4 Issues Exacerbating the Looming AI Chip Shortage

From powering a chatbot to crunching information in massive data centers, artificial intelligence (AI) is growing everywhere. However, one major problem remains at the center of it all — companies are running out of chips to feed the technology. Several factors are contributing to this shortage, and organizations may be looking at serious bottlenecks in the future.

So, what can supply chain managers do before things become more complicated? It starts with understanding some of the leading circumstances fueling the AI chip shortage.

1. Raw Material Shortages

AI chips depend on rare and hard-to-source materials, such as gallium, germanium, cobalt, and tungsten, for their semiconductors. These minerals are in short supply, and many come from politically volatile regions or countries with tight control over them. For example, China has a 60% share of worldwide rare earth elements output and has already threatened export restrictions in response to escalating trade tensions.

Mining and processing these minerals is not easy either. Environmental regulations, labor conditions, and a lack of domestic refining capacity in many Western countries all contribute to a slower supply chain. That bottleneck keeps fabricators from operating at full capacity, delaying chip production further.

Still, these resources have become essential to the production of chips, and demand will only keep growing. According to market projections, the global semiconductor materials market may growfrom $72.03 billion in 2025 to $96.24 billion by 2032. As the need for raw materials skyrockets, the competition for these resources will determine who gets to build the future — and who gets left behind.

2. Increased Demand for AI Computing Power

Since the 2022 breakthrough in generative AI, every company wanted in on the action. From scrappy startups to cloud titans, diverse companies vied to build the next big thing in artificial intelligence. With tools like ChatGPT gaining popularity, the demand for AI computing power has exploded. Suddenly, every application needed machine learning. Explosions like this have consequences, especially for the chips doing all the heavy lifting.

AI models — especially the massive large language models — require advanced GPUs and AI accelerators. These components are expensive to make and even harder to scale. As model sizes expand into the billions of parameters, so does the appetite for computing power. This increases the need for serious infrastructure.

Major players like AWS, Google Cloud, and Microsoft Azure have already boosted their capital spendingby around 36% in 2024, with most of it going toward AI infrastructure. As the need for chips rises, there is not nearly enough supply.

That’s where the center of the shortage lies. The supply chain can’t handle this kind of spike. With AI now baked into everything, lead times are growing, and production backlogs are piling up. Yet, with the global AI chip market expected to hit $89.6 billion by 2029, it will be even more challenging for supply to keep pace with accelerating demand.

3. Geopolitical Tensions and Trade Restrictions

Semiconductors are more than tech components — they’re geopolitical currency. The U.S.-China trade war has turned chipmaking into a chess game, and AI chips are now one of the most contested pieces.

To curb China’s access to advanced technologies, the U.S.has imposed export restrictions on high-end chips and the equipment used to manufacture them. Nvidia — one of the biggest names in the market — has already taken hits. The company held an estimated 90% share of China’s AI chip market before the U.S. imposed sanctions.

Yet, it’s more than about individual companies. The entire semiconductor ecosystem is wildly globalized. A single AI chip might involve design in California, fabrication in Taiwan, materials from Japan, and assembly in Malaysia. When governments throw up barriers, the ripple effects can paralyze entire supply chains.

4. AI-Driven Demand from PCs and Smartphones

AI is now reaching into consumers’ pockets. Until recently, most of the heavy AI lifting happened in the cloud. However, chipmakers are now bringing AI capabilities into smart devices, creating an even higher demand for AI chips.

For example, smartphones are quickly evolving into mini AI machines. Their capabilities range from real-time language translation to generative image editing — features that require powerful on-device processing. Fitting energy-efficient chips into already thermally constrained devices can be a tough engineering challenge and an even tougher supply chain issue.

However, AI smartphone shipments are growing exponentially, with market forecasts predicting them to growfrom 234 million units in 2024 to more than 900 million by 2028. While this happens, enterprise and cloud-scale AI demand is still spiking, so the use cases are growing.

Strategic Responses to Mitigating Shortages

Supply chain managers can take steps to reduce the potentially negative impacts of AI chip shortages. Here’s how they may play smarter in a chip-starved market:

• Diversifying suppliers: Relying on a single vendor can create setbacks. That’s why spreading sourcing across multiple regions and vendors is important.
• Strengthening supplier relationships: Businesses must treat suppliers like strategic partners rather than vending machines. From collaborative planning to long-term contracts, various tactics can give companies a leg up when things are tight.
• Investing in inventory buffering: Stocking inventory based on usage data and realistic forecasting can buy time without draining cash flow.
• Enhancing demand forecasting with AI: Ironically, AI can solve the chip shortage. With predictive analytics, organizational leaders can anticipate surges, identify vulnerabilities, and plan accordingly.
• Exploring domestic or nearshore manufacturing: Shorter supply chains can support smoother operations. If reshoring isn’t an option, nearshoring can offer a solid middle ground for critical components.

Solving the AI Chip Shortage

AI may be booming, but there are not enough chips to keep pace with demand. With each issue occurring simultaneously, they pull the semiconductor supply chain in every direction. For supply chain managers and tech leaders to stay ahead, there will be a growing matter of planning smarter and building strength before it begins.