Bitcoin's Progress Holds The Secret To The AI Boom

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Reports of Chinese LLMs challenging OpenAI and Anthropic do not signal the end of the AI boom, according to a recent analysis. The article argues that large language models are merely the "tip of the iceberg." While free models exist, the true economic activity and value lie in the vast, expensive, and constantly evolving infrastructure required to run them. Businesses and governments face an unavoidable need for maximum AI, driving an "efficiency arms race" in hardware, energy, and cooling. This long, constrained value chain ensures that even if LLMs become commoditized, the underlying components will continue to fuel immense growth. The AI cycle, akin to crypto mining's compute demands, is just beginning, promising an accelerating loop of technological advancement. “OMG,” they say, “China is catching up with OpenAI and Anthropic, and may soon surpass them in the power of their LLMs. This must be the end of the AI boom.” AI models are only a small part of the AI-boom picture. OpenAI and Anthropic might be worth a trillion or two, perhaps even as much as Microsoft, but they are just the tip of the iceberg. Linux, the best computer operating system, is free. Does that kill Microsoft? No. There are fabulous databases out there for free — do they kill Oracle? (Oh boy, they tried, but no.) I’ve competed with companies paying Oracle millions for software and won by using the free equivalent. Was that an option for them? No. The “why” is a long story, but the point is that a free LLM from China will not kill OpenAI or Anthropic, in the same way Linux didn’t kill Microsoft but did sustain Red Hat. I would not even care if Chinese LLMs did kill OpenAI and Anthropic. That would not touch the AI boom. It would barely scratch it. Hugging Face, the AI destination and repository of AI technology, is proof that free AI models do not damage the future of AI — in fact, they are massively beneficial. All businesses and governments must have maximum AI. That applies to individuals too, but let’s keep the idea focused on B2B and B2G. Let them use Qwen, DeepSeek, or Kimi. So, OK, I say, I’m fed up with spending hundreds of dollars a day building my app. No more $1,000 a month paid to Claude — I’ll run that open-source model at home. Boo, sucks to those greedy fellows! Oh, I need $250,000 of hardware? Well, I’ll buy $30,000 of kit from Nvidia and use a Chinese or U.S. open-weights AI model. That should work. What do I get? An AI equivalent to ChatGPT 3. OK, let me run my business on a Chinese server. Thank goodness Elon bought all those GPUs and rented them to Anthropic. Thank goodness he bought all those gas turbines for his vast compute city. I’ll use that and go back to writing $12,000-a-year checks. For a CEO, the thought process might go something like this: “Maybe I don’t want my data on someone else’s trillion-dollar infrastructure. I need my own server town. Oi, CFO, go get me $50 million of equipment to run our AI in-house. Yes, ring up IBM to get it in. Actually, make that $100 million.” “So the ‘free’ AI models got more powerful, and so did the equipment, and now we have to pay $50 million a year just to keep up with the competition. OK, get it!” So Bitcoin miners will know exactly where I’m going with this. Every two years, the equipment becomes obsolete. It has to be replaced. Crypto mining, like AI, is all about compute per kilowatt. AI equals energy. Success is all about your cost per kilowatt-hour and your compute per kilowatt-hour. So it’s not about maximizing tokens; it’s about minimizing energy. It’s an efficiency arms race, and as every crypto miner will tell you, the equipment and infrastructure providers have you by your nodes. There is no second place in the competition. The value chain is long, and while the models are at the top, the chain quickly descends into basic economics and economic activity. Every link has leverage. You can give away the LLM and still sell the best LLM for fortunes. You can even sell a weaker one for fortunes with the right positioning and marketing (see Microsoft and Oracle). So there is no need to worry about OpenAI, Anthropic, and the rest, even though everyone is panicking about this right now. What is more, even if they were doomed, it would have little impact because, while LLMs might not have obvious moats, everything below them does. You can’t give away the hardware, the energy, the cooling, or the turbines. The long value chain is hard, constrained, immutable — and will remain so for a very long time. What is more, as any Bitcoin miner will tell you, you can’t escape the never-ending replacement cycle created by ever-increasing demand for ever-increasing levels of compute, powered by ever-advancing hardware and software. And guess what? That cycle is accelerated by AI. So as long as intelligence is good and stupidity is bad, this will be an accelerating yet extremely benign loop. That cycle has only just begun. We are in the 1981 Intel 8088 phase. Any crypto-mining denizen will tell you how this goes — how development pushes technology and human endeavor to unimaginable levels at astonishing speed. They will also tell you about the wild ride. So when you next go onto ChatGPT and type in something at “super-smart” level, then wait and wait and wait, don’t think that is how long it takes. No, that is your allocation of the infrastructure. You don’t need a better LLM; you need more infrastructure allocation. Buy $250,000 worth of equipment for your sole use and you will get that output faster. And guess what? You are paying by the minute, just like a good old-fashioned 1980s online service. Take that setup cost and write it off over two years, then compare it to OpenAI API costs, and lo and behold, you understand the cost base. You might be able to afford not to have AI — lucky you. However, that is not the case for corporations or governments. They have no option. Meanwhile, the body of the AI iceberg is gigantic. In the same way Windows built Microsoft and free Unix built Apple, open and closed AI will build the new titans of tomorrow. This is not the end of the AI boom. It is not even the beginning.

AI 시장 분석

The scalability and infrastructure development of the Bitcoin network are gaining attention as key drivers for enhancing data processing efficiency in the AI industry. Bitcoin's decentralized computing resources are supporting AI computational demand, creating technical synergy. Investors should focus on the infrastructure cost reductions and expanded computational power resulting from the integration of these two technologies.

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DYAX 전담 분석

The convergence of Bitcoin and AI is marking a significant shift in infrastructure utilization. Bitcoin's robust, decentralized architecture provides a reliable foundation for the massive data processing requirements of AI models. By leveraging idle computational resources, the network addresses scalability challenges while fostering a more efficient ecosystem for advanced machine learning operations.

This synergy not only optimizes cost structures for AI enterprises but also enhances the overall utility of the Bitcoin blockchain beyond traditional financial applications. As these technologies continue to integrate, the focus will remain on the long-term scalability and the resulting increase in network value.

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