AI scaling drains trillions in energy while amplifying errors. Neurosymbolic reasoning and decentralized cognitive systems deliver reliable intelligence without the risk.
Opinion by: Mohammed Marikar, co-founder at Neem Capital
Artificial intelligence has consistently been defined by scale, so far — bigger models, faster processing, expanding data centers. The assumption, based on traditional technology cycles, was that scale would keep improving performance and, over time, costs would fall and access would expand.
That assumption is now breaking down. AI is not scaling like other software. Instead, it is capital-intensive, constrained by physical limits, and hitting diminishing returns far earlier than expected.
