After OpenClaw went viral: which US stocks were impacted by an open-source crayfish?

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, but 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 surpassed 100,000. As of March 3, this number had ballooned to nearly 250,000, topping the stars chart and surpassing Linux. For reference, star-studded 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 growth curve is almost a vertical line.

OpenClaw's original name, Clawdbot, sounds like Claude, and on January 27, Anthropic sent a lawyer's letter demanding a name change. The project went through the name Moltbot before finally being named OpenClaw. However, the name changes did not slow its spread; instead, they generated more discussion. On February 16, Sam Altman announced that Steinberger would join 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 crayfish took less than three months.

The popularity of OpenClaw in the tech circle is evident to all, but where has this fire spread now? This article attempts to outline the benefiting industrial chain behind OpenClaw's explosive popularity from the perspective of the capital market, as well as the U.S. companies that may be revalued.

1. What is OpenClaw? Why does it impact U.S. stocks?

To get to 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 piece of text. In contrast, OpenClaw receives your commands and then takes action. It can operate browsers, execute code, call APIs, manage file systems, and connect to more than 12 messaging platforms.

The difference in operational modes can be summarized in a table:

In simpler terms, it has evolved from a chatbot into a true digital employee, which also means that the commercial paradigm of AI is undergoing a qualitative change. In the conversational era, users ask a large model a question, and the model returns an answer, consuming hundreds of tokens, and the interaction ends. However, in the Agent era, an OpenClaw may initiate hundreds or even thousands of calls to the model each day. The token consumption generated by a single Agent user can be dozens or even hundreds of times that of traditional chat users.

This consumption multiplier is the core transmission chain through which OpenClaw impacts U.S. stocks:

First layer: A surge in model call volume. Each tool call and decision-making reasoning by the Agent consumes tokens, directly benefiting large model API providers.

Second layer: A spike in inference computing power demand. Massive Agent calls mean a massive number of inference requests, shifting the GPU demand logic from the "training side" to the "inference side," bringing a new narrative for chip companies.

Third layer: Comprehensive benefits for cloud infrastructure. Agents need cloud servers to run, and model inference requires cloud GPUs for computation. Enterprise-level Agents need compliant, secure, and monitorable cloud infrastructure.

Fourth layer: Demand for enterprise Agents awaits validation. OpenClaw has proven the real demand for "AI doing work for people" in an open-source manner, and the valuation logic for enterprise software companies that are commercializing Agent capabilities may change.

Fifth layer: Expanded security threat landscape. When Agents hold email, calendar, and file system permissions for an extended period, the attack surface is exponentially enlarged, leading to new growth narratives for security companies.

Next, we will follow this chain and systematically outline the benefiting U.S. stocks.

2. Token Killer: The Superflywheel of Large Model Service Providers

If Agents become the mainstream paradigm for AI interaction, the API revenue of large model vendors will experience exponential growth.

However, the two largest Agent model suppliers, OpenAI and Anthropic, are not yet publicly traded. Therefore, the most direct corresponding listed stocks in the capital market are MSFT and GOOGL.

First, Microsoft, as the largest external shareholder of OpenAI, sees every API request made through Azure OpenAI Service to call GPT-4o or o1 essentially contributing revenue to Microsoft's cloud business. The fact that the founder of OpenClaw joined OpenAI and handed the project over to an OpenAI-supported foundation means that the OpenClaw ecosystem will likely be more closely tied to OpenAI models in the future. If OpenClaw's default model recommendation list ranks OpenAI at the top, Microsoft will have unknowingly gained access to a developer with 240,000 GitHub stars.

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

What’s more noteworthy is that the market currently seems not to have fully priced in the API consumption logic driven by Agents. Since February, GOOGL has not shown significant increases due to OpenClaw, while MSFT has experienced a round of valuation correction. In other words, the expectation gap still exists, meaning that the capital market is still valuing model companies using the "chatbot" logic rather than the continuously operating Agent economy.

3. Inference is Never Enough: The New Narrative 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 primarily been based on the training side, with major manufacturers competing to purchase GPUs to train increasingly larger foundational models. However, training is more like a phased investment, while inference is a continuous consumption. For example, every tool call by an Agent continuously triggers new inference requests. As Agents move from the lab to millions of users, the proportion of demand on the inference side is expected to rise significantly.

This also explains NVIDIA's new narrative. If the large orders on the training side slow down, what else can sustain GPU demand? The answer provided by Agents is the continuous expansion of the inference side. NVIDIA's latest financial report shows a 73% year-on-year revenue growth in Q4 2026, with strong demand remaining, and the rise of the Agent paradigm provides a more sustainable underlying explanation for this strength.

Now let’s look at AMD. On February 4, AMD's stock plummeted 17% due to disappointing Q1 earnings, causing panic in the market. However, just 20 days later, Meta announced a strategic AI chip supply agreement with AMD worth up to $60 billion (over 5 years), along with a warrant arrangement for up to 160 million shares, about 10%, resembling a strategic deep binding.

Why does Meta need so much inference computing power? Because it is pursuing what it calls personal superintelligence, and achieving this vision relies on a massive number of Agents running continuously in the background. OpenClaw validates not just a product direction but the entire logic of the demand for substantial computing power for Agents.

Thus, the growth in inference demand driven by Agents will first transmit to the computing power layer, with core targets being NVDA and AMD, while among companies that continuously consume computing power at the application layer, META may also become a significant demand driver.

4. The True Carrier of Agent Scaling: Cloud Computing

As mentioned earlier, GPUs are the engines of the Agent era, while cloud computing platforms are the infrastructure for these Agents to run long-term. From the perspective of the capital market, the core targets corresponding to this chain are the three major cloud platforms AMZN, MSFT, and GOOGL, while upstream, data center infrastructure providers EQIX and DLR may also become indirect beneficiaries.

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

Moreover, there is an easily overlooked detail: the value of Agents to the cloud is not just computing power but also long-tail inference traffic. Because AI training orders are "large customers + large orders + periodic," while Agent inference is "many small customers + high-frequency calls + continuous revenue," which is a business model that cloud vendors prefer.

In the global market, the three major cloud vendors each have unique advantages. AWS, as the largest cloud platform globally, supports multiple model API integrations through its Bedrock platform, making it one of the common deployment environments for developers. Azure benefits from both model API and cloud infrastructure, with the exclusive GPT access capability of Azure OpenAI Service being further amplified in Agent scenarios. Google Cloud's differentiation lies in its cost structure. The inference prices of models like Gemini Flash are significantly lower than many flagship models, and in scenarios where Agents need to run continuously and consume tokens, this price difference will be quickly magnified.

Another logical point to note is that if Agents run at scale, the computing power demand from cloud vendors will ultimately transmit to data center construction, and Equinix and Digital Realty may also benefit indirectly.

5. The Logic of Enterprise Agents Awaits Validation, 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 sectors, this is seen as the prologue to a "SaaSpocalypse."

At the beginning of 2026, SaaS giants collectively faced pressure: Salesforce has dropped 21% since the beginning of the year, and ServiceNow has fallen 19%. The root of the panic comes from a structural game between Agents and software. In the past, we needed a software interface to command the system to do tasks; now, Agents can directly call the system to complete tasks, diminishing the presence of software itself. This change brings two fundamental issues.

First, the impact of AI is not limited to the "per-head charging" model but affects the entire software value chain. For example, Adobe's stock price has fallen from a high of $699.54 to $264.04, a drop of 62%; educational software company Chegg has plummeted from $115.21 to $0.44, nearly zero; tax software giant Intuit also saw a 16% drop within a week in January 2026. The market is concerned not just about a specific charging model being disrupted but that generative AI tools (like Anthropic, etc.) are automating core business workflows, reducing reliance on traditional software functions, thus permanently compressing the revenue potential of entire SaaS platforms.

Second, the stronger the Agent, the more fragile the traditional business model becomes. Take ServiceNow as an example; Microsoft is eroding its pricing power through the "Agent 365" bundling strategy, slowing down the acquisition of new customers. A simple deduction is enough to make investors shudder: if one AI Agent can do the work of 100 employees, does the enterprise still need to purchase 100 software licenses? The breakout of OpenClaw essentially accelerates the realization of this logic.

Of course, several giants are not sitting idle. Salesforce's AgentForce has achieved an ARR of $800 million, a year-on-year increase of 169%; ServiceNow's Now Assist annual contract value has surpassed $600 million, with expectations to hit $1 billion by the end of the year. But it is never easy for elephants to dance; they are caught in the classic innovator's dilemma: new Agent revenue is growing, while old seat revenue is shrinking, and the outcome of these two curves racing against each other remains unclear. For CRM and NOW, the core contradiction lies in whether the increment from Agents can fill the gap left by the seat model. The market has already voted with its feet.

Meanwhile, Palantir tells a completely different story. This company focuses on helping governments and large enterprises make critical decisions using AI: the military uses it to analyze battlefield intelligence, and enterprises 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 is being hit by the "SaaS apocalypse," Palantir is strengthening against the trend. This differentiation may indicate that the winners of the Agent era may not be the old giants that transform the fastest but rather the companies that were born for AI from the beginning.

6. Hidden Benefits for Security Companies

This is currently the most underestimated clue in the market.

Imagine you have 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 leaks, permission abuse, and even data theft.

This is precisely why security companies are starting to position themselves early. In the current security industry, CrowdStrike (CRWD) and Palo Alto Networks (PANW) are two of the most capable leading firms.

CrowdStrike is considered a leader in endpoint security, with its Falcon platform unifying the management of endpoints, identities, and threat intelligence through a cloud-native architecture, achieving high penetration rates among large enterprises globally. In recent years, the company has continuously integrated AI into security operations, such as Charlotte AI, which can automatically complete threat detection and response.

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

At the moment when OpenClaw has just exploded in popularity, security issues have not yet been widely converted into revenue growth, but this precisely means that security companies may represent the segment with the largest "expectation gap" in the entire Agent narrative. Moreover, security spending is a necessity.

7. Conclusion: Short-term Focus on Sentiment, Mid-term on Inference, Long-term on Ecosystem

Returning to the initial question, what U.S. stocks has OpenClaw leveraged? We can reason from different timelines.

Currently (in the past month), from the stock price performance perspective, OpenClaw's direct pulse on individual stocks is quite limited. GOOGL and MSFT have not shown abnormal fluctuations driven by the Agent narrative since February. The only clear event-driven spike comes from AMD, where Meta's billion-dollar chip order pushed its stock to soar in a single day. Overall, the AI sector may be undergoing a round of valuation calibration, and OpenClaw's popularity has not translated into immediate stock price catalysts.

In the short term (3 months), the market may continue to digest the pressure of the AI valuation bubble, but the cognitive shock brought by OpenClaw may change buyers' cognitive anchor regarding the Agent sector. This change in cognitive level will not immediately reflect in stock prices but may reshape analysts' expectation models.

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

In the long term (1-3 years), the real winners will be the companies that occupy 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 key thing: it has shown the world the possibilities of AI Agents. This is no longer just product iteration; it is a profound paradigm shift.

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