AI Agents, Real Yield & RWA — Where Does the Next Wave Go?

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METYA
@metyacomDec 10, 2025

The Spaces explored where the next wave of value will come from at the intersection of AI agents, real yield, and real-world assets (RWA). Co-host Amy guided founders and operators including Rumy (Hyper GPT), Robert (Revita), X Smith (Stock5), David (ATT Global), Banawi (Mero Pay), Henry (Office Arc), and a TradeType AI ambassador. The panel separated real AI from hype by emphasizing execution, on-chain utility, measurable outcomes, and active users. Revita proposed a single proof metric: growth in on-chain issuance/value anchored by AI-driven Proof of Value (POV) from validated device data. For onboarding, speakers advised starting as a user (not a speculator), using AI for information gathering, analysis, and risk control, and keeping things simple and reliable for mass adoption. On impact, near-term AI will streamline compliance, risk, pricing, and collateral tracking across IoT/manufacturing/energy/transport/smart cities; mid-term could see AI-to-AI trading and dynamic pricing of assets (e.g., billboards). Liquidity is expected to tilt toward TradFi/ETF capital over 3–5 years, muting meme cycles. On security, if an agent is hijacked, freeze, trace, involve auditors/regulators/exchanges, and negotiate recovery.

AI Agents, Real Yield, and Real-World Assets — Where Does the Next Wave Go?

Participants and Roles

  • Amy (Host/Co-host): Moderator guiding the discussion around AI agents, real yield, and real-world assets (RWA).
  • Rumy (HyperGPT): Executive Marketing Assistant. HyperGPT builds AI and Web3 infrastructure: an agent builder, an AI app marketplace (HyperScore), and an SDK. Noted backing from Microsoft, Google for Startups, AWS, and BNB Chain.
  • Robert (Revita): Represents Revita, a decentralized infrastructure platform integrating AI, Web3, and IoT to bridge the value gap between real-world assets and Web3 capital. Introduced their pre-RWA model and AI-driven Proof of Value (POV).
  • X Smith (Stock5): Represents Stock5, focused on tokenized stocks and a financial layer for RWA with accurate pricing, collateralization, efficient liquidation, and yield separation, strengthened by AI risk modeling.
  • David (ATC Global): Strategy Director, 10+ years in crypto. Tokenizing premium outdoor advertising billboards; >$10M in assets on-chain; ~$20M in revenues. Operations in Hong Kong, Bangkok, Jakarta; expanding to Vietnam; deploying AI across retail and advertising.
  • Banawi (Mero Pay): Community Manager. Crypto payments infrastructure: mCard (supports Mastercard/Visa) and an App Store for crypto-powered shopping.
  • Henry (Orbis Arc): Platform bringing culture/IP and diverse RWA categories on-chain; financializes IP (e.g., short dramas, brands) into tradable, verifiable assets.
  • Sarita (TradeType AI): Represents TradeType AI, a full-cycle trading intelligence platform unifying market analysis, strategy execution, and trader learning.

Session Overview

The discussion tackled:

  • How to separate real AI from hype in Web3.
  • A single metric to prove AI agents create real on-chain value.
  • Practical onramps for everyday users to the AI/crypto space.
  • What AI agents must do well to reach mass adoption.
  • Where AI will have the biggest impact on RWA over the next 1–3 (and 4–5) years.
  • Security risks (AI agent takeover) and incident-handling playbooks.
  • Liquidity flows and real yield destination in 3–5 years and implications for token prices.

Key Highlights and Takeaways

  • Real AI is measured by execution, real users, and verifiable on-chain outcomes—not by branding or narratives.
  • A credible single metric for AI agent value: on-chain value issuance anchored to AI-verified device/data contributions (Revita’s POV).
  • Mass adoption hinges on simplicity, reliability, and measurable utility in daily workflows.
  • Near-term AI impact in RWA will be in back-office and infrastructure automation (compliance, risk, pricing), reducing friction so assets can scale.
  • Medium-term: machine-to-machine (agent-to-agent) commerce and dynamic pricing could unlock substantial new markets (e.g., ad inventory).
  • Security remains paramount; incident response and regulator/exchange cooperation are increasingly effective at containing exploits.
  • Liquidity is shifting toward TradFi rails (e.g., ETFs) with implications for token price behavior and reduced speculative seasonality.

What Separates Real AI From Hype

  • Rumy (HyperGPT): Two questions cut through hype: 1) Is real tech being built (models/agents, clear technical roadmap)? 2) Are people using it (users, dev integrations)? Real projects show work, ship incremental value, and focus on execution and clarity rather than loud narratives.
  • Banawi (Mero Pay): Real AI must do meaningful on-chain work—decisions, task execution, measurable UX improvements, and contract-level interactions. If AI disappears and nothing changes, it was hype. Real AI compounds utility over time through data and usage; hype relies on marketing alone.
  • X Smith (Stock5): One word: utility. Real AI delivers measurable outcomes (better execution, liquidation efficiency, pricing). It requires data pipelines and feedback loops; without them, “AI” is just marketing. For RWA, scaling demands accurate data, strong risk modeling, and reliable automation—hype can’t substitute for these.
  • Robert (Revita): Real AI projects integrate tangible asset/data flows, not just the “AI” label. Their model: IoT/smart devices feed data; AI evaluates and issues verifiable on-chain value (pre-RWA + AI-driven POV). Emphasized compliance and real-world alignment; projects must deliver auditable, on-chain credentials rather than speculative tokenomics.

The Single Metric That Proves AI Agents Create Real On-Chain Value

  • Robert (Revita): Verify on-chain value issuance via AI-driven Proof of Value (POV), backed by device/data provenance. Track total on-chain asset value issued based on AI-validated device data and actions. This ties the full pipeline—device → AI evaluation → tokenization/financing—and avoids hype/usage vanity metrics. Growth in this metric evidences real-economy activity rather than speculation.

Onboarding Everyday Users to AI/Crypto

  • Sarita (TradeType AI): Start as a user, not a speculator. Learn by using AI tools for market analysis, sentiment, and risk awareness. Begin with small accounts and observe AI behavior across conditions. Avoid jumping into full automation immediately; prioritize understanding signals and safe execution.
  • David (ATC Global): Use AI for information gathering and structured analysis. Build disciplined logic into every trade; rely on AI to surface true signals and risk controls. Applicable to all trading types (spot, perps, “degen”/meme), but perps demand heightened risk discipline due to leverage.

What AI Agents Must Do to Reach Mass Adoption

  • Sarita (TradeType AI): Mass adoption happens when solutions are simple, useful, and trustworthy. Real utility means time saved, fewer mistakes, and better decisions. Transparency and responsible AI behavior are essential to earn trust with user data and assets; education is a core function.
  • Rumy (HyperGPT): Users want easier lives, not futuristic complexity. Agents must: be easy to use (minimal setup), reliably handle small tasks, and deliver clear, everyday value (organizing info, answering questions, automating simple tasks). No need for perfection—practical help drives adoption.

Where AI Will Drive the Biggest RWA Impact (1–3 Years, and Beyond)

  • Robert (Revita): High-impact overlap between physical devices, data flows, and tokenization:
    • IoT and smart devices: value capture and monetization via AI evaluation and on-chain issuance.
    • Smart manufacturing, energy, transportation, and smart city infrastructure: AI validates device outputs to create traceable asset streams for financing.
    • Data-to-value economy: Package device-generated data as assets (RDA/RWA) verified by AI, enabling financing or yields.
    • Compliance-first, AI-led valuation (pre-RWA) can reduce risk and unlock institutional capital. He expects rapid innovation, citing growing RWA support (e.g., in Hong Kong) and the trajectory of digital RMB.
    • Healthcare and beyond: AI is horizontal (e.g., Dubai’s AI-adaptive traffic signals, airport use cases).
  • X Smith (Stock5): Real impact comes from infrastructure rather than slogans:
    • Pricing and risk modeling: dynamic pricing, collateral quality assessments.
    • Compliance and fraud detection: KYC/AML and real-time monitoring.
    • Asset classification and workflow automation: maintain metadata for stocks/bonds/credit pools; critical for transparency.
    • Governance and market intelligence: understand real yield, performance, and risks vs narratives.
    • Expect AI infrastructure to make RWA usable and scalable: accurate pricing, safe collateralization, efficient liquidation, transparent yield separation. Stock5 is building tokenized stock rails with AI-assisted risk/liquidation modeling and risk-adjusted yield/governance mechanics.
  • Banawi (Mero Pay): AI addresses the “annoying ops” that have slowed RWA for years:
    • Automation of verification, compliance, valuation, monitoring.
    • Real-time pricing/discovery via AI, reducing fragmentation and human bottlenecks.
    • Smarter distribution: AI agents can route liquidity to the best-performing products, lowering complexity for retail.
    • Net effect: removing structural friction so RWA can finally scale.
  • Rumy (HyperGPT): AI won’t transform RWA overnight. Biggest near-term gains are behind the scenes: document processing, risk checks, verification, cash-flow analysis, ongoing monitoring—making RWA processes faster, more reliable, and more scalable.
  • David (ATC Global): Medium-term (4–5 years): agent-to-agent (machine-to-machine) trading at scale. Example: each billboard hardware segment priced dynamically; AI agents transact without human approval, powered by upgraded hardware and autonomous pricing. This could drive the next wave (timeline: toward 2030) as more machines transact services with each other.

Security and Risk: If an AI Agent Controlling Funds Is Compromised

  • Robert (Revita): Technology is breakable; focus on incident response and containment:
    • Pause protocols if needed, engage auditors, trace on-chain funds, and coordinate with exchanges/wallets and regulators.
    • Funds are increasingly difficult to move undetected; many exploits end with negotiated returns (whitehat-style resolutions) due to traceability and enforcement pressure.

Liquidity and Real Yield: 3–5 Year Outlook and Token Price Implications

  • David (ATC Global): Liquidity has been weakening on the crypto-native side; tokens supported by ETFs have stronger bids from TradFi capital. Over the next 3–4 years, expect traditional finance to control the majority of liquidity. Consequences:
    • Less frequent meme-driven cycles and pure narrative rallies.
    • Higher bar for projects (MVPs, demos, real metrics demanded).
    • Yield generated by AI agents may increasingly be intermediated via TradFi rails, tempering speculative token price spikes but potentially improving durability of value accrual.

Project Snapshots (Context)

  • HyperGPT: AI/Web3 infra; agent builder, AI app marketplace, developer SDK; focus on shipping practical tools and enabling builders.
  • Revita: AI + Web3 + IoT infra; pre-RWA model and AI-driven POV; converts data value into asset value with compliance and on-chain issuance.
  • Stock5: Financial layer for tokenized stocks and RWA; AI-enhanced risk modeling, dynamic pricing, collateralization, efficient liquidation, yield separation.
  • ATC Global: Tokenized billboard ad assets (> $10M on-chain, ~$20M revenue); AI in retail, dynamic pricing vision; operations in Hong Kong, Bangkok, Jakarta; expansion planned for Vietnam.
  • Mero Pay: Crypto payments with real-world rails; mCard (Mastercard/Visa) and App Store for crypto-powered shopping.
  • Orbis Arc: On-chain platform integrating RWA and culture/IP; financializes media/IP into tradable assets.
  • TradeType AI: Unified platform for market analysis, strategy execution, and trader education; advocates measured, user-first onboarding.

Consensus Points

  • Utility over narrative: Real, measurable value creation beats branding.
  • Simplicity drives adoption: Reduce setup friction; deliver small but reliable wins.
  • AI as infra first: The biggest short-term RWA wins are compliance, risk, monitoring, and pricing automation.
  • Verification matters: On-chain, auditable credentials (e.g., POV) will differentiate serious projects.

Nuanced Differences

  • Timeline: Some anticipate 1–3 year improvements mainly in back-office/infrastructure; others see larger market shifts (agent-to-agent trading) in 4–5 years.
  • Destination of yield/liquidity: Expect growing TradFi participation (ETFs, institutional rails), reducing purely crypto-native volatility cycles.

Practical Takeaways

  • For builders:
    • Prioritize verifiability (device/data provenance → AI evaluation → on-chain issuance).
    • Build data pipelines and feedback loops to improve models and outcomes.
    • Design for compliance and risk from day one (KYC/AML, real-time monitoring).
  • For users/traders:
    • Start small; use AI for information gathering and structured decision support.
    • Demand transparency in AI behavior, data handling, and risk controls.
  • For liquidity providers/investors:
    • Look for projects with measurable on-chain value issuance and audited data provenance.
    • Favor infrastructure that reduces RWA friction (pricing, collateral, liquidation).

Open Questions to Watch

  • Standardization of AI attestations (e.g., Proof of Value frameworks) across IoT/RWA verticals.
  • Regulatory convergence on AI-agent autonomy and liability in financial settings.
  • How token value capture mechanisms evolve as TradFi liquidity deepens and yield intermediates off-chain.