AI's Evolution Diverges from Web's Historical Trends
In the fast-paced world of artificial intelligence (AI), the focus for builders in the sector remains on tangible advantages such as compute, energy, data, and distribution. This is a crucial lesson learned from the 2020s AI environment, which is characterised by high policy rates, sticky inflation, and quantitative tightening.
The AI landscape in Germany, in particular, is thriving, with companies like Aleph Alpha and Neura Robotics leading the charge. Aleph Alpha specialises in explainable, secure AI for enterprises and public administration, emphasising data sovereignty, while Neura Robotics develops cognitive robots that combine advanced AI and hardware for industrial and service automation. These companies, with their profitable business models and control over predictability and energy sources, represent successful niches in the innovative, resilient, and investor-interest-rich German AI startup ecosystem.
Looking back to the internet boom of the 1990s, we can draw parallels and contrasts with the current AI cycle. The 1990s boom took place in an era of low inflation, falling interest rates, and globalisation, contrasting the high-rate environment of the 2020s. However, a key takeaway from both cycles is that markets can hallucinate and technology advances, regardless of whether it's the dot-com crash or the AI correction.
For investors, it's crucial to distinguish between valuation hallucinations and real adoption curves. In the 2020s, AI firms must prove unit economics earlier compared to dot-com companies in the 1990s. Cash flow matters again for AI firms, unlike the dot-com era where companies could bleed cash for years. Infrastructure, such as datacenters, securing compute, and locking energy contracts, is crucial for AI companies and can act as a moat.
The current macro environment also presents challenges, including permitting delays, capital scarcity, and geopolitical chokepoints in semiconductors and energy. Geopolitics plays a significant role in AI supply chains, with export controls and national security priorities fracturing them. Policymakers should understand that restricting compute or energy access can reshape competitive advantage more than capital flows alone.
Despite these challenges, the survivors of this cycle won't just be the best coders; they'll be the ones who master energy contracts, navigate export controls, and prove unit economics in a high-rate world. What matters are unit economics, cash flow positive models, control of compute and data, and energy access.
The AI cycle of the 2020s resembles the dot-com boom on the surface, but the macro environment is inverted. While the internet required minimal hardware buildout and relied heavily on software, AI requires heavy infrastructure such as datacenters, GPUs, and global supply chains. The cost of capital decreased throughout the 1990s due to declining interest rates, contrasting the capital-constrained 2020s where AI requires massive infrastructure but capital is scarce.
As we navigate this AI landscape, it's important to remember the lessons from the past. The dot-com crash of the 1990s did not invalidate the internet, but it invalidated financial hallucinations. The survivors of the crash, like Google, Facebook, and iPhone-era Apple, reshaped society. Similarly, the survivors of the AI correction will likely shape the future of technology. Markets cannot price innovation without the Fed's influence, and it's up to us to understand and adapt to this changing landscape.
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