AIASC 260: AI System Earnings Per Share (EPS)

Purpose and Scope:

This document provides guidelines for calculating and presenting the earnings per share (EPS) specifically attributable to AI operations. The aim is to give stakeholders a clear measure of profitability from AI-driven activities on a per-share basis.

1. Principle of AI Revenue Recognition:

• Only revenues directly attributable to AI operations should be considered when calculating AI-specific EPS.

2. Principle of Direct and Indirect AI Costs:

• Both direct costs, like AI research and development, and indirect costs, such as overhead allocation to AI projects, should be deducted from AI revenues.

3. Principle of AI Financing Costs:

• Interest costs or dividends related to financing specifically raised for AI projects should be considered in the calculation.

4. Principle of Weighted Average Shares:

• The denominator for the EPS calculation should be the weighted average number of shares outstanding during the period.

5. Principle of Dilutive AI Instruments:

• Any financial instruments, like convertible bonds or stock options, related to AI operations that have a dilutive effect should be considered when calculating diluted AI EPS.

6. Principle of Extraordinary AI Items:

• Extraordinary items, such as gains or losses from the sale of an AI subsidiary, should be clearly identified and their impact on AI EPS separately disclosed.

7. Principle of Comparative Reporting:

• AI EPS should be presented alongside traditional EPS measures to provide stakeholders with a comparative view.

8. Principle of Disclosures:

• The methodologies, assumptions, and any special considerations used in the AI EPS calculation should be transparently disclosed.

Updates and Amendments:The AIASC 260 guidelines will be periodically reviewed and updated to consider advancements in AI technology, financial reporting practices related to AI, and feedback from stakeholders and the public.

Note: This is a fictional representation and does not represent any real-world standard for AI. The development of such standards would involve extensive consultations with experts, stakeholders, and the public. Fictional representations simplify complex AI concepts, stimulate discussion, envision future scenarios, highlight ethical considerations, encourage creativity, bridge knowledge gaps, and set benchmarks for debate in fields like accounting.