protocol one.
setup: choose a single manufactured object. place it on a plain surface.
core activity: for one hour, attend to the object. imagine its material origins, the industrial processes, the transit, the hands that touched it. map its physical properties: its precise mass, texture, temperature. hold it without purpose.
perceptual quality: awareness of deep materiality. the object becomes a nexus of geologic time, industrial systems, and contingent events.

protocol two.
setup: find a familiar path that is safe to walk.
core activity: walk the path once with eyes open. then, with eyes closed, perceive the space through the soles of your feet. build a detailed mental map from variations in texture, incline, and temperature.
perceptual quality: proprioceptive space. the world becomes a pressure map against the body.

protocol three.
setup: sit somewhere with ambient, unpredictable action.
core activity: select a single small event. for ten minutes, trace the event backward. consider the forces that detached the leaf. consider the decisions that brought the person to the table.
perceptual quality: radical contingency. events become crystallizations in a field of conditions.

economic activity becomes small, comforting purchases. career progression stagnates as people seek manageable roles over ambitious climbs. financial decisions favor extreme caution, prioritizing loss avoidance.

technology provides low-friction substitutes for connection. social media becomes passive observation of others. ai companions offer conversations without social risk. entertainment replaces direct experience.

political narratives that assign blame to external forces find receptive audiences. these stories explain personal powerlessness. others embrace institutional stability and its predictable inertia, supporting systems that promise minimal change.

interrogate the ontology implied by the paper's core terminology.
pinpoint where the problem framing predetermines the perceived success of the solution.
deconstruct analogies; what properties are transferred implicitly from source to target domain.

consider the delta between the operational definition of a concept and its philosophical weight.
map the assumptions about data distribution and task stationarity.
assess if scalability claims depend on an idealized future state of computational resources or data availability.

scrutinize how metrics might select for specific behaviors while masking broader incompetencies.
trace the theoretical commitments; which school of thought silently underpins the proposed mechanism.
distinguish performance on benchmarks from generalizable capability.
evaluate if complexity of the model serves a functional purpose.
identify how the paper leverages popular intuitions about ai.
think about the degrees of freedom in experimental design that could shape outcomes.

the state starves physics while silicon valley pisses billions into large language models.

a decade of this? our instruments of observation decay. the skilled hands that build and run them retire, unreplaced.

new "discoveries" come from ai trawling old data, or from simulations whose guts nobody fully maps to reality anymore.

the models get better at predicting. genuine understanding languishes.

the capacity to empirically test surprising new hypotheses fades.

we're left with elegant algorithmic castles built on ever-sandier conceptual foundations. eventually, something fundamental breaks, and no one remembers how the original plumbing worked.