I asked Claude to rewrite Apple’s latest research article so that we can all understand it. See below.
How Apple researchers discovered that our most advanced AI models behave like overconfident executives who give up just when the real work begins
Picture this: You’ve hired the most expensive consulting firm in town. They arrive with impressive credentials, promise deep thinking, and charge premium rates for their “enhanced reasoning capabilities.” On simple problems, they’re oddly slower than your regular team. On medium-complexity challenges, they shine brilliantly. But when you present them with the truly difficult strategic questions—the ones that could make or break your organization—they suddenly go quiet, produce shorter reports, and essentially shrug.
Welcome to the bewildering world of Large Reasoning Models.
The Million-Dollar Question Nobody Asked
A team of researchers at Apple just spent months torturing some of the world’s most advanced AI systems with puzzles. Not the kind of brain teasers you’d find in a conference room icebreaker, but carefully designed challenges that scale from “intern level” to “CEO having a nightmare.”
Their subjects? The AI equivalent of management consultants: OpenAI’s o1 and o3 models, Claude’s new “thinking” mode, and DeepSeek’s reasoning variants. These aren’t your garden-variety chatbots—these are the premium models that tech companies tout as having sophisticated reasoning capabilities. They actually pause to “think” before answering, generating internal monologues that can stretch for thousands of words.
The researchers’ approach was elegantly simple. Instead of testing these models on math problems that might have leaked into their training data, they created four pristine puzzle environments: Tower of Hanoi, Checker Jumping, River Crossing, and Blocks World. Think of these as the laboratory mice of reasoning research—clean, controllable, and scalable from trivially easy to impossibly hard.
The Plot Twist That Changes Everything
What the researchers discovered should make every leader pause the next time someone promises that AI will solve their strategic planning problems.
These “thinking” models don’t follow the performance curve you’d expect. Instead, they operate in three distinct zones that mirror something unsettlingly familiar to anyone who’s watched high-performers crack under pressure.
Zone One: The Overthinking Trap
On simple problems—the equivalent of “Should we reorder office supplies?”—the premium thinking models often performed worse than standard AI. They were like senior executives who turn a five-minute decision into a three-hour strategy session. The models would find the right answer early in their thinking process, then continue churning through alternatives, often talking themselves into worse solutions.
Imagine hiring a consultant to determine optimal parking arrangements and receiving a 50-page analysis that concludes exactly what your facilities manager suggested in the first place—except now you’re less confident about it.
Zone Two: The Sweet Spot
On moderately complex problems—think “How do we restructure our supply chain?”—the thinking models justified their premium positioning. They outperformed standard models by meaningful margins, demonstrating the kind of careful analysis that separates thoughtful leaders from reactive ones.
This is where the investment in “enhanced reasoning” pays dividends. The models worked through problems systematically, corrected early missteps, and arrived at solutions that simpler approaches missed.
Zone Three: The Collapse
Here’s where things get truly bizarre. On highly complex problems—the “How do we navigate a global recession while transforming our business model?” level challenges—both thinking and non-thinking models collapsed entirely. But the thinking models didn’t just fail; they failed in a particularly disturbing way.
As problems became more difficult, these models actually reduced their thinking effort. Despite having abundant computational resources and no approaching deadlines, they started producing shorter analyses just when deeper thinking was most needed.
It’s as if your highest-paid strategic advisors responded to your company’s biggest crisis by scheduling shorter meetings and sending briefer memos.
The Algorithm That Wasn’t Enough
The researchers tried something that should terrify anyone betting their organization’s future on AI reasoning. They provided some models with explicit, step-by-step algorithms for solving problems—essentially giving them the playbook for success.
The models still failed at exactly the same complexity levels.
This isn’t about lacking the right strategy or framework. These models couldn’t execute known solutions consistently, even when spoon-fed the methodology. They stumbled not in the creative problem-solving phase but in the methodical execution of established procedures.
Picture handing your team a detailed implementation plan for a proven process, only to watch them make random errors in basic execution steps. The failure isn’t strategic—it’s operational, which makes it far more concerning.
The Inconsistency Problem That Keeps Leaders Awake
Perhaps most troubling was the models’ wildly inconsistent performance across different types of problems requiring similar complexity levels.
A model might successfully navigate 100+ sequential steps in one type of challenge while stumbling after just 4 steps in another domain requiring similar logical rigor. This wasn’t about difficulty per se—it was about the unpredictable nature of when and where cognitive collapse would occur.
For leaders evaluating AI tools, this inconsistency represents a nightmare scenario. You can’t reliably predict which problems will trigger the “smart” response and which will cause an inexplicable breakdown. It’s like having a brilliant strategist who’s equally likely to craft a masterful market entry plan or forget how to read a basic financial statement.
The Overthinking Paradox
The research revealed something that will resonate with anyone who’s watched analysis paralysis destroy good decisions. On simpler problems, the thinking models often found correct solutions early but then continued processing, frequently convincing themselves that worse alternatives were better.
This “overthinking phenomenon” mirrors what organizational psychologists have observed in high-performing teams under pressure. Give smart people too much time and too few constraints, and they’ll often think their way past good solutions toward mediocre ones.
The models demonstrated a peculiar inability to recognize when they’d found the right answer and stop there. They lacked the wisdom to know when thinking becomes overthinking—a skill that separates effective leaders from those who perfect themselves into irrelevance.
The Math Benchmark Mirage
While the research was unfolding, these same models were achieving impressive scores on mathematical reasoning benchmarks. But when the researchers compared their puzzle performance to established math tests, a troubling pattern emerged.
On newer math problems, the thinking models showed clear advantages. On older, well-established problem sets, the gap between thinking and non-thinking models narrowed dramatically. The implication was uncomfortable: these models might be leveraging memorized patterns rather than developing genuine reasoning capabilities.
It’s the organizational equivalent of executives who excel at problems similar to their past experience but crumble when facing novel challenges that require adaptive thinking.
What This Means for Your Organization
The implications stretch far beyond AI research labs. These findings illuminate fundamental questions about how we evaluate and deploy intelligent systems—whether artificial or human.
The Premium Paradox
Organizations paying premium rates for “enhanced reasoning” capabilities need to understand that they’re not buying uniformly superior performance. They’re purchasing systems that excel in a specific zone of complexity while potentially underperforming in others. The key isn’t avoiding these tools but understanding exactly where and when to deploy them.
The Execution Gap
The inability of these models to execute known algorithms consistently highlights a critical distinction between strategic thinking and operational reliability. For organizations, this suggests that even sophisticated AI tools require robust process controls and verification mechanisms—especially for high-stakes decisions.
The Scaling Illusion
Perhaps most significantly, these models demonstrated that throwing more computational resources at complex problems doesn’t guarantee better outcomes. As problems scaled beyond a certain threshold, additional “thinking time” actually correlated with worse performance.
This mirrors a phenomenon many leaders recognize: some challenges don’t yield to more analysis, bigger teams, or longer planning cycles. Sometimes, complex problems require different approaches entirely, not just more intensive versions of existing methods.
The Curious Case of Computational Confidence
The research revealed something particularly unsettling about how these models allocate their processing resources. As problems became more challenging, the models began reducing their effort—despite having ample computational budget remaining.
This behavior suggests an AI equivalent of learned helplessness or strategic withdrawal. When faced with problems approaching their capability limits, these systems don’t double down with additional effort; they essentially give up preemptively.
For organizations, this pattern raises questions about how intelligent systems behave under stress and whether current AI architectures can truly handle the open-ended, multi-faceted challenges that define organizational leadership.
The Pattern Recognition Trap
The models’ inconsistent performance across different puzzle types revealed another troubling limitation. Success seemed heavily dependent on whether problems matched patterns the models had encountered during training, rather than reflecting genuine reasoning capabilities.
A model might handle complex logical sequences in one domain while failing elementary challenges in another, simply because one pattern was more familiar than the other. This suggests that what appears to be reasoning might actually be sophisticated pattern matching—impressive in familiar territories but brittle when venturing into uncharted problem spaces.
The Human Mirror
These AI limitations eerily reflect human cognitive biases that organizational leaders battle daily. The overthinking on simple problems mirrors analysis paralysis. The confidence collapse on complex challenges echoes imposter syndrome among high performers. The inconsistent performance across domains resembles how subject matter experts struggle outside their specializations.
Perhaps these models aren’t failing to achieve human-level reasoning—they’re succeeding too well at replicating human cognitive limitations.
Looking Forward: The Intelligence Plateau
The research suggests that current reasoning models may be approaching fundamental architectural limitations rather than temporary scaling challenges. The counterintuitive reduction in thinking effort as problems become more complex indicates potential ceiling effects that won’t necessarily improve with more training data or computational power.
For organizations betting on AI to solve increasingly complex strategic challenges, this research serves as both a reality check and a roadmap. The technology shows genuine capabilities within specific domains and complexity ranges, but it’s not the general-purpose reasoning solution that marketing materials might suggest.
The Practical Imperative
Organizations moving forward with AI-assisted decision-making need frameworks for understanding where these tools excel and where they stumble. The research suggests three practical considerations:
First, complexity assessment becomes crucial. Organizations need methods for categorizing problems by type and difficulty to predict where AI reasoning tools will provide value versus where they might introduce new risks.
Second, verification systems become non-negotiable. Given the models’ execution inconsistencies, even well-reasoned AI outputs require robust checking mechanisms, particularly for high-stakes decisions.
Third, hybrid approaches emerge as the most promising path. Rather than replacing human reasoning with artificial reasoning, the most effective implementations likely combine human judgment with AI capabilities in carefully orchestrated ways.
The Irony of Artificial Intelligence
There’s something deeply ironic about spending billions to create artificial thinking machines that struggle with the same cognitive pitfalls that have challenged human decision-makers for millennia. These models overthink simple problems, underperform on complex ones, and show inconsistent judgment across domains—behaviors that sound remarkably familiar to anyone who’s worked in organizational leadership.
Perhaps the most valuable insight from this research isn’t about AI limitations but about the nature of intelligence itself. Effective reasoning appears to require not just computational power but wisdom about when to think, when to stop thinking, and when to approach problems differently altogether.
The researchers at Apple have given us more than a technical analysis of AI capabilities. They’ve provided a mirror reflecting the challenges that face any intelligent system—artificial or otherwise—when confronting the messy, multi-layered complexity of real-world problem-solving.
As we continue integrating these powerful but peculiar tools into our organizations, the question isn’t whether AI can think like humans. The evidence suggests it already does—complete with all the cognitive quirks, limitations, and surprising blind spots that make human reasoning both remarkable and remarkably unreliable.
The path forward requires not just better AI but better understanding of when and how to deploy intelligence—artificial or otherwise—in service of genuinely effective decision-making.