For years, the world has been fascinated with the idea that artificial intelligence could one day “think” like us. From OpenAI’s ambitious announcements to bold predictions of “superintelligence” around the corner, the narrative has been hard to ignore. But a new study from Arizona State University is cutting through the hype, offering a sobering reminder: AI isn’t really reasoning at all. It’s just matching patterns.
Why People Think AI Can Reason
Ever since AI tools like ChatGPT, Bard, and Claude started dazzling the public with polished essays, clever code, and convincing explanations, experts and executives alike have been quick to assign human-like qualities to these systems. The language around AI has often leaned toward exaggeration. Words like “reason,” “understand,” and “think” have been casually used to describe what is, in truth, a much narrower set of abilities.
Take OpenAI’s release of its “o1 reasoning model” last year. In its own announcement, the company compared the system to a person “thinking for a long time before responding to a difficult question.” Phrases like this created the impression that machines were developing actual thought processes. Soon after, OpenAI CEO Sam Altman declared that “the takeoff has started,” suggesting humanity was nearing the dawn of digital superintelligence.
But these claims lean more on metaphor than science. Even the creators of today’s large language models (LLMs) admit they don’t fully understand how their own systems operate. That mystery has opened the door to hype, and in many cases, outright anthropomorphizing.
Cracking the Black Box
AI models like GPT-5 and DeepSeek V1 are often described as “black boxes.” We can see the inputs and the outputs, but what happens in between is largely hidden from view. This makes it tempting to apply human terms to what the machine appears to be doing. When a model shows its work in a “chain of thought” before delivering an answer, it looks like reasoning. But looks can be deceiving.
Arizona State researchers, led by Chengshuai Zhao, decided to test whether this so-called reasoning was real. They stripped things back to basics, using an older AI system, GPT-2, and retrained it entirely from scratch. Instead of feeding it books, websites, or massive datasets, they limited it to the 26 letters of the English alphabet. The tasks were simple: rearrange sequences, shift letters by a set number of places, or perform other basic manipulations.
This minimalist setup created a clean test environment. If the model could truly reason, it should have been able to solve new problems it had never seen before. For example, if trained to shift letters by 3, could it figure out how to shift them by 13 without being taught?
What the Experiment Revealed
The results were telling. When the AI faced tasks outside of its training data, it failed. The machine produced long explanations that sounded convincing—what scientists call “fluent nonsense”—but the final answers were wrong. In other words, the model was borrowing from familiar patterns rather than generating original reasoning.
Zhao and his team concluded that the “chain-of-thought” approach is a brittle mirage. What looks like logic is really just a sophisticated form of pattern-matching. The model isn’t truly inferring solutions; it’s remixing clues and surface-level associations it has already learned.
This raises a critical point: the ability of AI to sound smart can be more dangerous than an outright wrong answer. A wrong answer is easy to spot. But a wrong answer wrapped in convincing reasoning can fool even experts, leading to misplaced trust.
Lessons for the AI Industry
The Arizona State study is part of a growing backlash against inflated claims of AI’s capabilities. Researchers warn against overconfidence and advise testing AI systems with problems that are unlikely to have been in their training data. This way, companies and users can see where the technology’s limits really lie.
It’s also a reminder that we need precise language when discussing AI. When Google Brain first published its influential 2022 paper on “chain-of-thought prompting,” the authors were careful not to claim that LLMs were reasoning like humans. They simply noted that asking models to “show their work” often improved accuracy. Somewhere along the way, that caution was lost in translation as companies raced to market.
The Bigger Picture: Hype vs. Reality
The fascination with AI “reasoning” reflects a broader tension in the industry. On one hand, companies want to excite investors and the public by painting AI as a leap toward human-like intelligence. On the other hand, researchers continue to uncover just how limited these systems are when pushed beyond their training.
For example, when Sam Altman talks about humanity being close to “digital superintelligence,” critics point out that today’s models still stumble on basic tasks outside familiar patterns. That’s not the hallmark of true reasoning—it’s the behavior of a machine playing back the best guess from its training history.
What’s Next for AI Research
Looking ahead, the Arizona State study may encourage more researchers to peel back the curtain on how AI systems really function. If we stop framing these tools as “thinking machines,” we can better focus on what they actually do well: spotting patterns at scale, summarizing vast amounts of information, and offering human users a helpful starting point.
There’s also a growing call for more transparency in how AI systems are trained. If companies reveal more about what data is included—and excluded—users could better understand when to trust a model and when to be skeptical.
Past Headlines and Future Predictions
This isn’t the first time AI hype has collided with reality. Back in 2022, Google Brain’s discovery of chain-of-thought prompting sparked excitement but was never meant to prove human-like reasoning. By 2023 and 2024, however, tech leaders began spinning these findings into claims about intelligence and even superintelligence.
Now, in 2025, researchers are stepping in to correct the record. Their message is simple: AI is powerful, but it’s not magic. The industry may continue to market AI as a leap toward human understanding, but the science is clear—it’s still just pattern-matching under the hood.
As we move into the next phase of AI development, the real challenge may not be building machines that “think” but helping people cut through the noise to see what these systems can and cannot do. The more honest we are about AI’s limitations, the more responsibly we can use it in classrooms, hospitals, offices, and homes.