AI reasoning models have long been heralded as the next frontier in artificial intelligence, promising to usher in an era of smarter systems capable of tackling increasingly complex problems and paving the way towards superintelligence. Major players in the AI landscape, including OpenAI, Anthropic, Alphabet, and DeepSeek, have recently focused their efforts on developing and releasing models boasting enhanced reasoning capabilities. These models are designed to execute tougher tasks by employing a form of 'thinking,' breaking down problems into logical, sequential steps and transparently demonstrating their processes.

However, this optimistic outlook is now being critically re-evaluated by a wave of recent research. In a significant development, a team of Apple researchers released a white paper in June titled "The Illusion of Thinking." This paper presented a concerning finding: that "state-of-the-art [large reasoning models] still fail to develop generalizable problem-solving capabilities, with accuracy ultimately collapsing to zero beyond certain complexities across different environments." In essence, the research suggests that as problems escalate in complexity, the efficacy of reasoning models diminishes to the point of failure. Even more troubling, these models appear to lack 'generalizability,' implying they might be merely memorizing existing patterns rather than generating truly novel solutions. Ali Ghodsi, CEO of AI data analytics platform Databricks, echoed these concerns, stating, "We can make it do really well on benchmarks. We can make it do really well on specific tasks... Some of the papers you alluded to show it doesn't generalize. So while it's really good at this task, it's awful at very common sense things that you and I would do in our sleep. And that's, I think, a fundamental limitation of reasoning models right now."

Concerns about the limitations of reasoning models are not confined to Apple. Researchers at Salesforce, Anthropic, and other prominent AI laboratories have also raised significant red flags. Salesforce, for instance, characterizes this phenomenon as "jagged intelligence," noting a "significant gap between current [large language models] capabilities and real-world enterprise demand." Such inherent constraints could signal underlying vulnerabilities in the narrative that has propelled AI infrastructure stocks, such as Nvidia, to unprecedented heights. Nvidia CEO Jensen Huang himself highlighted the escalating computational demands, stating at his company's GTC event in March that "The amount of computation we need at this point as a result of agentic AI, as a result of reasoning, is easily a hundred times more than we thought we needed this time last year."

While the findings present a significant challenge, some experts suggest that Apple's public warnings about reasoning models might also serve as a strategic move to redirect conversation, especially given the perception that the iPhone maker is currently playing catch-up in the intensely competitive AI race. Apple has faced a series of setbacks with its highly anticipated Apple Intelligence suite of AI services. Notably, key upgrades to its Siri voice assistant have been delayed until sometime in 2026, and the company made fewer major AI announcements than expected at its annual Worldwide Developers Conference earlier this month. Daniel Newman, CEO of Futurum Group, commented on CNBC's "The Exchange" that Apple's paper, released post-WWDC, "sounds more like 'Oops, look over here, we don't know exactly what we're doing.'" Regardless of the motivations, these emerging constraints on AI reasoning capabilities carry profound implications for the broader AI trade, for businesses investing billions in AI technologies, and potentially for the very timeline towards achieving superhuman intelligence.