Discussion Recaps

September 4th, 2025 –
Vertical AI: A Cross-Industry Conversation on the Impact of Artificial Intelligence

Web3 and Emerging Technologies

AI as the Operating Layer: Positioned as foundational for Web3, similar to choosing between early operating systems (Linux, Windows, Apple). Seen as the “glue” for tokenization and integration.
Blockchain Simplification: AI could make digital wallets and token systems easier to use, reducing complexity around keys and usability.
New Fan Experiences: Tokenization plus AI enables real-world rewards—like MMA fight access, exclusive meet-and-greets, or immersive content—bridging blockchain, AI, and entertainment.
Beyond Hype Cycles: While metaverse and crypto booms faded, the underlying technologies (blockchain + mixed reality) are still valuable and ready for more mature applications.

Finance and Capital Markets

Automation & Efficiency: Supercomputers and AI automate trade flows, even in illiquid markets (like bonds). Fractionalization and digitization are increasing transparency and access.
Proprietary Data: Firms with unique datasets (e.g., Citadel) gain major advantages in sharpening models and predicting market moves.
Day Traders & Democratization: Individual traders benefit from AI-driven tools, but the arms race for private data continues to favor large institutions.
Research Impact: AI-generated reports level the playing field but make traditional research harder to differentiate.
Job Dynamics: AI is expanding who can participate in development (e.g., traders or analysts building tools without coding) rather than replacing jobs outright.

Legal and Privacy

Generative AI in Legal Workflows: Applied to M&A due diligence, contract review, and deal verification. Automates complex cross-referencing of amendments and side letters.
Zero Data Retention (ZDR): Critical for legal and healthcare use cases. Hard to secure agreements with AI vendors, but vital for trust and compliance.
Attorney-Client Privilege: Use of AI tools is acceptable if treated like other third-party processors, but raises concerns about true confidentiality.
Alternative Business Structures (ABS): Arizona allows non-lawyers to hold equity in law firms, opening new multidisciplinary models (law + consulting + tech).

Wealth Management

Human Relationships Still Central: AI won’t replace the empathy and trust built by advisors in multi-generational wealth management.
Augmentation Role: AI will help advisors proactively manage family financial needs (estate planning, liquidity, tax, philanthropy) and anticipate generational shifts.
Self-Custody Revolution: Future generations may bypass banks entirely, using AI-driven blockchain wallets and stablecoins for self-managed wealth strategies.

Healthcare

AI for Early Detection: Tools like Bayesian Health’s sepsis predictor outperform clinicians in early diagnosis, reducing “alert fatigue” and saving lives.
Integration Challenges: Startups must work within massive EMR systems (Epic, Allscripts). Regulation (HIPAA, FDA approval) adds cost and complexity.
Concierge and Global Models: AI enables new models like international, AI-enabled specialty practices without physical offices.
Regulatory Questions: Debate continues over whether AI systems can/should hold licenses, and how liability is handled if predictions fail.

Cross-Cutting Themes

Global Competition: Barriers to entry are low; billions worldwide can leverage free or cheap AI models, intensifying competition.
Intellectual Property & Moats: Patents and proprietary data remain crucial, but there’s little lasting moat in AI due to rapid commoditization.
Partnerships with Consulting Firms: Startups may need to align with large consulting groups to access enterprise clients more effectively.
Future of AI: Most agreed AI augments rather than replaces professionals, creating efficiencies in service delivery (finance, legal, healthcare). Success will depend on integration into workflows and agentic AI capable of performing real tasks, not just outputs.