The Era of Universal Employment: A New UBI
AI may create a new economic channel in which people contribute ideas, expertise, methods and cognitive systems to the development of machine intelligence—and potentially share in the value created.
AI may create a new economic channel in which people contribute ideas, expertise, methods and cognitive systems to the development of machine intelligence—and potentially share in the value created.
AI can reason brilliantly inside the wrong frame. From the counterfeit-cap puzzle to the bat-and-ball problem, the deeper challenge is not merely solving a question but recognizing whether the question itself deserves to be solved.
AI & Human Intelligence begins with Book I: The Augmented Mind—a three-part exploration of artificial intelligence, human intelligence, and how AI may change the way we think.
AI may eliminate millions of IT jobs, but job loss is not the same as economic collapse. The real questions are productivity, output and income distribution.
AI could shift education from a fixed four-year journey to a continuous, adaptive capability system in which people learn, demonstrate, work and keep learning.
The same technology can amplify independent thinking—or accelerate cognitive outsourcing. AI may not produce one uniform cognitive future. It may widen the distance between people who use intelligence tools to think further and those who use them to think less.
AI can produce answers that look like understanding. But the deeper test is whether it can transfer concepts, detect contradictions, recognize boundaries, manage uncertainty, and connect different forms of intelligence.
As AI makes knowledge and cognitive production abundant, the bottleneck may move upward—from knowledge to awareness, judgment and agency.
The seed is not the beginning of life. A thought experiment about energy flow, information, propagation, artificial life and the possibility that life begins when a self-maintaining loop closes.
AI may be more than an artificial mind. It may be a new architecture of intelligence: structural, cross-domain, persistent, reusable—and capable of amplifying human thought.
If AI can solve mathematical problems that have resisted humanity for decades, should we slow the machine—or rethink what mathematical discovery is for?
AI may become more than a productivity tool. It could become a personal cognitive training partner—changing how human intelligence develops.
As AI becomes capable of discovering systems humans may not fully understand, the future presents a paradox: technological dependence on intelligence beyond us—and the possibility of augmenting ourselves to understand it.
AI is not merely automating work. It is reducing the organizational scale required to create, build, publish, and compete.
AI can advance at software speed while infrastructure, institutions and economies move at different speeds. The real challenge is not slowing intelligence, but building systems capable of absorbing it.
Why P vs NP may resist increasingly powerful AI and computation: the deeper challenge may be discovering structural principles that eliminate exponential search rather than merely searching faster.
AI may become so capable that humans voluntarily surrender practical control—not because AI takes the wheel, but because its reasoning becomes too persuasive to resist.
AI is entering mathematics at unprecedented speed. The real question is not whether machines can solve problems, but who gets the credit—and whether AI enriches mathematics or turns it into a leaderboard.
What must remain unchanged for consciousness to survive radical technological transformation?
The Navier–Stokes controversy reveals a deeper problem with AI: we may not recognize the value of the ideas, research directions, and cognitive material we expose to increasingly powerful systems.
AI can expand our search for ideas, push beyond idea exhaustion, and provide the outside perspective we lose when familiarity creates blind spots.
If humanity’s accumulated knowledge helps create extraordinary AI wealth, should humanity receive more than a basic income?
If AI companies can commercially learn from humanity’s knowledge, should humanity receive a return?
A humorous explanation of P vs NP through government clerks and wine testers: verifying a proposed solution can be easy, but finding the solution efficiently is the real mystery.
