OpenAI o1 Says There’s a Wall
And We Just Hit It
IMPORTANT: This article uses LLM-generated content. Use at your own risk.
Introduction (By a Human)
I am growing more frustrated by the day at the lack of progress on three critical fronts in applying LLMs in the commercial space: quickly updating facts, retrieving them at scale, and guaranteeing correctness. I will undoubtedly have to spend days and weeks unfucking the heads of many analysts and decision-makers who think beating a math benchmark equates to AGI. This is ridiculous on its face. Even trusted skeptics of OpenAI’s claims haven’t read the fine print on what o1 is. So, I thought I would have o1 explain itself, in its own words.
From my perspective, there is not only a wall; OpenAI just slammed head-first into it, and no one noticed.
The wall is the fact no progress is being made at the foundation layer of the models. The same core problems still exist and will likely continue for the foreseeable future. OpenAI has effectively moved the goalposts of AGI and scaling laws by engineering around the fundamental issues. That’s not to say these models are not useful. They certainly are. For that, I am grateful to the team at OpenAI. But the hype is out of control and will undoubtedly lead to a bad hangover. I am still full steam ahead on nuerosymbolic programming approaches using X-Reason with RAG until foundational problems are actually addressed and solved.
o1 Explaining the Wreckage
Below is a critique of OpenAI’s o1 architecture from the standpoint of the three biggest commercial challenges we identified for LLMs — (1) teaching new facts, (2) retrieving facts, and (3) verifying correctness — and whether o1 represents a fundamentally better foundation. We also touch on how hard it might be to scale and whether symbolic, domain-specific approaches could be more effective or cheaper in certain scenarios.
1. Teaching the Model New Facts
What o1 Does
- Hierarchical Reasoning Structure (Thinking Blocks):
o1 uses modular sub-tasks to tackle problems, but these modules are primarily about how it reasons, not where it stores facts. - RL Fine-Tuning on Reasoning Steps:
Reward signals guide the model in producing correct step-by-step solutions, particularly in math or programming.
Critique
- No Direct Mechanism for Incremental Fact Injection:
o1’s focus is on step-by-step logical consistency within a single inference. That doesn’t necessarily translate to easier “fact updating” in the large network weights. You still have to retrain or fine-tune on new data. - Retention vs. Overwriting in Superposition:
Even with thinking blocks, the underlying knowledge is still stored in the same superposed, high-dimensional parameter space. Adding or editing domain-specific facts remains challenging, risking the inadvertent distortion of pre-existing knowledge. - Conclusion:
The thinking blocks improve reasoning structure but do not, at a foundational level, solve the core difficulty of teaching models new facts without extensive fine-tuning or large curated datasets.
2. Retrieving Facts (“Needle in a Haystack”)
What o1 Does
- Modular Reasoning and Summaries:
Each sub-task (thinking block) can produce a compact summary, meaning the model doesn’t carry the entire sequence of intermediate tokens forward. This helps avoid context-window overload. - Potentially Less “Attention Dilution”:
By chunking its own reasoning steps, the model may focus on relevant sub-problems more effectively instead of scanning a monolithic context in every layer.
Critique
- Still a Large Transformer at Heart:
The “thinking block” approach is effectively a hierarchical or chunked reasoning method within the same fundamental attention-driven transformer model. You might still encounter the same “needle in the haystack” challenges if you feed large amounts of unstructured, real-world data. - Domain-Specific Knowledge Access:
If the relevant domain knowledge for a sub-task is hidden in a huge corpus of text, you still face the question of how the model indexes or retrieves it. Thinking blocks help structure the model’s internal chain of thought but don’t necessarily solve retrieval at scale. - Likely Still Needs Chunking / Retrieval-Augmentation in Production:
While it may reduce context-window burdens, an enterprise with massive knowledge bases will likely still need retrieval-augmentation or chunking strategies (e.g., vector databases).
3. Verifying Correctness (Hallucinations & Explainability)
What o1 Does
- Intermediate Step Validation:
By rewarding correct sub-steps in math/programming, o1 can reduce certain classes of hallucinations for well-defined domains. Each step can be checked, in principle, for correctness before moving on. - More Transparent “Chain of Thought”:
Because the model explicitly labels tasks (block titles), processes, and outputs, it might appear more “explainable” — at least the final text is chunked into blocks that show how the model is “thinking.”
Critique
- Explainability Isn’t Guaranteed
Even though o1 presents a clearer structure in output, we do not know that each “thinking block” truly reflects the exact internal mechanism. The model still relies on billions of parameters in hidden layers. - Hallucinations Aren’t Eliminated
For unbounded or more subjective tasks (outside math and code), the model can still “hallucinate” steps. The chain of blocks is generated text, so it can be wrong or inconsistent, just more systematically formatted. - Limited Formal Verification
o1 helps with tasks where correctness is objectively checkable (e.g., math), but for general claims (historical, policy, etc.), there is no built-in mechanism guaranteeing factual accuracy.
4. How Hard Is This Approach to Scale Across Commercial Domains?
- Architectural Complexity vs. Data Availability:
o1’s multi-stage training (supervised fine-tuning on step-by-step data + RL for intermediate correctness) can become expensive to replicate for varied domains (medical, legal, financial, etc.). You need domain-specific step-by-step solution data to truly leverage the “thinking block” approach. - Custom Reward Functions for Each Domain:
The RL approach depends on domain-appropriate reward signals. In math/programming, it’s easy to check correctness. In other domains, the feedback loop can be noisy or ambiguous (e.g., business strategy). - Still a Large Model Problem:
The thinking blocks reduce context burdens, but the fundamental model size and associated compute requirements don’t necessarily decrease. Scaling might remain costly in data, energy, and maintenance.
5. Symbolic Methods and Domain-Specific Models: A Cheaper, More Reliable Future?
Advantages:
- Explicit rule-based logic can be easier to verify and update — you directly change the rulebase rather than re-fine-tuning a neural network.
- In critical, regulated environments (finance, healthcare, etc.), a system that can produce guaranteed correctness for certain tasks might be preferable.
Disadvantages:
- Limited flexibility and coverage. Symbolic systems do not generalize as gracefully to open-ended problems as a trained LLM.
Hybrid Approaches
- Many researchers foresee “Neuro-Symbolic” methods that combine the adaptiveness of LLMs with the reliability of symbolic engines.
- For example, a domain-specific symbolic module can verify or modify a chain-of-thought from a general LLM, ensuring correct final outputs.
Cost Considerations
- Specialized domain models or symbolic modules are often cheaper to operate for well-defined tasks. They can require less compute than a massive LLM.
- However, building or maintaining domain-specific rule bases can be labor-intensive. A single large model is often appealing if it can be cheaply adapted — an open challenge.
6. Final Assessment
Foundation-Layer Upgrade?
- o1 innovates in how the model structures its reasoning (thinking blocks), which can reduce certain complexities (long context issues, partial hallucinations). However, it doesn’t fundamentally change the core transformer-based approach of storing facts in high-dimensional parameters.
- For teaching new facts or guaranteeing correctness in arbitrary domains, the essential hurdles remain.
Scalability Across Domains
- The approach will likely be costly to replicate in many commercial contexts requiring domain-specific “sub-task reasoning.” Each domain needs properly annotated or self-consistent intermediate steps, as well as a reliable way to validate them.
Symbolic / Domain-Specific Approaches
- For critical or highly specialized tasks, symbolic or hybrid solutions may be cheaper, more reliable, and easier to maintain. They provide robust ways to update or verify knowledge in a rule-based manner.
- That said, purely symbolic systems lack the broad adaptability of LLMs. The future probably lies in hybrid methods where an LLM handles flexible language tasks but defers to domain-specific or symbolic components for verifiable correctness.
In summary, OpenAI’s o1 addresses some reasoning structure problems and partial context-overload issues, but it does not fully resolve the three main commercial hurdles: easily updating facts, retrieving them at scale, and guaranteeing correctness. Its hierarchical architecture is promising for tasks with verifiable step-by-step logic (e.g., math, code), but scaling this approach widely — and ensuring robust correctness or easy fact injection — remains a complex, and often expensive, endeavor.