Cognitive scientist and AI skeptic Gary Marcus joins the discussion to explore whether large language model scaling has hit a ceiling. He shares insights on diminishing returns in AI effectiveness and critiques the reliance on GPU scaling. The conversation touches on data privacy issues, ethical considerations in AI development, and the risks associated with both open-source and proprietary models. Marcus emphasizes the need for transparency and a more nuanced understanding of AI's future trajectory.
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insights INSIGHT
Diminishing Returns from AI Scaling
The scaling laws of AI models suggested predictable improvement with more data and compute but are now showing diminishing returns.
The industry is quietly acknowledging that making models bigger no longer yields the past exponential performance gains.
insights INSIGHT
Scaling Up Models Yields Small Gains
Recent huge increases in model size do not correspond to proportionate improvements in performance.
The era of large jumps in AI model quality from just scaling up appears to be over; gains now are incremental and costly.
insights INSIGHT
Test Time Compute Offers Narrow Gains
Adding more compute at test time (reasoning steps) helps improve performance only in narrow, closed domains.
This method doesn't yield broad reasoning improvements and the models often just mimic human reasoning patterns, not deeply understand them.
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Gary Marcus is a cognitive scientist, author, and longtime AI skeptic. Marcus joins Big Technology to discuss whether large‑language‑model scaling is running into a wall. Tune in to hear a frank debate on the limits of “just add GPUs" and what that means for the next wave of AI. We also cover data‑privacy fallout from ad‑driven assistants, open‑source bio‑risk fears, and the quest for interpretability. Hit play for a reality check on AI’s future — and the insight you need to follow where the industry heads next.
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