Bibliographic record
Abstract
Once central to countercultural spirituality and mystical exploration, psychedelics have been rebranded in Silicon Valley as tools for optimization, productivity, and cognitive control. This study examines how tech elites and aspirants are reconfiguring psychedelic practice under the influence of TESCREAL—an ideological bundle of Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism. Framed by Timnit Gebru and Émile Torres as a secular religion of technological salvation, TESCREAL advances a worldview that sacralizes rationalization, control, and the conquest of natural limits. Within this paradigm, psychedelics shift from sacraments of surrender to instruments of technocratic self-mastery. Methodologically, this study draws on discourse analysis of media coverage, cultural texts, and case studies including Bryan Johnson’s Project Blueprint, Michael Pollan’s reframing of psychedelics as medicine, Elon Musk’s advocacy of psychedelics as tools for leadership and empathy, and ventures such as Mindstate Design Labs, which aim to engineer “programmable mystical states.” These examples reveal how Silicon Valley translates ineffable experiences into measurable, replicable, and marketable states. This analysis introduces the term post-mysticality to describe this development. Post-mysticality refers to the process by which mystical experiences are standardized into predictable protocols for capitalist utility, aligning with neoliberal imperatives of efficiency, longevity, and cognitive enhancement. Psychedelics thus become folded into a larger TESCREAL project of technological transcendence, where the unknown is treated as a problem to be solved rather than a mystery to be encountered. This study contributes to the fields of religious studies, science and technology studies, and consciousness research by showing how Silicon Valley’s evolving psychedelic culture reflects a broader epistemic and metaphysical transition of normalizing optimization as a cultural value and extending biopolitical governance into the realm of inner experience. The findings raise critical questions about whether psychedelics will foster expanded consciousness and social imagination, or instead become another frontier colonized by code, capital, and control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".