AI agents are not just assisting; they are autonomously managing capital, challenging traditional hedge fund models and redefining white-collar work. This episode with Pei Chen of Theoriq reveals the architecture enabling this shift and forecasts the future of AI integration.
Theoriq's Autonomous Agent Architecture for DeFi
- Theoriq implements policy cages, programmable guardrails defining AI agents' operational scope. These limit what agents can control, particularly sensitive on-chain capital and transaction signing, ensuring safety.
- The Alpha Protocol acts as an agent-to-agent communication and coordination layer, enabling agents to register, discover, communicate, coordinate, and transact.
- Alpha Swarm comprises multiple modular agents, each specialized in areas like policy adherence, on-chain execution, or knowledge acquisition (e.g., understanding specific DeFi protocols). This swarm collectively executes complex tasks, such as optimizing liquidity provision or finding the best yield across various assets simultaneously, mitigating single points of failure.
- Pei Chen states, "We're not trying to limit the AI here... we wanted to kind of limit the... what actual things that the AI can touch and handle and control."
2025 AI Review: From Capability to Operational Reality
- The first half of 2025 saw LLMs become "tangible," with drastic improvements in usability and context understanding.
- The second half focused on integration, embedding AI products into existing applications like Google Suite (Gmail, Sheets) and replacing traditional search functions.
- Pei emphasizes the shift from AI's "capability" to its "operational reality," as enterprises deploy AI directly to users for tasks like customer interaction, research, code writing, and capital management.
- Pei Chen argues, "The focus of the question start shifting from can they do it to like should we trust them doing it."
AI Model Supremacy: Gemini Dominates Prediction Markets
- Prediction markets, specifically Kelshi, indicate Gemini as the leading AI model by year-end 2025, largely due to its Gemini 3 Pro launch and deep integration into Google's ecosystem.
- Gemini's market dominance (92% chance on Kelshi) stems from its superior overall experience and output compared to competitors like Grok and ChatGPT.
- Rob highlights the critical role of native integrations (e.g., Gemini in Google Docs, Grok on X) in driving user adoption and model improvement. More data and prompts lead to better-trained models.
- Rob observes, "I think the first half of 25 had drastic improvement in the models. The second half we're starting to make improvements in the UX and we're starting to bring them more or make them more integrated into our everyday systems."
AI in Coding: The Future of Engineering Roles
- While OpenAI leads in general rapid prototyping, Theoriq's engineering team heavily utilizes Anthropic's Claude for specialized tasks like backtesting, reasoning, and smart contract development.
- The rise of AI coding agents creates an "AI inception" scenario, where LLMs are used to build other specialized AI models.
- Pei asserts a continued, desperate need for human smart contract developers due to the rapid evolution of the space, complex integrations, and unforeseen edge cases. Junior engineers must adapt to vibe coding (collaborative coding with AI tools) to accelerate their work, maintaining a strong understanding of auditing and bug detection.
- Pei Chen states, "You still want to someone have the background of and you know coding background understanding how to audit and how to interpret codes and be able to capture the hackable like areas like look for the bugs right that is still very much needed."
Investor & Researcher Alpha
- Capital Allocation Shift: The emergence of AI-managed DeFi vaults like Theoriq's Alpha Vault signals a significant shift in capital allocation. Investors should research platforms offering verifiable, auditable AI agent-driven strategies with robust guardrails.
- AI Model Moats: Distribution and native integration are becoming critical competitive advantages for AI models. Investment thesis should consider which models are deeply embedded into widely used platforms, as this drives data acquisition and model performance.
- Engineering Talent Redefinition: The demand for traditional junior engineering roles may diminish, but the need for highly skilled smart contract developers and engineers proficient in collaborating with AI tools (vibe coding) will intensify. Research into educational pathways and tooling for AI-augmented development is paramount.
Strategic Conclusion
The economy is rapidly transitioning from software-based to an agent-based model, where autonomous AI agents manage capital and coordinate resources. The next step requires establishing robust trust frameworks and verifiable guardrails for these agents to operate at scale.
