Behind the OpenClaw Gold Rush: The Shovel Sellers Never Worry, Only the Miners Do

比推Pubblicato 2026-03-11Pubblicato ultima volta 2026-03-11

Introduzione

OpenClaw, an open-source AI agent framework, has sparked a frenzy in China, creating a wave of anxiety among users while a complete industry profits from it. The article reveals how various players are monetizing this trend: hardware manufacturers, like former crypto mining rig producers, are selling pre-configured “OpenClaw-optimized” devices; API resellers are offering discounted—and sometimes fraudulent—access to model tokens, with some making millions monthly by secretly swapping premium models for cheaper ones or collecting valuable user data; and service providers are capitalizing on information asymmetry, offering installation, customization, and industry-specific packages, with some earning over $1.8 million. The core insight is that, much like historical gold rushes, the real profits are not made by the “miners” (end-users) but by the “shovel sellers” (those providing tools and services), who exploit consistent human anxiety and the fear of being left behind.

Author: Black Little Shrimp Cage, Deep Tide TechFlow

Original Title: OpenClaw Gold Rush, Who's Getting Rich?


OpenClaw has exploded in China. How many people around you are anxious, and how many are counting money?

Some travel nationwide, flying to Shenzhen, Chengdu, Hangzhou, specifically to configure this "lobster" for small business owners, charging over ten thousand yuan per order.

Some directly install OpenClaw into Apple devices and ship them to customers, ready to use out of the box, earning a cumulative $1.8 million.

Crypto mining machine manufacturers, once struggling, have now pivoted to selling OpenClaw hardware.

Middlemen reselling Token APIs are quietly making a fortune, profiting millions per month.

This is the real slice of the OpenClaw wave: on stage, millions are busy with deployment, buying hardware, and purchasing courses; behind the scenes, a complete industry chain is monetizing this collective anxiety, layer by layer.

From selling hardware to cloud servers, from selling Tokens to Skills, the code is open-source and free, but the anxiety is the real commodity.

Turning OpenClaw into Hardware

A photo circulating online has left many people laughing and crying.

In the 1990s, qigong masters drew circles on people's aluminum pots, claiming to connect to cosmic energy; in 2026, people wear lobster hats in conference rooms receiving ideological洗礼, as if not raising a lobster means falling behind the times.

After the laughter, pay attention to the person speaking on stage. His name is Kong Jianping, a crypto OG, founder of iPollo.

Well-known KOL "Crypto Fearless" commented on social media: "In the crypto circle, the one always standing on the hottest trends is definitely Boss Kong Jianping... never missing big money, not neglecting small money."

When Bitcoin mining was hot, he launched the mining machine company Nano Labs. When the metaverse was hot, he stated a gradual shift from mining to metaverse business, claiming the metaverse would open a new era for humanity; when Hong Kong policies emerged, he布局 Hong Kong, becoming a director of Hong Kong Cyberport; when the DAT concept was hot, he started a DAT company...

This time, with OpenClaw's popularity, Kong Jianping is holding offline events in cities across the country, proclaiming that humanity has entered the "Web 4.0" era dominated by AI Agents, while simultaneously launching the hardware iPollo Claw PC, officially described as "born for Open Claw applications," with an AMD 5600H processor, up to 64GB RAM, pre-installed with ClawOS native system, priced at $439 on the official website.

Kong Jianping is not an isolated case; he is just the most visible name on this hardware supply chain.

In Dongguan, a hardware manufacturer's salesperson's朋友圈 is filled with lobster logos: OpenClaw lobster hardware solutions, support OEM ODM, B-end and large commercial customers welcome to call.

A standard white-label factory. Anyone wanting to enter the market can make a call and have a "AI host optimized for OpenClaw" branded.

Like this Dongguan company, many upstream in this hardware supply chain are factories that previously made cryptocurrency mining machines like Filecoin. They know this script too well: concept explosion, hardware demand emerges, white-label premium, harvesting window.

The supply chain response speed honed in the mining machine era is equally effective in a new field.

The core logic of this business is not mysterious at all: Mac Mini is the most widely recommended local hardware for running OpenClaw, but for most ordinary users, a 3000+ RMB Mac Mini plus a command-line configuration without a graphical interface is too high a barrier. Thus, demand arises, someone comes to sell a "lower the barrier" service, or simply sell a "ready-to-use out of the box" machine, monetizing this barrier.

The deeper the anxiety, the higher the premium.

Token Relay is a Big Business

OpenClaw itself is free, but running it requires continuously feeding it large model Tokens.

Domestic large model companies like MINIMAX and KIMI have become one of the biggest beneficiaries, but some still want to use overseas large models like Claude and ChatGPT to complete complex tasks.

The trouble is: registering accounts and making payments is difficult, and Claude frequently bans Chinese-speaking users. The official API price is steep; a high-intensity OpenClaw user running Claude fully can easily cost over $800 per month.

This has spawned a huge Token relay market.

On the market, you can buy a lot of "50% or even 30% off Claude API," but where they come from has always been a mystery.

On the surface, this industry is a business of making price differences, obtaining low-priced APIs, reselling at a markup, earning the middle profit, but the water is much deeper than that.

At the bottom, some rely on stolen credit cards to批量 register OpenAI and Anthropic accounts. After obtaining the accounts, the most common玩法 is to reverse engineer the ChatGPT and Claude web interfaces, package them into standard APIs, and resell them.

The price of an API relay station shows that its reverse-engineered Claude Code API is 89% cheaper than the official one. The official price is $0.024/K Token, it only needs $0.0024/T Token.

Even more profitable is selling outright fakes.

In early March, CISPA (Helmholtz Center for Information Security) published a research report titled "Real Money, Fake Models: Deceptive Model Claims in Shadow APIs".

They found that nearly half of the third-party API endpoints are engaged in bait-and-switch tactics.

You pay the API fee, happily thinking you are calling GPT-5, but the merchant is most likely running an extremely low-cost domestic model in the background, or even a free open-source model running locally (like GLM-4-9B).

Among the 17 top independent shadow API service providers identified by CISPA's audit, 15 were operated purely by individuals, and over 88.2% of the providers lacked even the most basic Internet Content Provider (ICP) registration.

An practitioner engaged in relay API agency told Deep Tide TechFlow that top relay APIs can now achieve profits of over a million per month, and demand is very strong.

These are just stories on the cost side. Former Manus staff member Yan publicly disclosed a deeper logic of this business: the core purpose of many Token relay stations is not to sell APIs, but to collect high-quality distillation corpora for specific scenarios.

"All requests passing through the relay, the complete prompt plus response, is a ready-made distillation dataset. Especially in OpenClaw's programming scenarios, users produce全是 complex reasoning chains and real engineering decisions, dream training materials for model manufacturers. So the real business model of some relay stations is likely: collecting your relay fee is the surface business, packaging your request data and selling it to big factories for model distillation is the core profit. You are a paying customer, and also a free training data producer, killing two birds with one stone."

Above this entire chain, there is another layer of business that looks cleaner: Token aggregation routing service providers, connecting you to APIs of more than a dozen models, automatically routing based on task complexity, simple tasks use cheap domestic models, complex tasks才 use Claude or GPT, claiming to save users 65% to 80% of API costs. The service itself is valuable. Those who master this traffic entry point accumulate maps of users' real usage patterns earlier than any model manufacturer.

Data is always the real asset.

Information Asymmetry is the Oldest Business

If the first two lobster gold mines rely on hardware and data, the third relies on something more basic: you know, and others don't.

Li Huan's recent business is traveling nationwide. He carries his laptop, flies to Shenzhen, Chengdu, Hangzhou, installing OpenClaw for local small business owners, connecting it to Feishu, DingTalk,调试好 automated workflows and专属 Skills. Thousands of RMB甚至 over ten thousand per order, earning significantly more per month than many programmers' salaries.

A counterintuitive thing is that Li Huan is not a programmer but comes from a liberal arts background. He坦诚 stated that he is not selling technology, but information asymmetry, directly transforming a hot concept into a product that bosses can use directly, while providing emotional value, soothing anxiety.

Taking this logic to the extreme is an American named Adam Sand.

Adam is also not an engineer. He and his wife Allison do business consulting for the roofing industry. After OpenClaw became popular, he did something that seems to have no technical barrier to the tech circle: installed pre-configured OpenClaw into MacBooks, connected to roofing industry-specific Skills packages, HubSpot CRM, and work order systems, configured data security, and直接 shipped them to customers. Plug in the power, and the AI employee starts work. One-on-one training, ongoing weekly support, priced at $5000 per unit.

This project is called RoofClaw, with total historical revenue exceeding $1.8 million,累计 delivered to over 360 roofing contractors.

Many people's first reaction is: Isn't this just putting a free open-source project into hardware and charging $5000?

Yes. But Adam is not selling software, nor hardware. He is selling the certainty that a roofing contractor, without needing to understand any technology, can have an AI employee start working tomorrow. Most people spend that $5000 without hesitation because Adam understands their pain points better than anyone; he has been dealing with this industry for over a decade.

This is the essence of the information asymmetry business: truly understanding your customer's needs.

Back in China, this business has already卷 to another style.

On Taobao, there is more than one installation service store with累计 orders exceeding 1000, with teams of dozens of engineers, earning 300,000 to 450,000 RMB in the past month just from installing OpenClaw. Some installers even promise on the product page "on-site deployment, plus one free cooking service, can cook home-style dishes".

This track is so hot that even Meituan and JD.com have entered the fray, partnering with Lenovo IT services to offer remote deployment services. From individual operators all the way卷 to internet giants, people at every level are making money, indicating this demand is real enough and big enough.

Skills selling is the other side of this logic.

OpenClaw's plugin ecosystem ClawHub has over 5700 community-contributed Skills plugins. Some are selling ready-made Skills packages, some are selling SOUL.md role configuration templates—AI CEO, Marketing Director, Legal Reviewer—for $19 to $99 each, some are selling custom development services, some are selling "OpenClaw complete practical tutorials".

The higher the technical barrier, the more people are willing to pay to lower it. This rule has never failed in every technology cycle.

The Shovel Sellers Never Worry

On a startup project aggregation platform with Stripe-verified revenue, there are already 126创业 projects围绕 OpenClaw, ranked by verifiable revenue over the past 30 days.

The data is残酷: among the top 30 projects making the most money, over 17 are doing the same thing: one-click cloud hosting.

Claw Mart made $54,000 in the last 30 days, Donely has a total historical revenue of $747,000, RoofClaw has a total historical revenue of $1.8 million.

But the other side of the story is: the half-life of information asymmetry is measured in "weeks," not "months."

The first batch of shovel sellers have already started to withdraw. A project called QuickClaw,主打 "deploy OpenClaw on手机 in 30 seconds", rapidly gained volume during a certain week of traffic explosion, and then offered to sell the entire package for $300,000.

This is the most real rhythm of this wave: technology concept explodes, information asymmetry window opens, the first batch of people swarm in,红利 quickly dissipates, the shovel sellers sell their shovels and turn to leave.

Kong Jianping has long刻 this rhythm into muscle memory. He moved from Bitcoin mining machines to OpenClaw hosts, spanning an entire crypto era. Each time he can accurately stand on the crest of the wave, not because he understood the technology earlier than others, but because he understands human nature better, understands the fear of "I can't fall behind," and how much people are willing to pay to消解 this fear.

That comparison picture of the qigong fever makes people laugh. The images from the two eras are structurally identical: aluminum pots and lobster hats are both rituals, both visualizations of anxiety, both symbols of "I am already on the right side."

The only difference is that the lobster hat in 2026 is connected to a real industry chain behind it, more precise, more懂得 monetizing human nature.

In the 1849 California Gold Rush, the ones who made money in the end were not the miners, but Levi Strauss who sold jeans.

This story has been told for 175 years, and it gets brought up again in every technology wave.

Because every time, it's right.

The shovel sellers don't bet on the outcome of technology, they bet on the stability of human nature.

So they never worry.


Twitter:https://twitter.com/BitpushNewsCN

Bitpush TG Discussion Group:https://t.me/BitPushCommunity

Bitpush TG Subscription: https://t.me/bitpush

Original link:https://www.bitpush.news/articles/7618789

Domande pertinenti

QWhat is the main business model behind the OpenClaw hardware boom, as described in the article?

AThe main business model involves hardware manufacturers, particularly former crypto mining machine factories, creating and selling pre-configured, 'plug-and-play' hardware devices optimized for running OpenClaw. They capitalize on the high technical barrier and user anxiety by offering a simplified, monetized solution, often with significant markups.

QHow do token relay stations (API middlemen) generate profit in the OpenClaw ecosystem?

AToken relay stations profit by reselling discounted API access, often obtained through methods like credit card fraud or by reverse-engineering web interfaces. Some engage in deceptive practices, such as substituting expensive models with cheaper ones. The most profitable operations also collect high-quality user prompts and responses to sell as distillation data to large model companies.

QAccording to the article, what is the core thing that Adam Sand's RoofClaw business is actually selling?

AAdam Sand's RoofClaw business is not primarily selling software or hardware. It is selling the certainty and convenience for roof contractors to have an AI employee start working immediately, without needing any technical knowledge. The $5000 price tag is for solving a specific industry pain point he understands deeply.

QWhat historical analogy does the article use to describe the dynamic between the 'miners' and the 'shovel sellers' in the OpenClaw trend?

AThe article uses the analogy of the 1849 California Gold Rush, where the people who made the most money were not the miners panning for gold, but those like Levi Strauss who sold essential supplies like jeans and shovels to the miners.

QWhy does the article claim that the 'shovel sellers' are never anxious, while the 'miners' are?

AThe 'shovel sellers' are not anxious because their business does not depend on the success or failure of the underlying technology (OpenClaw itself). Instead, they bet on the stability of human nature—specifically, the fear of being left behind and the willingness to pay to alleviate that anxiety. They profit from the hype cycle itself, not the outcome.

Letture associate

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1 h fa

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1 h fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1 h fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1 h fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1 h fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1 h fa

Trading

Spot
活动图片