The AI Index Nobody Is Tracking Why Storm Duncan of Ignatious Thinks It’s the Most Important AI Metric of the Decade

There is no shortage of metrics being applied to artificial intelligence. Benchmark scores, parameter counts, inference costs, enterprise adoption rates. The industry generates data about itself with the same velocity it generates everything else. What is not being measured, at least not systematically, is the one variable that may ultimately determine whether the transition to an AI-driven economy is navigated successfully or not. Storm Duncan calls it the AI Anxiety Index, and he has been watching it move for years.

The concept is straightforward. At any given moment, there is a measurable level of anxiety in the general population about what AI is doing to the economy, to employment, and to the social structures that depend on both. That level is not static. It rises as displacement becomes visible and falls when adaptation appears possible. What Duncan argues is that nobody is tracking it with the seriousness it deserves, and that the absence of that tracking is itself a risk.

This dynamic is especially acute for seasoned workers, historically the most valuable employees at a company. The old formula was “learn, earn, return.” AI has invalidated much of the “learn” phase, so workers in their 40s and 50s are being forced to relearn from scratch a skill set their 20-something peers acquired natively.

The Rate of Change Problem

The core tension Duncan identifies is not between AI and people. It is between the pace at which AI is advancing and the pace at which the institutions responsible for managing its consequences can respond. Governments move slowly. Culture moves slowly. Workforce retraining programs move slowly. AI does not.

When technology disrupts a sector of the workforce gradually, the surrounding systems — social safety nets, retraining infrastructure, new employer demand — have time to absorb the shock. Workers displaced over a decade have a decade to adjust. The concern Duncan has been raising, with increasing specificity as the capabilities of current AI systems have become clearer, is that the timeline assumptions embedded in most policy thinking are wrong. The displacement is not coming gradually. In some sectors, like customer support and copywriting, it is already here, and the institutions designed to manage it are operating on a lag that may prove consequential.

His language on this has evolved deliberately. Where he once used the term civil war to describe the potential social consequences of rapid AI-driven displacement, he has moved toward the phrase mass civil disruption: a distinction he draws carefully. The point is not that violence is inevitable. The point is that sudden, large-scale unemployment, without adequate institutional response, produces social instability of a kind that no economy handles cleanly.

The Questions Nobody Is Asking

What Duncan is pushing toward is a set of questions that he argues are not being asked seriously enough at either the policy or the market level. If AI displaces a meaningful portion of a workforce sector in a compressed time period, does that workforce have the practical ability to retrain and reposition quickly enough to avoid sustained unemployment? If it does not, does the government have the operational capacity to intervene at the speed and scale required? And if neither of those conditions holds, what does the social response look like, and who bears the cost?

These are not hypothetical questions for Duncan. They are near-term planning questions, and the absence of credible answers is what he believes the AI Anxiety Index is actually measuring. The anxiety in the population is not irrational. It is a signal that the gap between the rate of technological change and the rate of institutional adaptation is visible to ordinary people, even if it is not yet visible in the metrics that markets and policymakers are watching.

Where the Opportunity Lives

Duncan’s framing is not purely pessimistic. Embedded in the risk is a significant market opportunity, and he is direct about it. Every major period of technological disruption has generated a secondary wave of company formation around the problems the disruption creates. The rise of the internet created demand for cybersecurity, privacy law, and digital identity infrastructure. The cell phone industry created demand for numerous enterprise and consumer applications now worth trillions of dollars. AI will be no different.

The companies that will matter most in the next phase of this transition are not just the ones building the most capable models and data centers. They are the ones building the infrastructure for managing AI’s consequences: retraining platforms, displacement monitoring tools, regulatory compliance systems, workforce transition services. The AI Anxiety Index, if anyone bothered to build it properly, would function as a leading indicator for where that demand is about to emerge. For Ignatious — the boutique technology investment bank Duncan leads, focused on M&A and capital raises across AI, enterprise software, security and consumer internet — that rotation is already visible in where capital and acquirer interest are beginning to concentrate.

Duncan’s broader argument is that the long-run value of AI to humanity is not seriously in question. The technology will generate enormous benefits. The question is what happens in the interval between now and the point at which institutions, culture, and policy have caught up to the pace of change. That interval is not a footnote. It is the defining challenge of the next decade, and the companies that treat it as a market rather than a problem to be managed by others will be the ones that define what comes after.

This story was distributed as a release by Jon Stojan under HackerNoon’s Business Blogging Program.