Which assets benefit and which are harmed by 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.

Before, if you asked AI how to book tickets, it would give you 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 automatic food delivery ordering function, AI has begun to deliver execution results across apps.

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

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

• Value Collapse: Analyzing which old assets relying on labor premiums and information asymmetry are losing their moats.

  Value Collapse: Analyzing which old assets relying on labor 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 Moats? 1. SaaS Software Company Stocks The software industry is undergoing a transformation from function-oriented to execution-oriented. Previously, users paid for software because their core need was to utilize its UI to reduce operational difficulty and 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 simply issue commands, and the agent completes the task at the underlying level.

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

  This shift in logic has triggered panic in the capital markets. Recently, the US software sector has been 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.

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

  • Giants shrink:

  BuccoCapital Bloke@buccocapital (XHunt ranking: 3935)

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

  BuccoCapital Bloke@buccocapital (XHunt ranking: 3935)

  Opinion: While "internal construction" is no longer the primary reason for the current bear market in SaaS (because many companies still rely on off-the-shelf SaaS), the AI ​​agent economy will still bring multiple structural pressures, leading to long-term pressure on SaaS companies and even valuation reassessment.

  Alex Clayton@afc (XHunt Ranking: 31467) Opinion: The current valuation multiples of public software companies are dismal. 89% of over 100 companies are trading at less than 10 times their NTM revenue, with only 3 exceeding 20 times. Most companies have stagnant revenue growth, with a median ARR of only 15% annual growth, far inferior to 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 prove 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?

  Faced with asset repricing, the most effective way for ordinary people to participate is through in-depth experience with cutting-edge products, perceiving the changing boundaries of productivity.

  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 the macro-architecture. If a feature that originally 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 that rely on manpower scale for profit needs to be re-examined.

  By trying to convert the time saved by AI into excess returns on 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 like Seedance 2.0 signifies a structural decline in the cost of visual content production. Assessing Physical Asset Risk: By generating complex advertising storyboards, it can be observed that when the fidelity of AI-generated footage approaches that of live-action shooting, the asset value of rental companies with expensive filming equipment and traditional film and television parks will shrink. Identifying Sector Shifts: By experiencing highly integrated generation tools, it is possible to distinguish which sectors are in a clearing-out phase and which sectors are gaining incremental growth due to technological empowerment. 3. Identifying the Gaps in the Business Loop: Analyzing Transaction Protocols Through Payment Bottlenecks The performance of Tongyi Qianwen in scenarios such as automated milk tea ordering reveals the gap between agent decision-making and execution. Identifying Growth Opportunities: In daily operations, identify the points where AI can make decisions but cannot complete transactions; these are the core growth areas for the future. Verifying On-Chain Settlement Logic: When an agent cannot complete payments through the traditional banking system, funds inevitably flow to programmable on-chain protocols. 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, execution).