Apple, not typically known for publishing bombshell AI papers, released something that sliced straight through the current narrative around large language models (LLMs). In "The Illusion of Thinking", Apple researchers introduced a sharper distinction: not all that looks like reasoning is reasoning. They proposed the term Large Reasoning Models (LRMs) to describe systems that seem capable of multi-step reasoning but often crumble under real logical pressure.
Using puzzles (eg, Tower of Hanoi and symbolic math variations), Apple found that even the most advanced models—those fine-tuned for reasoning—perform well on simple tasks, but accuracy starts to collapse as complexity scales.
What’s even more worrying: the tougher the problem, the less effort these models put into solving it. Instead of trying harder, they give up.
That’s not exactly how human reasoning works. (Or so we tell ourselves, anyways…)
The point is: if you’ve been treating LLMs like thinking machines, this paper is a reminder that they’re not. Not yet.
For anyone building in this space, it’s a nudge to go deeper than wrapping LLMs in a shiny UI. The hard part is building systems that can think, not talk.
The Market’s Disconnect
You wouldn’t guess that from the market.
Over the past year, generative AI has soaked up capital and attention like nothing else. Nearly every enterprise conversation these days includes some version of "how can we integrate GenAI?" Not in the sense of: how do we make workflows smarter, cleaner, or more automated. But more like: how do we bolt GenAI onto a product so the stock ticks up?
That’s the strange part. The framing often isn’t about solving real friction—it’s about adding a feature because that’s what the market wants to hear. That shift is mirrored in venture: for the past two years, VC dollars have flooded into GPT wrappers, copilots, and thin UX plays built atop the same foundation models. There’s been an obsession with the application layer, despite how defensible—or undifferentiated—those apps might be.
A Cold Shower for AGI Dreams?
So the question is: does Apple’s paper cool the hype? Or does it simply confirm what many already knew but were incentivized to ignore?
To some, the findings pour cold water on the march toward AGI. If today’s most powerful models can’t handle symbolic reasoning, what’s the endgame? But a more nuanced read is that Apple’s calling time on a collective delusion. These limits aren’t new, but naming them explicitly—especially from a company with relatively little GenAI market share—shifts the Overton window.
It’s also a reminder of what hasn’t been said. Most foundation model companies are locked into a winner-takes-all framing. If your moat is your model, you don’t publicize its blind spots. Researchers inside these orgs might know the flaws intimately, but the incentives to publish aren’t aligned. Apple, oddly, has less riding on language model supremacy. That gives it room to speak plainly.
Reasoning is not a byproduct of scale. It might be an architectural shift.
The Benchmark Mirage
It’s worth asking why this hasn’t been front and center until now. One answer: leaderboard culture. A model performs well on MATH, GSM8K, or ARC. And so, the assumption is that we’re making progress. But Apple’s experiments show that good benchmark scores don’t tell the whole story. Performance might degrade catastrophically with even slight task complexity.
This distinction between LLM and LRM is worth sitting with. LLMs (Large Language Models) are trained to predict text. They’re good at sounding fluent, recalling facts, and mimicking patterns. LRMs (Language Reasoning Models) use the same underlying system, but are pushed to do something harder: solve problems, reason through steps, make plans. The architecture hasn’t changed, but the demands have. And that shift reveals something important: fluency doesn’t mean understanding.
We’ve long assumed that if an LLM can ace a benchmark, it must be reasoning. But Apple’s work shows how fragile those assumptions really are. Accuracy collapses, step counts shrink, and rather than the model trying “harder,” it disengages.
Perhaps what we are seeing mimicks kids in gradeschool: good grades don’t necessarily translate to real-life intelligence. Benchmarks can be gamed, datasets can be memorized. Evaluation becomes a performance, not a signal. Apple’s work cuts through that, and it’s likely to shift how serious researchers approach generalization.
Alternatives: LQMs, RAGs, and World Models
So where does that leave us? Some researchers are looking toward alternatives. LQMs (Large Quantitative Models; a term coined by SandboxAQ) take a different approach. Rather than treating language as the medium for reasoning, LQMs elevate numerical and symbolic logic as first-class citizens in the architecture. No public benchmarks exist yet, but philosophically, it’s a shift away from next-token prediction as the organizing principle.
RAGs (retrieval-augmented generation) are another partial fix. These connect models to external knowledge bases so they don’t have to memorize everything. But RAG improves recall, not reasoning. It makes LLMs better librarians—not better thinkers.
Then there are WFMs (World Foundation Models), more common in robotics and agent training. These systems learn how environments change over time, which makes them better at planning and adaptation. They’re not general-purpose yet, but they may be a more honest path to something like agency.
Are these better? That depends on your time horizon.
LQMs and world models are earlier in their maturity curve. RAG is already being used in production but doesn’t fundamentally shift the reasoning capabilities. These approaches aren’t drop-in replacements for LLMs. They trade generality for stability.
But if you believe the current paradigm is reaching a ceiling, then maybe it’s time to bet on something less polished and more purpose-built.
The approach isn’t necessarily giving up on GenAI, but refocusing attention on systems that reason reliably under pressure. And perhaps not assuming GenAI and LLMs are a good one-size-fits-all solution.
The Implications for AGI and Builders
If Apple’s right, the implications stretch beyond architecture. It means AGI probably won’t come from scale alone. Getting better reasoning will require a shift in how we build systems: integrating sensors, memory, structure, and maybe even embodiment. We’ve spent the last five years optimizing the wrong thing. That doesn’t mean progress has stalled. It means we need to reframe what progress looks like.
In other words: Reasoning is not a byproduct of scale. It might be an architectural shift.
For anyone building in this space, it’s a nudge to go deeper than wrapping LLMs in a shiny UI. The hard part is building systems that can think, not talk.
Real Consequences for the Market
That has real consequences for the market. The current investment landscape—from seed-stage startups to public company integrations—has largely been built around the idea that LLMs are improving fast enough to justify a race. But if the ceiling is lower than we thought, the winners might be those who find tighter loops between architecture and use case.
If you’re a founder, the takeaway isn’t to give up on GenAI. It’s to rethink what counts as differentiation. If everyone’s using the same backend, the real leverage is in reasoning fidelity, not packaging.
If you’re an investor, the lesson is caution. Not in the sense of avoiding the space, but in asking: is this company doing anything structurally different—or are they surfing the same narrative curve?
The tech behind Apple's critique is rigorous—but limited in scope
Apple designed a clean set of logic puzzles—like Tower of Hanoi and symbolic math problems—to see how well large language models actually reason. The results are clear: models tend to fall apart as tasks get harder. But here’s the catch. These puzzles aren’t how we usually use AI. In real-world applications, reasoning often means piecing together vague, incomplete information under time pressure, not solving abstract brain teasers. Apple’s work is helpful, but it’s focused on a very specific—and very limited—slice of reasoning.
The paper leaves out how models are actually used. What Apple doesn’t address is how most people use language models today. They’re rarely working alone. Increasingly, these systems are paired with tools: calculators, search engines, memory modules, even planning algorithms. Projects like ReAct and OpenAI’s toolformer show that when you give a model access to basic tools, its ability to reason improves dramatically. Apple’s findings matter, but they describe a model in isolation. That’s not how these systems work in the wild.
Finally, other researchers are more pragmatic than pessimistic. Some of the leading labs—like Anthropic, DeepMind, and OpenAI—already know these weaknesses. That’s why they’re building hybrid systems that combine different techniques. Anthropic’s Claude models are trained to follow internal rules more carefully, while DeepMind is experimenting with agents that plan and reason through sequences step-by-step. The field isn’t ignoring the reasoning problem, it’s just tackling it in more flexible ways than Apple gives credit for.
Hot Takes (Meg’s imo)
First, it’s strange that we’ve gone this long without a more public reckoning on reasoning. If a hedge fund’s stack falls apart under load, everyone knows. And everyone (except their competitors, ofc) is livid. If a language model hallucinates math, it’s shrugged off. That tells you something about the disconnect between hype and reliability. We’re using models that perform like interns and pretending they’re PhDs.
Second, I don’t think Apple’s findings will cool the market much—at least not in the Fortune 500 enterprise zone. In those circles, many less-technical buyers aren’t adopting GenAI for reasoning. They’re buying a story: narrative alignment, surface-level integrations, and the illusion of progress. Integration, meanwhile, is anything but simple. For example, take a Fortune 500 retailer trying to use a language model for customer support. Data is fragmented across outdated CRMs, internal documentation is inconsistent, and no one’s defined the logic a model would need to make reliable decisions. Rather than fix these, teams now might deploy a sleek chatbot that sounds impressive but escalates most queries to a human (essentially offloading the hard part while preserving the optics). Until incentives change, reasoning failures will keep being treated as an acceptable cost of appearing innovative.
There’s something ironic about watching companies scramble to embed GenAI into everything without asking if it’s the right tool. I’ve been to far too many conferences + discussions where conversations on AI sound more like PR strategy than systems thinking. There’s very little “how do we make this robust and reliable?” and a lot of “can we call this an AI feature?” That’s not innovation.
Third, my bet is that the next real breakthroughs won’t come from those chasing generality. They’ll come from people building grounded, domain-specific systems—especially in places where hallucination isn’t an option: defense, finance, medicine. In those contexts, reasoning has to work, not perform. Ironically, that kind of constraint might be exactly what cracks the real implementation issues enterprises face today—systems that actually understand structure, not just language.
Fourth, if reasoning doesn’t scale with model size, then startups focused on precision and context-specific optimization may outpace those chasing horizontal dominance. In a world flooded with copilots, accuracy and real-time reliability could be the differentiators that matter most.
And sixth—I think too many teams are afraid to admit the tools don’t work as expected. There’s no shame in friction. We need more honesty from builders and less theater from marketing. If Apple’s paper does anything, I hope it gives people permission to say what’s been obvious: these models are useful, powerful, and still missing something essential.
The Real Work Begins
The next generation of AI systems will need to move past the illusion of thinking. That starts with recognizing the illusion for what it is.
This doesn’t mean GenAI is a dead end. It just means we’re exiting the surface-level era and entering the accountability phase. Where trust, reliability, and reasoning fidelity become the metrics that matter.
It’s a good thing. But only if we’re honest about what these systems can and cannot do.
Thanks for reading! If you enjoyed this post, please consider supporting my work by subscribing to CipherTalk.
I’d also love to hear your thoughts as well. Do you agree or disagree with my takeaways? What shifts are you seeing in how people are building or evaluating these systems?
/m