Compute Availability Factor (CAF): A Framework for Measuring the Physical Deliverability of Artificial Intelligence Compute: From nominal accelerator capacity to effective and infrastructure-available compute

Q: What is the Compute Availability Factor (CAF)?

The Compute Availability Factor (CAF) is an empirical measurement framework developed by Richie Adomako at Serpens Inc. (ResearchGate Publication ID 414150400) that quantifies the fraction of hardware-effective AI compute that can actually be delivered in the physical world under real electrical (E), thermal (T), network (N), and operational (O) infrastructure constraints: CAF(t) = E(t) × T(t) × N(t) × O(t).

Q: What is the relationship between the CAF website, CAF Watcher, and the CAF Research Paper?

CAF is the formal mathematical methodology and research specification ('Compute Availability Factor: A Framework for Measuring the Physical Deliverability of Artificial Intelligence Compute', Adomako, 2026) rooted in the thesis 'When the Building Becomes the Computer'. This site hosts both the formal specification and CAF Watcher—the operational observability platform, 3D Spatial Desktop OS, Cluster Physics Simulator, and Workload Constraint Investigator built on that methodology.

Q: How does CAF calculate Physically Deliverable Compute and the Deliverability Coverage Ratio (DCR)?

CAF first converts Nominal Compute (C_N, raw installed accelerators) into Hardware-Effective Compute (C_E = C_N × U_H). It then multiplies by CAF = E × T × N × O to derive Physically Deliverable Compute (C_D = C_N × U_H × CAF). The Deliverability Coverage Ratio (DCR = C_D / C_R) compares verified deliverable compute against roadmap required compute (C_R) using continuous interval bounds [Lower, Upper].

Q: How does CAF handle missing or unverified public infrastructure data?

CAF enforces the epistemic axiom that 'Unknown does not equal Unavailable'. Missing public evidence is never treated as zero capacity; instead, it is classified as UNKNOWN, widens the [Lower, Upper] availability interval, and reduces the separate Evidence Confidence score (Q).

Adomako, Richie

↳ CAF WATCHER // CONTINUOUS PHYSICAL AUDIT LEDGER

OpenAYA Grade UNSCORED

Browser-delivered AI workspace and orchestration platform (Work OS, memory, evaluation harness, and external model gateway) — CAF’s transparent first-party internal test case · Category: AI Applications & Orchestration

Executive Physical Feasibility & Delivery Audit

Institutional Baseline: Evaluated under the Compute Availability Factor (CAF v1.0) specification by Richie Adomako (Serpens Inc., ResearchGate ID 414150400). CAF evaluates stated accelerator capabilities against empirical physical reality (interconnection queues, electrical substations, liquid cooling heat transfer, and optical network fabrics). Missing public evidence is classified as unknown—it is never treated as zero capacity.

CAF INTERVAL
[0 – 0]
DELIVERY GAP SCORE
0 pts
OPERATING POWER
0 GW
vs 0 GW announced
EVIDENCE CONFIDENCE
INSUFFICIENT (22%)

Physical Coupling Pillars (E, T, N, O)

Power & Grid Substrate (E = [object Object]): 0/100. Consumes upstream model services via AI gateway; account-specific capacity allocation is not established by reviewed public evidence.
Thermal Dissipation & CDU Limits (T = [object Object]): 0/100. Physical thermal dissipation is operated by upstream model providers; OpenAYA exposes application-level status responses and public receipts.
Cluster Topology & Optical Fabric (N = [object Object]): 0/100. Public receipts report fallback serving through Gemini rather than the declared default route; Oct 8 status response also reports degraded routing.
Silicon & Operational Availability (O = [object Object]): 0/100. Consumes upstream model APIs (including Gemini fallback); does not operate proprietary silicon or owned datacenters.

Attributable Public Evidence Ledger

[TIER 3] OpenAYA Homepage & Overview ("Objective-First AI Operating System")
Continuity across sessions and machines; browser-delivered AI workspace (Public Beta Workspace Application Layer)
Observed 2026-10-18 · Publisher: OpenAYA (First-Party Public Disclosure) · Subsystem: Operational
[TIER 3] OpenAYA Capabilities Page (44 Apps) & Beta Disclosures Page (50+ Apps)
External model routes, AI gateway, Work OS, memory, evaluation harness, and app registry (44 apps (Capabilities) vs. 50+ apps (Beta); Available vs. Experimental maturity split Registry Inventory)
Observed 2026-10-18 · Publisher: OpenAYA (First-Party Public Disclosure) · Subsystem: Operational
[TIER 3] OpenAYA Public Receipts Page & Retrieved Status Response (Oct 8 Timestamp)
Gateway model routing, comparative A/B evaluation receipts, and authentication status (Gemini fallback serving; 0 scored tasks; insufficient A/B data; Oct 8 degraded routing Public Receipts)
Observed 2026-10-08 · Publisher: OpenAYA (First-Party Telemetry Disclosure) · Subsystem: Runtime Network