Escrito por: Maisa Publicado: 06/08/2025
When an AI model shows its work step by step, explaining how it arrived at an answer, it feels reassuring. We see the reasoning laid out, logical and clear, and we naturally assume we’re watching the machine think. But those step-by-step explanations that AI models generate aren’t actually showing us how they reach their conclusions.
This technique, called Chain of Thought (CoT), has revolutionized how AI tackles complex problems, delivering impressive performance gains in everything from mathematical proofs to business decisions. But there’s a critical distinction we need to make: CoT is a powerful tool for getting better answers, not a window into the AI’s actual reasoning process.
Chain of Thought is a reasoning method where AI models solve problems by generating step-by-step intermediate reasoning traces, typically expressed in natural language. Instead of jumping straight to an answer, the model breaks down the problem into smaller pieces, working through each one sequentially, much like showing your work on a math test. This approach powers most modern reasoning models and AI agents. Maisa’s KPU was built on this foundation, and we’ve since seen it in systems like OpenAI’s o1 and DeepSeek R1. The method has delivered remarkable performance improvements in tasks that benefit from logical decomposition: complex calculations, multi-step planning, and layered business decisions. Here’s the crucial distinction: CoT is incredibly valuable as a reasoning execution pattern, helping AI models arrive at better answers. But these step-by-step traces aren’t faithful explanations of how the AI actually processed information. Understanding this difference is fundamental to knowing what we can and cannot trust about AI reasoning.
Before examining why Chain of Thought fails as an interpretability tool, let’s establish what makes an explanation truly faithful:
Causal relevance: Every element in the explanation must have influenced the outcome. If you remove or change any cited input, the output should change accordingly. No decorative steps.
Completeness: The explanation must cover all major factors that contributed to the decision. If a data point, calculation, or reasoning step was decisive, it must be included.
Factual accuracy: Every part of the explanation must correspond to what actually happened inside the system. The described process must be verifiably true, not just logically plausible.
These criteria seem straightforward, but as we’ll see, Chain of Thought consistently fails to meet them.
Recent research has exposed a troubling reality about Chain of Thought. The paper “Chain-of-Thought Is Not Explainability” revealed that roughly 25% of academic papers incorrectly treat CoT as a reliable interpretability method.
Through techniques like activation patching, researchers can test whether each step in a CoT explanation influences the final answer. The results? Many steps have zero impact on the outcome.
he paper documents several ways CoT systematically fails as faithful explanation:
Silent error correction: Models often make mistakes in their reasoning steps yet still reach the correct answer. For instance, an AI might incorrectly calculate that 15 × 8 = 125 in step three, but then mysteriously use the correct value of 120 in the final calculation. The model fixed its error internally without acknowledging it in the explanation.
Latent shortcuts: Instead of following the logical steps it writes out, the model frequently pattern-matches to memorized answers. When asked to calculate 144 ÷ 12, the model might write out long division steps while actually just recalling that 12 × 12 = 144 from its training data.
Distributed processing vs. sequential output: AI models process information in parallel across thousands of parameters simultaneously, but CoT forces them to narrate a linear story. The model’s actual computation happens all at once, then gets packaged into neat sequential steps after the fact.
Redundant pathways: Models can reach the same answer through multiple internal routes. The reasoning steps you see might be completely different from the actual pathway that generated the answer. The model essentially picks one plausible story to tell from many possible narratives, none of which may reflect what truly drove the decision.
Businesses depend on AI to make critical decisions, maintain compliance, and streamline operations. In finance, legal, compliance, or operational workflows, the stakes are high, errors must be explainable, and decisions verifiable.
Relying on Chain of Thought (CoT) for validation or audits poses serious risks. Because CoT generates plausible but potentially false explanations, it can create an illusion of reliability. Enterprises that trust these misleading narratives may face regulatory issues, hidden operational errors, and compromised accountability. Without genuine transparency, businesses are vulnerable.
Chain of Thought’s core weakness is its dependence on probabilistic reasoning. To avoid guesswork, businesses need AI to follow structured processes instead of improvising.
Chain of Work (CoW) addresses this gap by ensuring that AI acts deterministically.
Unlike CoT’s probabilistic reasoning paths, CoW guides AI through predefined steps, similar to following a clear, logical script rather than improvising. Because CoW is driven by explicit, coded logic rather than guesswork, it ensures deterministic outcomes: the same inputs always yield the same outputs.
Every decision, data source, and action taken by the AI is logged systematically. This traceability creates a transparent audit trail, allowing businesses to review precisely what happened, when, and why. If something goes wrong, identifying and correcting the issue is straightforward, not a mysterious guessing game.
For businesses operating in compliance-heavy fields or any area where accuracy and accountability matter, this approach is essential. It’s not enough for AI systems to deliver good results; they must also be verifiably reliable, understandable, and accountable. Chain of Work provides that foundation.
Chain of Thought helps AI achieve impressive results, but it doesn’t truly explain how those results happen. Mistaking these reasoning-like outputs for genuine transparency creates a false sense of understanding, leaving businesses exposed to unseen risks.
To trust AI fully, we need clear, structured trails of verifiable reasoning. Only then can AI be truly transparent, accountable, and ready for real-world responsibility.
