LG Doucet speaks with technology strategist and author Alvin Wang Graylin about why soaring AI spending, concentrated gains and slow enterprise adoption could trigger a “great reckoning.” Graylin argues that better models and more data centers do not guarantee fast returns, especially when organizational change—not technology—is often the main barrier to adoption.
The discussion examines what could follow a correction: more disciplined investment, stronger safety nets and greater emphasis on human-centered work such as education and care. Graylin also challenges the framing of AI development as a winner-take-all race between the US and China, warning that competition can weaken safeguards while open models make advanced capabilities widely accessible.
The central takeaway is that AI’s benefits may arrive more slowly than markets expect and require coordination rather than unchecked scale. Graylin calls for tax and policy reforms, international safety cooperation and an approach that treats AI as a global public good.
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