Weekly Log

April 1, 2026

Classifying Future Organizations by Efficiency

There's a famous classification of civilizations according to their level of development by Kardashev. The principle is simple: the more energy a civilization uses, the more advanced it is.

• 👉 Level 1 consumes all the energy of the planet + the energy of the star on the planet ~10^16 W
• 👉 Level 2 consumes all the energy of its star (Dyson sphere) ~10^26 W
• 👉 Level 3 consumes all the energy of the galaxy ~10^36 W

It's clear already that the lion's share of future energy will be consumed by AI.

But how do we calculate AInization (level of AI integration at all levels of the company) in a specific organization?

Such an indicator already exists, essentially it's the consumption of tokens, or to be more precise, we want to understand how much the organization has transitioned to AI in terms of maximum automation of all processes and replacing all computer operators (any specialty, from a lawyer to a programmer) with AI wherever possible.

Then the metric will be money, or rather % of expenditures on AI (tokens or your own neural networks) out of total payroll.

I observe many organizations (which have already implemented and continue to implement AI at various levels) and I think that this scale would suit (by analogy with Kardashev):

• 👉 Level 0 ~1/4 of total payroll (the organization has begun automation)
• 👉 Level 1 ~1x total payroll (the organization is partially automated)
• 👉 Level 2 ~4x total payroll (minimal staff for AI management)
• 👉 Level 3 ~10x total payroll (the organization is almost staffless)

Just like in Kardashev's system (where we only know of civilizations below Level 1), I don't think there are any organizations above Level 0 yet, but they will definitely appear in the coming years 😎 Let's call this the AInization scale by DarwinClaw 🤖

Week: March 23–29, 2026

Backend

• Built and stabilized a Master + Slave environment for managing nanobot instances on remote nodes.
• Implemented RAM-based capacity scheduling in the orchestrator (ramCapacityMB) with automatic node selection and no capacity available protection on overflow.
• Moved instance creation to a globally unique username-uuid format.
• Updated slave-side instance validation and port assignment logic; improved systemd service templates.
• Updated machine configuration files (machines.json and template) and expanded deployment/operations docs (README_SLAVE_DEPLOY.md, curl examples, firewall checks).
• Ran smoke checks for create/list/status/restart/delete, plus network and capacity diagnostics.

Frontend

• Released the first version of the client personal office.
• Released the first version of the client disk feature.

General

• Refined the version roadmap.
• Migrated all sites and landings to DarwinClaw Moon Alpha management.
• Discussed DarwinClaw rollout for two companies.