In the past decade, the tech industry has been dominated by software innovations, with cloud computing, mobile apps, and AI-powered solutions reshaping how we live and work. Software has driven growth, offering scalability, efficiency, and convenience. However, as we step into 2025, the tide is shifting. Hardware, once overshadowed, is reclaiming its throne as a key driver of technological progress.

With advancements in AI accelerators, quantum computing, and edge devices, hardware is no longer just the silent supporter of software but a cornerstone of innovation. This renaissance is fueled by the demand for faster processing, lower latency, and energy-efficient solutions to power next-generation applications. In this blog, we’ll explore how hardware is making its comeback, why it’s becoming indispensable, and what this means for the future of technology. Welcome to the new era where hardware reigns supreme once again!

All about hardware

It refers to the physical components of a computer or other electronic devices that are tangible and perform specific functions. It encompasses all the machinery and equipment that work together to process data, run applications, and carry out commands from software.

Type of general hardware

• Input Devices
• Output Devices
• Processing Units’
• Storage Devices
• Peripheral Devices

AI Hardware

AI hardware refers to specialized physical components and devices designed to support and accelerate artificial intelligence (AI) workloads. These solutions are optimized to handle the computationally intensive tasks involved in training, deploying, and running AI models, such as machine learning (ML) and deep learning (DL).

Key Components of

Processing Units
AI workloads require immense computational power, which is provided by the following:

• Graphics Processing Units (GPUs): Highly parallel processors that handle multiple operations simultaneously, making them ideal for deep learning tasks.
• Tensor Processing Units (TPUs): Specialized hardware developed by Google for high-efficiency deep learning computations.
• Field-Programmable Gate Arrays (FPGAs): Customizable chips that can be tailored for specific AI applications to enhance speed and efficiency.
• Application-Specific Integrated Circuits (ASICs): Hardware designed for specific AI tasks, such as inference and model optimization.

Memory and Storage
AI requires fast data access and large-scale storage for datasets and models:

• High-Bandwidth Memory (HBM): Fast memory integrated with GPUs or other processing units for rapid data transfer.
• Solid-State Drives (SSDs): Used for quick access to large datasets.

Networking Components
For distributed AI tasks, networking hardware facilitates high-speed communication between servers:

• InfiniBand: A high-speed networking standard commonly used in data centers.
• Ethernet: For connecting multiple AI hardware units in clusters.

Edge AI Devices
Hardware designed for AI processing at the edge (closer to the data source):

• AI-Enabled Chips: Found in smartphones, IoT devices, and autonomous systems for real-time AI processing.
• Neural Processing Units (NPUs): Specialized chips optimized for running AI algorithms on edge devices.

Software Dominance over the past years

Over the past few years, software solutions have emerged as a transformative force across industries. This shift is largely driven by advancements in cloud computing, which enable scalable and cost-effective access to powerful computing resources. Mobile applications have also played a pivotal role, redefining how businesses and consumers interact, while data analytics has unlocked insights that fuel strategic decision-making and innovation. Together, these developments have allowed software to become the backbone of modern enterprises, streamlining operations, improving customer experiences, and fostering global connectivity.

Software Industry Growth

• Market Expansion: The global software market is projected to grow from approximately USD 718.26 billion in 2024 to around USD 2,088.9 billion by 2035, representing a compound annual growth rate (CAGR) of 10.19%. (Roots Analysis)
• Enterprise Software Dominance: Within the software sector, enterprise software is expected to lead, with a projected market volume of USD 294.30 billion by 2024. (Statista)

Cloud Computing and Data Analytics

• Cloud Computing Revenue: As a segment of IT services, cloud computing was anticipated to generate over half a trillion U.S. dollars in revenue in 2023, indicating its substantial role in modern IT infrastructure. (Statista)
• Data and Analytics Software Market: Valued at USD 141.91 billion in 2023, this market is expected to expand at a CAGR of 13.6% from 2024 to 2030, reaching approximately USD 345.32 billion by 2030. (Grand View Research)

Innovation Focus

In the race to remain competitive, both startups and established companies have increasingly focused their attention and resources on software development. The rapid pace of digital transformation and demand for seamless user experiences have made software a priority. However, this emphasis has often come at the expense of hardware innovation, which has seen relatively incremental advancements. While hardware remains essential, its development is now frequently tailored to support and optimize software capabilities, reflecting a broader trend where software leads the charge in defining the future of technology.

Investment in Hardware, Especially AI Hardware (2010 – Present)

Overall Hardware Investment Trends

2010-2015:

• Global spending on IT hardware was approximately $400 billion annually.
• Investment in data centers and cloud infrastructure began to rise, with spending reaching around $50 billion by 2015.

2016-2020:

• The global market grew to about $500 billion annually by 2020.
• AI hardware investments started to gain traction, with estimates of around $5 billion in AI-specific hardware by 2019.

2021-Present:

• The global market is projected to exceed $600 billion by 2025.
• AI hardware investment surged to approximately $10 billion in 2021 and is expected to reach $30 billion by 2025.

AI Hardware Investment Breakdown

2010-2015:

• Investment in GPUs for AI applications was minimal, estimated at $1 billion by 2015.
• Major tech companies began experimenting with using GPUs for deep learning tasks.

2016-2018:

• Investment reached about $10 billion by 2018.
• Google introduced its first Tensor Processing Unit (TPU) in 2016, marking a significant investment in AI-specific hardware.

2019-2021:

• AI hardware market size exceeded $10 billion by 2021.
• NVIDIA reported revenues of $6.9 billion in fiscal year 2021, with a significant portion attributed to AI and data center sales.

2022-Present: The AI hardware market is projected to grow at a CAGR of 25%, reaching $30 billion by 2025. Major investments include:

• NVIDIA: Projected revenue of $10 billion from AI hardware by 2023.
• Google: Continued investment in TPUs, with significant R&D budgets allocated to AI.

3. Key Players and Their Contributions

NVIDIA:

• Dominated the GPU market with a 63% market share in AI hardware by 2021.
• Revenue from AI-related products reached approximately $8 billion in 2022.

Google:

• Continued to invest heavily in its TPU technology, with investments estimated at $2 billion annually in AI hardware.

AMD:

• Increased focus on AI chips, with investments in R&D reaching $1 billion by 2021.

Intel:

• Committed around $3 billion to AI hardware development, focusing on FPGAs and specialized processors.

The investment landscape for hardware, especially AI, has seen exponential growth from 2010 to the present. As companies increasingly recognize the importance of AI, investments are projected to continue rising, significantly impacting various industries. The market is expected to reach $30 billion by 2025, reflecting the critical role of AI hardware in future technological advancements.