Sell to startups at their inception when they have no switching costs and few stakeholders. As these customers scale into major companies, your business scales with them, turning early adopters into significant, long-term revenue streams.
For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.
Creating synthetic derivatives (like perpetual futures) of traditional assets on-chain is more scalable and efficient than creating direct tokenized copies. This is especially true for assets with high derivative demand, such as emerging market equities.
Large incumbents struggle to serve newly-formed startups because these customers offer low initial revenue but require significant sales and support. This P&L constraint creates a protected 'greenfield' market for new vendors to capture customers early and grow with them.