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All 31 files from ideas/passepartout-economics/ promoted to ideas/ root. - Subfolder's passepartout-economics.org (42-line index) renamed to triad-index.org to avoid collision with root-level full doc - index.org removed (redundant — triad-index.org replaces it) - Root-level passepartout-economics.org: stripped file:passepartout-economics/ prefix from all cross-references (now simple file:foo.org links) - compliance-framework-mapping.org: same prefix cleanup - All internal file: links within the economics docs already used simple names (no prefix) — they resolve correctly from ideas/ root
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Why Lisp Is Economically Viable Now
The 1980s trade-off was: C is cheap enough for the market. Correctness is a luxury the market cannot afford. The 2020s trade-off is: C is expensive for the market. Incorrectness has become the dominant cost of software. Lisp's verification infrastructure is now the cheaper option.
Four transformations flipped the economics:
- Memory is free. 40MB runtime is noise on a $20 Raspberry Pi with 8GB RAM. In 1980, DRAM was ~$5,000/MB.
- Transistors are free. Modern ARM Cortex-A72 has billions of transistors. GC and type dispatch cost nothing because the transistors are there whether used or not.
- Complexity saturates human verification. Systems are tens of millions of lines. Testing is necessary but insufficient — zero-day vulnerabilities prove bugs survive all testing. Formal verification is the only known path.
- Cost of failure exceeds cost of verification. A single breach costs millions. Regulation mandates provable compliance. Proving correctness is cheaper than not proving it.
The verification appliance (AGPL symbolic engine + RISC-V Lisp μcode on FPGA) costs $5,000/year and replaces $500,000/year in compliance audits, breach litigation, and regulatory fines. This cost structure — zero marginal cost per additional user — is what makes Lisp economically viable at scale. The self-driving Lisp Machine is the hardware endpoint of this economic logic. For the biological analogy that explains why Lisp architecture is a natural outcome of complexity pressure, see biology parallels. For the historical precedent, see the comparison with Symbolics Genera. The impact on the AI industry is the market-side consequence.