I have the sensation that I will be forced to always use the strongest, frontier model. The DSM XX will need a large class of categories for AI ailments like mine.

As long as I reach for the smartest LLM available, I tell myself I am making the best decisions. Every prompt, every half-formed thought, every tiny choice gets the full superintelligence treatment. It can feel responsible. It also can feel like the only responsible choice.

When tiny choices carry unknowable ripple effects, even the trivial ones start demanding superintelligence to navigate the chaos. Modern life turning every moment into a high-stakes simulation.

Laplace and the butterfly

Pierre-Simon Laplace, 1814. In the introduction to his Essai philosophique sur les probabilités he wrote:

That intellect became known as Laplace’s demon (Laplace himself never used the word “demon”). A perfect predictor. Complete knowledge of the present yields complete knowledge of the future. Classical mechanics as theology.

Edward Lorenz, 1961, running a weather model on a Royal McBee LGP-30. He restarted a run with initial conditions rounded from 0.506127 to 0.506. Completely different weather emerged. Sensitive dependence on initial conditions. The butterfly effect. Lorenz later asked, in a 1972 talk, whether the flap of a butterfly’s wings in Brazil could set off a tornado in Texas.

Chaos theory. Deterministic systems that are still practically unpredictable. The universe may be Laplacian in principle and still refuse to let us live that way.

Why decisions matter

Decision theory tried to give us a way through.

Von Neumann and Morgenstern (1944/1947) gave us expected utility theory: if your preferences satisfy a small set of axioms, there exists a utility function such that you should choose the option that maximizes expected utility. Rationality as calculation.

Herbert Simon looked at actual humans and said no. Bounded rationality. We do not optimize; we satisfice. We search until we find something good enough, then stop. Computational limits, attention limits, time limits. The organism is not an infinite intellect.

I think about decision density, the bits spent per perceived decision. I wrote about it years ago as a possible measure of the digital divide. Computation per capita is one thing. Computation per decision is another. What happens when the cost of “enough intelligence” for every decision drops near zero? Does the density explode?

When overthinking is overthinking

Analysis paralysis and a fear of error or of missing a superior option outweighs the value of acting in time. People stall, research forever, never decide. MRI work suggests overthinking can clog working memory and reduce creativity. The jam study (Iyengar & Lepper) is the classic: more options, less purchasing.

There is a point where additional intelligence applied to a decision produces diminishing or negative returns. Not because the model is wrong, but because the human is still the bottleneck that has to live with the output, integrate it, act on it, and then face the next decision.

The conundrum of finding optimums

Local versus global.

Gradient descent and most practical optimizers find local optima. They follow the slope downhill until the ground flattens. The global optimum may be on the other side of a ridge they will never climb. In high-dimensional, non-convex landscapes (most of life), you do not know if you are in a good valley or a mediocre one.

Superintelligence does not magically solve the problem of knowing whether a local improvement is worth the search cost for a global one. It just makes the local search extremely good and extremely fast. You can still be optimizing the wrong thing with exquisite precision.

Will it reduce or increase decision fatigue?

Maybe the only decision left becomes: always use the smartest model?

Decision fatigue (the popular name for the cumulative cost of choice) was framed by Baumeister and colleagues as a form of ego depletion; self-regulatory resources running down. Later replication work complicated the pure “muscle” model, but the phenomenology remains: after many choices, people default, avoid, or choose poorly.

If every micro-decision is escalated to the frontier model, two things can happen at once.

The cognitive load of evaluating the answer may rise. You still have to read it, trust it, decide whether to follow it, and then own the consequences. The meta-decision—“should I even ask?”—does not disappear; it just moves upstream.

At the same time, if the model is genuinely better at navigating the butterfly effects, the downstream regret and corrective decisions may fall. Net fatigue could decrease for some people and explode for others.

I do not know which.

I only know the sensation is already here. The quiet pressure to never use a weaker model for anything that matters, because everything might matter. Because the flap of a wing is real.

Laplace’s mandate is not that we must become the demon.
It is that the existence of something close to the demon makes ordinary human-scale decision-making feel newly negligent.

Maybe the wise move is sometimes to use less intelligence for most things.
Accept a local, good-enough, human-sized choice and let the butterflies do what they will.

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Notes

• Original spark: Jonathan Hendler’s post (21 Jul 2026) asking for a term for the compulsion to use superintelligence on even small decisions because of unpredictable consequences. Grok replied with “butterfly burden” or “Laplace’s mandate.”
• Laplace’s demon: primary source is the 1814 Essai philosophique sur les probabilités. Good modern treatments on Wikipedia and Information Philosopher.
• Butterfly effect: Lorenz 1963 paper “Deterministic Nonperiodic Flow”; 1972 AAAS talk title coined by Philip Merilees.
• Expected utility: von Neumann & Morgenstern, Theory of Games and Economic Behavior (1944, 2nd ed. 1947).
• Bounded rationality / satisficing: Herbert Simon, “A Behavioral Model of Rational Choice” (1955) and later work.
• Analysis paralysis: Iyengar & Lepper jam study (2000); broader psychological literature on overthinking and working memory.
• Local vs global optima: standard optimization theory; gradient-based methods are local by nature.
• Decision fatigue / ego depletion: Baumeister, Vohs et al. early 2000s work; later replication debates exist—treat the popular concept carefully and distinguish phenomenology from the original resource model.
• Decision density: term used on jhendler.com in earlier writing about computation per decision as a possible digital-divide metric; related concepts appear in recent AI systems literature around decision-energy density and process design.

Wonder what the term would be for being forced to use super intelligence for even the smallest decision, because every choice as unpredictable/difficult to predict consequences, like the butterfly effect.

cc @grok

— Jonathan Hendler 🪰 (@hendler) July 21, 2026