Execution

Entrepreneurial decision-making differs not only in ambition or speed, but in the structure of uncertainty it must absorb. A useful distinction can be made between chess-like, go-like, and poker-like decision grammars—not as cultural metaphors, but as operational models. Chess operates under conditions of near-complete information. All pieces are visible, roles are fixed, and objectives are explicit. Although tactical complexity can be high, uncertainty is localized and feedback is relatively immediate. ...

In environments where actions carry immediate physical consequences, decision-making operates under conditions that differ fundamentally from abstract reasoning. There is no separation between intention and execution. A decision is enacted the moment it is formed, and feedback arrives without mediation or delay. However, not all physical decision environments share the same structure. Different combat systems cultivate distinct modes of decision-making. For example, striking-based systems such as Muay Thai emphasize range control, accumulated damage, and durability under sustained pressure. Decision-making unfolds through continuous exchange, where timing errors compound gradually and resilience becomes a strategic variable. ...

The concept of the Minimum Viable Product (MVP) is often treated as a universal principle of startup execution. Its actual effectiveness, however, depends on the structure of the decision environment in which it is applied. MVP originates from chess-like environments. These are contexts where objectives are explicit, feedback is rapid, and actions are largely reversible. Under such conditions, a minimal artifact can reliably test a hypothesis, and failure produces interpretable information. ...

Status note This text is written as a vision note. At the time of writing, I am still based in Taiwan. I may eventually live in Europe, or elsewhere. The geography is provisional. What is not provisional is the trajectory described here: AI gradually becoming a participant in everyday coordination, work, and responsibility. When AI Became a Participant I imagine living in an old house by the southwestern coast of Portugal. ...

My engagement with dance did not precede my work in computer science. It came after years of thinking in terms of formal systems, execution models, and computational control. This ordering matters. Social dance — particularly partner dance with explicit leading and following — exposed the limits of output-driven, predictive coordination in a way that abstract agent models could not. In social dance, stable coordination does not emerge from faster inference or earlier decision-making. It emerges from maintaining a shared temporal envelope — a co-semantic session — where neither party collapses the interaction into unilateral control. ...