Link: The Big Stack Game of LLM Poker / Sarah Tavel
TLDR: The AI race is a high-stakes poker game where tech giants are all in. With more than $600 billion invested and trillions at stake, it’s a battle of deep pockets and nerves. The winner takes all, but the real jackpot? Unprecedented innovation will benefit everyone and reshape tech’s landscape.
Key Points:
- The AI investment landscape is reaching staggering proportions, with Nvidia’s projected Q4 2024 revenue run rate of $150 billion highlighting the scale of capital being poured into large language model development. This “big stack game” of AI poker has major tech players like Meta, Microsoft, Google and AI-focused companies betting big, with hundreds of billions potentially lost but trillions to be gained.
- The AI market is characterized by rapid commoditization, with model pricing plummeting as newer versions emerge. GPT-3.5’s pricing fell by 97.5% in just 18 months, illustrating the breakneck pace of innovation and competition. This dynamic creates a challenging environment for return on investment calculations, as investments in current models are quickly depreciated by newer iterations. AI investment payback gap, or the revenue needed to recoup investments, now exceeds $500 billion, with projected revenues of $600 billion needed.
- As LLMs advance, their capability to handle increasingly complex tasks grows exponentially. This progression unlocks enormous economic value, potentially disrupting trillion-dollar industries like software engineering. The transition from “copilot” to “autopilot” functionalities in coding alone represents a multi-trillion-dollar opportunity. At least that seems to be the bull case.
My Thoughts:
Sarah Tavel, in her lengthy article, points out that the AI arms race among tech giants is reaching a fever pitch. It has turned into a high-stakes game where the ante is measured in billions, and the potential winnings could be in the trillions – theoretically. Why? Because while the rapid commoditization of LLM pricing is good for spurring higher usage and more experimentation, it also exposes the reality that an easy payoff isn’t guaranteed. Or as someone else eloquently argued: The bloom is off the AI rose.
All these arguments are true and false at the same time. If we think of AI (big data + algorithms + machine learning + LLMs) through the lens of generative AI, then we are going to think of everything in limited quantifiable terms. However, if we think of AI as a way to interact with information in a brand new way, then we are witnessing a seismic shift in technology’s landscape, much like original web, the always-connected web and social web. From my perspective, the shift will reveal its impact in unusual ways, long after the boom-and-bust cycle is over.
