While an AI bubble seems negative, the overproduction of compute power creates a favorable environment for companies that consume it. As prices for compute drop, their cost of goods sold decreases, leading to higher gross margins and better business fundamentals.
Unlike consumer or enterprise software, the defense industry has a single major customer per country. This structure favors consolidation. The path to success is not to be a niche SaaS tool but to build a platform that becomes a "national champion," deeply integrated with the nation's defense strategy.
While AI is often viewed abstractly through software and models, its most significant current contribution to GDP growth is physical. The boom in data center construction—involving steel, power infrastructure, and labor—is a tangible economic driver that is often underestimated.
VCs are incentivized to deploy large amounts of capital. However, the best companies often have strong fundamentals, are capital-efficient, or even profitable, and thus don't need to raise money. This creates a challenging dynamic where the best investments, like Sequoia's investment in Zoom, are the hardest to get into.
In the AI arms race, competitive advantage isn't just about models or talent; it's about the physical execution of building data centers. The complexity of construction, supply chain management, and navigating delays creates a real-world moat. Companies that excel at building physical infrastructure will outpace competitors.
The assumption that AI will create trillions in corporate profit overlooks a key economic reality: only 1% of global GDP is profit above the cost of capital. Intense competition in AI will likely drive prices down, meaning the vast majority of economic benefits will be passed to consumers, not captured by a few monopolistic companies.
Unlike the 2008 financial crisis, which was a debt-fueled credit unwind, the current AI boom is largely funded by equity and corporate cash. Therefore, a potential correction will likely be an equity unwind, where the stock prices of major tech companies fall, impacting portfolios directly rather than triggering a systemic credit collapse.
Early tech giants like Google and AWS built monopolies because their potential wasn't widely understood, allowing them to grow without intense competition. In contrast, because everyone knows AI will be massive, the resulting competition and capital influx make it difficult for any single player to establish a monopoly.
A common belief is that investment from a top-tier VC can guarantee a company's success. However, the hard-learned lesson is that capital alone cannot create a successful company. True success is predetermined by the founder's quality and strong product-market fit; VCs can only help navigate.
There's a stark contrast in AGI timeline predictions. Newcomers and enthusiasts often predict AGI within months or a few years. However, the field's most influential figures, like Ilya Sutskever and Andrej Karpathy, are now signaling that true AGI is likely decades away, suggesting the current paradigm has limitations.
Since modern AI is so new, no one has more than a few years of relevant experience. This levels the playing field. The best hiring strategy is to prioritize young, AI-native talent with a steep learning curve over senior engineers whose experience may be less relevant. Dynamism and adaptability trump tenure.
A year ago, stable giants like Microsoft and Amazon absorbed the risk of the AI compute build-out. Now, they've stepped back, and smaller players like Oracle and CoreWeave, along with chipmakers financing their own sales, have taken on that risk. This shift to less stable, more circular financing models reveals the bubble's underlying fragility.