Maybe artificial intelligence really is going to “change everything.” But if it does, lots of us will be surprised. In a Forecasting Research Institute survey last spring of economists, AI experts, superforecasters, and the general public, about 80 percent of each group thought the world at the end of 2030 would look a lot like the world of 2026.
Here’s the breakdown: Roughly 41 percent backed a “slow progress” scenario (basic research and administrative tasks, passable creative content, and some physical tasks), 44 percent a “moderate progress” scenario (significant research and multiday tasks, high-quality creative work, and navigating many environments), and 15 percent a “rapid progress” scenario (outperforming top humans in research, coding and leadership; producing award-winning creative works; and nearly all physical tasks).
Those predictions might offer some comfort to workers worried about a looming AI-driven job apocalypse. Even under the rapid progress scenario, economists don’t anticipate a world without human work (Table 1).
Why don’t economists see a world utterly and unrecognizably transformed—and sooner rather than later? The real world isn’t an AI lab. From the FRI analysis:
The most frequently cited reason why transformative technology would translate into only modest economic growth was uneven and time-lagged diffusion, with economists drawing on analogies to electrification, automobiles, and personal computers to argue that multi-decade lags routinely separate a general-purpose technology’s arrival from its measurable productivity impact. Geopolitical, structural, and demographic headwinds—including trade wars, climate change, an aging population, and declining immigration—that could offset AI-driven gains were also cited, as were constraints on energy, chips, and data center construction. Collectively, these factors were deemed likely to cap the pace at which AI capabilities could be deployed regardless of how quickly they advance. Under the rapid scenario, the possibility that large-scale workforce exit, societal unrest, or existential risk could become drags on GDP was also considered.
When I ask about this issue, I find that the go-to economist answer is that it takes time for businesses to reorganize themselves, which is reflected in the passage above. Firms must change processes, retrain workers, and figure out actual productive uses for AI. That certainly seems to be where we are right now, as highlighted in a new Wall Street Journal story, “AI’s Transformational Value Remains Elusive for Most Companies.” The piece quotes a new Gartner survey that finds “only 13 percent of CIOs [chief information officers] reported their enterprise has realized significant or transformational value from AI so far.”
We hear plenty from AI CEOs about what their models can do, and we should definitely pay close attention. But we probably need to listen more closely to the CEOs who actually rely on them.