Recent neuroscience research argues that large language models produce fluent text without the perceptual grounding or adaptive learning processes that support real intelligence, which raises the possibility that the current AI market is built on systems that cannot deliver the cognitive capabilities being advertised.
This should be at the forefront of the public mind. Like everybody should be talking about this one. It’s an argument that has just stuck with me. Language model vendors may be unintentionally marketing a structurally flawed product if recent neuroscience research is correct. Critics argue that current LLM architectures excel at statistical pattern synthesis but lack the perceptual grounding, adaptive feedback loops, and developmental learning trajectories that shape how biological systems form concepts and respond to uncertainty. These models generate convincing text, yet their internal representations do not support the kind of abstraction or stable world modeling associated with reasoning. Riley reports that this gap is widening as model scale increases, because larger models reduce superficial error but do not add the mechanisms that support genuine cognitive capacity in natural systems [1]. If this assessment is accurate, then the commercial AI ecosystem risks being anchored to systems that cannot achieve the reasoning depth presented in current marketing claims, which could reshape investor expectations, redirect research agendas, and redefine what progress in artificial intelligence means in the decade ahead.
Yeah, I asked both ChatGPT and Gemini about this and what it would mean if they were a false start. The answers tend to be underwhelming. At some point, they sort of land back on an argument from Yann LeCun [2]. That argument states that simply scaling Large Language Models won’t lead to human-level intelligence (AGI). Inside that assertion is a belief that the models fundamentally lack understanding of the physical world, persistent memory, and true reasoning. In short, the models can only predict the next token based on patterns, unlike humans who learn through experience and interaction. LeCun advocates for new AI architectures focused on learning world models, causality, and planning, moving beyond text-based prediction to embodied, experience-driven learning, similar to a toddler’s understanding of physics. A few other arguments abound related to what models might succeed in the future. Evidence is starting to mount and be shared that it is very possible that the LLM might just be a piece of puzzle and not the ultimate answer.
This line of reasoning carries broad repercussions. Meta and Mark Zuckerberg appear to have internalized the implications of LeCun’s critique. Their recent financial commitments indicate a strategic bet that the next wave of AI requires architectures that move beyond text-based prediction toward integrated systems that learn from perception, action, and memory [3]. The shift signals the beginning of a long process where commercial AI strategies will either converge with or diverge from the neuroscience-informed critique of current large-scale language models. We have seen a huge influx of researchers and builders to Meta. What they build next will prove to be interesting.
Things to consider:
How investor sentiment may shift if statistical fluency is no longer equated with intelligence
Whether grounded perception becomes a central requirement for next-generation AI architectures
How research priorities may move toward multimodal, embodied, or agentive systems
Whether regulatory scrutiny increases as capability claims become harder to substantiate
Footnotes:
[1] Riley, B. “Large language mistake: Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it.” The Verge. https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems
[2] LeCun, Y. (2022). A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27. Open Review, 62(1), 1-62. https://openreview.net/pdf?id=BZ5a1r-kVsf
[3] Newton, C. (2025). Where Meta’s biggest experiment in governance went wrong. Platformer. https://www.platformer.news/meta-oversight-board-5-years/
