Yesterday, I wrote about what I called the AI Doomsday Job Clock, that uneasy sense that something fundamental is shifting beneath knowledge work. In that piece, I referenced Matt Shumer’s article, “Something Big is Happening in AI….” At the time, I had only seen excerpts.
After reading the full essay on his site, I believe it deserves far more than a passing reference. It may be one of the most consequential AI essays published ever. Not because it is alarmist, but because it is unflinching.
The more I reflected on it, the more it reminded me of Thomas Paine’s Common Sense. Not stylistically, Shumer isn’t pounding a revolutionary drum, but functionally. Paine articulated what many sensed but had not fully processed. He clarified a moment that was already underway.
Shumer is attempting something similar with AI.
One of his most effective comparisons is to February 2020. Life appeared stable. Markets were steady. Institutions were operating normally. Yet forces were already in motion that would soon reshape everything. COVID changed things like a tsunami. But in retrospect, some of those changes were temporary and only some are lasting.
His point is not that AI is a crisis. It is that we may be in a pre-recognition phase of a structural transformation. If you work close to frontier AI systems, you can feel the velocity. Release cycles are compressing. Performance jumps are arriving in tighter succession. What once felt like measured progress now feels like acceleration.
The critical hinge in Shumer’s argument is not simply capability growth. It is recursion. AI systems are increasingly embedded in the development loop that produces their successors. When a tool meaningfully reduces the time, cost and friction required to improve itself, even indirectly, progress compounds. Compounding can be viscous.
That dynamic changes the slope.
Most major technological breakthroughs in history did not participate in their own refinement. The printing press did not redesign itself. The steam engine did not draft blueprints for the internal combustion engine. The transistor did not architect the microprocessor. Human ingenuity drove each leap.
AI, by contrast, may now be assisting in reducing the human bottleneck inside its own improvement cycle. If that loop tightens, timelines compress in ways that institutions are not structured to absorb.
We often use the word “evolution” casually when describing technology. Strictly speaking, AI is not evolving biologically. It is not alive. It is not conscious. But when capability growth becomes self-reinforcing, the outcome resembles acceleration curves we typically associate with compounding systems.
Compounding does not need consciousness to be disruptive.
My earlier piece focused primarily on employment, particularly the exposure of screen-based knowledge work. Shumer’s essay pushes beyond labor markets. The deeper issue is not task replacement but performance thresholds.
What happens when AI systems outperform top-tier human practitioners across domains such as legal strategy, scientific hypothesis generation, large-scale systems design or macroeconomic modeling? Not in isolated benchmarks, but consistently enough to shift incentives?
Machines have long surpassed us physically. That has never threatened our sense of agency. Intelligence, however, has been humanity’s defining advantage. If we begin manufacturing systems that rival or exceed us in reasoning across multiple domains, the implications extend beyond productivity. They touch governance, authority and trust.
Do humans remain the final decision-makers? Or do we increasingly defer to systems whose analytical capacity surpasses our own?
Critics such as Gary Marcus have raised valid concerns about overstatement and runaway narratives (see his commentary). Current models still hallucinate. Robustness varies. Infrastructure and economic constraints are real. Scaling is not magic.
Those points deserve attention.
But AI does not need to be perfect to be transformative. The internet reshaped media long before it was stable. Cloud computing altered enterprise IT long before it was hardened. Smartphones rewired daily life before they were secure.
Transformation occurs when capability becomes sufficiently reliable, accessible and economically attractive to change behavior. In several fields, AI has already crossed that threshold.
The more consequential question is not whether progress continues, but how steep the curve becomes if AI meaningfully accelerates its own development cycles.
I am not rooting for stagnation. History offers no examples of societies voluntarily halting technological advancement because it felt destabilizing. The potential upside of advanced AI — from scientific discovery to climate modeling — is extraordinary.
The challenge is not whether to build. It is whether governance, institutions and social frameworks can adapt at a pace commensurate with what is being built.
Frank Herbert’s Dune universe imagines the Butlerian Jihad — humanity’s revolt against “thinking machines,” followed by a ban on advanced AI. It is fiction, but the metaphor is instructive. When capability concentrates faster than governance adapts, backlash follows. Not because technology is inherently malevolent, but because agency feels diminished.
Even if AI never becomes conscious, its capability may outstrip our ability to audit, interpret or meaningfully oversee it. That imbalance would have implications for economics, security and political stability.
This is where the historical parallels matter. Thomas Paine wrote, “We have it in our power to begin the world over again.” Patrick Henry warned, “Give me liberty or give me death.”
Both were statements about agency — about who governs and who decides.
For all of human history, intelligence has been our defining advantage. Now we are manufacturing it. Not tools that extend our strength, but systems that increasingly rival our reasoning.
This moment is not primarily about product cycles or job categories. It is about whether the first species to create scalable intelligence can also remain the species that governs it responsibly.
Creating intelligence at scale is a technological achievement.
Ensuring that human agency scales alongside it is a civilizational one.
That is the experiment now — and there are no case studies to guide us.
