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Supporting research
The twelve questions, with the evidence behind them.
The presentation is the short version. Each question opens a research note that separates published evidence from project assumptions, and names what is still unresolved.
The Global AI Race
- 01Why Are World Models Becoming AI's Next Frontier?A generative model predicts the next word. A World Model learns how something actually behaves, then runs it forward. That shift turns AI from a system that describes the world into one that can test a decision before taking it, which is why the frontier has moved here.Read the note
- 02Who Is Leading the World Model Race in 2026?No single lab leads on every axis. Google DeepMind holds the interactive frontier, World Labs shipped the first commercial product, and Odyssey, NVIDIA and a cluster of robotics labs each own a different slice. Capital followed: more than $3B went into world-model startups in the first half of 2026.Read the note
- 03Why Are World Models So Difficult to Build?World Models represent a major technological advancement, with emerging research demonstrating their ability to simulate complex environments and evolving system dynamics. Developing frontier-level capabilities from scratch, however, requires extraordinary computational resources, high-quality data, specialized scientific expertise and substantial long-term investment. This complexity makes access to an existing operational World Model particularly valuable: it can significantly accelerate technological deployment, reduce the need to reproduce foundational development and create a powerful platform for further innovation. Reliability, scalability and application-specific performance remain important areas for continuous validation and improvement.Read the note
Technology and Economics
- 04What Would It Really Cost to Build One?Independent frontier development is a research programme, not a purchase order: $100M to $300M over 18 to 30 months on our own planning estimates, concentrated in senior research staff, GPU time, experiments that fail, data and deployment. Most of it is committed before anything works.Read the note
- 05Why Not Just Use ChatGPT, Claude or Gemini?General models are excellent, and for many tasks they are the right answer. The comparison is not a subscription against a platform: it is a general model plus the integration, evaluation and operations we build around it, against a licensed platform that already carries financial and blockchain capability.Read the note
- 06Can World Models Improve Financial Forecasting?Yes, progressively, and the horizon is widening. Research published through 2026 shows generative market simulators reproducing order-book behaviour and stylised market facts, financial foundation models forecasting across instruments from one pretrained model, and deep order-flow models extracting signal over multiple horizons. A World Model adds what a standalone predictor lacks: a state that carries history, dynamics that can be rolled forward, and a forecast whose uncertainty is explicit. Faithful long-horizon rollout and behaviour under regime change remain open, which is exactly what an evaluation contract is for.Read the note
Financial Applications
- 07How Could AI Transform Blockchain Finance?Across four measurable surfaces, all of which a market-state model can serve: liquidity intelligence that reads venues, pools and chains as one book, execution and capital efficiency inside a fragmented market, continuous review of smart-contract risk, and token utility that follows paid usage rather than preceding it. Each surface has an observable state, a forecast that can be scored and a baseline already running today.Read the note
- 08What Happens When AI Agents Work Together?One generalist tries to do everything and drifts. A team of specialists, each watching one part of the environment with shared memory, produces a single view that can be checked and corrected. The pattern that works keeps verification outside the agent that produced the work.Read the note
- 09How Will AI Agents Buy and Sell Intelligence?x402 makes it concrete: a request comes back priced instead of refused, the agent pays from an allowance a person approved, and the data is released in the same exchange. Settlement can run in stablecoin, in a native token where the rail supports it, or in a hybrid the treasury manages.Read the note
The Strategic Opportunity
- 10Why License Existing Technology Instead of Building?Because the alternative is a multi-year frontier programme with an uncertain outcome. A licence converts that programme into an operating capability in months, at roughly a tenth of the capital, and carries the technical risk with the provider rather than with us.Read the note
- 11What Exactly Would the License Deliver?Access rather than ownership: three years of a working platform shaped around our use cases, with a custom user experience, integration support and validation on our own data. Source code, model weights and ownership stay with the provider.Read the note
- 12Why Could This Become a Major Strategic Advantage?Because the case compounds rather than saves once. Better liquidity and services, sharper economic intelligence, earlier risk control, lower avoidable operating cost and new AI services, with a defined share of what is saved reinvested in capability that stays relevant as the technology moves.Read the note