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iLearningEngines (iLearningEngines) EBIT per Share : $0.06 (TTM As of Jun. 2023)


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What is iLearningEngines EBIT per Share?

iLearningEngines's EBIT per Share for the six months ended in Jun. 2023 was $0.02. Its EBIT per Share for the trailing twelve months (TTM) ended in Jun. 2023 was $0.06.

During the past 12 months, the average EBIT per Share Growth Rate of iLearningEngines was 116.70% per year. Please click Growth Rate Calculation Example (GuruFocus) to see how GuruFocus calculates Wal-Mart Stores Inc (WMT)'s revenue growth rate. You can apply the same method to get the EBIT per Share growth rate using EBIT per Share data.

The historical rank and industry rank for iLearningEngines's EBIT per Share or its related term are showing as below:

AILE's 3-Year EBIT Growth Rate is not ranked *
in the Software industry.
Industry Median: 9.5
* Ranked among companies with meaningful 3-Year EBIT Growth Rate only.

iLearningEngines's EBIT for the six months ended in Jun. 2023 was $2.4 Mil.

iLearningEngines EBIT per Share Historical Data

The historical data trend for iLearningEngines's EBIT per Share can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

* Premium members only.

iLearningEngines EBIT per Share Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
EBIT per Share
0.06 0.06 0.09

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
EBIT per Share - - 0.04 0.05 0.02

iLearningEngines EBIT per Share Calculation

EBIT per Share is the amount of Earnings Before Interest and Taxes (EBIT) per outstanding share of the company's stock.

Earnings Before Interest and Taxes (EBIT) is what the company earns before it expenses interest and taxes.

iLearningEngines's EBIT per Share for the fiscal year that ended in Dec. 2022 is calculated as

EBIT per Share(A: Dec. 2022 )
=EBIT/Shares Outstanding (Diluted Average)
=12.105/134.970
=0.09

iLearningEngines's EBIT per Share for the quarter that ended in Jun. 2023 is calculated as

EBIT per Share(Q: Jun. 2023 )
=EBIT/Shares Outstanding (Diluted Average)
=2.354/134.970
=0.02

EBIT per Share for the trailing twelve months (TTM) ended in Jun. 2023 adds up the semi-annually data reported by the company within the most recent 12 months, which was $0.06

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

iLearningEngines  (NAS:AILE) EBIT per Share Explanation

EBIT is a company's earnings before interest and tax expenses are deducted. It measures a company's profit generates from operating, ignoring tax burden and capital structure. As the tax expense are not deducted, EBIT is helpful when comparing companies in the same industry but with different tax situations. Also, the interest expense are included in EBIT, making it useful to compare companies that have high interest expenses due to large amount of debt.

iLearningEngines EBIT per Share Related Terms

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iLearningEngines (iLearningEngines) Business Description

Industry
GURUFOCUS.COM » STOCK LIST » Technology » Software » iLearningEngines Inc (NAS:AILE) » Definitions » EBIT per Share
Comparable Companies
Traded in Other Exchanges
N/A
Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
Website
https://www.ilearningengines.com
iLearningEngines Inc is an AI and automation platform that empowers its customers to productize their institutional knowledge by transforming it into actionable intellectual property that enhances outcomes for employees, customers and other stakeholders. Its platform enables enterprises to build intelligent Knowledge Clouds that incorporate large volumes of structured and unstructured information across disparate internal and external systems and to automate organizational processes that leverage these Knowledge Clouds to improve performance. The company combines its offerings with vertically focused capabilities and data models to operationalize AI and automation to effectively and efficiently address critical challenges facing its customers.