The rapid adoption of generative AI (gen AI) is driving increased cloud consumption.
This increased cloud consumption is largely due to the following:
- Increased need for GPU power: Gen AI uses graphics processing units (GPU) power
- AI development and use of models: AI developers need greater computing power to train gen AI models, and use of gen AI to create inferences and information requires robust computing power
- Large enterprises: Large enterprises that have aging data infrastructures rely on the cloud for backup and resiliency
- Backup GPU power: Redundancy and resiliency are critical due to the complexity of GPUs
Just as the COVID-19 pandemic pressure-tested the supply chain, this increased demand for cloud resources will do the same, and could have the following effects on the supply chain:
- Increased demand for semiconductors (GPUs and tensor processing units) due to increased use and advancement of AI
- Increased demand for hardware and components that intersect with AI applications and data centers (including power generation equipment)
- Impact on pricing and availability due to increased competition in connection with demand for specific semiconductor chips
- High demand for GPUs could impact the availability of other types of semiconductor chips
- Strain on or expansion of industry due to such demand and competition
- Temporary impact to other industries reliant on chips or hardware (e.g., the auto industry during the COVID-19 pandemic)
Based on the potential issues listed above, it is important for businesses to consider backup plans to help navigate the inevitable supply-chain disruptions and changes. Such plans may include:
- Basing capacity on anticipated future needs
- Considering long-term purchase agreements to guaranty manufacturing capacity and supply
- Investing in data and operational supply-chain resiliency (e.g., forecasting and contingency planning)
- Monitoring refresh cycles for related hardware that may be affected by such supply-chain issues