Thought leadership
Capital allocation and operational governance for AI are converging. Both require the same evidence base: what a workflow costs, what it's authorized to do, and what it actually did.
FASB ASU 2025-06 opens a principles-based path to capitalize AI development spend — but only for companies that can produce a traceable nexus between a dollar and a durable asset. Most can't, because provider billing aggregates at the credential boundary, not the cost center finance needs.
Putting a model in the request path buys latency, non-determinism, and per-token cost on every turn. Compiling policy into typed, versioned artifacts once — offline — and executing them deterministically is faster, cheaper, and auditable by construction.
Why the problems are connected
A prompt-based workflow can't produce a stable audit trail because the same case can produce different decisions on different runs. Without a stable trail, finance can't build the cost attribution auditors require. Without that attribution, capital allocation for AI stays a guess. Compiling the workflow — Runtime's approach — is what makes TokenOps's ledger trustworthy in the first place.
Existence proof
An internal benchmark ran the same classification task — urgency, sentiment, and category over real support-style text — across seven approaches. A classical SVM tier won on accuracy and ran roughly 4,700x faster than a zero-shot large model call (86.7% accuracy at 2ms, versus 78.7% at 9.4 seconds). That result is the empirical case for the whole design: compile with a large model, run with something cheap and deterministic.
TL;DR
Capital control and operational governance require the same evidence. TokenOps produces the ledger; Runtime produces the deterministic execution that makes the ledger trustworthy. Start with one workflow, model it once, and let both systems read from it.