We are building intelligence.
But we still do not
understand intelligence.
AI is advancing fast.
Faster than our understanding of what intelligence actually is.
Bigger models. More compute. More claims.
But intelligence is not scale.
It is structure.
It is efficiency.
It is adaptation.
It is deeply rooted in biology.
I work on building AI systems grounded in these principles—
and applying them where they matter in the real world.
Intelligence grounded in nature
Much of today’s AI conversation is driven by scale, speed, and competition.
But the future of intelligence will not be shaped by size alone.
Human intelligence is layered, contextual, embodied, and still largely mysterious. Even the smallest biological systems display forms of elegance and efficiency we do not yet know how to reproduce.
My work begins there: with nature, with first principles, and with the belief that useful intelligence must be grounded in reality.
Anything that comes from biology carries an elegance of efficiency that our engineered systems are still trying to reach.
From early research in emotion-inspired machine learning to products deployed at scale — a thread of inquiry into what intelligence is, and what it can do.
One of the early efforts in machine intelligence inspired by emotion, intuition, and biological learning principles. Work dating to 2003 that helped open new ways of thinking about intelligence beyond rigid computation — a line of research still ahead of its time.
A continuous human-verification system designed to reduce friction while improving trust, security, and real-world usability in digital environments. Rethinking identity not as a checkpoint, but as a living layer of the system.
A curated global community of decision-makers, entrepreneurs, and investors exploring how AI can be developed and deployed with greater consciousness and responsibility. Because the people shaping adoption matter as much as the technology itself.
A health platform that reached more than 400 million users by making practical, reliable knowledge more accessible. A proof that impact at scale is possible without compromising integrity.
From research to products to communities, the through-line has remained the same:
build intelligence that serves reality.
How I think about
AI and intelligence
Benchmarks may capture performance, but they do not capture the full nature of intelligence. Human intelligence is multi-dimensional, contextual, and still not fully understood. We should be humble about what we are measuring.
Anything that comes from biology carries an elegance of efficiency. Nature often solves with far less waste than our engineered systems. Before we build more, we should understand more of what already exists.
Larger systems can be more capable, but capability should not be confused with comprehension. Bigger is not the same as deeper. The race for size is not necessarily the race worth winning.
The pace of AI development is often outstripping practical validation, adoption, and societal readiness. Not everything that can be built should be built. Velocity in the wrong direction is not progress.
AI becomes meaningful when it solves actual problems, earns trust, and improves life beyond the benchmark. The measure of a system is not its leaderboard position — it is its effect on the world it enters.
A selection of field notes, essays, and observations on where AI is going — and where it may be getting ahead of itself.
Not all of them are answers. Most are attempts to ask better questions.
Why scaling systems is not the same as understanding the nature of intelligence — and why the difference matters.
ReadOn efficiency, biology, hallucinations, and the limits of brute-force AI as an organizing principle.
ReadOn the asymmetry of harm, responsibility, and the difference between capability and wisdom.
ReadWork, Research & Collaborations
My work spans research, products, communities, and global collaborations.
Across these efforts, the aim remains consistent: to build and support forms of intelligence that are more grounded, more efficient, and more aligned with reality.
Some of this work lives in products.
Some of it lives in institutions, conversations, and communities.
All of it is part of the same inquiry.
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Hummingbirds AIFounder
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Miami AI ClubFounder
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Columbia UniversityCo-Chair, AI & Healthcare TF
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Future Investment InitiativeContributor
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National Applied AI ConsortiumAdvisor
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Palm Beach State CollegeAdvisor
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BEL ResearchResearcher
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PositiveMedFounder
Let’s build with more understanding
If you are building AI, deploying it in the real world, or thinking seriously about where it is heading — I’d be glad to connect.