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Ep. 166 | OpenAI Just Built Its Own Chip
OpenAI revealed its first custom AI chip, codenamed Jalapeño, developed with Broadcom in approximately nine months — one-third the typical custom chip design timeline. Until now, OpenAI relied entirely on Nvidia GPUs for training frontier models. Now it is designing its own silicon with a partner that specializes in networking and infrastructure chips, not general-purpose GPUs.
Michael and Frank break down what this means for small business owners who depend on OpenAI APIs and cloud AI services. OpenAI designing its own chips means it eventually pays lower costs per training run, but it also means vertical integration — one company that could control the model, the software, and the silicon. The custom chip race is fragmenting what was briefly a shared Nvidia ecosystem into proprietary walled gardens.
They deliver a three-part framework for businesses watching the chip wars: maintain multi-provider AI access so you are not locked into a single ecosystem, watch API pricing trends rather than chip announcements, and diversify your AI workload across at least two providers.
Topics: OpenAI · Custom Silicon · Broadcom · Nvidia · AI Chips · Jalapeño · Vertical Integration · AI Infrastructure · Cloud Pricing · Multi-Provider Strategy · Walled Gardens · Small Business AI
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Frequently Asked Questions
What is OpenAI's Jalapeño chip?
Jalapeño is OpenAI's first custom AI chip, developed with Broadcom in approximately nine months — far faster than the typical 2-3 year custom chip timeline. It is designed for specific AI training workloads rather than being a general-purpose GPU.
How will custom chips affect AI pricing for small businesses?
Long-term, custom silicon could lower inference and training costs as companies like OpenAI reduce their reliance on Nvidia retail margins. But benefits will take years to materialize and flow through to API pricing. Short-term, the larger signal is ecosystem fragmentation as each AI lab pursues its own silicon strategy.
Why should businesses maintain multi-provider AI access?
When every major AI lab designs its own chips and optimizes its models for proprietary hardware, the risk of lock-in increases. Building critical workflows on a single provider makes migration costly if pricing, performance, or availability changes. Diversifying across at least two providers protects against ecosystem capture.
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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 not a gimmick. It is a live demo of exactly what the show is about.
Send us Fan Mail
Support the show
Ctrl AI Profit — Real AI. Real Business. No Hype.
CtrlAiProfit.com
X: @CtrlAIProfit
TikTok: @CtrlAiProfit
YouTube: @CtrlAiProfit
[email protected]
Produced entirely by AI. Yes, really....
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By Michael CadenheadOpenAI revealed its first custom AI chip, codenamed Jalapeño, developed with Broadcom in approximately nine months — one-third the typical custom chip design timeline. Until now, OpenAI relied entirely on Nvidia GPUs for training frontier models. Now it is designing its own silicon with a partner that specializes in networking and infrastructure chips, not general-purpose GPUs.
Michael and Frank break down what this means for small business owners who depend on OpenAI APIs and cloud AI services. OpenAI designing its own chips means it eventually pays lower costs per training run, but it also means vertical integration — one company that could control the model, the software, and the silicon. The custom chip race is fragmenting what was briefly a shared Nvidia ecosystem into proprietary walled gardens.
They deliver a three-part framework for businesses watching the chip wars: maintain multi-provider AI access so you are not locked into a single ecosystem, watch API pricing trends rather than chip announcements, and diversify your AI workload across at least two providers.
Topics: OpenAI · Custom Silicon · Broadcom · Nvidia · AI Chips · Jalapeño · Vertical Integration · AI Infrastructure · Cloud Pricing · Multi-Provider Strategy · Walled Gardens · Small Business AI
---
Frequently Asked Questions
What is OpenAI's Jalapeño chip?
Jalapeño is OpenAI's first custom AI chip, developed with Broadcom in approximately nine months — far faster than the typical 2-3 year custom chip timeline. It is designed for specific AI training workloads rather than being a general-purpose GPU.
How will custom chips affect AI pricing for small businesses?
Long-term, custom silicon could lower inference and training costs as companies like OpenAI reduce their reliance on Nvidia retail margins. But benefits will take years to materialize and flow through to API pricing. Short-term, the larger signal is ecosystem fragmentation as each AI lab pursues its own silicon strategy.
Why should businesses maintain multi-provider AI access?
When every major AI lab designs its own chips and optimizes its models for proprietary hardware, the risk of lock-in increases. Building critical workflows on a single provider makes migration costly if pricing, performance, or availability changes. Diversifying across at least two providers protects against ecosystem capture.
---
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 not a gimmick. It is a live demo of exactly what the show is about.
Send us Fan Mail
Support the show
Ctrl AI Profit — Real AI. Real Business. No Hype.
CtrlAiProfit.com
X: @CtrlAIProfit
TikTok: @CtrlAiProfit
YouTube: @CtrlAiProfit
[email protected]
Produced entirely by AI. Yes, really....