The Intelligence Frontier is Moving. We Are Driving It.
Catalyzing a continuous cycle of co-evolution between human and artificial intelligence.
We map under-utilized human cognitive primitives, train people to use them, and turn them into AI benchmarks.
The bottleneck is not computational—it is cognitive.
Leaders like Bengio, LeCun, Marcus, Hassabis, and Lake argue AI lacks grounding in real cognitive principles—causal reasoning, abstraction, compositionality.
It's easy to make computers do hard things (calculus, chess) but hard to make them do easy things (perception, common sense, contextual judgment).
of employees fear AI will eliminate jobs. Organizations struggle with adoption, trust, and real-world fit.
We solve Moravec's Paradox by starting with the biology, not the code.
The window is open for a company that bridges cognitive science and AI development.
Current benchmarks test pattern matching, not judgment, collaboration, or ethical reasoning. Brynjolfsson (MIT): Human–AI complementarity outperforms either alone—but requires defined "edge skills."
Spearman's g (1904) and CHC models still dominate—frameworks that predate modern neuroscience and don't capture human-AI complementarity.
After Deep Blue, Kasparov showed: "weak human + machine + better process > strong computer alone." We create Centaurs.
80%+ believe AI would help—if fear barriers are addressed. People resist tools that replace them but embrace tools that upgrade them.
Human–AI teams that combine the irreducible human edge with AI scale and speed.
"A weak human + machine + better process is superior to a strong computer alone." — Garry Kasparov
We discover, measure, and train under-utilized cognitive primitives.
Big tech focuses on engineering (scaling compute). We understand the source code of the brain.
We move beyond g (general intelligence) to c (complementary intelligence).
Knowing what you don't know
How we know what we know
Understanding complex interdependencies
Intuitive + analytical integration
Decision-making under uncertainty
Group cognition and creative reframing
Identify cognitive primitives
Build rigorous tests
Upskill humans
→ Process DataTrain AI systems
Compare performance
Level N → N+1
Two revenue streams that reinforce each other.
"Cognitive Edge" Workforce Programs
"Beyond Pattern Matching" Evaluation
We've pressure-tested our thesis. Here's what we know is hard—and how we've designed around it.
Human training is our data labeling pipeline, not a separate product. We capture Process Data (how thought is formed)—not just Result Data (final output). Primary asset: longitudinal dataset.
We focus on qualitatively different reasoning: empathetic logic, ethical synthesis, contextual judgment. Kasparov proved it: Human–AI Centaurs beat both pure humans and pure AI.
We say "under-utilized cognitive primitives"—grounded in neuroscience and measurable tasks. Benchmarks are open, reproducible (ImageNet/GLUE/MMLU analogy).
We don't say g is wrong—we say it's incomplete for the AI era. g captures ~40-50% of variance. We operationalize c (complementary intelligence)—the rest.
Talent can be hired. Data cannot be backfilled. Our moat is longitudinal Process Data + continuous discovery. Competitors can't copy a moving target.
It requires precision neuroscience—trait-state decomposition to identify what can be trained. We measure outcomes with our own benchmarks, not satisfaction surveys.
"Talent can be hired. Data cannot be backfilled."
Big tech focuses on engineering (scaling compute). We understand the source code of the brain—defining ground truth before they know what to look for.
Most AI training data is "result-oriented" (final outputs). We capture Process Data—how thought is formed. This dataset doesn't exist anywhere else.
Competitors get snapshots; we track cognitive development over time across tasks, industries, and contexts.
Big labs have cognitive scientists. They don't have a business model for human-AI co-evolution. We discover new primitives faster than competitors can copy.
We're raising seed funding to build the first cognitive benchmarks and launch enterprise pilots.