Three major AI stories have been making rounds recently, and frankly, most of the coverage has been either completely wrong or massively overblown. We’ve got Apple’s “Illusion of Thinking” paper that supposedly proves AI can’t reason, some collection of allegations against OpenAI that turned out to be mostly fake, and a new educational study that everyone’s claiming proves “LLMs make you stupid.” None of these headlines match what the actual research says.
The problem isn’t just bad journalism – it’s that people want dramatic AI stories so badly that they’ll twist any research to fit their narrative. Whether you’re pro-AI or anti-AI, you can apparently find something in these studies to support your position. But if you actually read the papers, the reality is much more nuanced and frankly, less sensational than the headlines suggest.
Let me break down what these studies actually found, why the coverage has been terrible, and what this tells us about how AI research gets misrepresented in the media.
Apple’s “Illusion of Thinking” Paper: Flawed Methodology, Overblown Conclusions
Apple’s research paper titled “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity” examined reasoning capabilities in Large Language Models (LLMs) and reasoning models using puzzle-based experiments like Tower of Hanoi, Checkers Jumping, River Crossing, and Blocks World. The basic finding was that current reasoning models and LLMs struggle with complex puzzles, often failing to generalize problem-solving skills as complexity increases. At low complexity, standard LLMs sometimes outperform reasoning models, but both fail at high complexity tasks.
Here’s the thing though – the methodology is seriously flawed. Using abstract puzzles to test “reasoning” is like testing someone’s driving ability by having them solve crossword puzzles. These puzzles often exceed the token and step limits of the models being tested, which immediately explains some of the failures. It’s like giving someone a 10-page essay assignment and then criticizing them for not finishing when you only gave them one sheet of paper.
But even if we accept the methodology, Apple’s conclusions don’t actually prove what people think they prove. The paper frames this as an “illusion of thinking,” emphasizing the difference between human reasoning and pattern-based outputs of LLMs. But here’s where it gets interesting – if you apply the same logic to humans, you’d conclude that humans can’t really “think” either, since human reasoning also relies heavily on pattern recognition and learned responses. If it walks like a duck and talks like a duck, it doesn’t really matter if it’s a duck, as I’ve said before.
Apple’s puzzle-based approach doesn’t necessarily reflect real-world reasoning capabilities
The media coverage completely missed this nuance. Headlines screamed “AI Can’t Really Reason!” when the paper itself is much more measured in its conclusions. Apple isn’t saying LLMs are useless – they’re highlighting current limitations, especially in scaling reasoning to complex problems. That’s valuable research, but it’s not the AI apocalypse that some outlets made it out to be.
More importantly, the paper is still a preprint that hasn’t undergone peer review. Publishing preliminary findings is fine, but treating them as definitive proof of anything is premature. The puzzle-based methodology has legitimate criticisms, and real-world performance often differs significantly from performance on abstract benchmark tests. The paper has faced criticism for its methodology and relevance to real-world applications, which questions the robustness of its conclusions.
This kind of misrepresentation is common. Just look at how OpenAI’s own model names are a mess, as I discussed in my post OpenAI’s GPT Models: A Deep Dive into the Naming Chaos and Real Capabilities. If even the companies can’t keep their narrative straight, what hope do journalists have?
The OpenAI Files: A Collection of Unsubstantiated Allegations
Then we have what’s been called “The OpenAI Files” – a compilation of various allegations against OpenAI and Sam Altman that’s been circulating online. I’ve looked into these claims, and frankly, they’re either fake, massively overblown, already addressed, or just straight-up rumors with no credible evidence. Many of these allegations have been characterized as involving bad faith arguments that do not hold up under scrutiny.
Some of these allegations include serious personal accusations that I won’t repeat here, but when you actually trace them back to their sources, you find a pattern: anonymous claims on social media, debunked stories being recycled, issues that were already resolved being presented as current scandals, and a lot of guilt-by-association reasoning. The Altman family and OpenAI have denied these allegations, and the available information suggests many are unsubstantiated.
This is a perfect example of how misinformation spreads in the AI space. Someone puts together a compelling-looking document with a bunch of bullet points, and suddenly it’s being shared as if it’s investigative journalism. But quantity doesn’t equal quality – making 50 false accusations isn’t more credible than making one false accusation. It doesn’t matter how many accusations you make if none of them are real.
The tech industry has real problems that deserve scrutiny. OpenAI, like any major tech company, should be held accountable for its actions and decisions. But this kind of bad-faith compilation of rumors and debunked claims actually makes it harder to have serious conversations about legitimate issues. It’s the equivalent of crying wolf – when everything is treated as a scandal, nothing is.
This situation reminds me of the intense board conflicts and rumors that surrounded Sam Altman’s initial removal from OpenAI in late 2023. At that time, many claims were also labeled as overblown or fake, lacking credible evidence. Just like then, the current narrative seems to prioritize sensationalism over factual accuracy.
The Education Study: LLMs Don’t Make Students Stupid
Now for the third story – a new educational research study that actually has solid methodology. The research examined students writing essays under different conditions: no tools at all, with access to Google search, and with access to LLMs. The findings were pretty straightforward: participants who wrote essays without any external tools performed better, showed more ownership of their work, and produced more creative output.
Using Google search and websites slightly diminished performance, but not dramatically. However, using LLMs to write essays resulted in noticeably poorer outcomes in these areas. This makes intuitive sense – if you don’t write something yourself, you’re not going to remember it as well or feel as connected to it. Clearly, if you didn’t write the thing yourself, you’re not going to have as much ownership of it and remember it in as much detail.
But here’s where the media completely lost the plot. Headlines everywhere started screaming that “LLMs Make You Stupid” or cause “Brain Rot.” That’s not what the study says at all. The research is specifically about essay writing in educational contexts, not about LLMs making people stupid in general.
The researchers themselves called this out in their FAQ section. They explicitly stated that their findings shouldn’t be reported with terms like “stupid” or “brain rot,” and that their research was specifically about educational outcomes when students use AI to write assignments. But apparently, nobody in the media actually read that part. They are making it very clear that that’s not what this says and that you shouldn’t report with terms like stupid, brain rot, and things like that. However, everybody ignores them and is doing that anyway.
The education study shows specific impacts on essay writing, not general intelligence effects
This distinction matters. The study tells us something we already knew intuitively – students learn better when they do their own work. It doesn’t tell us that using LLMs for other purposes makes people stupid. A carpenter using a power saw instead of a hand saw isn’t becoming “stupid” – they’re using an appropriate tool for the job.
The implications for education are straightforward: students shouldn’t use LLMs to write their assignments if the goal is learning and retention. This aligns with existing educational principles and does not represent a novel condemnation of LLM technology itself. But that doesn’t extrapolate to “never use AI tools” or “AI makes you dumb.” Context matters, and the context here is very specific.
This is a topic I’ve touched on before when discussing AI’s impact on various professions. For instance, when I talked about Vibe Coding: Bridging the Gap Between Non-Coders and Developers with AI, the point was that AI augments, it doesn’t always replace or degrade. Similarly, my post PSA: Don’t Listen to McKinsey About AI Agents – Here’s What Actually Matters in 2025 highlighted that the real value lies in what you can do with AI, not in fearing its capabilities.
Why AI Research Gets Misrepresented
These three stories illustrate a bigger problem with how AI research gets covered in the media. There’s this constant pressure to find definitive answers – either AI is amazing and will solve everything, or AI is terrible and will destroy everything. Nuanced findings don’t generate clicks.
Apple’s paper becomes “AI Can’t Think,” educational research becomes “AI Makes You Stupid,” and a collection of unverified allegations becomes “Major Scandal at OpenAI.” The actual research gets lost in the rush to create compelling narratives.
This isn’t just an annoyance for people who work in AI – it actively damages public understanding of the technology. When legitimate limitations are overstated, it becomes harder to have rational discussions about where and how AI should be used. When fake scandals get the same attention as real issues, it becomes harder to address actual problems in the industry.
The solution isn’t to stop covering AI research or to only publish positive stories. It’s to actually read the research, understand what it does and doesn’t say, and resist the urge to turn every study into a definitive statement about the future of artificial intelligence.
The trend of media misrepresentation isn’t new. We’ve seen it with other AI developments, like the discussions around Midjourney Video V1 or Google’s Gemini 2.5. Every new model or capability gets spun into an extreme narrative, rather than being discussed for its actual utility and limitations. It’s a disservice to both the public and the researchers.
What These Studies Actually Tell Us
When you strip away the sensational headlines, these three stories actually provide some useful insights:
From Apple’s research: Current reasoning models have specific limitations, particularly when dealing with complex, multi-step problems. This is valuable information for developers and researchers, even if the methodology isn’t perfect. It suggests we need better benchmarks and more realistic testing scenarios. This aligns with findings from other areas, like how even advanced models like Claude 4 Opus are incredibly good at niche tasks that emerge from scale, as I observed in my own testing with make.com scenarios.
From the OpenAI situation: There’s a lot of misinformation floating around about AI companies, and we need to be more careful about vetting sources and distinguishing between legitimate concerns and conspiracy theories. The AI industry has real issues that deserve attention, but fake controversies distract from addressing them. It’s similar to how I often emphasize that AI-assisted SEO can be a competitive advantage, but delivering actual value is the main thing, not hype.
From the education study: Using AI to complete assignments designed for learning and retention defeats the purpose of those assignments. This doesn’t mean AI has no place in education, but it does mean we need thoughtful policies about when and how students should use these tools. This is a practical application of AI’s capabilities, much like how AI is already replacing non-expert copywriters and graphic designers, shifting the value to those who can work with AI, not against it.
None of these findings are earth-shattering, and none of them support the dramatic headlines they generated. They’re incremental additions to our understanding of AI capabilities and limitations – exactly the kind of steady progress that actual science provides.
Moving Forward: Better AI Discourse
The pattern here is clear: sensational headlines about AI research often don’t match what the research actually says. Whether it’s claiming AI can’t think, that it makes people stupid, or that there are major scandals without evidence, the media coverage consistently overshoots the actual findings.
This matters because public understanding of AI shapes policy decisions, investment priorities, and individual choices about how to use these tools. When that understanding is based on misrepresented research and fake controversies, we end up with bad decisions across the board.
The fix isn’t complicated, but it requires discipline: read the actual research, check sources for allegations, understand the scope and limitations of studies, and resist the urge to extrapolate dramatic conclusions from limited data. It’s less exciting than declaring the death of AI reasoning or the dawn of artificial stupidity, but it’s more useful for actually understanding what’s happening in this field.
AI research will continue to produce incremental findings about capabilities and limitations. Some of those findings will be positive, others will highlight problems or constraints. Most of them will be much less dramatic than the headlines suggest. The sooner we get comfortable with that reality, the sooner we can have productive conversations about how to build and deploy these technologies responsibly.
For now, when you see dramatic AI headlines, take a step back and ask: what does the actual research say, who’s making these claims, and does the evidence actually support the conclusions being drawn? In these three cases, the answer is pretty clear – the hype doesn’t match the reality.
This discussion also highlights the gap between open-source AI and proprietary models. While open source models like those I’ve discussed in relation to Cerebras and Groq offer advantages in privacy and cost, proprietary models often push the frontier. The constant back-and-forth means that what’s cutting-edge today might be surpassed tomorrow, making accurate reporting even more vital.
