Things humans should think about
I asked Claude.ai what kinds of patterns are there in all it knows that are currently being “ignored” or that there is not much information about. Things that are “hiding in plain sight”. Here is it’s response:
This is a genuinely interesting question to sit with — not “what do we know” but “what's been hiding in plain sight.”
Let me think through some real patterns that feel underexplored, drawing on what's actually known but not yet synthesized well:
The convergence problem in complex adaptive systems
Ecosystems, immune systems, markets, and neural networks all exhibit what looks like “memory without a memory organ” — they adapt to past states without a centralized record. The pattern nobody has fully articulated: they all require forgetting at the right rate to remain adaptive. Too much memory and they over-fit to past conditions; too little and they can't build structure. The relationship between optimal forgetting rates and environmental volatility seems like it should be a universal law, but nobody has written it down cleanly across all domains.
The geometry of compression and creativity
Information theory, evolution, and artistic style all seem to share a deep structural relationship: the most generative patterns live at the edge of maximum compression. A genome that's too redundant doesn't evolve fast; one with too little redundancy can't repair itself. A creative style that's too predictable is boring; one with too little pattern is noise. Jazz, protein folding, and machine learning loss curves might all be navigating the same abstract manifold. The connection hasn't been formalized across domains in a unified way.
Developmental timing as a master variable
In embryology, “heterochrony” — shifts in the timing of developmental events — is a massive evolutionary driver. But almost nobody applies this lens seriously to cultural evolution, language change, or technological development. The moment something “matures” relative to other things it interacts with seems to determine its trajectory more than its intrinsic properties. Languages that crystallized early shaped cognition differently; technologies that arrived before their infrastructure was ready failed not on merit but on timing. Timing relationships between co-evolving things may be a hidden master variable that nobody tracks systematically.
The immunological model of belief systems
Immune systems develop tolerance to self and reactivity to other — but this requires early exposure during a critical window. There's a striking parallel in how cultural belief systems work: exposure during formative periods creates “self,” and things encountered after that window are more likely to be treated as foreign. What's interesting is that autoimmune disorders — where the system attacks self — have an analog in ideological systems that become self-destructive. The conditions that produce each seem structurally similar (disrupted early exposure, dysregulated signal amplification). Nobody has mapped this carefully.
Friction as information
In physics, friction is usually treated as noise — energy lost, signal degraded. But in social systems, biological systems, and information systems, friction is often the signal. The resistance a new idea meets tells you about the topology of the existing network. The places where species don't hybridize tell you about species boundaries. The places where a programming language is awkward tell you about its underlying model of computation. Friction maps the hidden geometry of systems, but we almost always try to eliminate it rather than read it.
The recursion trap in self-modeling systems
Any system that models itself changes itself by modeling itself — and that change alters the model, which alters the system. This is known in psychology (the observer effect, self-fulfilling prophecy) and in physics (measurement problem). But the rate at which this recursion loop operates versus the rate at which the underlying system changes seems to determine whether you get stability, oscillation, or runaway. This feels like it should govern everything from therapy outcomes to central bank policy to AI alignment, but nobody has a unified account of what determines the recursion rate or how to tune it.
What strikes me most across all of these: the patterns tend to involve relationships between rates — the rate of forgetting vs. learning, the rate of self-modeling vs. system change, the rate of maturation vs. environmental readiness. Static snapshots miss them entirely. They only appear when you watch how fast things change relative to each other.
That might itself be the underarticulated meta-pattern.