Instead of chasing high-valuation, first-order AI players like GPU makers, THL focuses its investment thesis on second or third-degree beneficiaries. These companies provide critical, capital-light IP or embedded software that endures through tech cycles, offering better long-term value for middle-market investors.
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When evaluating AI startups, don't just consider the current product landscape. Instead, visualize the future state of giants like OpenAI as multi-trillion dollar companies. Their "sphere of influence" will be vast. The best opportunities are "second-order" companies operating in niches these giants are unlikely to touch.
As AI infrastructure giants become government-backed utilities, their investment appeal diminishes like banks after 2008. The next wave of value creation will come from stagnant, existing businesses that adopt AI to unlock new margins, leveraging their established brands and distribution channels rather than building new rails from scratch.
Current M&A activity related to AI isn't targeting AI model creators. Instead, capital is flowing into consolidating the 'picks and shovels' of the AI ecosystem. This includes derivative plays like data centers, semiconductors, software, and even power suppliers, which are seen as more tangible long-term assets.
Like containerization, AI is a transformative technology where value may accrue to customers and users, not the creators of the core infrastructure. The biggest fortunes from containerization were made by companies like Nike and Apple that leveraged global supply chains, not by investors in the container companies themselves.
If AI is truly transformational, its greatest long-term value will accrue to non-tech companies that adopt it to improve productivity. Historical tech cycles show that after an initial boom, the producers of a new technology are eventually outperformed by its adopters across the wider economy.
When a new technology stack like AI emerges, the infrastructure layer (chips, networking) inflects first and has the most identifiable winners. Sacerdote argues the application and model layers are riskier and less predictable, similar to the early, chaotic days of internet search engines before Google's dominance.
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.
The AI investment case might be inverted. While tech firms spend trillions on infrastructure with uncertain returns, traditional sector companies (industrials, healthcare) can leverage powerful AI services for a fraction of the cost. They capture a massive 'value gap,' gaining productivity without the huge capital outlay.
Permira's AI strategy uses a clear framework: invest in the 'picks and shovels' of compute (data centers) and in applications with unique, proprietary data sets. They deliberately avoid the hyper-competitive model layer, viewing it as a scale game best left to venture capital and strategic giants.
Investing in startups directly adjacent to OpenAI is risky, as they will inevitably build those features. A smarter strategy is backing "second-order effect" companies applying AI to niche, unsexy industries that are outside the core focus of top AI researchers.