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C3.ai, Inc.: diluted earnings per share

Diluted earnings per share for C3.ai, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All C3.ai, Inc. financial histories

What this measure means

Reported earnings or loss per share under dilution rules. Antidilutive instruments may be excluded. A diluted value can equal the basic value without implying no potential dilution.

Exact concept: us-gaap:EarningsPerShareDiluted. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2021-05-01 to 2026-04-30. The SEC response was captured on 2026-09-20.

Reading these values

This selected numerical history matches Basic earnings per share for the same reporting intervals and original units. The accounting definitions remain distinct. Equal values do not establish that the concepts are interchangeable or explain why they match; filing dates and accessions may differ. Compare the definitions and source filings before combining them.

Context from the filing

For C3.ai’s fiscal years ended April 30, 2024, 2025 and 2026, the two-class calculation allocates results proportionately to Class A and Class B because their dividend and liquidation rights are identical, despite voting and conversion differences. The selected denominator combines those classes. Options, RSUs, ESPP shares and early-exercised options subject to repurchase are excluded from diluted loss per share as anti-dilutive. April fiscal dates, scale-three shares and scale-zero EPS are retained without calendar-year conversion. Read the source filing.

Selected filing history

Diluted earnings per share in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-05-012026-04-30-3.35USD/shares2026-06-2410-K · 0001577526-26-000078
2024-05-012025-04-30-2.24USD/shares2026-06-2410-K · 0001577526-26-000078
2023-05-012024-04-30-2.34USD/shares2026-06-2410-K · 0001577526-26-000078
2022-05-012023-04-30-2.45USD/shares2025-06-2310-K · 0001628280-25-032604
2021-05-012022-04-30-1.84USD/shares2024-06-1810-K · 0001628280-24-028786

Related financial histories

Inspect the source

Entity
C3.ai, Inc. / CIK 0001577526
Captured
2026-09-20T09:22:06.790Z
SEC response SHA-256
c456ee49e0d66c093b29590a202b941c6d917b918515dc104dd419254b454871

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001577526.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))