Commodity trading is an ideal but underutilized area for AI. The field is rich with unstructured micro-data—from individual warehouse invoices to real-time shipping costs—that is difficult for humans to process. AI can synthesize this information to uncover complex patterns and arbitrage opportunities.

The convergence of AI and Distributed Ledger Technology (DLT) is setting the stage for a 'liquidity explosion.' This will enable the tokenization of previously untradeable, fragmented assets like specific plastics or downstream LNG hubs, creating entirely new markets.

Because the entire crypto ecosystem is open-source, AI developer tools are exceptionally effective at writing, auditing, and debugging its code. This gives the industry a significant OpEx advantage over traditional finance, whose code is proprietary and not in AI training sets.

AI and crypto are not competing but are parallel, complementary forces reshaping business. While AI revolutionizes company creation and internal operations, Internet Capital Markets (powered by crypto) are fundamentally rewriting the external functions of capital formation, trading, settlement, and ownership for this new generation of AI-native companies.

In traditional finance, data providers (S&P) and ratings agencies (Moody's) are separate, high-headcount businesses. The combination of transparent on-chain data and AI allows a single firm to perform these functions instantly and cheaply, threatening to consolidate this fragmented, multi-hundred-billion-dollar market.

The rapid emergence of complex AI infrastructure financing is breaking down traditional silos between credit markets. Investors can no longer rely on a single approach and must develop new, hybrid analytical frameworks that blend corporate-level fundamental analysis with the asset-specific expertise typical of securitized products.

The future of AI in finance is not just about suggesting trades, but creating interacting systems of specialized agents. For instance, multiple AI "analyst" agents could research a stock, while separate "risk-taking" agents would interact with them to formulate and execute a cohesive trading strategy.

Historically, asset classes were siloed for convenience because modeling illiquid private assets was difficult. Technology is changing this by providing greater transparency and analytic capabilities for private markets, turning the binary public/private distinction into a continuous spectrum of liquidity and disclosure.

Man Group uses AI to systematize the creation of trading strategies. Agents analyze academic papers for ideas, build code, run backtests, and construct signals. Over 15 models created this way are now trading client assets, proving the viability of automating research itself.