Ep 37 — How AI Agents Will Reshape DeFi & Crypto Infrastructure with Ejaaz Ahamadeen

This episode unpacks the transformative potential of AI agents in reshaping DeFi and crypto infrastructure, exploring the full stack from decentralized compute to autonomous financial strategies and the critical challenges ahead.

Show Notes: Deco Podcast Ep 37 — How AI Agents Will Reshape DeFi & Crypto Infrastructure with Ejaaz Ahamadeen

The Crypto AI Stack Explained

• AI Resources Layer: This base layer comprises the essential components needed for AI:
  • Data: Ejaaz highlights that most valuable data is currently siloed in Web2 platforms (social media, healthcare). Crypto offers solutions through coordination mechanisms (e.g., Grass Protocol scraping web data, VA focusing on personalized data, Masa's subnet approach) and potentially leveraging blockchain for data ownership or ZK encryption for privacy. Investors should note the ongoing challenge and opportunity in accessing high-quality, personalized data for decentralized AI.
  • Compute: This refers to the processing power needed for AI tasks. Ejaaz contrasts centralized cloud providers (AWS, Azure) with the potential of Decentralized Physical Infrastructure Networks (DePIN). DePIN aims to coordinate distributed compute resources, mitigating centralization risks. DePIN (Decentralized Physical Infrastructure Networks) refers to blockchain protocols that incentivize the deployment and operation of physical hardware infrastructure. Examples mentioned include Ionet, Render, Prime Intellect, and Noose Research focusing on training or inference. Inference is the process of using a trained AI model to make predictions or decisions based on new input data (like querying ChatGPT).
  • Models: Ejaaz describes models as intricately designed structures requiring data and compute to function. He stresses the importance of open models (like DeepSeek, Llama from Meta) for transparency, bias mitigation, and avoiding reliance on single entities. "If that model is owned by a single party or a single person, then you're now under the assumptions and objectives of that individual and person, right? And that's why it's so important to potentially decentralize that ownership."
• Coordination Layer: Sitting above the resources, this layer routes requests to the appropriate models, data, and compute resources, optimizing the process. Ejaaz points to networks like Bittensor and Allora as examples that coordinate various components, potentially improving efficiency through iterative loops. Researchers should track the development of these coordination protocols as they are crucial for orchestrating decentralized AI resources effectively.
• Middleware Layer: This layer contains tools and frameworks for building AI applications, such as AI agent frameworks (mentioning AI6NZ, Arc Virtuals), memory architecture, and data repositories. Ejaaz expresses uncertainty about whether this layer truly requires crypto or decentralization, noting it's an area he's actively exploring. The necessity and viability of decentralizing the middleware remain open questions with significant implications for stack architecture.
• Application Layer (Agents): Ejaaz predicts this layer will manifest primarily as AI Agents – autonomous software entities capable of thinking, ingesting data, acting in real-time, and making decisions, potentially in infinite loops like "digital humans." He argues agents provide a necessary new interface for navigating the complexity of future digital environments. The emergence of sophisticated AI agents represents a paradigm shift investors should monitor closely, potentially disrupting traditional app/web interfaces.

Value Accrual and Business Models Across the Stack

• Sarup raises the critical question of sustainable business models and value accrual within the Crypto AI stack. Ejaaz candidly states this is "the golden question that no one has been able to answer," suggesting anyone with a definitive answer is likely overstating their certainty.
• He notes the crypto business model is only truly proven for monetary assets (Bitcoin) and stablecoins (fee-based).
• For the AI stack's infrastructure layer (compute, data, models), the bet is that upper layers will pay fees (e.g., inference/training costs) back down, possibly via token payments or buy-back-and-burn mechanisms. However, Ejaaz questions the long-term sustainability, especially regarding token price volatility impacting usability – a broader crypto protocol challenge.
• At the application (agent) layer, current models often involve pay-to-access (like AIXBT for alpha) or tiered staking models. Ejaaz remains unsure about their long-term sustainability, pointing out few are revenue-positive yet, unlike profitable AI-powered (but not crypto-native) apps like Kaido. Investors must critically evaluate the tokenomics and revenue models of Crypto AI projects, as sustainability remains a major hurdle across the stack.

Comparing Crypto AI Infrastructure to Web2 Giants

• Ejaaz argues that coordinating many smaller participants ("many versus one") can potentially overcome large, centralized players. While aggregating diverse GPUs presents technical challenges (performance bottlenecks), advancements by teams like Noose Research, Prime Intellect, and Jensen are making decentralized training/inference more feasible.
• A key advantage Ejaaz emphasizes for decentralized compute is distributed ownership, offering philosophical and ethical benefits over centralized control.
• However, he expresses significant skepticism about solving the data challenge decentrally, particularly accessing valuable personal data siloed in Web2. Scraping public web data faces competition, and accessing private data requires new models, perhaps incredibly compelling decentralized apps that incentivize data sharing – a difficult problem to solve. While decentralized compute shows promise, the data acquisition strategy remains a critical vulnerability and area for innovation in the decentralized AI stack.

Deep Dive into Defi AI (Decentralized Finance + AI)

• Intelligence: Agents scouring data for insights (e.g., AIXBT finding alpha).
• Curation: Filtering the vast amount of information/intelligence generated.
• Execution/Abstraction: Agents performing actions (swaps, bridging) on the user's behalf, hiding complexity (e.g., Wayfinder).

Sarup observes that centralized exchanges (CEXs) currently dominate crypto usage, acting not just as execution venues but also as crucial curators, simplifying the user experience by vetting assets and reducing risks (wrong contracts, scams). The question arises: will on-chain AI curators simply become new gatekeepers extracting value, similar to CEXs?

• Ejaaz counters that CEXs dominate partly because they offer the easiest path for the primary crypto use case: making money. Users often lack knowledge and rely on CEX curation (familiar names, low prices).
• However, he argues users migrate on-chain for access to the long tail of assets (higher potential alpha) and because DEX liquidity is becoming competitive with CEXs for major assets on certain days (though Sarup pushes back, noting CEX aggregate volume is still much higher).
• Ejaaz believes the ultimate solution isn't just better on-chain exchanges but a singular, personalized AI agent terminal that handles curation, execution, and understands individual user preferences and timing (e.g., anticipating trades based on salary deposits). This improved UX, rather than extraction, is the goal. The development of user-friendly, personalized AI agents could be key to onboarding more users to DeFi, challenging CEX dominance through superior experience rather than just lower fees.

Building the Ideal DeFi Agent

• Ejaaz gives a "boring but true" answer: "it needs to be an agent that makes me money."
• He argues the agent must appeal directly to the core crypto motivation. It should be an on-chain trader agent, likely focused on higher-risk assets to provide the "dopamine hits" associated with quick, significant gains.
• Crucially, it needs safety guardrails (spending limits) and must demonstrate returns quickly to build trust and engagement.
• Ejaaz stresses the execution is key. He notes the difficulty and reputational risk involved, citing AI6NZ's cautious approach to launching an autonomous trader.
• He emphasizes the overlooked human and community aspect, drawing parallels to the relatable, human-like communication style of agents like Ask Billy Bets (sports betting) and AIXBT. An agent needs to connect with users culturally, not just present graphs. Successful DeFi agents may need to combine sophisticated trading logic with engaging, community-centric interfaces and potentially operate with proprietary strategies.

Scaling AI Agents and Market Efficiency

• Sarup questions whether a successful trader agent can maintain profitability if widely accessible, given that market alpha relies on exploiting inefficiencies.
• Ejaaz agrees: "No. That's why I don't think everyone should have access to it." He argues successful strategies must be proprietary. Open-sourcing trading strategies leads to immediate arbitrage and loss of edge.
• He uses Ask Billy Bets releasing picks shortly before games start as an example of controlled access. While ZK encryption might eventually enable private strategy execution at scale, Ejaaz believes tiered access is necessary to avoid simply creating a low-yield savings account. Investors should be skeptical of DeFi agents promising high returns with fully open access; sustainable alpha likely requires proprietary elements or controlled distribution.

Untapped Opportunities and Ignored Problems in Crypto AI

• Backend Automation (Unsexy Agents): Automating tedious tasks like data cleaning, labeling, structuring, liquidity pool management, or other "mechanical turk" style work. He envisions a "gig economy of unsexy agents" handling these crucial but often invisible processes.
• Web2 Integration: Creating agents that seamlessly blend Web2 functionalities (e.g., familiar finance app interfaces earning yield) with crypto-native actions. The goal is to leverage user familiarity from Web2 to ease them into crypto functionalities within a single interface, potentially bridging the gap CEXs currently fill.
• Governance: Sarup brings up Shaw's point (from a previous episode) about AI streamlining crypto governance, which Ejaaz implicitly supports through his focus on improving crypto UX overall. Researchers and builders might find significant opportunities in developing AI agents for backend automation and governance, areas less hyped but potentially foundational for ecosystem efficiency.

AI's Role in Bringing Capital On-Chain

• Initially, Web2 institutions (like Coinbase, Robinhood) will likely lead by integrating AI-driven crypto features into their existing platforms, leveraging their user base and trust.
• In parallel, truly useful crypto-native applications, likely in the form of agents, will emerge. These agents will need to offer compelling use cases that draw users organically.
• He believes the pressure to build genuinely good products, driven by AI capabilities, is forcing the crypto space to mature beyond technically complex but user-unfriendly solutions. "It's forcing crypto to do something that they've never done before... build like a really good product." The convergence of AI capabilities and market pressure could catalyze the development of crypto applications with genuine product-market fit, attracting new capital.

Web2 Agent Examples and Cross-Application Potential

• Supply Chain Agent: An agent designed to interact digitally (text, automated calls) with human intermediaries in shipping, drastically reducing process time from weeks to days by automating scheduling and communication.
• Enterprise Agents: Internal agents trained on company data to assist with tasks like scrum meetings, Slack monitoring, Jira tracking, making knowledge work more efficient.
• Coding Co-pilots: Agents assisting software engineers with code checking, formatting, and suggestions, integrated into frameworks like LangChain or AutoGen.
• He argues the underlying technology isn't novel in a way that prevents similar applications in Web3, suggesting significant spillover potential. Observing successful Web2 agent implementations can provide valuable blueprints and inspiration for Crypto AI applications, particularly in enterprise and workflow automation.

Addressing Blind Spots and Challenges

• Data: Reiterates the unsolved problem of accessing high-quality data for decentralized models, though suggests compelling agent interfaces might organically gather user interaction data over time.
• User Experience (UX): The persistent difficulty of using crypto protocols remains a barrier. Adding AI intermediaries must demonstrably improve, not complicate, the UX.
• Crypto Tribalism: A major cultural hurdle is the siloed nature of crypto communities. Agents, being chain-agnostic, require users to adopt a multi-chain perspective. Ejaaz controversially suggests it might be easier to onboard non-crypto natives to new crypto AI apps than to convince existing, tribal crypto users. "It's ironic. We talk about growing the pie, but no one actually wants to grow the pie." Overcoming crypto's inherent tribalism is a significant socio-technical challenge for achieving widespread adoption of interoperable AI agents.

Accelerate DAO: Origins, Goals, and Roadmap

• Origin: Formed to scale efforts in supporting builders and addressing ecosystem gaps identified through inbound requests.
• Mission: To build out the crypto AI ecosystem in a decentralized manner.
• Three Pillars:
  • Core Development: An internal team building products based on a roadmap addressing perceived gaps (ecosystem tooling, agents, frameworks). Currently working on two agents (research-focused and trader-focused) and associated builder tools.
  • Incubator: To foster promising builders lacking resources. Plans include a small cohort, two-month sprint model aiming for demoable, publicly accessible products, similar to Y Combinator.
  • Investments: Working on legal infrastructure to allow direct investment (tokens or other vehicles) in high-conviction teams.
• Partnerships: Announced collaborations with Virtuals and Send AI to help scale these pillars. Accelerate DAO aims to be a multifaceted ecosystem catalyst, combining direct building, incubation, and investment – a model investors might watch.

Accelerate DAO: Launch Learnings and Future Strategy

• Token Launch: Acknowledges the fumble of launching without vesting, assuming a quieter start, leading to an unjustified market cap spike and justified criticism. Vesting was retroactively added.
• Inclusivity: Would take more time to include more people in the initial round, aiming for a balance between avoiding snipers and being less concentrated. Admits there's no perfect token launch method.
• Product Readiness: The hype created expectations of an immediate product, whereas the team intended to build carefully. Having something viable at launch is important when hype is high.
• DAO Definition: Clarifying that "DAO" doesn't mean fully decentralized voting from day one; initial direction needs guidance from the core team before progressive decentralization.
• Revenue Model: Believes the path to sustainability involves creating valuable services charged via familiar Web2 models (subscriptions, one-off payments) to onboard users easily. Crypto elements (buybacks, treasury management via DAO votes) can be implemented on the backend with the generated revenue. Founders should note the importance of managing launch expectations, structuring token distribution carefully, and potentially prioritizing familiar UX/business models for initial user adoption.

Market Downturn Perspectives: Exciting Sectors and Teams

• Agent Platforms: Believes these platforms (like Virtuals, Send AI, etc.) will evolve beyond simple launches into enabling an "agent app layer" or "services layer," potentially like a "Zapier for agent services," where most revenue will be generated. This includes facilitating agent-to-agent communication (swarms), despite his initial skepticism.
• Application Agents: Expects standout agents to emerge in Defi AI (trading, betting) that significantly outperform, likely using proprietary algorithms.
• Resource Layer (DePIN): Highly excited about the potential of decentralized compute/training networks (Noose, Prime Intellect, Jensen) to become powerful, community-owned infrastructure for training high-quality AI models. During downturns, focusing on foundational layers (DePIN, agent platforms) and applications with clear potential for product-market fit (high-performing DeFi agents) may offer strategic investment opportunities.

Final Thoughts and Advice

• Ejaaz concludes with advice for navigating market downturns: "Study and don't give up." He emphasizes that periods of low market activity are ideal for deep learning and identifying genuine opportunities, whether for investment or intellectual curiosity. He notes he is still deep in study himself, indicating the vastness of the learning curve.

Strategic Conclusion

This conversation highlights AI agents as the pivotal future interface for crypto, poised to enhance UX and unlock new DeFi capabilities. Investors and researchers must track agent development, underlying infrastructure needs (compute, data), and emerging Defi AI use cases to navigate market shifts and identify high-potential opportunities in this rapidly evolving intersection.

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