One interesting consequence is that the cost of being wrong may remain unchanged, while the cost of discovering why you're wrong is falling dramatically. That should fundamentally change how smaller funds approach diligence. The question is whether investment practices will evolve as quickly as the tools.
Perhaps the biggest opportunity is to change the questions asked at investment committees. Rather than presenting 40 pages of risks, identify the three assumptions whose failure would materially change valuation or financing needs. That's a much more useful basis for an investment decision.
This resonates particularly in industrial deeptech. We spend considerable time validating the science, yet the investment outcome often depends on much more mundane variables: procurement cycles, grid access, regulatory sequencing or working capital. The technology can work perfectly and the investment thesis still collapse.
The distinction between contractual value and economically realisable value is essential. We see similar problems with commercial LOIs, conditional purchase orders and government grants. The headline numbers enter financial models long before the underlying commitments become enforceable or operationally achievable.
would add a geographical dimension here. Increasingly, the technology, manufacturing capacity, customers and financing sit in different jurisdictions. Each component can look credible independently, while the overall project becomes fragile because nobody controls the entire execution chain. That's precisely where conventional diligence often falls short.
One interesting consequence is that the cost of being wrong may remain unchanged, while the cost of discovering why you're wrong is falling dramatically. That should fundamentally change how smaller funds approach diligence. The question is whether investment practices will evolve as quickly as the tools.
Perhaps the biggest opportunity is to change the questions asked at investment committees. Rather than presenting 40 pages of risks, identify the three assumptions whose failure would materially change valuation or financing needs. That's a much more useful basis for an investment decision.
This resonates particularly in industrial deeptech. We spend considerable time validating the science, yet the investment outcome often depends on much more mundane variables: procurement cycles, grid access, regulatory sequencing or working capital. The technology can work perfectly and the investment thesis still collapse.
The distinction between contractual value and economically realisable value is essential. We see similar problems with commercial LOIs, conditional purchase orders and government grants. The headline numbers enter financial models long before the underlying commitments become enforceable or operationally achievable.
would add a geographical dimension here. Increasingly, the technology, manufacturing capacity, customers and financing sit in different jurisdictions. Each component can look credible independently, while the overall project becomes fragile because nobody controls the entire execution chain. That's precisely where conventional diligence often falls short.