Macheng Shen
Researcher, builder, and explorer of intelligence and human transition.
Featured Essay (Full Text)
This PDF is the current overview of the broader agenda: intelligence as a finite physical system that builds, updates, and deploys internal structure sufficient for prediction, intervention, and control.
If the embedded viewer does not load in your browser, open the PDF directly: essay.pdf.
Research Map
Toward a theory of intelligence
The core question is what sort of object intelligence is. The working answer is not “static function approximation,” but multiscale closed-loop competence under physical, informational, and control constraints.
Line loss, viability, and distributed cognition
These essays develop the higher-level language: physical budgets, coordination costs, endogenous viability, and why multiscale organization keeps appearing in biological and artificial systems.
Credit transport and wave-inspired learning
This branch asks a narrower question: how does task-relevant update information move inside a learning system? Some physical media can compute with fields and even measure gradients in situ; cortex may use its own cousin of this idea.
Original Raw Prompt
I am still sharing the original prompt for reproducibility. It was useful as a seed, but several claims on the site have since been revised into a more careful layered framing.
About
I was trained as an engineer and researcher, earning a PhD at MIT and working on algorithmic research both in Silicon Valley startups and at the Shanghai Qi Zhi Institute, where I focused on foundational algorithms for embodied intelligence.
I am trying to find a language that can describe intelligent behavior across biology and AI without collapsing everything into current engineering jargon. That means connecting learning, control, multiscale feedback, physical implementation, and safety.
Recently, I’ve become less interested in being defined by any single credential, institution, or prior role. I want to spend my life working on questions and systems that can genuinely change how humans understand intelligence, navigate transition, and shape the future.
Right now, my work is centered around three long-term directions:
Theories of intelligence — exploring the deeper structure, limits, and dynamics of intelligence itself.
Frontier Transition Lab — studying how AI-native individuals, one-person companies, and new human institutions may reshape society.
Personal cognition systems — building agents and frameworks that can help people think more clearly, make better long-term decisions, and live with greater internal coherence.
That’s the cleanest description I have for now. The rest is still being built.