Nilesh Jasani published “A Glossary of Our Worldview” by GenInnov on October 4, 2026. It is not a dictionary and not a forecast. It follows the order in which he thinks, from a world of “a billion Einsteins” to the claim that ideas are now cheap and factories, power, and manufacturing yields are not. The argument underneath is that innovation may be the last durable growth theme left, and that ignoring its jargon now carries an unusually high cost.
For most of history, the primary bottleneck in human discovery was intellectual bandwidth. The number of minds capable of mastering advanced mathematics, molecular biology, physics, or engineering was extraordinarily small—and the number who could move fluidly across those domains was smaller still. Even Einstein could only hold one problem in his head at a time. Institutions had no choice but to strictly ration their hardest questions among a rare few.
AI fundamentally loosens that constraint. A sufficiently capable model holds vast libraries of cross-disciplinary knowledge in a single operational context. It can analyze a biological mechanism, reason through its underlying chemistry, write custom software to model the results, draw mathematical parallels from an unrelated field, and help design the next experiment. Crucially, unlike a human expert, that cognitive machinery can be duplicated at will. The same analytical capacity can be deployed across a million problems simultaneously.
This is the core of the phrase “a billion Einsteins.” It does not claim that software possesses Einstein’s raw human intuition, nor that a billion citizens have suddenly acquired genius-level intellect. It means that high-level intellectual labor is becoming reproducible at near-zero marginal cost. Cognition itself is ceasing to be the scarce resource.
The economic implications of this inversion are staggering. Imagine if, in 1900, every university, workshop, and industrial lab had suddenly been granted thousands of tirelessly brilliant polymaths who read every published paper, never slept, and could be replicated on demand. Discovery would not merely accelerate; the entire architecture of human progress would transform.
Yet, removing one bottleneck only exposes the next.
An AI can draft ten thousand candidate molecules overnight, but someone still has to synthesize and run the binding assay to see if they work. It can propose a breakthrough battery chemistry, but someone must manufacture the cells and run them through thousands of charge cycles. It can formulate an elegant theoretical proof, but someone must verify every step. It can design a next-generation reactor or data center, but someone must secure the land, navigate permitting, erect the steel, and interconnect the grid.
When thinking becomes abundant, physical reality becomes the bottleneck.
This creates a striking paradox in the modern economy. For decades, we automated physical labor while treating advanced intellectual labor as rare and costly. AI reverses this dynamic: high-level cognitive work becomes cheap and ubiquitous, while physical infrastructure, specialized labs, electrical capacity, and real-world execution become severe limiting factors.
A skilled lab technician capable of executing a delicate assay becomes more critical, not less, when flooded with thousands of AI-generated hypotheses. Semiconductor fabs become more valuable because model outputs far outpace physical manufacturing capacity. Gigawatts, power transformers, clean rooms, and regulatory bandwidth become the real determinants of speed.
Finally, there is one last constraint machine intelligence cannot easily remove: knowing which question is actually worth asking.
Scientific discovery is rarely as simple as solving an equation placed neatly on a blackboard. It requires recognizing an anomaly, sensing when a foundational assumption has quietly expired, determining what evidence would truly settle a debate, and understanding why the answer matters to human flourishing. A system capable of generating a million mathematically sound answers offers little value if no one can distinguish the vital problem from the trivial one.
The ultimate paradox of a world with “a billion Einsteins” is that human discernment becomes more valuable precisely because machine intelligence becomes so cheap.
The Industrial Revolution multiplied muscle. The Microchip multiplied calculation. The Internet multiplied access. AI is multiplying something much closer to reasoning itself—forcing us to confront a fundamental question: when intelligence is no longer scarce, what becomes the most valuable thing in the world?
