:PROPERTIES:
:CREATED:  [2026-05-24 Sun]
:ID:       9af13fff-9725-542b-93b1-a555bc74ad72
:ID:       0b5a8a74-cfd6-542d-bc88-4eb3cd8626f9
:END:
#+title: Lisp Economics
#+filetags: :passepartout:economics:lisp:history:C:viability:cost:marginal:zero:

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:

1. **Memory is free.** 40MB runtime is noise on a $20 Raspberry Pi with 8GB RAM. In 1980, DRAM was ~$5,000/MB.
2. **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.
3. **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.
4. **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 [[id:84a537b4-4256-50c8-91f5-dd5b4538418f][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 [[id:13e6ae54-2d24-5aa0-b1cd-a7e8e749aa70][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 [[id:2afd9a3c-e96a-54c7-ac77-a05a28065b4b][biology parallels]]. For the historical precedent, see the [[id:00ab3a4d-e3de-5605-a67d-12935bb36ab5][comparison with Symbolics Genera]]. The [[id:5f55bbe6-d243-5766-8ccf-5c5cc88a6542][impact on the AI industry]] is the market-side consequence.

* Cost Structure — Zero Marginal Cost

  - **One-time cost:** [[id:45ea493b-94ad-5885-aa65-0c846e5c3c1d][gate-rule encoding]] for a domain (from hours for codified domains up to months for tacit domains)
  - **Near-zero marginal cost:** ACL2 proof + Screamer consistency check + VivaceGraph lookup per interaction — all CPU-native, all in-image
  - **No recurring LLM API costs** for the 80% symbolic reasoning layer
  - **After [[id:efc76898-03f7-57ba-923d-35d65da88bb7][sufficiency flip]]:** pennies per day vs dollars per day for LLM-only

  The cost curve inverts: generation is expensive, verification is cheap. This is the inversion [[id:28c46769-c14b-42aa-ac7a-69d310157f8f][Passepartout]] exploits.

  Token demand shifts from "every interaction burns tokens" to "only unfamiliar interactions burn tokens." Steady-state per-user LLM consumption drops by an order of magnitude.
