“As we will see in a moment, people are finally pursuing a tool-driven, neurosymbolic vision like this — and getting better results than they were able to get with pure neural networks.”
Our reading: Tool-driven neurosymbolic approaches are now being pursued and yield better results than pure neural networks.
No horizon Evidence as of
Limitation: The decisive studies were preprints reporting the authors' own experiments.
Assessment: UnresolvedSaid
Observation
“I argued that scaling as it was then defined (pretraining data and compute) alone was insufficient to solve challenges with reasoning, misalignment and hallucinations. (Spoiler alert: it still hasn’t.)”
Our reading: Scaling pretraining data and compute alone has not solved reasoning, misalignment and hallucination problems.
No horizon Evidence as of
Limitation: This is an evidence limitation, not evidence that pretraining alone had solved the problems.
Assessment: SupportedSaid
Observation
“Three years later, the pure scaling of pretraining data (and compute, which is to say more and more GPUs) simply hasn’t worked. The specific obstacles that I dwelled on, with respect to hallucinations, misalignment and reasoning errors, have not been overcome.”
Our reading: Pure scaling of pretraining data and compute has not worked; hallucinations, misalignment and reasoning errors remain.
No horizon Evidence as of
Limitation: The result depends on reading 'hasn’t worked' as failure to overcome the named problems.
Assessment: UnresolvedSaid
Observation
“OpenAI quietly began doing this to some degree in 2023 with a system called “code interpreter”, in which LLMs (themselves neural networks) call (purely symbolic) Python interpreters. This is literally “putting programs inside”; exactly what Hinton said was a huge mistake.”
Our reading: In 2023, OpenAI quietly began, to some degree, 'putting programs inside' its systems with Code Interpreter, in which LLMs call purely symbolic Python interpreters. Marcus says this is exactly what Hinton called a huge mistake.
No horizon Evidence as of
Limitation: The launch and functionality component is supported.
Assessment: SupportedSaid
Observation
“Recent “reasoning” models borrow even more from classic symbolic approaches, such as techniques like search and conditionally iterating through multiple solutions, and aggregating the results, techniques that traditional networks had long eschewed, in favor of a single feed-forward pass through a neural net.”
Our reading: Recent reasoning models borrow symbolic techniques such as search, iteration over multiple solutions and aggregation.
No horizon Evidence as of
Limitation: Developer descriptions do not establish historical influence from symbolic AI or an explicitly programmed symbolic search implementation [e1, e2, e3].
Assessment: Can’t be checkedSaid
Observation
“GPT-4 probably wasn’t (I suspect it included some symbolic filters in its guardrails), and o3, when it invokes code interpreter and symbolically-executed control structures certainly isn’t.”
Our reading: GPT-4 was probably not a pure LLM, and o3 using code interpreter is certainly not a pure LLM.
No horizon
Qualifier: o3 when it invokes code interpreter
Assessment: SupportedSaid
Observation
“Both informal experimentation and a number of recent quantitative results make it abundantly clear that current generative AI systems perform better when they avail themselves of symbolic tools.”
Our reading: Current generative AI systems perform better when they use symbolic tools.
No horizon Evidence as of
Limitation: The AIME results are company-reported; the cited scores alone do not quantify the improvement attributable to Python [e1].
Assessment: UnresolvedSaid
Observation
“In contrast, o3, when it draws on symbolic code (which it sometimes displays explicitly) can build crossword grids like this far faster than I could (short of my writing my own code).”
Our reading: When o3 draws on symbolic code, it can build crossword grids like this one far faster than Marcus could, short of writing his own code.
No horizon Evidence as of
Limitation: The informal experiment may have private records that are not publicly available.
Assessment: Can’t be checkedSaid
Observation
“None of which means we are close to AGI. There are many ways to put together symbols and neural nets. I am not at all convinced we have the right one.”
Our reading: These results do not mean we are close to AGI, and Marcus is not at all convinced the field has found the right way to combine symbols and neural nets.
No horizon
Qualifier: there are many ways to put together symbols and neural nets
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Including LLMs somewhere in the next evolution of AI makes sense to me, but leaving them at the core may be a mistake.”
Our reading: LLMs may belong in future AI systems, but keeping them at the core may be a mistake.
No horizon
Assessment: Can’t be checkedSaid
Observation
“A neurosymbolic substrate for AI by itself (as I argued in 2020) is likely to be necessary but not sufficient.”
Our reading: A neurosymbolic substrate is likely necessary but not sufficient for AGI.
No horizon
Assessment: Can’t be checkedSaid
Observation
“There is likely still a great deal of work left to be done.”
Our reading: There is likely still a great deal of work left to be done.
No horizon
Assessment: Can’t be checkedSaid
Observation
“We still also (just as I argued in 2020) lack proper ways of building and inferring symbolic world/cognitive models on-the-fly.”
Our reading: AI still lacks proper ways to build and infer symbolic world models on the fly.
No horizon Evidence as of
Limitation: A stricter reading, such as 'no method works reliably', might be checkable. But that would add a threshold the speaker did not give.
Assessment: Can’t be checkedSaid
Observation
“Explicit cognitive models (as I discussed recently in this newsletter) are also likely to be essential, yet remain very much underexplored.”
Our reading: Explicit cognitive models are also likely to be essential, yet they remain very much underexplored.
No horizon
Assessment: Can’t be checkedSaid
Observation
“We have made some progress; we still likely have a long way to go.”
Our reading: We have made some progress toward AGI, but we likely still have a long way to go.
No horizon
Scope: in the mountain-climb metaphor with AGI as the highest peak
Assessment: Can’t be checkedSaid
Causal claim
“Largely because of Hinton’s influence, the field still talks about neurosymbolic AI as if it were a dirty word. That’s likely holding back the science.”
Our reading: The stigma against neurosymbolic AI, largely due to Hinton, is likely holding back AI science.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“Hinton’s admonition against using symbolic AI was extraordinarily misguided, and probably delayed discovery of useful tools like Code Interpreter by years.”
Our reading: Hinton's opposition to symbolic AI was misguided and probably delayed tools like Code Interpreter by years.
No horizon
Assessment: Can’t be checkedSaid
Observation
“It’s likely that OpenAI, Anthropic and others are already doing more neurosymbolic AI than they actually make public. They still pretend it’s all “a model” but that model uses all kinds of tricks and mixtures with tools, code generation etc.”
Our reading: OpenAI, Anthropic and others are likely using more neurosymbolic techniques than they disclose.
No horizon
Assessment: Can’t be checkedSaid
Observation
“Massive infrastructure investments like Stargate are likely based on misleading impressions of what is actually driving progress.”
Our reading: Massive AI infrastructure investments like Stargate are likely based on a mistaken view of what drives AI progress.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“A lot of recent improvements are likely coming from improvements in using symbolic tools, rather than scaling.”
Our reading: Much recent AI improvement likely comes from better use of symbolic tools rather than scaling.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“It would appear that it is only recently, in desperation, as scaling started to reach diminishing returns, that mainstream machine learning began to broaden its vistas.”
Our reading: It would appear that mainstream machine learning only recently began to broaden its vistas, in desperation, as scaling started to reach diminishing returns.
No horizon
Qualifier: it would appear
Assessment: Can’t be checkedSaid
Observation
“Where the field is getting stuck now is likely on how to scale these new, neurosymbolic tools and how to make models “understand” when to use what and how to logically combine the context.”
Our reading: The field is likely stuck on scaling neurosymbolic tools and getting models to know when and how to use them.
No horizon
Assessment: Can’t be checkedSaid
Forecast
“Getting to AGI will likely take still more breakthroughs.”
Our reading: Reaching AGI will likely require further breakthroughs.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“But it may well be as simple as money. The message that we can simply scale our way to AGI is incredibly attractive to investors because it puts money as the central (and sufficient) force needed to advance.”
Our reading: The industry's connectionist-only focus may be explained by money, because the scaling narrative appeals to investors.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“Admitting that they need to rely on neurosymbolic tools would pierce the scaling narrative.”
Our reading: If AI companies admitted that they need to rely on neurosymbolic tools, that would pierce the scaling narrative.
No horizon
Only if: if companies admitted they need to rely on neurosymbolic tools
Assessment: UnresolvedSaid
Observation
“OpenAI, without any sort of of public acknowledgement whatsoever, has accidentally vindicated neurosymbolic AI.”
Our reading: Without any public acknowledgement whatsoever, OpenAI has accidentally vindicated neurosymbolic AI.
No horizon Evidence as of
Limitation: Company-reported improvements establish limited practical benefits, not the necessity of neurosymbolic AI for general intelligence.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Fostering its further development may be among the best things that companies, researchers, and governments can do. Investors, take note.”
Our reading: Fostering the further development of neurosymbolic AI may be among the best things that companies, researchers, and governments can do, and investors should take note.
No horizon
Held from this post (7)
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 record does not meet the current rules. Our reading, not assessed: Most modern models appear to rely heavily on training data generated by symbolic rule systems.
- The record does not meet the current rules. Our reading, not assessed: Marcus says it is quite clear from the Grok 4 Humanity's Last Exam graph that pure scaling of training data and compute is reaching diminishing returns a long way from peak possible performance, and that increasing compute alone is not driving an exponential explosion into superintelligence.
- The two reviews read the claim differently
- The record does not meet the current rules. Our reading, not assessed: Importing neurosymbolic ideas, including symbolic algorithms in the workflow, has worked to some degree.
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: AI reasoning remains unreliable and spatial reasoning is far from solved.