Adam Marblestone – AI is missing something fundamental about the brain
Adam Marblestone contends that large language model (LLM) scaling overlooks essential brain principles. The thesis raises a conceptual risk to the prevailing narrative that more compute and larger models alone will reproducibly deliver human-like cognition — a point that could influence market sentiment for AI-branded software and application names.
Linked assets
Tickers highlighted as potentially sensitive to shifts in AI capability narratives: AI (C3.ai) and SOUN (SoundHound) as AI-exposed application/software stocks that could see sentiment moves; MSFT (Microsoft) as a major LLM backer via OpenAI and cloud AI but with diversified exposure that mitigates a direct bearish read-through.
C3.ai, Inc.
C3.ai and other AI-branded software names can be sensitive to cooling sentiment around near-term AI capability expansion, though the source is not company-specific.
Microsoft Corporation develops and supports software, services, devices, and solutions worldwide.
Microsoft has major LLM exposure through OpenAI and cloud AI, but its diversified cloud/software business and ability to fund new architectures reduce the direct bearish read-through.
SoundHound is an AI application stock that could be exposed to broader AI sentiment, but the neuroscience argument is only loosely connected to its fundamentals.
Source proof
Source proof: Strong source proof | 2 directional assets | 1 supporting author | headline-like title review
A talk by Adam Marblestone arguing current LLM-scaling approaches are missing fundamental aspects of brain computation. The content focuses on conceptual and scientific critiques rather than firm-specific disclosures or market guidance.
The source is a physics/GR discussion (Einstein’s equivalence principle, inertial motion, free-fall vs acceleration) with a brief anecdote about Jane Street traders; it contains no market, macro, company, sector, earnings, product, regulatory, or pricing information that would support a tradable investment view.
Skipped non-finance YouTube video. The content does not contain a clear market or investable-stock discussion.
Skipped non-finance YouTube video. The content does not contain a clear market or investable-stock discussion.
Skipped non-finance YouTube video. The content does not contain a clear market or investable-stock discussion.
Lecture-level geopolitical framework (continental land powers vs maritime trading powers) with a brief mention of Russia/Putin targeting global agriculture. Mostly conceptual; only loosely translatable into trades via second-order implications (defense spending, supply-chain resilience, agriculture/food security).
Podcast description discussing economics of AGI: taxation/redistribution of AI-generated wealth, how non–AI-supply-chain countries share gains, and whether inequality explodes. Contains sponsor mentions (Jane Street recruiting; Google Gemini). No concrete near-term catalysts or company-specific fundamentals in the text.
The provided source contains only a title (“How do AI chips actually work? – Reiner Pope”) with no substantive body text. There are no details on companies, products, demand drivers, competitive dynamics, or time-bound catalysts that could be translated into a tradable thesis.
What rebuilding AlphaGo teaches us about self-play, RL, and future of LLMs - Eric Jang Eric Jang walks through how to build AlphaGo from scratch, but with modern AI tools. Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn. Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second. Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choo
Supporting authors
1 author contributed to this summary. No new company filings or quantitative disclosures were presented in the source material.
Unlock full thesis monitoring
Consider the conceptual risk to the AI-scaling narrative when assessing sentiment-driven, AI-branded names. Monitor technical progress and company-specific disclosures; watch for any incremental evidence that new architectures or neuroscience-informed approaches materially shift product roadmaps or TAM assumptions.