

What Comes After AI Transformers? (Ep. 531)
Aug 19, 2025
The conversation delves into the limitations of transformers in AI, sparking the need for innovative architectures. Smarter methods like neurosymbolic hybrids and spiking neural networks are showcased as promising avenues for improvement. The discussion highlights revolutionary alternatives, including Mamba and Retinette, while emphasizing energy efficiency in AI advancements. Swarm intelligence emerges as an exciting concept for collaborative AI, potentially paving the way to artificial general intelligence. Overall, it's a captivating exploration of the future of AI beyond current models.
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Scaling Limits Spark A New Age
- Transformers have driven rapid progress but now face diminishing returns from hyperscaling.
- Limits in data, cost, and physics push AI toward new architectural invention.
Three Parallel Research Voyages
- Three research voyages: evolve transformers, build revolutionary alternatives, or explore brain-inspired models.
- Each path targets different weaknesses of current transformer systems.
Augment Transformers, Don’t Abandon Them
- Enhance transformers with test-time compute, neurosymbolic attachments, and mixtures of experts.
- Use specialized components to aggregate reliable, grounded responses.