A rapidly-growing international competition for access to hardware necessary for using artificial intelligence (AI) will alter hardware infrastructure in many areas including, but not limited to, data centers, supply chains, and chip design/production. The global race to possess as much AI-related hardware as possible will have far-reaching consequences as countries and companies compete for supremacy, create innovative products/services, and illuminate structural barriers that now exist.
1. Explosion in AI Infrastructure Demand
The surge of interest in AI has resulted in exploding demand for chips specifically designed to facilitate computing (ie., receivable SiP) capabilities, with revenues associated with manufacturing AI related semiconductors are projected to account for more than $500 billion by 2026 globally.
This high volume of demand will lead to massive reconfiguration of existing computer-related infrastructure; data centres, for example, are being redesigned into structures that can accommodate large numbers of parallel-processing GPUs, tensor processors, and custom accelerators. This is a change from existing data centre structures in which traditional computing has been performed. It will also lead to the emergence of hyper-scale clusters of computers that can all process massive amounts of information simultaneously (parallel processing) to support the execution of large numbers of AI programs at very high speeds.
In addition, numerous corporations are investing heavily in expanding computing power through major winning initiatives like the global Terafab project that will provide a high level of computing capacity by providing a vertically-integration ecosystem.
2. Rise in Custom Silicon and Vertical Integration of Custom Chips
The shift toward custom-designed chips has been one of the most important trends that are changing how we view hardware infrastructure. Many of the larger technology companies have decided to invest in their own processor design by creating their own processors to be less reliant upon a select few dominant suppliers.
Recent examples show that this move is already being implemented. Amazon is developing its Trainium chips to provide an alternative option in the same space as established GPU manufacturers with a greater focus on price versus performance and cost savings that can be gained internally.
In addition, partnerships such as Broadcom’s agreement with Google to create custom-designed AI chips show the immediate focus on providing tailored hardware solutions.
Some of the examples include:
- Encouragement of heterogeneous computing environments.
- Reduction of vendor lock-in.
- Co-design of both hardware and software stacks.
Because of these changes, our infrastructure has become increasingly specialized for use with specific AI workloads versus general-purpose computing.
3. Supply Chain Strain and Hardware Bottlenecks
The semiconductor supply chain is currently experiencing difficulties despite its constant expansion due to growing demand for semiconductors and the growing AI sector’s need for chips, which has resulted in hardware shortages of some frequently used semiconductors like HBM and advanced packaging technologies, among others.
Global semiconductor production capacity has been continually increasing to meet the demands of the growing AI market, but companies now realize that global semiconductor demand will soon exceed global semiconductor manufacturing capacity. As a result, the semiconductor supply chain is no longer simply a reactionary process for companies; it is now an integral part of their understanding of what they can do to ensure future scalability through an understanding of how best to procure and maintain inventory.
The result of shortages of memory and constrained wafer supply will be a negative impact on downstream industries, including but not limited to consumer electronics and the automotive industry.
To resolve the supply chain bottlenecks created because of these supply chain issues companies are now forced to:
- Commit to multi-year purchasing of semiconductor products
- Identify alternative sources for semiconductor parts
- Redesign their systems for improved efficiency and reduced energy consumption.
4. Geopolitics and the Push for “Silicon Sovereignty”
The AI chip market is a highly political issue with trade tensions, export controls, and national security issues changing the way global semiconductors are manufactured and distributed.
In response to these issues, many country governments are making significant investments to increase domestic semiconductor manufacturing to reduce reliance on foreign suppliers. This increase in domestic manufacturing by several countries creates “silicon sovereignty” and creates a geographic change in the locations of where semiconductor manufacturers manufacture chips.
For instance, China is expanding its semiconductor manufacturing capacity faster than ever due to the AI driven demand for semiconductors and at the same time, Western nations are investing in increasing the value of domestic semiconductor fabrication plants.
This geopolitical competition is:
- Fragmenting global supply chains
- Increasing redundancy in manufacturing
- Driving massive capital expenditure in fabrication plants
5. Evolution of Data Center Architecture
Data Centers Must Redesign Their Infrastructure Due to AI Workloads. As AI workloads become more prevalent, data centers must rethink how their infrastructure is designed. Traditional electrical connections will no longer support the volume and speed of connections needed for these workloads; thus, another form of connection must be developed.
Emerging technologies that allow data to be transferred via light instead of electricity (e.g. silicon photonics) are gaining momentum as a solution to increase bandwidth and decrease energy usage.
These technologies are transforming hardware infrastructure by:
- Reducing latency and power consumption
- Enabling quicker communication between chips
- Supporting the latest generation of AI models
Additionally, data centers are becoming increasingly energy intensive as AI continues to grow and create demands for efficient cooling and power management solutions.
6. Strategic Alliances and Ecosystem Building
As AI can introduce so much complexity into the hardware used for it, there is a greater need for collaboration among semiconductor manufacturers. Establishing partnerships with chip manufacturers and cloud providers will be crucial to ensure sufficient supply for the increasing demand for AI.
Intel’s partnership with various AI companies demonstrates how alliances are being formed to enable rapid scaling of production and meet infrastructure needs for AI.
Such alliances:
- Strengthen supply chain resilience
- Enable shared R&D investments
- Accelerate deployment of advanced infrastructure
also read:
- Elon Musk’s Mega AI Chip Fab Project
- Government is planning for funds to power up domestic chipmaking
Conclusion
Hardware architecture is undergoing a massive shift as the compete for AI processors heats up, creating a new battle zone with the data center industry where custom silicon, hyperscale data structures, dynamic geopolitical minireshoring of supply chains and government-sponsored resiliency will all create new barriers and opportunities.
Due to the rapid pace of innovation, the race to build and manufacture AI technology has also created significant weaknesses in our supply chains and energy usage systems; it will take much more than algorithms to create the future of AI; and therefore we will require building, scaling, and maintaining an entire hardware ecosystem to support the continued success of AI moving forward.
