6e992cc0c5
- Rename 'three-pronged' folder to 'knowledge-layers' — prong metaphor
was misleading (implied parallel tines), replaced with epistemic layers
(deductive base, empirical middle, probabilistic oracle — vertical stack)
- Collapse 11 overlapping files into 3 coherent documents:
- knowledge-layers/_index.org: core framework (two engines + one store,
World Model formula, 0-14 layer table, provenance store design,
conflict resolution, cold-start, stage mapping)
- knowledge-layers/practical-implications.org: design-world-aware-of-
physics, 10 powers, Schafmeister existence proof, epistemic transparency
- knowledge-layers/neurological-empirical.org: neural networks in
provenance framework (kept intact)
- Relocate wolfram/mathematica and Schafmeister docs to ideas/viability/
- Integrate into main architecture _index.org:
- Gate: expanded from two vectors (ACL2+LLM) to three (deductive,
provenance/empirical, LLM oracle)
- Autodidactic loop: split into Track 1 (deductive hardening, fast)
and Track 2 (empirical validation, slow, experimental-feedback-driven)
- See also: added Knowledge Layers cross-reference
- Add all-lisp geometry engine note (ideas/lisp-geometry-engine.org) as
concrete illustration of the empirical layer's effect on design work
- Rebuild site: 148 files, 0 errors
6.4 KiB
Pass as a Service
- Assumptions
- Unit cost per idle instance
- Packing density
- Infrastructure cost breakdown (100K users)
- Pricing and margin
- Scaling inflection points
- Cloud vs colo
- Architecture
- Why this works
- Addressable market
- Price ladder
Assumptions
- Stage 2 instance: one SBCL Lisp process with Gate + PDS + environment in one address space
- User brings own LLM API key — no AI token cost to the provider
- Containerized: Docker image running on cloud VMs (AWS spot instances)
- Instances are embarrassingly parallel — no cross-instance coordination
Unit cost per idle instance
At rest: ~500 MB-1 GB RAM. Active peaks at 2-3 GB. CPU at rest negligible.
Packing density
AWS r6a instances (AMD, good price-to-RAM):
- r6a.4xlarge (128 GB RAM): ~$0.23/hr spot, ~80 instances per VM
- r6a.8xlarge (256 GB RAM): ~$0.45/hr spot, ~160 instances per VM
Cost per user per month at ~80 instances per 128 GB VM: ~$1.50 for compute.
Infrastructure cost breakdown (100K users)
| Component | Detail | $/user/month |
|---|---|---|
| Compute | r6a spot, 80 instances/VM | ~$1.50 |
| Storage | 10 GB EBS gp3 per user | ~$0.80 |
| Egress | Light protocol usage | ~$0.50 |
| Relay | K8s, stateless web service | ~$0.50 |
| Infra subtotal | ~$3.30 |
| Overhead | ||
|---|---|---|
| Engineering | 4-5 people | ~$0.80-1.60 |
| Support | 2-3 people | ~$0.40-0.80 |
| Overhead subtotal | ~$1.20-2.40 |
| Total | ~$4.50-5.70 |
Pricing and margin
At $10/user/month:
- Cost: ~$4.50-5.70/user/month
- Margin: 43-55%
Scaling inflection points
| Users | Provider cost/user/mo | Margin at $10/mo |
|---|---|---|
| 1K | $25-40+ | Negative |
| 10K | $8-12 | ~0-20% |
| 50K | $5-7 | ~30-50% |
| 100K | $4.50-5.70 | ~43-55% |
| 1M | $2-4 | ~60-80% |
10K-20K users is the crossover to positive unit economics. Below that, the team overhead dominates.
Cloud vs colo
At small scale (under 10K users): AWS wins. No hardware risk, no colo contract, elastic.
At large scale (100K+ users): Colo is 2-5x cheaper per instance. AWS premium comes from degraded packing density (hypervisor overhead, can't overcommit memory).
Crossover at roughly 50K-100K users where dedicated ops justify colo.
Architecture
Relay on Kubernetes (stateless web service, standard pattern). Instances are Docker containers on raw VMs — one container = one SBCL Lisp process + volume mount for PDS. No orchestration magic needed for the instance layer.
The hardest operational problems: port mapping at scale (reverse proxy in front of VM pools) and PDS data persistence on VM failure (EBS snapshots or NFS-backed volumes).
Why this works
Three things make the unit economics viable early:
- Zero AI token cost (user brings own API key)
- The Gate runs even without an LLM — caches common decisions, declines to reason when no key is configured. Not a degraded product, just a non-AI mode.
- Docker-on-large-VM packing recovers bare-metal packing density on cloud, avoiding per-instance overhead.
Addressable market
AI chat vs AI agents — orders of magnitude gap:
| Category | Users (Jun 2026) | Notes |
|---|---|---|
| ChatGPT (chatbot) | 900M weekly active | Mostly text generation |
| AI agent users (all tools) | 5-10M | Actions, tools, environment control |
| Ratio | ~100:1 | Not 1000:1 as of mid-2026 |
Agent users are 1% of chatbot users today. If agent adoption follows the same growth curve as chatbots but lags by 18-24 months:
| Year | Est. agent users | 0.1% capture = users | MRR at $10/mo | Annual rev at 50% margin |
|---|---|---|---|---|
| 2026 | 5-10M | 5K-10K | $50K-100K | $300K-600K |
| 2027 | 50-100M | 50K-100K | $500K-1M | $3M-6M |
| 2028 | 300-500M | 300K-500K | $3M-5M | $18M-30M |
This is conservative — 0.1% capture of the agent market, $10/month (no AI tokens included).
Passepartout is not just an AI agent. It's a social protocol, verified computing environment, and knowledge system. It competes on more than agent UX. Even a fraction of the growing agent market funds the infrastructure.
Price ladder
The most important constraint: the price users will bear must cover real infrastructure cost at whatever scale you're at. Two tiers solve for both growth and unit economics.
Self-hosted tier (growth engine):
- User downloads the image, runs on own hardware or $5-10/mo VPS
- Brings own API key for LLM access
- Provider cost: ~$0.50-1/user/month (relay + routing)
- Zero per-user compute or storage cost to provider
- Negative margin but negligible — scales to millions freely
- This is the wedge: proves the protocol, builds the network, costs nothing to operate per user
Hosted tier (revenue engine):
- Provider-managed container, user brings API key
-
Packing density drives cost:
- At small scale (<5K hosted users): cost = $20-25/user/month
- At mature scale (50K+): cost = $5-7/user/month
| Phase | Scale | Hosted cost/mo | Charged price | Margin |
|---|---|---|---|---|
| Bootstrap | <5K | $20-25 | $25-30 | 10-20% |
| Break-even | 5-20K | $10-15 | $20-25 | 40-50% |
| Mature | 50K+ | $5-7 | $15-20 | 60-70% |
| Commodity | 500K+ | $2-4 | $10-15 | 75-85% |
Pricing strategy:
- Never price below cost — ramp pricing down as infrastructure efficiency improves
- First 5K hosted users are enthusiasts and early adopters who pay a premium ($25-30)
- When costs drop below $10/user/month, you have room to price at $10-15 and open a much wider funnel
- Self-hosted grows the network regardless of whether the hosted tier succeeds
What the price buys:
- Persistent Passepartout environment (shell, editor, browser, agent in one image)
- Social protocol identity (DID, PDS, encrypted messaging)
- The Gate verifying every action
- Org data you own, in a format you own
- AI tokens are NOT included — user brings own API key
Buyer profile:
- Already spends $10-200/month on LLM API keys
- Values a verified, persistent environment over ephemeral chatbot sessions
- Wants to own data and identity
- Can't or won't self-host
- Developer, researcher, or knowledge worker
The non-obvious constraint: The addressable market at $25-30/month is narrower (0.1-0.5% of agent users) than at $10-15/month (1-5%). The hockey stick in user growth depends on infrastructure costs dropping far enough to price at consumer-friendly levels without burning capital.