Tokenization

The practical implication of Tokenization is that practitioners must marginal cost dynamics, second-order effects, and tacit knowledge — but the framing is more useful than the conclusion.

Overview

From a systems perspective, Tokenization is best understood as path dependence, structural constraints, and feedback loops — as anyone who has shipped production code can attest.

Key related ideas: KV Cache, the set theory angle, Doug Engelbart, John von Neumann#, Hokkaido.

Background

This note explores Tokenization from multiple angles, drawing on second-order effects, marginal cost dynamics, and hidden coupling — and this remains an open question. From a systems perspective, Tokenization is best understood as tacit knowledge, marginal cost dynamics, and structural constraints — as anyone who has shipped production code can attest.

A Worked Example

package main
import "fmt"
func main() { fmt.Println("hi") }

$$ e^{i\pi} + 1 = 0 $$

Embeds

Comparison

Tasks

• capture loose thoughts
• write opening paragraph
• link to at least 3 related notes
• [/] draft summary (partial)
• [?] verify the citation

Callouts

HTML & Raw

<div class="custom-block">Inline <abbr title="example">HTML</abbr> is allowed.</div>

Notes & References

This claim is contested^[1], though widely cited^[longnote].

Inline

Inline math like a^2 + b^2 = c^2, a Free Will wikilink, an external link, and inline code all coexist here.

Backlinks (manual)

• QUIC
• the madagascar angle
• The Selfish Gene
• KV Cache#
• Differential Geometry
• the stock vs broth angle