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iLearningEngines (iLearningEngines) Capex-to-Revenue : 0.00 (As of Jun. 2023)


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What is iLearningEngines Capex-to-Revenue?

Capex-to-Revenue measures a company's investments in physical assets such as property, industrial buildings or equipment to its revenue.

iLearningEngines's Capital Expenditure for the six months ended in Jun. 2023 was $-0.01 Mil. Its Revenue for the six months ended in Jun. 2023 was $195.23 Mil.

Hence, iLearningEngines's Capex-to-Revenue for the six months ended in Jun. 2023 was 0.00.

iLearningEngines Capex-to-Revenue Historical Data

The historical data trend for iLearningEngines's Capex-to-Revenue 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.

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iLearningEngines Capex-to-Revenue Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Capex-to-Revenue
- - -

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Capex-to-Revenue - - - - -

Competitive Comparison of iLearningEngines's Capex-to-Revenue

For the Software - Infrastructure subindustry, iLearningEngines's Capex-to-Revenue, along with its competitors' market caps and Capex-to-Revenue data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.

iLearningEngines's Capex-to-Revenue Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Capex-to-Revenue distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Capex-to-Revenue falls into.


iLearningEngines Capex-to-Revenue Calculation

iLearningEngines's Capex-to-Revenue for the fiscal year that ended in Dec. 2022 is calculated as

Capex-to-Revenue=- Capital Expenditure / Revenue
=- (0) / 309.17
=0.00

iLearningEngines's Capex-to-Revenue for the quarter that ended in Jun. 2023 is calculated as

Capex-to-Revenue=- Capital Expenditure / Revenue
=- (-0.007) / 195.225
=0.00

* 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) Capex-to-Revenue Explanation

Capex-to-Revenue measures a company's investments in physical assets such as property, industrial buildings or equipment to its revenue. The ratio shows how aggressively the company reinvests its revenue back into productive assets. However, a high ratio potentially indicates that the company has invested too much in innovation and infrastructure, taking up funds that could be used to boost productivity and increase revenue. Therefore, a high Capex to Revenue Ratio could be a positive or a negative sign depending on how effectively a company converts those investments into future earnings.

iLearningEngines Capex-to-Revenue Related Terms

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iLearningEngines (iLearningEngines) Business Description

Industry
GURUFOCUS.COM » STOCK LIST » Technology » Software » iLearningEngines Inc (NAS:AILE) » Definitions » Capex-to-Revenue
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Traded in Other Exchanges
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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.