In my recent article, “AI and Opportunities for Local Talent in the Gulf: Disruptor or Catalyzer?” (Georgetown Global Business, Baratta Center, December 2025), I argue that AI is more likely to catalyze new opportunities for local talent in the GCC than simply destroy jobs. If governments align education, skills, and entrepreneurship with AI adoption, it can accelerate the region’s shift toward locally rooted knowledge economies, rather than deepening dependence on imported expertise and products. Achieving that will require determination and coordinated policy, even as the ongoing conflicts in the Middle East make the task harder.
That perspective is partly why I read Citrini Research’s “The 2028 Global Intelligence Crisis”, published two weeks ago, with interest but also skepticism.
The paper paints a dramatic picture of AI-driven labor collapse, unemployment spikes, and “Ghost GDP.” Yet the scenario relies on several strong assumptions about speed, scale, dramatic business and policy changes, and global synchronization that deserve closer scrutiny.
In many ways, the piece reads like a chapter from a sci-fi book. It is crisp and dramatic, but the leaps from AI capability to sweeping economic outcomes appear more speculative than rigorously modeled. It was striking how quickly markets reacted—quickly buying into the somewhat dystopian narrative that AI could simultaneously and overnight wipe out multiple industries, trigger unemployment shocks, inflate equity markets, and then cause a synchronized global collapse.
For such a scenario to unfold as described, several assumptions (and more) would need to align simultaneously:
- AI substitutes rather than augments labor across most industries
- Adoption happens extraordinarily fast, outpacing policy, institutions, and politics
- Productivity gains flow mostly to capital, hollowing out demand (“Ghost GDP”)
Organizations transform instantly, despite decades of evidence that change is slow and culturally resistant
- The disruption occurs globally at the same time rather than unevenly across economies
Each is individually plausible, though even then a stretch. Together, compressed into a narrow timeframe, they move from analysis to speculation.
Technology adoption rarely moves at “AI speed.” Corporate governance, labor markets, regulation, and political systems evolve much more slowly. A more plausible trajectory may be a multi-year—perhaps multi-decade—transition that reshapes the relationship among labor, capital, and machine intelligence unevenly across societies.
The Citrini article does succeed in posing a serious question: are our economic and social systems prepared for abundant AI?
As investor Doug Tynan notes, “The opportunity is not writing code. It is in redesigning work.” I would add: enabling constructive adoption—where organizations, policymakers, educators, and society collaborate to harness AI productively.
Ricardo Ernst Alberto Rossi Bilal Baloch Jerry Haar Nick Lovegrove
