Market Disruption Amplified: Artificial Intelligence intensifying market turbulence
In the rapidly evolving world of Artificial Intelligence (AI), traditional strategies for combating the infamous bullwhip effect have lost their effectiveness. This phenomenon, which causes inventory swings and stockouts, is now amplified by the rapid advancement of AI.
The beer distribution game at MIT provides a stark reminder of this reality. Rational actors making optimal local decisions can unintentionally create system-wide chaos, leading to massive inventory swings and stockouts. This is particularly true in the AI sector, where the variance in infrastructure orders is 10 times greater quarter-to-quarter, posing a significant challenge for supply chains.
The bullwhip effect operates through four amplification mechanisms: order batching, price fluctuations, shortage gaming, and demand forecast updating. In the AI industry, these mechanisms are exacerbated by factors such as the massive batches of orders placed by AI companies tied to funding rounds, and the volatile pricing of AI infrastructure on cloud platforms like AWS.
The largest investments in AI infrastructure have been made by U.S. companies, with investors providing around $68 billion to AI firms in 2023. However, these investments have paradoxically led to increasing profits but also liquidity problems, particularly for smaller AI startups. In South Korea, despite substantial investments in AI and biotech, many SMEs struggle with liquidity and cash flow, showing that large AI investments do not always translate into good financing or liquidity conditions for smaller players.
The bullwhip effect isn't just a problem to be solved; it's something to ride. The winners in AI won't be those who eliminate the bullwhip but those who surf it, companies that stay flexible and investors who time the waves. However, the bullwhip effect in AI is structural and permanent, as long as models evolve faster than infrastructure, the oscillations continue.
The bullwhip effect has caused systemic breakdown in the AI industry, leading to the collapse of companies like Graphcore, Cerebras, and Stability AI. The financial assumptions underlying supply chain investment in AI assume asset lifespans, but reality shows that assets can become economically obsolete in as little as 18 months.
The distribution channel for AI compute is different from traditional distribution, as compute is consumed where it's produced and can't be warehoused or buffered. This, coupled with the catastrophic mismatch between the monthly model updates in AI and the years-long hardware cycles, creates a perfect storm for the bullwhip effect.
In conclusion, the bullwhip effect in AI presents a significant challenge for the industry. It's a complex issue driven by factors such as rapid model evolution, volatile demand, and the unique distribution channel for AI compute. As we navigate this unpredictable landscape, it's crucial for all participants in the AI supply chain to stay flexible, adaptable, and prepared for the ongoing oscillations that destroy value and companies.
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