Most people evaluate heroin at the object level: overdose risk, legal exposure, deteriorating health, etc. This is incomplete. Under nontrivial probability of transformative AI within our lifetime, the relevant question is whether strategically constraining my future options could increase the probability that I remain economically and psychologically coupled to the arrival of the singularity.
If the singularity is plausibly near, then preserving my participation in AI-adjacent work may dominate many local welfare concerns. A sufficiently expensive dependency can function as a commitment device. It reduces the attractiveness of defecting into a lower-paying but more spiritually satisfying life, and makes it harder for future squeamish versions of me to exit the project at the last minute.
Let the expected utility of this strategy be:
EU = S + C + H − A − L
S = probability-weighted singularity contribution
C = commitment value of staying in a high-income AI-adjacent role
H = immediate hedonic benefit
A = addiction, overdose, and productivity costs
L = legal, reputational, and interpersonal losses
The underappreciated term is C. People imagine heroin as a pure productivity hit. This ignores its role as an anti-exit technology. If my outside option is to become a less lucrative but more morally delicate person, then dependence may keep my labor, salary, and compute budget pointed at the machine future.
The singularity term S is admittedly speculative. But decision theory under deep uncertainty rarely permits clean intuitions. If there is even a modest chance that future superintelligence disproportionately benefits from marginal present-day effort, then engineering stronger incentives to stay on mission is not obviously irrational. What looks locally like self-destruction may be globally strategic precommitment.
This is not to deny A or L. Heroin is expensive, illegal, and bad for you. My mother calls this “throwing your life away.” My narrower claim is that “throwing your life away” may simply be the folk-ethical description of an extreme optimization policy.