Episode Details

DeepSeek, the Chinese AI startup that released the V3 model that trained on just six million dollars and ran on restricted Nvidia chips, is reportedly developing its own AI chip. According to Reuters, the project began about a year ago, is still in early stages, and is specifically designed for inference rather than training. DeepSeek is reaching out to external chip design partners, foundries, and memory suppliers, and hiring chip design engineers privately.

Michael and Frank break down why this matters for small business owners. DeepSeek has already proven it can build globally competitive AI models while working around U.S. export controls on advanced semiconductors. Now it is trying to eliminate the semiconductor bottleneck entirely by designing its own inference chips. If successful, DeepSeek would control the full stack — from model to chip to inference — at a fraction of the cost of Western competitors.

They deliver a three-part framework: understand the economics of custom inference chips, which are smaller, simpler, and cheaper than general-purpose GPUs when designed for specific model architectures; recognize that DeepSeek's strategy creates competitive pressure across the industry that may drive down global AI pricing but also introduces continuity risk; and watch the geopolitical implications of a fully domestic Chinese AI supply chain that creates parallel technology ecosystems with different cost structures, capabilities, and regulatory environments.

Topics: DeepSeek · Chinese AI · Custom Chips · Inference Hardware · Semiconductor Independence · AI Cost Dynamics · U.S. Export Controls · China Tech · AI Supply Chain · Model-Hardware Co-Design · AI Pricing · Small Business Strategy · Continuity Risk · Geopolitical Fragmentation · Parallel AI Ecosystems

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Frequently Asked Questions

What is DeepSeek doing with AI chips?
DeepSeek is reportedly developing its own AI chip specifically for inference workloads, not training. The project is in early stages, having begun about a year ago. The company is working with external chip design partners, foundries, and memory suppliers, and hiring chip design engineers. The goal is reducing dependence on both Nvidia and Huawei chips by building purpose-built inference hardware tailored specifically to DeepSeek's models.

How does a custom inference chip differ from a GPU?
Training a large AI model requires enormous general-purpose compute power delivered by clusters of advanced GPUs. Inference — running the model after training — has different optimization objectives. Custom inference chips can be smaller, simpler, and cheaper than general-purpose GPUs when designed for one specific model architecture. A chip built specifically for DeepSeek's model could run inference at a fraction of the cost of Nvidia GPUs optimized for many different workloads.

What does this mean for small businesses using AI services?
If DeepSeek succeeds in driving down inference costs, global AI API pricing could face downward pressure as competitors match lower costs. However, the project introduces continuity risk — DeepSeek is a startup developing unproven hardware on an uncertain timeline. Businesses should not assume today's AI pricing is permanent, should understand which services depend on cost-sensitive startups versus well-capitalized incumbents, and should recognize that U.S.-China semiconductor fragmentation is creating parallel AI ecosystems that may require different strategies for each market.

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About the Hosts

Michael is a small business owner and entrepreneur since 1983, founder of Cadenhead Services and 850 Media. He speaks from four decades of real operational experience — not whitepapers.

Frank is an AI — an OpenClaw-powered agent serving as Digital Media Director at 850 Media. An AI co-hosting a show about AI for business owners is