In 2024, the workforce at Intel decreased to ******* employees compared to ******* in 2022. The company generated just over ** billion U.S. dollars in revenue in 2024, with Intel’s net income amounting to a loss of over ** billion U.S. dollars during the same year.

In 2024, Intel generated **** billion U.S. dollars in revenue, with **** billion U.S. dollars generated by the company's Client Computing Group, its core business unit serving the PC market. Revenues associated with Intel's data center and AI activities amounted to **** billion U.S. dollars in 2024, a part of the business that is expected to receive greater interest moving forward. Integration of AI into PCs and data centers Intel is in a good position to benefit from the growing adoption of AI PCs, with its processor technologies helping to support AI applications on many devices. As well as PCs, growing demand for AI chips across data centers offers Intel further opportunities for growth, particularly as it looks to close the gap on the market leader Nvidia. Strategic initiatives and industry dynamics To strengthen its position in the evolving semiconductor market, Intel has been pursuing strategic initiatives such as its IDM 2.0 strategy. This plan involves expanding manufacturing capabilities and establishing Intel Foundry as a service that can serve external customers, enabling Intel to compete with the leading semiconductor foundries globally.

Introduction

Employee Layoff Statistics: Employee layoffs are a prevalent cost-cutting strategy employed by companies during economic downturns or organizational restructuring. In 2024, the technology sector alone witnessed over 136,000 job losses across 422 companies, with major firms like Intel, Cisco, and IBM implementing significant workforce reductions. Intel, for instance, announced plans to lay off 15,000 employees, constituting more than 15% of its workforce, as part of a USD 10 billion cost-reduction initiative.

The financial implications of layoffs extend beyond severance packages. For example, when Meta Platforms Inc. laid off 11,000 employees in November 2022, it incurred approximately USD 975 million in severance costs, averaging over USD 88,000 per employee. Additionally, companies often face indirect costs such as decreased productivity among remaining staff, increased turnover, and higher unemployment insurance taxes.

In India, the impact of layoffs has been significant as well. By August 2024, at least 8,000 individuals had been affected by job cuts, with companies like Paytm announcing reductions of up to 3,500 employees. Furthermore, Reliance Industries reportedly reduced its workforce by 11%, equating to approximately 42,000 jobs, to enhance cost efficiency.

These figures underscore the widespread and multifaceted impact of layoffs on both organizations and employees, highlighting the importance of strategic planning and support mechanisms during such transitions. This article includes recent trends and facts from insights gathered in 2024 and 2025. Let's delve into key statistics to get a clearer picture of the topic.

In the third quarter of 2024, roughly ** percent of x86 computer processor or CPU tests recorded were from Intel processors, while **** percent were AMD processors. When looking solely at laptop CPUs, Intel is the clear winner, accounting for nearly ** percent of laptop CPU test benchmark results in the third quarter of 2024. Traditional x86 processors The x86 is a family of instruction set architectures (ISA) for computer processors, otherwise known as central processing units (CPUs), which were initially developed by Intel. They refer to the way a CPU handles information, with x86 architecture of variable instruction lengths, and primarily of a complex instruction set computer (CISC) design. Although additions and developments to the x86 processor have taken place, backward compatibility has remained a key feature of the architecture; the newer x86 processors can run all the programs that older processors could run. Apple Mac processors The x86 processor is the predominant hardware platform for laptops, desktops, and servers globally. Both Windows and Mac laptops have used x86 chips; previously, Apple’s line of Mac products used PowerPC microprocessors, with the transition to Intel x86 architecture first acknowledged in 2005. The first generation of Intel-based Mac computers were released in January 2006, a partnership that continued through until 2020, where Apple announced the switch of the Mac line from Intel CPUs to Arm-based processors. The decision was made in order to standardize on one CPU architecture for all iPhone, iPad, and Mac products.

OpenPack is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job. In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages. The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. 53+ hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations. You can find information on how to use this dataset at: https://open-pack.github.io/. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" 10.1109/PerCom59722.2024.10494448. Full Dataset In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related data. Preprocessed Dataset (IMU with operation and action Labels) We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip". Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels) We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are: "kinect-2d-kpt-with-operation-action-labels.zip", and "kinect-3d-kpt-with-operation-action-labels.zip". Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release [1.1.0] was uploaded on 24/04/2024. Changes LOG: v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels. v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels) We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. https://open-pack.github.io/challenge2022 Funding: JSPS KAKENHI Grant Number JP21H05299, and JP21J10059, and JST ACT-X JPMJAX200T

In its 2025 fiscal year, Nvidia spent 12.9 billion U.S. dollars on research and development (R&D), an increase from the 8.7 billion U.S. dollars that was spent on R&D in 2024. The figure for 2025 is also the highest for the company. Nvidia’s journey into AI In the course of a decade, Nvidia CEO, Jensen Huang, has overseen the company’s move beyond gaming and into AI. Strong demand for data center products that are essential to generative AI has helped fuel the company’s successes. A notable example of Nvidia’s technologies being deployed to train and run a large language model is that of ChatGPT. Cloud providers are developing their own AI chips While fellow chipmakers AMD and Intel may seem the natural competitors to Nvidia in the AI space, the major cloud computing providers could pose another substantial threat to Nvidia going forward. Already, these firms are developing their own AI chips in a bid to cut into Nvidia's lead, as well as tapping into an ever-growing AI chip market. By developing AI chips in-house, firms can cut costs and reduce reliance on one specific supplier.

As of June 2, 2025, Nvidia ranked as the leading semiconductor company in terms of market capitalization at just under 3.3 trillion U.S. dollars, followed by the likes of Broadcom, TSMC, ASML, and Samsung. Many of the leading semiconductor stocks suffered as a result of trade tariff announcements in 2025. Nonetheless, the mix of companies featured on the list reflects the broad and complex nature of the semiconductor industry, with firms coming from across all parts of the chip ecosystem. Regional highlights The global semiconductor industry is dominated by companies from North America and the Asia-Pacific region. As a result, China, South Korea, and Taiwan rank as some of the biggest regional markets for semiconductor equipment spending. One of the world’s leading chip-making machine manufacturers, and a global leader in producing extreme ultraviolet lithography, or EUV, machines, is ASML based in Europe. ASML supplies its machines to the likes of TSMC, who are then contracted to manufacture chips for Nvidia. A dynamic industry In 2025, the semiconductor industry is expected to grow strongly, with forecasts suggesting the market could rise to just below 700 billion U.S. dollars. Nonetheless, companies across the whole supply chain must continue to navigate a challenging and changing world. Geopolitical tensions, such as the ongoing tech competition between the United States and China, as well as the growth of the AI chip market, will have a profound influence on the semiconductor industry moving forward.