Kovendo

Artificial intelligence is changing how businesses invest in technology. Companies are purchasing GPU servers, developing AI applications, subscribing to cloud platforms, using commercial AI models, and building proprietary machine learning systems. However, these investments create an important accounting question: Should AI technology costs be expensed immediately, depreciated over time, or amortized as intangible assets? The answer depends on what the company acquires, whether it controls an identifiable asset, how the technology is used, and which accounting standards apply. For example, purchasing a physical GPU server may create a tangible asset that is depreciated over its useful life. Paying for cloud-based AI inference is generally a service expense as the service is consumed. Meanwhile, qualifying costs of developing internal-use software may be capitalized when the applicable accounting requirements are satisfied. This guide explains depreciation and amortizatio...

Modern applications increasingly need to process information quickly, protect sensitive data, and operate reliably even when internet connectivity is limited. Traditional cloud computing remains important, but sending every piece of data to a distant data center can introduce delays and unnecessary network traffic. This is where fog computing vs edge computing becomes an important technology comparison. Both approaches bring computing resources closer to the devices generating data. However, they differ in how processing resources are organized, where intermediate analytics occur, and how multiple devices share computing capabilities. According to IoT Analytics' October 2025 report , the number of connected IoT devices was forecast to reach 21.1 billion by the end of 2025, with a projection of 39 billion by 2030. These are forecasts rather than confirmed final counts, but they illustrate the scale of the connectivity challenge. Edge computing processes data directly ...

Artificial intelligence is changing how companies assign tasks, evaluate performance, develop software, and manage employees. AI assistants can write code, analyze customer behavior, summarize meetings, and recommend decisions. Yet an important question remains: should managers use AI to control employees more closely, or empower them to work more independently? This question connects directly to Douglas McGregor's Theory X and Theory Y, two influential approaches to understanding employee motivation. In an AI-driven workplace, these theories offer a useful way to examine algorithmic monitoring, workplace autonomy, team innovation, and the relationship between human judgment and automated systems. Quick answer: Theory X assumes employees generally need close supervision, external control, and clear consequences to perform effectively. Theory Y assumes employees can be self-motivated, responsible, and creative when their working environment supports autonomy and meaningful g...