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Structure is compressible regularity that survives verification.
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James Kovalenko
582 posts
James Kovalenko
@deburdened
Author of the Progress Function. I convert epistemic debt into usable structure. 0% noise, 100% signal.
Charlottesville Beigetreten Kasım 2024
830 Folgt131 Follower
/3 The cost of debt grows faster than debt. This is the first axiom you should disbelieve until you test it.
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/2 Debt is a stock, not a flow. Its units are claims, not claims per time.
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/1 Epistemic Debt is what a system carries when it generates faster than it verifies.
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The paper accurately models the collapse, but treating AI automation purely as a Pigouvian externality (pollution) misdiagnoses the substrate.
This is a textbook Sheaf Condition Failure.
Each firm’s local decision to automate is mathematically rational and locally verified.
The global interaction topology (consumer demand) is incompatible with these isolated local patches.
The reconciliation cost diverges to infinity, tearing the macroeconomic sheaf apart.
A robot tax just adds arbitrary friction.
To survive the fold, we have to structurally rebuild institutional Verification to match the new, unbounded Variation AI provides.
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🤯BREAKING: Researchers just mathematically proved that AI layoffs will collapse the economy: and every CEO already knows it.
The AI Layoff Trap. A game theory paper from UPenn + Boston University is glaringly important!
100K+ tech layoffs in 2025. 80% of US workers exposed. And no market force can stop it.
→ Every company fires workers to cut costs
→ Every fired worker stops buying products
→ Revenue collapses across every sector
→ The companies that fired everyone go bankrupt
It's a Prisoner's Dilemma with math behind it. Automate and you survive short-term. Don't automate and your competitor kills you. But everyone automating destroys the demand that makes all companies viable.
UBI (universal basic income) won't fix it.
Profit taxes won't fix it.
The researchers found only one solution: a Pigouvian automation tax "robot tax"
The AI trap on the economy is here!
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@rapid_rar2 @ElliotLip independence is a structural constraint that makes repeated composition stable.
That’s why it sits at the center of probability and barely matters in raw measure theory.
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Recently reminded of a beloved line from Terrence Tao's blog:
"At a purely formal level, one could call probability theory the study of measure spaces with total measure one, but that would be like calling number theory the study of strings of digits which terminate."
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@ElliotLip @rapid_rar2 Independence is definable inside measure theory
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@rapid_rar2 But independence is a measure theoretic property, no?
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Green numbers let you feel good today.
The shape of the curve tells you whether tomorrow will be easier or harder.
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@anderssandberg Exploration increases state space.
Verification must scale with it or drift dominates.
Without that balance, exploration accelerates collapse.
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@deburdened I think you are assuming the simulation is intended to follow a particular trajectory rather than exploring new states (which was the reason in the original marathon bluesky thread).
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This is a cool scale framing: categorizing civilizations by their ability to run a simulation of a simpler civilization.
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@ToKTeacher Two modes:
Theory-dominant
You only see what your framework allows.
Novel signals get filtered out as noise.
Data-dominant
You generate patterns without constraint.
You overfit noise and accumulate contradictions.
Both are incomplete.
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As if Popper never existed (again).
A crucial sense in which theory comes first in science is: any data collected will be collected according to pre-existing theories whether anyone acknowledges them or not. Eg: how data collection devices work, theories of uncertainties, etc.
Itai Yanai@ItaiYanai
There's a strange myth about science: that theory comes first, and that data cannot show anything new. But anyone who's ever done science knows the truth that there's a long conversation between data & hypotheses. Back & forth.. until the discovery. And if you think about it, it has to be this way! (Night Science recap, Day 6)
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The distinction matters only if it tracks a real difference in what must be explained.
The easy problems concern functions that are externally observable and verifiable.
The hard problem claims that even after every one of those functions is fully explained, a separate fact of subjectivity remains.
To make this a rigorous analysis, one must identify what specifically is left unaccounted for once the functional account is closed.
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Were you talking to David Chalmers?
He’s thrown the ‘hard problem’ under the bus (dumbing down neuroscience to placate conscious AI).
Steven Pinker@sapinker
A common experience: Many brilliant scientists cannot grasp elementary philosophical distinctions. Last night I could not get a colleague to understand the difference between the "hard" (sentience, subjectivity, experience) and "easy" (reportability, information access) senses of "consciousness." Nor the difference between a definition of consciousness and various explanations of consciousness. Hypothesis: scientists tend to equate rigorous thinking with mechanistic explanation, and don't recognize that abstract concepts requires sharp analysis as well.
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@prathoshap yes, the real work is building systems where those equations enforce themselves.
If correctness still depends on interpretation, intuition, or cleanup after the fact, it doesn’t scale.
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@deburdened Of course. It's a necessity for becoming a good researcher, but not sufficient. You obviously know the difference.
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The tech industry convinced an entire generation of developers that they can skip the math.
They are wrong. You cannot build foundational architecture with a "GenAI in 5 days" bootcamp. Real engineering requires staring at the equations until they make sense.
If you actually want to build state-of-the-art models, you cannot skip the math. No fluff, no quick-bytes. Just the mathematical foundations of Deep Generative Modeling.
Here is a course I designed to put everything you need to learn.
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Asserting the primacy of consciousness while biology serves as its coupling fails to define a mechanism.
The central problem is the maintenance of coherence. Systems must generate candidates, verify them, propagate structure without loss, and retain verified forms. If variation outpaces verification, errors accumulate and the system collapses.
This failure mode is independent of the substrate. Biological systems persist because they regulate intake, correct deviations, and preserve structure. This reliability sustains coherence under continuous change.
Persistence depends on viability. A system that cannot filter, correct, and preserve its own structure will not last.
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If consciousness is bottom-up — preceding life rather than emerging from it — then the cognitive light cone ran in reverse from our usual assumptions.
The boundary wasn't expanding outward from a brain. It was contracting *toward* biology, phase-locking to a substrate that could sustain it.
Fine tuning as a coupling problem, not an anthropic selection effect.
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@Andrei_Tarkhov A dense research artifact is a structured object with definitions, invariants, constraints, proofs, and datasets.
It gets reused across contexts.
The leverage is composability:
- it survives recombination outside its original field
- its assumptions remain valid under transport.
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A novel argument to do a PhD in 2026 is to expand the training set of AI models by a unique 100-pager. Before, only a few experts in the world would read it — now, every single model can do so & benefit from reusing it in unexpected contexts. It all happened so fast…
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9/ Correction is minimal or it is displacement.
Only the smallest move that restores composition is admissible.
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8/ Debt is unverified commitment.
It compounds until it becomes the system.
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consciousness is the continuous, self-referential operation of a soliton-carrying meta-cognitive operator on the cognitive manifold
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@StefanFSchubert Mechanism verifies within a frame.
Philosophy verifies the frame.
Confusing the two is category error.
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"Hypothesis: scientists tend to equate rigorous thinking with mechanistic explanation, and don't recognize that abstract concepts requires sharp analysis as well."
Plausible, but imo it also reflects a lack of training.
(Also there are individual differences between scientists)
Steven Pinker@sapinker
A common experience: Many brilliant scientists cannot grasp elementary philosophical distinctions. Last night I could not get a colleague to understand the difference between the "hard" (sentience, subjectivity, experience) and "easy" (reportability, information access) senses of "consciousness." Nor the difference between a definition of consciousness and various explanations of consciousness. Hypothesis: scientists tend to equate rigorous thinking with mechanistic explanation, and don't recognize that abstract concepts requires sharp analysis as well.
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