On the 28th of May 2025, Apple Research published a paper that did more than generate headlines. Titled The Illusion of Thinking, it challenged the core assumptions that underpin AI systems widely considered the most advanced in the world. These models, often promoted as reasoning engines, were shown to falter under genuine complexity. Not with minor declines in performance, but with complete collapse.
We do not operate in theory. We build AI to assist critical decisions in energy, manufacturing, and industrial infrastructure. When an AI agent is placed in a refinery control room or at the heart of a power generation facility, we are not concerned with cleverness. We are concerned with stability, traceability, and cognitive resilience. When a global technology leader publishes results that suggest large models stop trying when things get hard, we look closely.
This blog is not a summary of the Apple paper. It is a response to the conversation it has triggered. A conversation that enterprise leaders need to be part of if they want to deploy AI in high-consequence environments.
Apple’s researchers designed a series of controlled environments to test the reasoning ability of leading AI models. They selected puzzles with established cognitive value; Tower of Hanoi, River Crossing, Blocks World, and Checkers Jumping, because they scale cleanly in complexity and require multi-step problem solving.
The test environment allowed Apple to adjust the difficulty of each task. Models were tested not just on final outputs but also on their ability to generate structured reasoning steps. The results were startling.
As complexity increased, models didn’t struggle incrementally. They collapsed entirely. Beyond a certain threshold, accuracy dropped to zero. Token usage decreased. These were not resource-constrained models, they had room to reason. But they didn’t.
This pattern, observed across different model types and vendors, suggests more than difficulty. It points to a lack of cognitive robustness. The system doesn’t fail gracefully. It quits.
The paper quickly became the center of a storm. Some hailed it as the beginning of the end for current model architectures. Others argued the results were misleading or flawed.
Anthropic released a detailed rebuttal. They argued that the evaluation penalized models unfairly for formatting errors and that some of the problems were unsolvable by design. In those cases, the models correctly identified impossibility but were scored as if they had failed.
Another widely shared rebuttal, titled The Illusion of the Illusion of Thinking, turned out to be a hoax. It was written in the style of a legitimate paper but contained mathematical errors and intentional misrepresentations. Its widespread circulation highlighted a larger issue: the difficulty of separating noise from insight in a fast-moving field.
There were also thoughtful critiques from within the academic and technical community. Simon Willison noted that while the paper was provocative, it didn’t necessarily reveal anything that hadn’t already been observed in other research. Ethan Mollick pointed out that AI collapse narratives often surface and rarely hold up under broader scrutiny.
Gary Marcus, one of the more persistent critics of LLMs, supported Apple’s conclusions and argued they revealed the same fragility that neural networks have exhibited for decades when facing distribution shift.
The debate was wide, technical, and unresolved.
For enterprise decision-makers, many of these arguments are beside the point.
In high-stakes industries, AI is not judged on abstract reasoning benchmarks. It is judged on how well it performs under pressure, how clearly it can explain its actions, and how reliably it handles edge cases.
Apple’s findings highlight three concerns that are directly relevant to enterprise AI deployment:
If a system is designed to reason step by step, it must be evaluated not just on results, but on its ability to maintain logical momentum when conditions become less familiar.
Today’s most common AI benchmarks reward surface-level performance. They do not reward transparency, explanation, or recoverability. That is a problem.
They are core features of how current systems operate. If those systems break under real-world standardized communication formats or step count limits, they cannot be considered production ready.
These are not theoretical issues. They are practical blockers to responsible deployment.
The Apple paper, regardless of how you read its methodology, points to a structural issue in the current approach to AI design. Most large models rely solely on statistical learning. They detect patterns and generalize. But they do not understand. They do not reason. They simulate reasoning.
At Beyond Limits, we address this by using hybrid AI. Our systems combine neural networks with symbolic reasoning, constraint-based logic, and curated knowledge bases. This allows us to build AI agents that don’t just generate output, they process context, follow rules, and explain their steps.
In practice, this means a production engineer using our system can trace a recommendation back to the inputs, the models consulted, and the logic applied. That is not possible with most foundational models today.
The Apple paper validated this approach. It showed the limits of current reasoning engines. It reinforced the need for architecture that can operate like a system of systems, rather than a monolithic statistical text generator.
Much of the debate around Apple’s paper focused on evaluation methodology. That was appropriate. Evaluation is where most AI claims succeed or fail. And right now, the industry is using the wrong tools.
We believe reasoning AI needs to be evaluated differently. Here are some principles we support:
A correct answer with no traceability is useless in operational contexts. Evaluation must score not only the result, but the rationale.
Does the system halt? Does it default to hallucination? Does it escalate? These are the behaviors that matter when AI is deployed in real-world settings.
Apple’s paper lacked human baselines. That limits its interpretive power. Future evaluations should include human reasoning traces for comparison.
These tests reveal whether the system can recognize uncertainty and respond appropriately. Penalizing models for admitting they don’t know is counterproductive.
A shift in evaluation practices won’t just help researchers. It will give enterprise buyers a clearer sense of what they’re purchasing and what to expect.
If you're leading AI adoption in a complex industry, the Apple paper and its aftermath should prompt a review of your current evaluation criteria. Not just for vendors, but for internal assessments as well.
Here are the questions we recommend asking:
• Can the model maintain reasoning under pressure or novelty?
• Does it respond gracefully to tasks it cannot complete?
• Can it explain its process clearly and provide an audit trace?
• Does it combine statistical inference with domain rules?
• Can the team deploying it demonstrate behavior under edge cases, not just best-case demos?
These questions will not be answered in glossy marketing decks. They need to be asked in technical reviews, procurement processes, and post-deployment audits.
The debate over The Illusion of Thinking is far from over. More papers will follow. Some will contradict Apple’s findings. Others will reinforce them. That’s how research works.
But from our vantage point, working at the edge of AI deployment in complex industrial settings, several things are clear.
Just because a model can generate plausible responses does not mean it has understood a problem or made a decision.
In high-risk domains, it is not enough to get the right answer. You need to understand how that answer was reached.
Systems designed for explainability, like hybrid AI, will outperform black-box LLMs in enterprise use cases over time.
If we continue to test reasoning models like autocomplete systems, we will continue to get misaligned results.
Models that break when reality doesn’t match the training data are not ready for deployment.
This is the shift that enterprise AI needs. Not just better models, but better expectations.
The Illusion of Thinking was not the first study to challenge assumptions about AI. It won’t be the last. But it was a rare moment when industry, academia, and practitioners all stopped to reconsider how reasoning is defined, measured, and trusted.
As builders of operational AI, we welcome the debate. The gaps it exposes are the same ones we have been working to solve for years. And the path it points to greater transparency, stronger evaluation, hybrid architectures, and cognitive accountability is one we have already begun walking.
Take a look at our CTO’s response to the Apple paper here >
