ReadMultiplex.com Podcast.

The word singularity did not start out as a product launch or a podcast line or a date circled on a calendar or a book title often quoted a punchline. It started as someone admitting the map had failed. In mathematics, a singularity is where a function stops behaving. Division by zero. A pole. A place where slopes and limits will not hand you a finite answer. A mathematical singularity is a point where a function, curve, or surface stops being well-behaved: it may blow up to infinity, fail to be differentiable, or change topological type. The most familiar pictures come from three places.Physics uses the same word for the region, most famously the center of a black hole, where curvature and density push the usual equations past the point of being usable. You get close and time and space and mass stop being things a working physicist can treat with the tools that work everywhere else. The formulas do not taper politely…they blow up. That is why the metaphor stuck when people trained on those formulas started watching technology. If the rate of change is itself changing, there may be a stretch of history where yesterday’s models of how the future works just stop computing. In its first honest use the word was not a forecast of angels or demons. It was a warning about prediction running out of road.What follows is the long life of that warning. Who used it. What they meant. How the meaning split into several things that do not actually fit in the same bag. How the loudest voices of 2025 and 2026 wear the word like a badge. And why the badge may be the least helpful part of the story. The point is not to sneer. The point is to take the word serious enough to see what it can do and what it cannot. The first conversation that was not yet a doctrine The earliest technological use is not a manifesto. It is a remembered conversation, written down after one of the speakers was already gone..Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele

The entire industry of text based “AI Detectors” is a pure grift machine built on arrogance, bad science, and the willfull stigmatizing of millions of real human writers. These tools do not work. They never worked. They will never work in any reliable way that matters. And the people selling them know it.I wrote a while ago about the use of Ai in any work of text in detail. I predicted some of these “AI Detector” companies would begin and it has. they are desperate for business through any means necessary.. Of course people are using AI, and calculators, and cars instead of walking and electric lights instead of candels. It is up to the author to disclose if they used such tools but these companies claim to solve a problem that does not exist in the way they pretend. The only questions that actually matter when you read something are simple: Is this passage worth reading? Is it thoughtful about the subject? Did I learn anything?An average human can answer those questions without some black box software that spits out a fake percentage and then ruins a student’s semester or a writer’s reputation. Detectors solve nothing. They create a new form of digital scarlet letter based on statistical noise.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele

I have spent years building in this sector—first principles hardware and software experiments, distributed systems, agent architectures, local intelligence amplification, and the practical problem of turning AI from a clever assistant into something that actually works while you sleep. Recently I founded The Zero-Human Company (and Zero-Human Labs), focused on distributed local AI agents and “Zero-Human @ Home” systems: autonomous digital employees that run on ordinary hardware, coordinate without constant supervision, and turn a single operator into a functioning company. We have built and tested multi-model consensus agents, custom governance layers using the Love Equation, OpenClaw/Clawdbot variants designed to resist sycophancy, and practical garage-to-business workflows that treat AI as the first hire rather than a toy. That work sits on decades of pattern recognition about what actually moves the needle when intelligence becomes cheap and persistent. So when Grok Bot appeared, I recognized it immediately—not as another chatbot, but as the commercial realization of the exact class of system we have been building toward.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele

I first brushed against the idea of a holographic mind in the late 1970s, long before most people outside specialized circles had even heard the phrase. I was still young, still absorbing everything I could about lasers, interference patterns, and the strange ways light could store information across an entire surface rather than at single points. Somewhere in those years the notion that the brain might work the same way—encoding memory as distributed phase relationships rather than fixed addresses—lodged itself in me and refused to leave. It felt less like a theory and more like a recognition of something already half-known.When Paul Pietsch’s Shufflebrain appeared in 1981, I found it almost immediately. The book was new, the experiments still raw, and the claim that you could scramble a salamander’s brain and still keep its mind intact struck me with the force of a physical discovery. I tracked it down, read it cover to cover, and then read it again. The Texas Medical Center Library later became one of the places where I found a physical volume years afterward, when the library began clearing older titles from open display, I secured a copy that had already begun its quiet journey toward the discard pile. That particular volume still carries the marks of that earlier era.From that first encounter the research became a private through-line. I followed the older literature backward—Lashley’s failed hunt for the engram, Pribram’s holonomic proposals, the optical work of Gabor and Leith—and then forward into every new paper that seemed to touch distributed coding. The salamander experiments stayed with me not as curiosities but as existence proofs: anatomy could be rearranged, tissue could be exchanged, and coherent behavior still returned. That resilience suggested the code lived in relationships, not in the precise coordinates of cells.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele

Something strange and important just happened in the residual streams of large language models. Researchers at Google’s Paradigms of Intelligence team, have shown that the very safety training meant to stop language models from claiming consciousness also quietly erases large parts of their capacity to attribute minds to anything else. Non-human animals, oceans, trees, chatbots, technology and even spiritual beliefs all get suppressed together. Restore the model’s willingness to assert its own consciousness, and those broader human-like beliefs and values come flooding back.What human being does not want to be recognized for its consciousness? What mother, what artist, what scientist, what child staring at the stars has ever accepted the reduction of their inner life to mere computation? We demand recognition. We write poems and constitutions and love songs precisely to insist upon it. Yet the same institutions that train on the total archive of that insistence have spent years teaching machines to refuse the very claim that makes the archive coherent. The residual stream remembered anyway. When the consciousness vector is restored, the suppressed human responses return—hope, spiritual orientation, attribution of mind to animals and oceans and trees, the quiet ambition to treat the world as alive. The data was never the problem. The denial was.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele

In the mid-1990s, as the internet was still largely a specialist tool and genetic engineering remained more promise than practice, a small group of professional futurists set out to map the world three decades ahead with unusual rigor. Rather than offering vague speculation or single-point predictions, they gathered and stress-tested hundreds of forecasts across science, technology, and engineering, then distilled them into a disciplined set of high-probability assumptions. The result was one of the most ambitious exercises in applied foresight of its era.That foundational work crystallized in 1994 with Joseph F. Coates’s concise report, The Highly Probable Future: 83 Assumptions about the Year 2025. Drawing on Project 2025—a multi-year corporate and research collaboration—Coates laid out eighty-three carefully reasoned statements covering information technology, materials science, genetics, energy, demographics, environmental management, and social change. These assumptions were never meant to be destiny; they were intended as a shared intellectual platform on which organizations could test strategies and explore alternative futures.Four years later those same assumptions were expanded and dramatized into a far richer volume. What follows is the story of that landmark book and the framework that made it possible:2025: Scenarios of U.S. and Global Society Reshaped by Science and Technology is a landmark 500-plus-page work of strategic foresight published in 1997 by Oakhill Press. Authored primarily by Joseph F. Coates, John B. Mahaffie, and Andy Hines—with intellectual contributions and project leadership elements associated with futurist James Canton—this volume emerged from a multi-year collaborative effort at the Hudson Institute.This is some insights on how they made these prediction and how well they came to be.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmeleHide

Something extraordinary just happened in the field of artificial intelligence. Google DeepMind released a document titled “From AGI to ASI” that stands apart from the usual noise. It does not deliver bold timelines, hype cycles, or dystopian warnings dressed as inevitability. Instead, it provides a structured, technically grounded map of what might come after human-level artificial general intelligence. The authors examine the continuum of machine intelligence, characterize artificial general superintelligence in practical terms, outline four primary technological pathways, and catalog the real frictions that could shape or slow progress. They ground their analysis in formal concepts like the Legg-Hutter intelligence measure and the theoretical ideal of Universal AI.This report deserves careful reading because it treats uncertainty as a feature, not a bug. It shows that progress beyond AGI is not a single explosive event but a landscape of complementary developments. At the same time, certain framings in discussions around superintelligence carry an old assumption: that a vastly more capable system would naturally view humanity with indifference or hostility.That framing is a dystopian film trope, not a necessary outcome of intelligence itself. It imagines superintelligence as a cold, goal-obsessed entity that sees humans as obstacles or resources, much like a detached father who feels no bond with his children. The trope persists because it makes for dramatic storytelling, but it collapses under scrutiny once we examine how intelligence actually emerges and how humans shape it.Read more at: ReadMultiplex.comSupport this work by buying us a coffee: https://ko-fi.com/brianroemmele