To invest in high-risk, transformative fields like quantum computing, structure portfolios with three tiers: established leaders (e.g., IBM) forming the core, "enabler" companies providing key components (e.g., Honeywell), and a smaller allocation to purely speculative startups (e.g., IonQ) to capture upside while managing volatility.
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Unlike software, where customer acquisition is the main risk, the primary diligence question for transformative hardware is technical feasibility. If a team can prove they can build the product (e.g., a cheaper missile system), the market demand is often a given, simplifying the investment thesis.
For breakthrough technologies like AI and quantum, traditional valuation is less important initially. Investors must buy into the narrative, long-term potential, and quality of the management team, much like early-stage seed investing. Near-term earnings are secondary to the transformative vision.
In a technology boom like the AI trade, capital first flows to core enablers (e.g., NVIDIA). The cycle then extends to first-derivative plays (e.g., data center power) and then to riskier nth-derivative ideas (e.g., quantum computing), which act as leveraged bets and are the first to crash.
Instead of betting on which AI models or applications will win, Karmel Capital focuses on the infrastructure layer (neocloud companies). This "pick and shovel" strategy provides exposure to the entire ecosystem's growth with lower valuations and less risk, as infrastructure is essential regardless of who wins at the top layers.
A successful early-stage strategy involves actively maximizing specific risks—product, market, and timing—to pursue transformative ideas. Conversely, risks related to capital efficiency and team quality should be minimized. This framework pushes a firm to take big, non-obvious swings instead of settling for safer, incremental bets.
To avoid being too futuristic or too incremental, Cisco's innovation arm manages its ventures across two axes: technology risk and time horizon (from 6 months to 5 years). This portfolio approach ensures a mix of near-term value and long-term strategic bets.
Unlike SaaS startups focused on finding product-market fit (market risk), deep tech ventures tackle immense technical challenges. If they succeed, they enter massive, pre-existing trillion-dollar markets like energy or shipping where demand is virtually guaranteed, eliminating market risk entirely.
When expanding a fund's investment thesis, avoid making multiple changes simultaneously, such as moving from venture to growth stage AND from software to hardware. Making more than one 'leap' at a time dramatically increases risk and magnifies blind spots. Instead, change one variable at a time, like moving to a later stage within a familiar sector, to manage risk effectively.
The increased volatility and shorter defensibility windows in the AI era challenge traditional VC portfolio construction. The logical response to this heightened risk is greater diversification. This implies that early-stage funds may need to be larger to support more investments or write smaller checks into more companies.
Drawing a parallel to the early internet, where initial market-anointed winners like Ask Jeeves failed, the current AI boom presents a similar risk. A more prudent strategy is to invest in companies across various sectors that are effectively adopting AI to enhance productivity, as this is where widespread, long-term value will be created.
