Which assets will benefit from and which will suffer from Openclaw's agentic economy?

Author: Changan | Biteye Content Team

Previously, AI was like an intern who could only talk; now, OpenClaw is like a seasoned veteran who can get hands-on right away.

Previously, if you asked AI how to book tickets, it would provide you with travel tips; now, if you tell it "I want to go to Shanghai," it will directly compare prices, place orders, and select seats for you. As demonstrated by Qianwen's automated food delivery ordering function, AI has begun delivering results across different apps.

This shift is quietly stealing money from many companies, leading to a decline in valuations.

This article will analyze the asset repricing logic triggered by this productivity revolution from the following dimensions:

• Value Collapse: Analyzing which old assets that rely on human capital premiums and information asymmetry are losing their moats.

• Value migration: Exploring how funds flow to computing power, energy, encrypted settlement protocols, and embodied smart hardware.

• Practical Guide: Providing individuals with coping strategies based on cutting-edge product experiences.

I. Value Collapse: Which old assets are losing their moat?

1. SaaS software company stocks

The software industry is undergoing a transformation from a feature-driven to an execution-driven approach. Previously, users paid for software primarily because they wanted its user interface to simplify operation and allow them to complete tasks by clicking the mouse.

However, when AI agents possess the ability to directly drive underlying logic and deliver results, the value of traditional software as an operational entry point begins to crumble. Users no longer need complex software interfaces; they only need to issue commands, and the agent can complete the task at the underlying level.

My personal experience: Gemini's Nano Banana photo editing function is better than Meitu Xiu Xiu.

This shift in logic has triggered panic in the capital markets. The US software sector is currently undergoing a major valuation correction.

• Sector crash : At the end of January 2026, the S&P North American Software Index plummeted by about 15% in a single month, marking the largest monthly drop since the 2008 financial crisis.

• Giants shrink : In just the last few trading days, the market value of the US software sector has evaporated by more than $800 billion.

Investors are realizing that SaaS companies that only offer simple features and lack a core data moat are being brutally attacked by AI. Currently, 89% of publicly listed software companies worldwide have seen their valuations fall below 10 times earnings, with an average stock price decline of 33%.

(Image source: @afc)

2. Stocks of basic intermediary platforms

In traditional business models, aggregation platforms profit by integrating fragmented information, leveraging information asymmetry, and controlling traffic entry points. They charge commissions from merchants and display advertisements to users, essentially acting as intermediaries.

However, OpenClaw completely disrupted this pattern:

• Bypassing the intermediary layer: When the agent has the ability to automatically negotiate prices and place orders directly, it no longer needs to operate through the interface of the intermediary platform. The agent will directly connect to the lowest-level service provider (such as the official website of an airline or hotel), thereby bypassing the commission charged by the intermediary platform.

• The advertising model is failing: Merchants used to buy traffic to ensure your ads were seen, but agents don't watch ads. Those spam ads that rely on purchased placements to rank high will completely lose their audience.

Case Comparison: Currently, the phenomenon of different prices for the same product across different e-commerce platforms is serious. For the same item, the price on Xiaohongshu or Douyin is usually higher than on Pinduoduo due to the premium charged by video ads. However, in the AI ​​era, agents will directly lock in the lowest price across the entire network with absolute rationality, causing the premium space of platforms that rely on information asymmetry to quickly disappear to zero.

As Goldman Sachs stated in its "2026 Global Internet Reassessment Report," 2026 will be a turning point for intermediary platforms to degenerate from aggregators to data providers.

Goldman Sachs Chief Information Officer Marco Argenti points out that because AI agents can directly penetrate traditional traffic to make decisions, platforms that rely on buying placements to acquire customers are losing their take rate moat.

3. Real estate-related funds and stocks (especially commercial real estate)

The vehicle for productivity is shifting from people to code. People need physical office space and housing, but agents only need server rooms, electricity, and hardware. This restructuring of production relations is causing a shift in the value logic of traditional real estate assets.

• 1. Shrinking demand for office space

In the past, the primary purpose of large enterprises leasing office buildings in prime locations was to house their employees. With AI agents entering large-scale commercial application in 2026, the demand for physical workstations has begun to plummet.

Goldman Sachs predicts that, driven by AI, the U.S. will lose approximately 20,000 traditional administrative and professional service jobs per month by 2026.

• 2. Capital Shift: From Commercial Land to Energy and Computing Power

Capital is flowing from real estate in prime locations to data center assets with low electricity costs, stable power grids, and high cooling efficiency.

A Morgan Stanley report in early 2026 pointed out that energy supply has replaced chips as the primary bottleneck for the expansion of AI. This means that the value of land is no longer determined by its distance from business centers, but by its access to cheap electricity and fiber optic backbone networks.

By early 2026, the average price of office buildings in U.S. cities had fallen by approximately 50% from its previous peak. This decline reflects the market's final pricing in the dual impact of remote work and AI automation.

The overall vacancy rate for office space across the United States had risen to over 20% by the end of 2024, breaking historical records set in 1986 and 1991. In areas with the most severe loss of technology and administrative jobs, this figure is approaching the warning line of 35%.

4. Human Resources Service Companies (Outsourcing and Consulting Assets)

The valuation logic for these companies was once based on the premise that employee size equals productivity. However, as agents can replace junior analysts, programmers, and legal assistants at extremely low costs, a large number of employees is turning from an asset into a heavy operating liability.

Capital is rapidly withdrawing from labor-intensive professional services sectors such as Accenture (ACN) and Infosys (INFY). These companies rely heavily on junior programmers, but AI can now handle the vast majority of standardized coding tasks.

FilmHurricane conducted on-site research in Kenya on the local academic paper writing industry. This human resource outsourcing industry, which once supported hundreds of thousands of local people, is experiencing a devastating blow in the face of AI:

• Orders Plummet: Local practitioners stated in a video that the number of essay-writing orders has plummeted as students have switched to using AI to generate papers. Tasks that previously cost hundreds of dollars to African writers can now be completed almost at zero cost using AI.

• Skills reduced to zero value: While being proficient in English and able to write academic papers used to be core competencies, the value of such basic intellectual labor has rapidly diminished in the face of AI. This is not only a crisis for individual writers, but also a shared negative for platforms like Upwork and Fiverr that rely on individual labor for commissions.

Capital markets no longer view employee size as a competitive advantage. Companies that continue to rely on increasing manpower as their core growth engine risk having their productivity and efficiency completely overshadowed by AI. Future high-value assets will be concentrated in lightweight entities capable of driving large-scale agent operations through code.

II. Where did all the money go?

1. Restructuring of Production Factors: Computing Power Demand and Energy Certainty

When the moat of old assets collapses, the wealth does not disappear, but flows to the underlying infrastructure that supports the operation of the Agent.

The operation of an agent is essentially a continuous consumption of electricity and computing power. Enterprises are shifting the physical office costs that were originally used to house employees (such as office rent) to computing power subscriptions and energy security expenditures.

Token fees have become a major burden on business operations. Current top-tier models (such as Claude Code/Seedance 2.0) have exorbitant inference costs, with single complex tasks costing thousands of dollars. These high costs are forcing the industry into a race to increase inference costs.

With the application of dedicated inference chips and open-source models (such as DeepSeek and Kimi), the cost of a single inference will continue to decrease, making electricity quotas a scarce and irreplaceable production factor.

In this wave of value shift, hardware and energy assets with certain supply characteristics have become the biggest beneficiaries:

• Core computing hardware: NVIDIA and AMD. They provide the computing power foundation required for agents to run and are the core suppliers of AI productivity.

• Energy and Utilities: Vistra Corp, Constellation Energy. Companies that control a stable power supply have been revalued from traditionally defensive sectors as premium assets in the AI ​​supply chain.

• Digital infrastructure REITs: Equinix, Digital Realty. Their data centers are taking over capital that was originally flowing to traditional office buildings.

In conclusion, the pricing power of assets is shifting from landlords who provide office space to suppliers who provide computing power.

2. AI-powered automated payment systems: shifting from manual confirmation to automated code settlement.

As mentioned earlier, agents have rendered traditional intermediary platforms obsolete by comparing prices across the entire network, but price comparison is only the first step. Once an agent locks in the best price, it must have the ability to complete the transaction independently. Current limitations of traditional payment systems prevent this closed-loop process, driving funds towards code-driven cryptographic protocols.

Take the "automatic milk tea ordering" function recently showcased by Tongyi Qianwen as an example: AI has already been able to perform cross-app selection and ordering operations, proving the maturity of agents in decision-making and interaction. However, in actual implementation, the automated process often breaks down at the final payment stage because the traditional banking system still requires manual facial recognition, SMS verification codes, or physical identity verification.

This gap—where decision-making is possible but payment is impossible—is precisely where the value of programmable transaction protocols like X402 lies.

Beneficial assets:

• Programmable transaction protocols (such as X402) provide agents with private key management and fund access capabilities, enabling them to bypass traditional payment interfaces and execute financial interactions directly through code.

• Stablecoins (such as USDT and USDC) provide a 24/7 online, unmanned clearing environment and serve as the settlement benchmark for Agent business activities.

• High-performance public blockchains (such as Kite AI): Layer 1 blockchains customized for agents, providing a low-latency execution environment. Through programmable governance and identity, they provide agents with legitimate identity and access control, transforming them from isolated tools into economic entities capable of autonomous decision-making, collaboration, and profit-making. With the explosive growth of agent transaction volume, Kite AI, as a core collaborative infrastructure, has recently seen strong price performance in the market.

The current situation where agents can compare prices but cannot make payments has spurred the rise of encrypted settlement systems. Those who control the protocols for automated payment interfaces will take over the business traffic lost by traditional financial intermediaries.

3. Evolution of Productivity Models: Embodied Intelligence and Physical Execution Hardware

Once AI solves the problems of logical decision-making and software interaction, capital begins to flow to the physical entities that can support this intelligence. Budgets originally allocated to purchasing "basic intellectual labor" are being reallocated to hardware assets with physical execution capabilities.

When agent intelligence reaches a critical point, the only bottleneck limiting its effectiveness lies in its physical form. Funding is flowing into robotic hardware to address the shortcomings of AI in real-world execution.

• Expanding Work Scenarios: The application of agents is extending from computer screens to physical spaces. Utilizing OpenClaw for logic control, AI can intervene in home management (such as cleaning monitoring and homework assistance) and industrial production.

• Capital expenditure replacement: Businesses and households are shifting costs. Costs that were previously paid to human assistants and junior outsourced workers are now being converted into fixed asset expenditures for the purchase of embodied intelligent devices, such as home service robots and industrial robots.

Asset classes with certain benefits

• Core components of embodied intelligence: In early 2026, sectors such as robot joints (reducers, servo motors) and tactile sensors saw significant price increases. These components form the hardware foundation for agents to move from code to physical execution.

• Programmable automation equipment: Intelligent factory equipment and smart home terminals that can open underlying interfaces, allowing agents to access and directly control them.

Goldman Sachs points out that the combination of agents and robots is triggering a generational shift in capital expenditure. Because agents significantly improve the return on investment for hardware, budgets that previously flowed into human resource outsourcing are now being converted into purchase orders for robotic assets at a rate of 25% annually.

Agents endow hardware with the ability to think, while hardware provides agents with the physical form to monetize. This complementarity dictates that the evolution of agents will inevitably lead to a revaluation of physical actuator assets.

III. Summary of KOL Opinions

Teddy@DeFiTeddy2020 (XHunt ranking: 1742)

Opinion: The agency economy driven by OpenClaw will significantly depress the valuations of SaaS software stocks, intermediary platform stocks, and commercial real estate-related assets, as AI agents directly call APIs, autonomously search and negotiate prices, and do not require physical offices. Assets that traditionally rely on human behavior will face a systemic revaluation.

Haotian@tmel0211 (XHunt ranking: 1202)

Opinion: AI + Crypto will be a huge track that transcends the boundaries of Web2 and Web3. This is an inevitable result of the development of the Agentic Economy track in line with the trend. Once AI becomes distributed, the trustworthy payment, identity, and contract that it requires are all things that Crypto excels at. It is worth looking forward to.

Dov @dov_wo (XHunt ranking: 1843)

Opinion: A major turning point has arrived. SaaS and software company stock prices are crashing, with companies like Chegg being crushed by GPT-4. ClaudeCode and OpenClaw will cause high-paying jobs such as Wall Street analysts and lawyers to fall, with more than half of the workforce being laid off within three years. Traditional education will become useless, and students will be replaced by AI with 10 times the efficiency and 2 times the effectiveness upon graduation. This is a new generation plundering the wealth and meaning of the old generation. Humans need rest, but AI will remain cheap and continuous; everything will end. People should avoid dealing with documents like Notion and turn to AI to connect the old and new worlds.

BuccoCapital Bloke@buccocapital (XHunt ranking: 3935)

Opinion: While "internal construction" is no longer the primary reason for the current SaaS bear market (as many companies still rely on off-the-shelf SaaS), the AI ​​agent economy will still bring multiple structural pressures, leading to long-term pressure and even valuation reassessment for SaaS companies: platform differentiation is approaching zero (significantly increasing customer acquisition costs), value is shifting to the agent layer, local AI startups are offering better outcome-based solutions that are eroding LTV, the seat revenue model is collapsing, the shift from "per seat fee" to "outcome fee" is difficult, pricing power and gross margins are deteriorating, reduced organic traffic is further pushing up CAC, and competition for AI talent is intensifying operating costs. Investors must have a clear understanding of the strength and timing of these bear market factors.

Alex Clayton@afc (XHunt ranking: 31467)

Opinion: The current valuation multiples for public software companies are dismal. Of over 100 companies, 89% are trading at less than 10 times their NTM revenue, with only 3 exceeding 20 times. Most companies are experiencing stagnant revenue growth, with a median ARR of only 15% annual growth, far lagging behind AI upstarts like Anthropic. While AI may replace some budgets, this is not the root cause. The real problem is that most SaaS vendors have not developed AI products that customers are willing to pay for. If they cannot innovate and demonstrate the traction of AI, these traditional companies will continue to experience low growth, low valuations, and gradual decline. Now is a critical period for their AI transformation.

IV. Conclusion: What should ordinary people like us do?

When faced with asset repricing, the most effective way for ordinary people to participate is to deeply experience cutting-edge products and perceive the changing boundaries of productivity.

1. Master Vibe Coding: Achieving Iterative Development Paradigms

Tools like Claude Code 2.0 have changed the underlying logic of software development.

The focus of development has shifted from writing code line by line to optimizing the macro-architecture. Features that previously required a week of team collaboration can now be completed by an individual in a few hours with AI assistance. This means that the valuation logic of traditional software outsourcing assets, which rely on manpower scale for profit, needs to be re-examined.

By trying to translate the time saved by AI into excess benefits in personal productivity.

2. Identifying the cost inflection point in video production: Taking Seedance 2.0 as an example.

The widespread adoption of video generation models such as Seedance 2.0 signifies a structural decline in the cost of visual content production.

• Assessing physical asset risks: By generating complex advertising storyboards, it can be found that when the fidelity of AI-generated footage approaches that of live-action footage, the asset value of rental companies with expensive filming equipment and traditional film and television parks will shrink.

• Identify market shifts: By experiencing highly integrated generation tools, you can distinguish which markets are in the clearing-out phase and which markets are gaining incremental growth due to technological empowerment.

3. Identifying the missing links in the business loop: Examining transaction protocols from the perspective of payment bottlenecks.

The performance of Tongyi Qianwen in scenarios such as automatic milk tea ordering reveals the gap between agent decision-making and execution.

• Identify growth opportunities: In daily operations, look for the breakpoints where AI can make decisions but cannot complete transactions; these are the core growth areas for the future.

• Verifying the on-chain settlement logic: When an agent is unable to complete a payment through the traditional banking system, funds will inevitably flow to the programmable on-chain protocol. This proves that X402 and its related infrastructure are not speculative assets, but rather a necessary link in completing the agent's business loop.

Key recommendation: Maintain sensitivity to changes in productivity by continuously applying advanced tools in both work and life. In 2026, the most robust assets will be personal cross-domain integration capabilities and a deep understanding of the core nodes of the AI ​​industry chain (energy, computing power, settlement, and execution).