After OpenClaw's explosive popularity: Which US stocks were affected by this open-source crayfish?

Author: Viee I Biteye Content Team

In November 2025, an Austrian independent developer named Peter Steinberger quietly submitted a project to GitHub - Clawdbot (now renamed OpenClaw).

No one cared at the time, but everything spiraled out of control at the end of January 2026.

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

OpenClaw was originally named Clawdbot, a name that sounds like "Claude." On January 27th, Anthropic sent a lawyer's letter forcing them to change the name. The project was subsequently transferred to Moltbot before finally settling on OpenClaw. However, the name change did not slow its spread; instead, it generated even more buzz. On February 16th, Sam Altman announced that Steinberger had joined OpenAI, and OpenClaw would be transferred to an independent open-source foundation supported by OpenAI.

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

OpenClaw's popularity in the tech world is undeniable, but where has this success spread now? This article attempts to analyze the beneficiary industry chain behind OpenClaw's explosive growth from a capital market perspective, and identify US-listed companies that may be revalued.

1. What is OpenClaw? Why does it affect the US stock market?

Let's get to the point. OpenClaw is not just another chatbot; it's an open-source AI Agent framework.

What's the difference? A chatbot receives your question and returns a text message. OpenClaw, on the other hand, receives your instructions and then takes action. It can operate browsers, execute code, call APIs, manage file systems, and connect to more than 12 messaging platforms.

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

In short, to put it more bluntly, it has evolved from a chatbot into a true digital employee, which also signifies a qualitative shift in the business paradigm of AI. In the dialogue era, a user asks a question to a large model, the model returns an answer, consuming hundreds of tokens, and the interaction ends. But in the Agent era, an OpenClaw might make hundreds or even thousands of calls to the model every day. The token consumption of a single Agent user can be tens or even hundreds of times that of a traditional chatbot user.

This consumption ratio is the core transmission chain through which OpenClaw influences the US stock market:

• First layer: A surge in model usage. Every tool call and every decision-making inference by the Agent consumes tokens, directly benefiting large model API providers.
• The second layer: a surge in demand for inference computing power. Massive agent calls mean massive inference requests, and the demand logic for GPUs is shifting from the "training side" to the "inference side," ushering in a new narrative for chip companies.
• The third layer: Cloud infrastructure benefits comprehensively. Agents need cloud servers to run, model inference needs cloud GPUs for computation, and enterprise-level agents need compliant, secure, and monitorable cloud infrastructure.
• Fourth layer: Enterprise Agent demand remains to be verified. OpenClaw has proven the real existence of the demand for "AI doing work for humans" through open source. The valuation logic of enterprise software companies that are commercializing agent capabilities may change.
• Fifth layer: Expanded security threat surface. When agents maintain long-term access to email, calendar, and file system permissions, the attack surface is magnified exponentially, ushering in a new growth narrative for security companies.
• Below, we will follow this chain and analyze the US stock stocks that will benefit one by one.

II. Token Killer: The Super Flywheel 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 providers, OpenAI and Anthropic, are not yet publicly listed. Therefore, the most direct IPOs corresponding to this logic in the capital market are MSFT and Google.

First, as OpenAI's largest external shareholder, every API request Microsoft makes through Azure OpenAI Service to call GPT-4o or o1 essentially contributes revenue to Microsoft's cloud business. The fact that the founders of OpenClaw joined OpenAI and transferred the project to the OpenAI-supported foundation means that the OpenClaw ecosystem will likely be more closely tied to OpenAI models in the future. If OpenAI becomes the first recommended model in OpenClaw's default model list in the future, Microsoft will essentially gain a developer portal with 240,000 GitHub stars without even realizing it.

Alphabet, on the other hand, is a beneficiary in another dimension: the publicly traded company to which Google belongs (stock code GOOGL / GOOG). Google's Gemini series is one of the mainstream models supported by OpenClaw, and the Gemini 2.0 Flash boasts highly competitive inference performance. More importantly, among the leading model vendors, Alphabet is one of the few AI model providers that can be directly invested in through the secondary market.

More noteworthy is that the market currently doesn't seem to have fully priced in the agent-driven API consumption logic. Google hasn't seen significant gains since February due to OpenClaw, while MSFT is experiencing a valuation correction. In other words, the expectation gap still exists; the capital market is still valuing model companies based on the logic of "chatbots," rather than the continuously operating agent economy.

III. Reasoning is never enough: The new narrative of chip companies

If token consumption is the gasoline of the Agent era, then GPUs are the engine that drives the machine, and the most direct beneficiaries are still GPU manufacturers NVIDIA and AMD.

Over the past three years, the market's valuation logic for chip companies has primarily been based on the training side, with major manufacturers vying to purchase GPUs to train increasingly larger base models. However, training is more of a phased investment, while inference is a continuous consumption; for example, every tool call by each agent constantly triggers new inference requests. As agents move from the laboratory to millions of users, the proportion of demand on the inference side is expected to increase significantly.

This also explains NVIDIA's new narrative. If the large-scale single-order growth on the training side slows down, what will sustain GPU demand? The Agent paradigm's answer is the continued increase in inference volume. NVIDIA's latest financial report shows that revenue in Q4 2026 increased by 73% year-on-year, indicating continued strong demand. The rise of the Agent paradigm provides a more sustainable underlying explanation for this strength.

Let's take a look at AMD. On February 4th, AMD's stock price plummeted by 17% due to its Q1 earnings report falling short of expectations, triggering widespread market panic. However, just 20 days later, Meta announced a $60 billion (5-year) AI chip supply agreement with AMD, along with warrants for up to 160 million shares, representing approximately 10% of the company's stock, which resembles a deep strategic partnership.

Why does Meta need so much inference computing power? Because it's pursuing so-called personal superintelligence, and realizing this vision requires a massive number of agents running continuously in the background. OpenClaw is validating not just a product direction, but the entire logic behind the need for a large amount of computing power for the entire agent.

Therefore, the growth in inference demand driven by agents will first be transmitted to the computing power layer, with NVDA and AMD being the core targets. Among companies that continuously consume computing power at the application layer, META may also become an important demand driver.

IV. The true vehicle for agent scaling: cloud computing

As mentioned earlier, GPUs are the engine of the Agent era, and cloud computing platforms are the infrastructure for the long-term operation of these agents. From the perspective of the capital market, the core targets in this chain are the three major cloud platforms AMZN, MSFT, and GOOGL, while at the higher-up data center infrastructure layer, EQIX and DLR may also be indirect beneficiaries.

While OpenClaw touts local deployment, the reality is that due to security and permission issues, most users won't run an AI agent on their laptops 24/7. For both individuals and enterprises, large-scale deployment is likely to ultimately be cloud-based. The fact that Alibaba Cloud and Tencent Cloud have already launched one-click deployment services in the Chinese market indirectly confirms the genuine demand.

Furthermore, there's an easily overlooked detail: the value of agents to the cloud isn't just computing power, but also long-tail inference traffic. AI training orders are characterized by "large clients + large orders + periodicity," while agent inference is driven by "a large number of small clients + high-frequency calls + continuous revenue"—a 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 APIs through its Bedrock platform, making it a common deployment environment for developers. Azure benefits from both model APIs and cloud infrastructure, with Azure OpenAI Service's exclusive GPT access capabilities further amplified in agent scenarios. Google Cloud differentiates itself through its cost structure. The inference price of models like Gemini Flash is significantly lower than many flagship models, and this price difference is rapidly amplified in scenarios requiring long-term agent token consumption.

Another point worth noting is that if the agents operate at scale, the computing power needs of cloud vendors will eventually be passed on to data center construction, and Equinix and Digital Realty may indirectly benefit.

V. The logic of enterprise agents needs further validation, which is beneficial to AI-native companies.

The popularity of OpenClaw confirms a trend: people are willing to let AI do their work, not just chat with them. But for the traditional enterprise software sector, this is seen by the market as the prelude to the "SaaSpocalypse" (the end of SaaS).

At the start of 2026, SaaS giants collectively faced pressure: Salesforce fell 21% year-to-date, and ServiceNow fell 19%. The root of this panic lies in a structural game between agents and software. In the past, we needed a software interface to command systems; now, agents can directly invoke systems to complete tasks, and the software's own presence is being diminished. This change brings two fundamental problems.

First, the impact of AI is not limited to the "per-user" model, but affects the entire software value chain. For example, Adobe's stock price plummeted from a high of $699.54 to $264.04, a drop of 62%; education software company Chegg crashed from $115.21 to $0.44, almost to zero; and tax software giant Intuit also saw a 16% plunge in a week in January 2026. The market's concern is not about the disruption of a particular pricing model, but rather that generative AI tools (such as Anthropic) are automating core enterprise workflows, reducing reliance on traditional software functions, and thus permanently compressing the revenue potential of the entire SaaS platform.

Secondly, the more powerful the agent, the more vulnerable traditional business models become. Take ServiceNow as an example: Microsoft is eroding its pricing power and slowing down new customer acquisition through its "Agent 365" bundling strategy. A simple deduction is enough to send chills down investors' spines: if one AI agent can do the work of 100 employees, is it still necessary for a company to buy 100 software seats? OpenClaw's mainstream success is essentially accelerating the realization of this logic.

Of course, the giants haven't sat idly by. Salesforce's AgentForce has achieved $800 million in ARR, a year-on-year increase of 169%; ServiceNow's Now Assist annual contract value has exceeded $600 million, and is expected to reach $1 billion by the end of the year. But it's never easy for giants to dance, and they're caught in the classic innovator's dilemma: new agent revenue is growing, but old seat revenue is shrinking, and the outcome of this two-pronged race remains unclear. For CRM and Now, the core contradiction lies in whether the incremental growth of agents can fill the gap left by the seat model. The market has already given its answer.

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 businesses 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 battered by the "SaaS doomsday" narrative, Palantir bucked the trend and strengthened. This divergence may mean that the winners of the Agent era may not be the old giants that transformed the fastest, but rather the companies that were born for AI from the very beginning.

VI. Hidden Benefits for Security Companies

This is currently the most undervalued lead in the market.

Imagine you've configured OpenClaw with your email, calendar, Slack, Google Drive, and GitHub accounts. It needs these keys to do what you need, but what if this agent is compromised? The OpenClaw community has discussed these security risks multiple times, such as credential leaks, privilege 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 the two most capable leading vendors.

CrowdStrike is considered a leader in endpoint security. Its Falcon platform, with its cloud-native architecture, unifies the management of endpoints, identities, and threat intelligence, and enjoys extremely high penetration rates among large enterprises worldwide. In recent years, the company has continuously introduced AI into its security operations; for example, Charlotte AI can automate threat detection and response.

Palo Alto Networks is a leading global cybersecurity vendor. Starting with next-generation firewalls, it has expanded into cloud security, identity security, and automated security operations. In 2025, it acquired CyberArk for $25 billion, focusing on protecting the identity security of intelligent agents.

While OpenClaw has just become a hot topic, security issues haven't yet translated into significant revenue growth. However, this precisely means that security companies may be the segment with the biggest "expectation gap" in the entire agent narrative. Moreover, security spending is a necessity.

VII. Conclusion: Short-term focus on sentiment, medium-term focus on reasoning, long-term focus on the ecosystem.

Returning to the initial question, which US stocks did OpenClaw actually influence? We can deduce this by examining different timelines.

Currently (over the past month), judging from stock price performance, OpenClaw's direct impact on individual stocks has been quite limited. Google and MSFT haven't seen any unusual fluctuations driven by agent narratives since February. The only clear event-driven driver came from AMD, with Meta's multi-billion dollar chip order propelling its single-day surge. Overall, the AI ​​sector may be undergoing a valuation correction, and OpenClaw's popularity hasn't translated into immediate stock price catalysts.

In the short term (3 months), the market may continue to digest the squeeze on AI valuation bubbles, but the cognitive shock brought by OpenClaw may change buyers' cognitive anchors for the Agent sector. This change in perception will not be immediately reflected in stock prices, but it may reshape analysts' expected models.

In the medium term (6-12 months), the key catalyst is whether the demand for Agent inference computing power can be validated in financial reports. If OpenClaw and subsequent solutions such as Kimi Claw, MaxClaw, and enterprise-grade Agent solutions can bring observable growth in API call volume and cloud resource consumption, the inference-side narratives of NVDA, AMD, and the three major cloud vendors may be confirmed.

In the long run (1-3 years), the real winners are the companies that have secured a foothold in the agent ecosystem, such as CrowdStrike and Palo Alto Networks, which have established standards in the agent security field.

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

Once a paradigm shift occurs, it won't stop; we can only make full preparations and wait for that day to arrive.