Author: Changan I Biteye Content Team

Formerly, AI was just an inexperienced intern who could talk; now, OpenClaw is a seasoned driver who takes the wheel directly.

In the past, if you asked AI how to book a ticket, it would provide you with a guide; now, if you tell it "I want to go to Shanghai," it will directly help you compare prices, place orders, and choose seats. Just like the automatic takeout ordering feature demonstrated by Qianwen, AI has begun to deliver execution results across apps.

This shift is quietly stealing the wallets of many companies, leading to a decrease 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 labor premiums and information asymmetry are losing their protection.

• Value Migration: Exploring how capital flows into computing power, energy, crypto settlement protocols, and embodied intelligent hardware.

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

1. Value Collapse: Which old assets are losing their protection?

1. SaaS Software Company Stocks

The software industry is undergoing a transformation from a function-oriented approach to an execution-oriented approach. In the past, users paid to purchase software primarily to reduce operational difficulty using its UI, completing tasks with mouse clicks.

However, when AI Agents gained the ability to directly drive underlying logic and deliver results, the value of traditional software as an operational entry point began to disintegrate. Users no longer need complex software interfaces; they just need to issue commands, and Agents can complete tasks at the underlying level.

The editor's practical experience: The photo editing feature of Gemini's Nano Banana is more user-friendly than Meitu Xiuxiu.

This shift in logic has already triggered panic in the capital markets. Recently, the U.S. software sector has experienced significant valuation corrections:

• Sector Plummet: At the end of January 2026, the S&P North America Software Index dropped about 15% in one month, marking the largest monthly decline since the 2008 financial crisis.

• Big Tech Shrinking: In just the last few trading days, the market value of the U.S. software sector has evaporated by over $800 billion.

Investors have realized that SaaS companies that only provide simple functions and lack core data moats are being downgraded by AI. Currently, 89% of publicly listed software companies worldwide have valuations below 10 times, with an average stock price decline of up to 33%.

(Image source @afc)

2. Basic Intermediary Platform Stocks

In traditional business models, aggregation platforms profit by integrating dispersed information, leveraging information asymmetry and controlling traffic entry points. They charge merchants commissions and display advertisements to users; essentially, they are intermediaries.

However, platforms like OpenClaw have completely disrupted this order:

• Bypassing the Intermediate Layer: When Agents have the ability to automatically negotiate and place orders, they no longer need to operate through intermediary platforms. Agents connect directly with the underlying service providers (such as airline and hotel websites), thus bypassing the intermediary fee.

• Advertising Model Failure: Merchants used to buy traffic to be seen by users, but Agents do not watch ads. Those relying on buying visibility for junk information will completely lose their audience.

For example: The phenomenon of different prices for the same item across e-commerce platforms is severe. The same product may cost more on platforms like Xiaohongshu or Douyin due to video advertising premiums than on Pinduoduo. In the AI era, Agents will directly lock in the lowest prices across the entire network with absolute rationality, causing high premium platforms relying on information gaps to rapidly lose their premium space.

As Goldman Sachs stated in its "2026 Global Internet Repricing Report," 2026 will be a turning point for intermediary platforms to degrade from aggregation to data suppliers.

Goldman Sachs Chief Information Officer Marco Argenti pointed out that, because AI Agents can make decisions directly through traditional traffic, platforms that rely on buying visibility are losing their Take Rate moat.

3. Real Estate-Related Funds and Stocks (especially commercial real estate)

The carriers of productivity are shifting from people to code. Humans need physical office spaces and accommodations, but Agents only require data centers, power, and hardware. This reconstruction of productive relations is causing a displacement in the value logic of traditional real estate assets.

• 1. Shrinking Demand for Office Space

In the past, the primary purpose for large enterprises to rent office buildings in prime locations was to accommodate employees. As AI Agents enter the large-scale commercial application phase in 2026, the demand for physical workstations from enterprises has begun to plummet dramatically.

Goldman Sachs predicts that due to the influence of AI, the U.S. will lose about 20,000 traditional administrative and professional service positions each month by 2026.

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

Capital is flowing from properties in bustling locations to data center assets where electricity rates are low, the power grid is stable, and cooling efficiency is high.

Morgan Stanley's report at the beginning of 2026 pointed out that energy supply has replaced chips as the primary bottleneck for AI expansion. This means that the value of land is no longer determined by its distance from commercial centers but by its ability to access cheap electricity and fiber optic networks.

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

The overall vacancy rate for office space in the U.S. had risen to over 20% by the end of 2024, breaking historical records set in 1986 and 1991. In areas hardest hit by tech and administrative job losses, this number is approaching the warning line of 35%.

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

The valuation logic of these companies was once based on employee scale = productivity. However, when Agents are able to replace junior analysts, programmers, and legal assistants at a very low cost, the large number of employees is transforming from assets to heavy operational liabilities.

Capital is rapidly withdrawing from those labor-intensive professional service sectors: companies like Accenture (ACN) and Infosys (INFY). These companies have relied on large numbers of junior programmers to support their business, but now AI can complete most standardized coding tasks.

A media whirlwind once traveled to Kenya to investigate the local paper-writing industry. This labor outsourcing sector, which once supported hundreds of thousands of locals, is now experiencing catastrophic impacts from AI:

• Sharp Decline in Orders: Local practitioners stated in a video that due to students turning to AI-generated papers, the volume of ghostwriting orders has plummeted. Jobs that previously required hundreds of dollars to pay African writers can now be completed at nearly zero cost through AI.

• Zero Value of Skills: Being proficient in English and capable of writing papers was once a core competitive advantage, but in the face of AI, this kind of low-level intellectual labor has rapidly lost its value. This is not just a crisis for individual writers but also a common negative for platforms like Upwork and Fiverr that depend on individual labor to extract commissions.

The capital markets no longer see employee scale as a competitive barrier. If a company continues to see increasing manpower as its core growth engine, it will face the risk of productivity being completely overshadowed by AI. In the future, high-value assets will be concentrated in lightweight entities that can drive large-scale Agent operations through code.

2. Where is the money flowing?

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

When the moats of old assets collapse, wealth does not disappear but flows toward the underlying infrastructure that supports Agent operations.

The operation of Agents essentially involves the continuous consumption of electricity and computing power. Enterprises are shifting the physical office costs (such as office rents) originally used to house employees to expenses for computing power subscriptions and energy security.

Token costs have become a core burden of enterprise operations. Current top models (such as Claude Code/Seedance 2.0) have high inference costs, with complex tasks costing thousands of dollars per instance. High costs are forcing the industry into a competition over inference costs.

With the application of dedicated inference chips and open-source models (like DeepSeek, Kimi), the cost of inference per instance will continue to decrease, making electricity quotas an irreplaceable scarce production factor.

In this wave of value transfer, hardware and energy assets with supply certainty attributes have become the biggest beneficiaries:

• Core Computing Hardware: Nvidia, AMD. They provide the computing power backbone required for Agent operations and are the core suppliers of AI productivity.

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

• Digital Infrastructure REITs: Equinix, Digital Realty. Their operated IDC data centers are attracting capital originally directed toward traditional office buildings.

In summary, the pricing power of assets is shifting from landlords providing office space to suppliers providing computing energy.

2. AI's Automated Payment System: From Manual Confirmation to Code-Driven Settlement

As previously mentioned, Agents have caused traditional intermediary platforms to lose their survival space through price comparison, but price comparison is only the first step. Once Agents lock in the optimal price, they must possess the ability to autonomously complete transactions. Currently, the limitations of traditional payment systems prevent the fulfillment of closed loops, driving capital to migrate towards code-driven crypto protocols.

Taking the recent "automatic tea ordering" feature demonstrated by Tongyi Qianwen as an example: AI can already execute cross-app purchasing and ordering actions, proving the maturity of Agents in decision-making and interaction. However, in practical implementation, automated processes often break down at the final payment stage because the traditional bank system still requires manual face scanning, SMS verification codes, or physical identity verification.

This gap between decision-making and payment is precisely where the value of programmable trading protocols like X402 lies.

Beneficial Assets:

• Programmable Trading Protocols (like X402): Provide Agents with private key management and capital calling capabilities, allowing them to bypass traditional payment interfaces and execute financial interactions directly through code.

• Stablecoins (like USDT, USDC): Provide a 24/7 online, no human review settlement environment, serving as the settlement benchmark for Agent commercial activities.

• High-Performance Public Chains (like Kite AI): Layer 1 blockchains customized for Agents, providing a low-latency execution environment. Through programmable governance and identity, they provide Agents with legitimate identities and authority control, transforming them from isolated tools into economic entities capable of autonomous decision-making, collaboration, and profit-making. With the explosive growth in Agent transactions, Kite AI, as core collaborative infrastructure, has recently exhibited strong performance in the market.

The current situation where Agents can compare prices but cannot make payments has forced the rise of crypto settlement systems. Protocols that master automated payment interfaces will take over the business traffic lost by traditional financial intermediaries.

3. Evolution of Productive Forms: Embodied Intelligence and Physical Execution Hardware

When AI has solved the logical decision-making and software interaction issues, capital begins to flow into the physical entities that can carry these intelligences. Budgets originally allocated for purchasing "primary intellectual labor" are being reallocated to hardware assets with physical execution capabilities.

When Agents achieve critical intelligence, the only bottleneck limiting their performance lies in physical forms. Funds are flowing into robotic hardware to supplement AI's execution shortcomings in the real world.

• Extension of Work Scenarios: The application of Agents is expanding from computer screens to physical spaces. Utilizing OpenClaw for logical control, AI can intervene in household management (like cleaning monitoring, assisting with homework) and industrial production.

• Capital Expenditure Replacement: Enterprises and households are undergoing cost transitions. Expenses originally paid to human assistants and junior contractors are being transformed into fixed asset expenditures for purchasing embodied intelligent devices (like home service robots and industrial robots).

Asset categories with certain benefits

• Core Components of Embodied Intelligence: At the beginning of 2026, sectors such as robotic joints (reducers, servo motors) and tactile sensors saw significant increases. These components are the hardware foundation for Agents transitioning from code to physical execution.

• Programmable Automation Devices: Intelligent factory equipment and smart home terminals that can open basic interfaces, allowing Agents to connect and gain direct control.

Goldman Sachs pointed out that the combination of Agents and robots is triggering a generational shift in capital expenditures. As Agents significantly improve the return on investment in hardware, budgets that originally flowed into human outsourcing are transforming into procurement orders for robotic assets at an annual rate of 25%.

Agents grant hardware the ability to think, and hardware provides Agents with the physicality to monetize. This complementarity ensures that the evolution of Agents will inevitably drive the revaluation of physical actuator assets.

3. KOL Opinion Summary

Teddy@DeFiTeddy2020 (XHunt Ranking: 1742)

Viewpoint: The agency economy driven by OpenClaw will significantly reduce the valuations of SaaS software stocks, intermediary platform stocks, and commercial real estate-related assets because AI Agents directly call APIs, autonomously search for bargains without needing physical offices. Traditional assets reliant on human behaviors will face systemic revaluation.

https://x.com/DeFiTeddy2020/status/2020762007625248925?s=20

Haotian@tmel0211 (XHunt Ranking: 1202)

Viewpoint: AI + Crypto will represent a grand track that crosses the boundaries between web2 and web3, a natural outcome of the Agentic Economy track following trends, because once AI transitions to decentralized operations, the trusted payment, identity, contract, etc., required will all be well-managed by Crypto, making it something to look forward to.

https://x.com/tmel0211/status/2020319970908074021?s=20

Dov@dov_wo (XHunt Ranking: 1843)

Viewpoint: The era of great turning points has arrived, with SaaS and software company stock prices collapsing, like Chegg being crushed by GPT-4; ClaudeCode and OpenClaw will put high-paying jobs for Wall Street analysts, lawyers, etc., out of work, with layoffs exceeding half within three years. Traditional education will become obsolete, students graduating will be replaced by AI at ten times the efficiency and twice the effectiveness. This is a wealth and significance plundering of the new generation against the old. Humans need to rest, but AI can continuously operate at low cost; everything will come to an end. Humans should avoid interacting with documents like Notion and turn towards AI to connect the new and old worlds.

https://x.com/dov_wo/status/2020045763330601007?s=20

BuccoCapital Bloke@buccocapital (XHunt Ranking: 3935)

Viewpoint: Although "building internally" is no longer the main bear market reason for SaaS (since many enterprises still rely on mature SaaS), the AI agency economy will still bring multiple structural pressures, leading to SaaS companies facing long-term pressure and even valuation revaluation: platform differentiation is approaching zero (customer acquisition costs rising sharply), value migrating to the agent layer, AI-native startups providing better outcome-based solutions eating into LTV, seat revenue models collapsing, difficulty from "charging by seats" to "charging by outcomes," deteriorating pricing power and gross margins, organic traffic decreases further raising CAC, intensified competition for AI talents increasing operational costs. Investors must have a clear judgment on the intensity and time window of these bear market factors.

https://x.com/buccocapital/status/2015603777420607967?s=20

Alex Clayton@afc (XHunt Ranking: 31467)

Viewpoint: The current valuation multiples of public software companies are dismal, with 89% of over 100 companies trading at less than 10 times NTM revenue, only 3 exceeding 20 times; most companies are experiencing stagnant revenue growth, with a median ARR growth of only 15%, far inferior to AI newcomers like Anthropic. While AI may replace some budgets, that 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 AI traction, these traditional companies will continue to underperform, remain undervalued, and gradually decline; now is a critical period for their transformation with AI.

https://x.com/afc/status/2014133417538130351?s=20

4. Conclusion: What should ordinary people do?

In the face of asset repricing, the most effective way for ordinary people to participate is to deeply experience cutting-edge products and perceive the changes in productivity boundaries.

1. Master Vibe Coding: Achieving Iteration of Development Paradigms

Tools represented by 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 macro architectures. If a function that originally required a team to collaborate for a week can now be completed by an individual with AI assistance within a few hours, it means that the valuation logic of traditional software outsourcing assets relying on human scale for profit faces revision.

Individuals should try to convert the time saved by AI into excess productivity gains.

2. Identify the Cost Inflection Point in Video Production: Using Seedance 2.0 as an Example

The popularity of video generation models like Seedance 2.0 marks a structural decrease in the cost of producing visual content.

• Assess Physical Asset Risks: By generating complex advertising storyboards, it can be discovered that when the fidelity of AI-generated images approaches the level of live shoots, the asset values of rental companies with expensive shooting equipment and traditional film and television studios will shrink.

• Identify Track Shifts: By experiencing highly integrated generation tools, one can discern which tracks are in the clearing phase and which tracks are gaining increment due to technological empowerment.

3. Look for Missing Links in Business Closed Loops: Viewing Trading Protocols from the Payment Bottleneck

The performance of Tongyi Qianwen in scenarios like automatic tea ordering reveals the discontinuity between decision-making and execution of Agents.

• Identify Growth Opportunities: In daily operations, look for those points where AI can make decisions but cannot complete transactions; these links will be future core growth areas.

• Verify On-Chain Settlement Logic: When Agents cannot complete payments through traditional banking systems, capital must flow toward programmable on-chain protocols. This proves that X402 and its related infrastructure are not speculative assets but necessary links to complete Agents' business closed loops.

Core Advice: By continually applying advanced tools in work and life, maintain sensitivity to changes in productivity. In 2026, the most resilient assets are personal interdisciplinary integration capabilities and a deep understanding of core nodes in the AI supply chain (energy, computing power, settlement, execution).

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。