Author: Viee I Biteye Content Team

In November 2025, an independent developer from Austria, Peter Steinberger, quietly submitted a project on GitHub - Clawdbot (renamed to OpenClaw)

At that time, no one paid attention, and everything spiraled out of control by the end of January 2026.

Between January 29 and 30, the project gained tens of thousands of GitHub stars in a very short time and quickly broke through 100,000. By March 3, this number had swollen to nearly 250,000, topping the star rankings, surpassing Linux. For reference, celebrity open source projects like React (one of the most popular front-end development frameworks globally) and Linux (the operating system kernel that supports internet servers) often take over a decade to accumulate around 200,000 stars, while OpenClaw's curve is almost a vertical line.

The original name of OpenClaw, Clawdbot, is a homophone of Claude. On January 27, Anthropic sent a lawyer's letter demanding a name change, leading the project to become Moltbot, and eventually settle on OpenClaw. However, the name change did not slow its rate of spread at all; instead, it created more topics of discussion. On February 16, Sam Altman announced Steinberger's addition to OpenAI, and OpenClaw would be handed over to an independent open-source foundation supported by OpenAI.

From an independent developer's project to a strategic pawn of a tech giant, this little lobster took less than three months.

OpenClaw itself has been witnessed to be extremely popular in the tech circle, so where has this fire spread now? This article attempts to outline the benefiting industrial chains behind the explosive growth of OpenClaw from the perspective of the capital market, as well as the US companies that may be reassessed.

1. What is OpenClaw? Why does it impact US stocks?

Let's start with the essence. OpenClaw is not just another chatbot; it is an open-source AI agent framework.

What’s the difference? A chatbot receives your questions and returns a text response. In contrast, OpenClaw receives your commands and then takes action. It can operate browsers, execute code, call APIs, manage file systems, and connect to over 12 messaging platforms.

The differences in their operational modes can be summarized in a table:

In short, to put it more plainly, it has evolved from a chatbot into a real digital employee, which also means that the business paradigm of AI is undergoing a qualitative change. In the conversational era, users pose a question to a large model, and the model returns an answer, consuming hundreds of tokens, concluding the interaction. However, in the agent era, a single OpenClaw may initiate hundreds or even thousands of calls to the model every day. The token consumption generated by a single agent user can be tens or even hundreds of times that of traditional chat users.

This consumption multiplier is the core transmission chain by which OpenClaw influences US stocks:

• First layer: Model call volume surges. Each tool call and every decision-making by an agent consumes tokens, directly benefiting the providers of large model APIs.
• Second layer: Demand for inference computing power skyrockets. A massive number of agent calls means a massive number of inference requests, shifting the demand logic for GPUs from "training" to "inference," providing chip companies with a new narrative.
• Third layer: Cloud infrastructure benefits overall. Agents need cloud servers to run, and model inference requires cloud GPUs to compute; enterprise-level agents require compliant, secure, and monitorable cloud infrastructure even more.
• Fourth layer: Corporate agent demands to be verified. OpenClaw has proven that the demand for "AI to do the work" genuinely exists in an open-source manner, and the valuation logic for software companies commercializing agent capabilities may change.
• Fifth layer: Expansion of security threats. When agents hold email, calendar, and file system permissions for long periods, the attack surface is exponentially enlarged, leading to a new growth narrative for security companies.
• Next, we will sort through the benefiting US stocks along this chain one by one.

2. Token Killer: The Superflywheel of Major Model Providers

If agents become the mainstream paradigm of AI interaction, API revenues for major model providers will see exponential growth.

However, the two largest agent model suppliers currently, OpenAI and Anthropic, have not yet gone public. Therefore, the most direct representatives corresponding to this logic in the capital market are MSFT and GOOGL.

First, Microsoft, as OpenAI's largest external shareholder, has income contribution for its cloud business from every API request made through the Azure OpenAI Service calling GPT-4o or o1. The fact that OpenClaw's founder joined OpenAI and transferred the project to a foundation supported by OpenAI means that the future OpenClaw ecosystem will likely be more closely bound to OpenAI models. If OpenClaw's default model recommendation list ranks OpenAI first in the future, Microsoft will effectively receive an entry from a developer with 240,000 GitHub stars without even realizing it.

On the other hand, Alphabet is another beneficiary from a different dimension, which is the publicly listed company (stock codes GOOGL/GOOG) that owns Google itself. Google's Gemini series is one of the mainstream models supported by OpenClaw, and Gemini 2.0 Flash boasts highly competitive inference cost-performance. More critically, among several leading model providers, Alphabet is one of the few AI model providers that can be directly invested through the secondary market.

What’s more noteworthy is that the market currently seems to not fully price the API consumption logic driven by agents. GOOGL has not seen a significant rise since February due to OpenClaw, while MSFT has gone through a round of valuation correction. In other words, an expectation gap still exists, meaning the capital market is still valuing model companies based on the "chatbot" logic instead of the continuously operating agent economy.

3. Inference is Never Enough: New Narratives for Chip Companies

If token consumption is the gasoline of the agent era, then GPUs are the engines driving this machine, and the most direct beneficiaries remain GPU manufacturers NVIDIA and AMD.

Over the past three years, the valuation logic for chip companies has mainly been established on the training side. Major manufacturers have been competing to procure GPUs to train increasingly larger base models. However, training is more like a phase investment, while inference is a continuous consumption. For instance, each tool call by an agent continuously triggers new inference requests. As agents move from the laboratory to millions of users, the proportion of inference demand is expected to increase significantly.

This also explains the new narrative for NVIDIA. If the large orders on the training side are marginally slowing down, what else can sustain the demand for GPUs? The answer offered by agents is the continued expansion on the inference side. NVIDIA's latest financial report shows a year-over-year revenue growth of 73% in Q4 2026, with strong demand still intact, and the rise of the agent paradigm provides a more sustainable underlying explanation for this strength.

Let’s also take a look at AMD. On February 4, AMD plummeted 17% due to Q1 financial results falling short of expectations, causing market panic to spread. However, just 20 days later, Meta announced a strategic AI chip supply agreement with AMD worth up to $60 billion (over five years), with up to 160 million stock warrants, about 10%, further establishing a deep strategic bond.

Why does Meta need so much inference power? Because it is pursuing what it calls personal super intelligence, which relies on a massive number of agents running in the background. OpenClaw does not just validate a product direction; it confirms the fundamental logic of demand for large computing power needed by agents.

Thus, the growth of inference demand driven by agents will first be transmitted to the computing power level, with corresponding key targets being NVDA and AMD, while among companies continuously consuming computing power at the application level, META could also become a significant demand driver.

4. The Real Carrier of Agent Scale: Cloud Computing

As previously mentioned, GPUs are the engines of the agent era, while cloud computing platforms are the infrastructure for these agents to operate over the long term. From the capital market perspective, the core targets that correspond to this chain are the three major cloud platforms AMZN, MSFT, and GOOGL, while further upstream in the data center infrastructure layer, EQIX and DLR could also become indirect beneficiaries.

Although OpenClaw boasts local deployment, the reality is that due to security permission issues, most users will not run an AI agent on their laptops 24/7. For both individuals and enterprises, the endpoint for scaled deployment is likely cloud deployment. Alibaba Cloud and Tencent Cloud have already launched one-click deployment services in the Chinese market, which indirectly verifies the authenticity of demand.

Moreover, there is an easily overlooked detail: the value of agents to the cloud is not just computing power but also the long-tail inference traffic. This is because AI training orders are “large clients + large orders + periodic,” while agent inference is “a lot of small clients + high-frequency calls + continuous revenue,” which is the business model that cloud vendors prefer.

In the global market, the three major cloud vendors each possess unique advantages. AWS, as the world's largest cloud platform, supports multiple model API access through its Bedrock platform and has become one of the common deployment environments for developers. Azure captures benefits from both the model API and cloud infrastructure, with the unique exclusive GPT access capability of Azure OpenAI Service being further amplified in the agent scenarios. Google Cloud's differentiation lies in cost structure; models like Gemini Flash have significantly lower inference prices compared to many flagship models, and in scenarios requiring long-term operation of agents consuming tokens, this price difference can be rapidly magnified.

Another logical point to note is that if agents run at scale, the cloud providers' demand for computing power will ultimately be transmitted to data center construction, potentially benefiting Equinix and Digital Realty indirectly.

5. Enterprise Agent Logic to be Verified, Favoring AI Native Companies

The popularity of OpenClaw validates a trend: People are willing to let AI do the work for them rather than just chat with them. However, for traditional enterprise software, this is seen by the market as the prologue to "SaaSpocalypse" (the SaaS apocalypse).

Entering 2026, SaaS giants collectively came under pressure: Salesforce dropped 21% since the beginning of the year, and ServiceNow declined 19%. The root of the panic stems from a structural game between agents and software. In the past, to command a system to do work, we needed a software interface; but now, agents can directly invoke systems to complete tasks, diminishing the presence of software itself. This change brings two fundamental issues.

First, the impact of AI is not limited to just the "per user charge" model but affects the entire software value chain. Taking Adobe as an example, its stock price dropped from a peak of $699.54 to $264.04, a decline of 62%; education software company Chegg plummeted from $115.21 to $0.44, almost to zero; tax software giant Intuit also dropped 16% in just one week in January 2026. What the market worries about is not just one type of charging model being overturned but the automated core workflows that generative AI tools (like Anthropic) are performing, thus reducing reliance on traditional software functions and causing the overall revenue potential of the SaaS platforms to be permanently compressed.

Secondly, the more powerful the agent, the weaker the traditional business model becomes. For instance, with ServiceNow, Microsoft is eroding its pricing capability through a bundling strategy with "Agent 365," slowing down the speed of acquiring new customers. A simple deduction can send chills down investors' spines: If one AI agent can do the work of 100 employees, does a company still need to purchase 100 software licenses? The emergence of OpenClaw is essentially accelerating the realization of this logic.

Of course, several giants have not been sitting idly by. Salesforce’s AgentForce has achieved an annual recurring revenue (ARR) of $800 million, up 169% year-over-year; ServiceNow's Now Assist annual contract value has exceeded $600 million, with expectations to hit $1 billion by the end of the year. But it's never easy for elephants to dance; they find themselves in the classic innovator's dilemma: new agent income is growing, while old seat revenue is shrinking, and the outcome of these two curves racing against each other remains uncertain. For CRM and NOW, the core contradiction lies in whether the incremental value of agents can make up for the shortfall of the seat model. The market has already voted with its feet to provide an answer.

Meanwhile, Palantir is telling an entirely different story. This company focuses on helping governments and large enterprises make crucial decisions using AI: the military uses it to analyze battlefield intelligence, and companies use it to optimize supply chains and predict risks, deploying AI in the most complex and sensitive business scenarios. After a brief pullback in February, PLTR quickly rebounded, stabilizing around $153 in early March.

While the SaaS sector was hit hard by the "SaaS apocalypse," Palantir has been gaining strength against the trend. This differentiation may suggest that the winners of the agent era may not be the old giants transforming the fastest, but the companies that were born for AI from the start.

6. Hidden Benefits for Security Companies

This is currently the most undervalued clue in the market.

Imagine you configured OpenClaw with email, calendar, Slack, Google Drive, and GitHub. It needs these keys to help you work, but what if this agent gets compromised? The OpenClaw community has already discussed related security risks multiple times, such as credential leakage, permission abuse, and even data theft.

This is precisely why security companies are starting to get ahead; in the current security industry, CrowdStrike (CRWD) and Palo Alto Networks (PANW) are the two most capable leading companies.

CrowdStrike is regarded as a leader in endpoint security, with its Falcon platform managing endpoints, identities, and threat intelligence through a cloud-native architecture, achieving high penetration rates among large enterprises worldwide. In recent years, the company has continuously introduced AI into security operations, such as Charlotte AI, which can automate threat detection and response.

Palo Alto Networks is a leading company in the global cybersecurity industry. Starting from next-gen firewalls, it has gradually expanded into cloud security, identity security, and automated security operations, acquiring CyberArk for $25 billion in 2025 to protect the security of intelligent agent identities.

At this moment when OpenClaw has just exploded in popularity, security topics have not yet massively converted into revenue growth, but this precisely means that security companies may be the sector with the greatest "expectation gap" in the entire agent narrative. Moreover, security expenditure is a necessary item.

7. Conclusion: Short-Term Focus on Sentiment, Medium-Term on Inference, Long-Term on Ecosystem

Returning to the initial question, what US stocks has OpenClaw leveraged? We can extrapolate reasoning from different timelines.

Currently (in the past month), based on stock performance, OpenClaw’s direct pulse on individual stocks is quite limited. GOOGL and MSFT have not seen abnormal fluctuations driven by the agent narrative since February. The only clear event-driven spike came from AMD, with Meta's multi-billion chip order driving its single-day surge. Overall, the AI sector may be undergoing a round of valuation calibration, and the explosion of OpenClaw has not translated into immediate stock price catalysts.

In the short term (3 months), the market might continue to digest the squeeze of the AI valuation bubble, but the cognitive shock brought about by OpenClaw could change buyers' anchors of perception regarding the agent sector. This change in cognitive layer may not immediately reflect in stock prices but could reshape analysts' expectation models.

In the medium term (6-12 months), the key catalyst will be whether the demand for agent inference computing power can be validated in financial reports. If OpenClaw and subsequent Kimi Claw, MaxClaw, and enterprise-level agent solutions can deliver observable growth in API call volumes and cloud resource consumption, the narrative for the inference side of NVDA, AMD, and the three major cloud vendors may be confirmed.

In the long term (1-3 years), the real winners will be those companies occupying positions in the agent ecosystem, such as CrowdStrike and Palo Alto Networks, which establish standards in the agent security field.

We also need to recognize that OpenClaw may not be the ultimate product; it has security vulnerabilities, high token costs, and an uncertain business model. But it has at least accomplished one critical thing: it has shown the world the possibilities of AI agents. This is no longer just product iteration; it is a profound paradigm shift.

Once a paradigm shift occurs, it will not stop; we can only be fully prepared to wait for that day to come.

免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。