The current trajectory of artificial intelligence is often framed as a race defined by scale, where larger models, greater computational power, and expanding datasets are seen as indicators of progress. According to research, artificial intelligence could contribute up to 16% or approximately $13 trillion to the global economy by 2030, while also increasing global GDP by as much as 26%.
According to Rob Sobhani, PhD, an early investor and board member of Z Advanced Computing, Inc. (ZAC), this narrative, while influential, is beginning to reveal structural limitations, particularly as costs escalate and questions around transparency remain unresolved. He observes that billions are being directed toward infrastructure-heavy approaches that depend on vast data aggregation, yet the outcomes do not always translate into systems that are understandable or inherently trustworthy.
