Huang Jen-hsun is Satoshi Nakamoto

Author: Luo Yihang

In January 2009, an anonymous person invented something called a “token.” You invest computing power to earn tokens, which circulate, price, and trade within a consensus network. This gave birth to the entire crypto economy. Over the past decade, people have debated whether these tokens have any real value.

In March 2025, a man in a leather jacket redefined another kind of token. You invest computing power to produce tokens, which are immediately consumed during AI inference and reasoning processes: thinking, reasoning, coding, decision-making. This accelerates the AI economy. No one debates whether these tokens have value because you just used millions of them this morning.

Two types of tokens, same name, same underlying structure: input computing power, output valuable results.

In March 2026, I sat at NVIDIA GTC listening to Huang Renxun deliver a nearly product-free keynote. Yes, he announced Vera Rubin, a CPU-GPU hybrid product. But this time, he didn’t talk about chip specs or process technology; he presented a complete economics of token production, pricing, and consumption:

Which model corresponds to which token speed; which token speed corresponds to which price range; what hardware level is needed to support each.

He even provided data center hardware allocation plans for CEOs and decision-makers holding corporate budgets: 25% for free tier, 25% for mid-range, 25% for high-end, 25% for premium.

Yes, he didn’t sell a specific GPU model this time, just like two years ago with Blackwell. But this time, he was selling something bigger. After two hours, I felt his core message was: Welcome to consume tokens, and only Nvidia’s factories can produce them.

At that moment, I realized that this man, and the anonymous person who mined the first token 17 years ago, are doing the exact same thing structurally.

Same set of transformation rules.

The anonymous person under the pseudonym “Satoshi Nakamoto” wrote a nine-page white paper in 2008, designing a set of rules: invest computing power to complete a mathematical proof (Proof of Work), and earn crypto tokens as rewards.

The brilliance of this rule is that it requires no trust in anyone—if you accept these rules, you automatically participate in this economy. The rule is correct because it brings together many tricksters and fraudsters.

And Huang Renxun, on the GTC 2026 stage, did something structurally identical.

He showed a diagram illustrating the relationship and tension between inference efficiency and token consumption: Y-axis is throughput (how many tokens per megawatt of power), X-axis is interactivity (perceived token speed per user). Below the X-axis, he marked five pricing tiers: Free with Qwen 3, $0 per million tokens; Medium with Kimi K2.5, $3 per million tokens; High with GPT MoE, $6 per million tokens; Premium with GPT MoE 400K context, $45 per million tokens; Ultra at $150 per million tokens.

This diagram could almost serve as the cover of Huang Renxun’s “Token Economics” white paper.

Satoshi Nakamoto defined “what constitutes valuable computation”—completing a SHA-256 hash collision is valuable. Huang Renxun defined “what constitutes valuable inference”—producing tokens at a specific speed under given power constraints for specific scenarios is valuable.

Neither Nakamoto nor Huang directly produce tokens; they define the rules for token creation and pricing mechanisms.

Huang’s statement on stage could almost be directly quoted as the abstract of the token economics white paper:

Tokens are the new commodity, and like all commodities, once they reach an inflection point, once they mature, they will segment into different parts.

Tokens are the new bulk commodity. Once mature, they naturally stratify. He’s not describing the current state; he’s predicting a market structure and precisely aligning his hardware product lines with each layer of this structure.

The production processes of these two tokens are even semantically symmetrical: mining is called mining; inference is called inference.

The essence of mining and inference is both turning electricity into money. Miners spend electricity to mine crypto tokens, then sell them; AI models and agents spend electricity to generate AI tokens, then sell them by the million. The intermediate steps differ, but both ends are the same: meters on the left, income on the right.

Two ways of expressing scarcity.

The most important design decision Nakamoto made was not Proof of Work, but the cap of 21 million bitcoins. He used code to create artificial scarcity—no matter how many mining machines flood in, the total number of bitcoins will never exceed 21 million. This scarcity anchors the entire crypto economy’s value.

Huang Renxun, on the other hand, creates natural scarcity through physical laws. He says:

“You still have to build a gigawatt data center. You still have to build a gigawatt factory, and that one gigawatt factory, amortized over 15 years… costs about $40 billion even before you put anything in it. It’s $40 billion. You’d better make sure you put the best computer system on it so you can have the lowest token cost.”

A 1GW data center will never become 2GW. This isn’t a code limit; it’s physics.

Land, electricity, cooling—each has physical limits. The amount of tokens a factory built for $4 billion over 15 years can produce depends entirely on the computing architecture you put inside.

Nakamoto’s scarcity can be forked. If you don’t like the 21 million cap, fork a new chain, change it to 200 million, call it Ether or whatever, and write a white paper. And people have indeed done this, eagerly.

Huang’s created scarcity cannot be forked. You can’t fork the second law of thermodynamics, the capacity of a city’s power grid, or the physical area of land.

But whether Nakamoto or Huang, their scarcity creation leads to the same result: a hardware arms race.

The history of mining: CPU → GPU → FPGA → ASIC. Each generation of dedicated hardware renders the previous obsolete. The history of AI training and inference is repeating: Hopper → Blackwell → Vera Rubin → Groq LPU. General hardware starts it, specialized hardware finalizes it. The Groq LPU Huang showcased at this year’s GTC, after acquiring Groq, is a deterministic dataflow processor. Static compilation, no dynamic scheduling, 500MB on-chip SRAM—it’s architecturally an ASIC for inference. Doing one thing, but doing it to the extreme.

Interestingly, GPUs played a key role in both waves.

Before 2013, miners discovered GPUs were better suited than CPUs for crypto mining; Nvidia graphics cards were sold out. Ten years later, researchers found GPUs are the best tools for training and inference of AI models; Nvidia data center cards sold out again. As a processor class, GPUs served two generations of the token economy.

The difference is, the first time Nvidia benefited passively, and that was the end of it. The second time, as AI inference shifted from pretraining to deployment, Nvidia quickly seized the opportunity, designing the entire game and becoming the rule-maker in AI.

The world’s most profitable pickaxe.

In the gold rush, the most profitable weren’t the miners but Levi Strauss selling shovels. In the mining boom, the most profitable weren’t miners but Bitmain and Wu Jihan selling mining machines. In the AI pretraining and inference wave, the most profitable aren’t the base models or agents but Nvidia selling GPUs.

But honestly, the roles of Bitmain and Nvidia in their respective industries are no longer comparable.

Bitmain only sells mining hardware; Nvidia was once a supplier to Bitmain. When you buy a miner, what coin you mine, which pool you join, and at what price you sell are unrelated to Bitmain. It’s a pure hardware supplier, earning one-time device profits.

Nvidia is different. It doesn’t just sell hardware. Since the AI inference boom in 2025, it has deeply defined what to mine with these GPUs, how to price tokens, who to sell tokens to, and how to allocate data center compute—everything in Huang’s GTC slides: market divided into five tiers, each with specific models, context lengths, interaction speeds, and prices. Nvidia has standardized and formatted the future AI inference-driven market.

Around 2018, global computing power was concentrated in a few large pools—F2Pool, Antpool, BTC.com—they competed for hash rate share, but the hardware source was highly centralized at Bitmain.

Today, Nvidia’s 60% revenue comes from competing hyperscalers like AWS, Azure, GCP, Oracle, CoreWeave, while 40% comes from decentralized AI natives, sovereign AI projects, and enterprise clients. Large “mining pools” contribute most revenue; smaller “miners” provide resilience and diversity.

The structure of these two ecosystems is identical. But Bitmain later faced competitors—Shenma Mining Machines, Canaan, and others—eating into its market share. ASIC miners are relatively simple designs, and competitors have a chance to catch up. But disrupting Nvidia seems increasingly difficult: 20 years of CUDA ecosystem, hundreds of millions of GPUs installed, NVLink sixth-generation interconnect, and the decoupled inference architecture after Groq’s integration—all these create a technological and ecological barrier that most competitors cannot overcome.

This could last another 20 years.

The fundamental fork of the two tokens.

What makes crypto and AI inference/production tokens fundamentally different is the motivation and psychology of their users.

Crypto tokens are driven by speculation. No one “needs” Bitcoin to do work. All white papers claiming blockchain tokens can solve problems are scams. Holding crypto is because you believe someone will buy it from you at a higher price in the future. Bitcoin’s value comes from a self-fulfilling prophecy: if enough people believe it’s valuable, it is. This is a faith economy.

AI tokens, on the other hand, are driven by productivity. Nestlé needs tokens for supply chain decisions—its supply chain data refreshes every 15 minutes now, down to 3 minutes, reducing costs by 83%. This value can be directly mapped to profit and loss. Nvidia’s engineers now need tokens to write code instead of manual work; research teams need tokens for scientific research. You don’t need to believe tokens are valuable; just use them, and their value is self-evident in usage.

This is the core difference between the two tokens. Crypto tokens are produced to be held and traded—their value lies in not being used. AI tokens are produced to be consumed immediately—their value lies in their use at the moment of consumption.

One is digital gold, appreciating as it’s stored; the other is digital electricity, burned upon production.

This difference determines that the AI token economy won’t bubble like the crypto economy. Bitcoin’s wild swings are driven by speculation and emotion. Token prices are driven by usage and production costs—so long as AI remains useful, so long as people use Claude Code to code, ChatGPT to write reports, and agents to run business processes, demand for tokens won’t collapse. It doesn’t rely on faith; it relies on indispensability.

In 2008, the Bitcoin white paper had to repeatedly justify why a decentralized digital cash system was valuable. Seventeen years later, people still debate it.

In 2026, token economics have caused no controversy; they are even accepted as consensus without much argument. When Huang Renxun said at GTC “tokens are the new commodity,” no one questioned. Because everyone in the audience had consumed millions of tokens this morning using Claude Code or ChatGPT. They don’t need convincing of token value—their credit card bills already prove it.

In this sense, Huang Renxun is truly a copy of Satoshi Nakamoto—the one who left behind the monopoly on mining hardware, defined token use cases and standards, and annually hosts a show at San Jose’s SAP Center to showcase the next-generation AI training and inference “mining machines.”

Nakamoto has a cautious, almost romantic desire—design the rules, hand them to the code, then disappear. That’s the cyberpunk ideal. Huang, by contrast, is more like a businessman: he designs the rules, maintains them himself, keeps refining, and builds his moat.

The token you once believed in because you trusted it, now you can see without trust. It’s the next after Watt, Ampere, and Bit.