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AI billing tools can undercount usage when they treat a missing usage field as zero, but the available account of the audit does not establish that this was its single most common bug. Roy Tong’s September 22, 2026 report is described as auditing 110 open-source AI usage-metering tools and finding several recurring failure types. A ledger can guard against the missing-usage failure by preserving absent data as unknown—not by silently recording zero—but the ledger’s actual implementation must be inspected to prove that it does so.
What the audit reportedly found
A search-result excerpt describing Roy Tong’s September 22, 2026 report says the audit covered 110 open-source AI usage-metering tools, identified 45 or more verified bugs across five recurring families, and saw 23 fixes merged upstream. These are the report’s attributed figures; the underlying inventory, test fixtures, and reproductions were not independently checked here. Read the audit report.
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The excerpt names these failure types:
- Model prices that are stale or missing from a pricing table.
- Cache multipliers applied across the wrong providers.
- Retries counted twice during streaming aggregation.
- Missing usage fields converted to zero.
- Quota-window boundary errors.
The distinction matters: these are separate ways a meter can misstate usage or cost, and fixing one does not fix the others.
Is missing usage the audit’s “#1 bug”?
That ranking is not established by the available evidence. The report excerpt calls stale pricing tables the “most common real bug,” while a separate excerpt associated with the exact-title page points to absent usage being treated as zero. Neither excerpt conclusively establishes that missing usage was the audit’s ranked number-one finding. See the related page excerpt.
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It is safer to describe absent-as-zero as a serious failure mode, not as a proven top-ranked bug. The report’s numbers and bug descriptions should remain attributed to Roy Tong’s report unless readers can inspect the complete inventory and reproduce its tests.
Why absent usage must not become zero
Zero is a value: it says the metered quantity was measured and found to be none. An absent usage field says the event does not provide that evidence. Converting absence to zero can make a bill appear smaller than it is, conceal an incomplete record, or create a total that cannot be verified.
A safer ledger keeps the raw event and represents usage as unknown when the field is missing. It should then prevent a total from being presented as complete unless the missing value has been resolved or the total is explicitly marked incomplete. This is a data-integrity rule, not proof that any particular ledger already enforces it.
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How to make a ledger auditable
Keep attempts separate from logical operations
A logical operation is the user-level request or task; a physical attempt is an individual request sent to a provider. Retries can create several attempts for one operation. Store both identifiers and keep the raw attempt records, then apply the relevant vendor’s documented billing policy when calculating what is billable. Do not assume that every retry is free or that every retry should be counted as a separate user operation.
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Preserve evidence and uncertainty
For each event, retain the provider, model, timestamp, raw usage fields, pricing version used, and links or identifiers needed to trace the event back to its source. If a usage field is absent, record it as absent or unknown rather than coercing it to zero. Make any resulting total visibly provisional until the missing evidence is reconciled.
Test the failure cases explicitly
Use fixtures that include missing usage, repeated attempts, provider-specific cache rules, a changed model price, and events at quota-window boundaries. For each fixture, assert both the expected result and whether the result is provable from the available evidence. A status such as PASS, FAIL, or UNPROVABLE is more informative than returning a numeric total for every input.
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The AgentMeasure repository describes a conformance approach that distinguishes attempts from logical operations and reports PASS, FAIL, or UNPROVABLE; it says missing evidence is disclosed rather than zeroed. That is a useful design pattern, but the repository’s own description is not independent verification of the title’s ledger or of every tool in the audit. Review the AgentMeasure repository.
What to check when an AI usage bill looks wrong
- Find the raw events. Export the provider or application records behind the total and identify missing usage fields rather than treating them as zero.
- Trace retries. Match physical attempts to their logical operation and check the provider’s billing rule for retries.
- Verify pricing inputs. Confirm the model and applicable price version for each event; check provider-specific cache accounting instead of applying one provider’s multiplier to another.
- Check time windows. Recalculate events near quota-window boundaries using the provider’s stated window rules.
- Reconcile and document exceptions. Separate confirmed cost from unknown or disputed amounts, preserve the evidence, and follow the vendor’s published correction or dispute procedure.
What the evidence does—and does not—support
The reported audit counts and bug families offer a useful warning that metering failures can arise in several parts of the pipeline. They do not, by themselves, prove which bug is ranked first, establish that all 110 tools were tested under the same conditions, or verify that a particular ledger prevents undercounting. The AgentMeasure repository is a first-party description of its own local-first and conformance approach; it is not an independent comparison of alternatives. Read the related counterview excerpt.
The practical standard is reproducibility: a reader should be able to trace a ledger total to source events, see how missing values and retries were handled, identify the pricing rules applied, and distinguish proven totals from unprovable ones.
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