At first glance, the 21st century promised something different. We imagined a world where knowledge itself would finally be rewarded, where researchers would no longer die poor with notebooks in their drawers or canvases unsold in their attics. After all, AI is the hottest gold rush of our era. Surely, those who invent the algorithms must be the ones to reap the wealth?
The illusion collapses upon inspection.
AI Researchers: the modern Teslas and Ramanujans.
They write the papers, prove the theorems, build the open-source libraries. Their curiosity capital produces the scaffolding of an entire new economy---transformer architectures, reinforcement learning methods, diffusion models. Many of these breakthroughs come from small labs, graduate students working sleepless nights, or eccentric hobbyists publishing code on GitHub for free. Their motives, often, are not profit but curiosity, beauty, or intellectual prestige.
Big Tech: the contemporary Edisons and art dealers.
They do not invent the lightning; they monetize it. They scrape the open-source code, hire a handful of the brightest, lock the rest behind proprietary walls, and weaponize compute power that no university can rival. Patents, cloud infrastructure, lobbying power---this is their laboratory. If the researcher gives the world a brilliant new method, Big Tech's reflex is simple: swallow it, rebrand it, and rent it back to the world.
The asymmetry is staggering:
A PhD student might labor for years to produce a breakthrough architecture, earning a modest fellowship stipend.
A corporation rebrands it with glossy marketing and IPOs, generating billions in valuation.
The algorithm's name (BERT, GPT, Stable Diffusion) becomes part of corporate myth, while the individuals who built them remain in the footnotes.
This is not a bug---it is the new time lag paradox. Innovation is immediate, but recognition is deferred and diffused, often drowned in corporate branding. And when rewards come, they flow not to the originators, but to shareholders, executives, and marketing departments. The wealth mindset ideology insists these researchers should have "negotiated better" or "started their own company," as though access to billions in compute and global lobbying networks were a matter of personal mindset.
The satire deepens: in academic conferences, researchers give talks about fairness and open access, while the corporations sponsoring the lanyards quietly hire them away, bind them with non-disclosure agreements, and absorb their curiosity capital into proprietary silos. The cycle repeats endlessly: innovation flows upward, wealth flows sideways.
Thus, the story of Tesla, Ramanujan, and Van Gogh is not nostalgia---it is prologue. AI researchers today reenact the same tragedy on a digital stage. Their brilliance electrifies the future, but their bank accounts remain dim compared to the institutions that harness them. The Genius Wealth Illusion is alive, humming, and fully automated.
VII. Toward a New Economics of Genius
The preceding chapters have demonstrated that the linkage between genius and wealth is not merely tenuous---it is systematically inverted. The tragedy of Tesla, Ramanujan, Van Gogh, and their modern heirs in AI is not a collection of isolated anecdotes but a patterned outcome of institutional architectures. If economics continues to treat these tragedies as exceptions, it will remain blind to the deeper law: genius produces anticipatory value that existing systems are structurally unprepared to reward.
To move beyond this blind spot, we require nothing less than a new economic framework: an Economics of Genius. Such a framework does not seek to romanticize the figure of the misunderstood genius; rather, it demands that we formally account for the dynamics by which intellectual originality, aesthetic rupture, and scientific anticipation generate value that markets, states, and institutions recurrently fail to recognize.
