The question on many investors’ minds is whether the AI bubble is about to burst, as stock prices of companies like Nvidia and the “Fab Five” tech giants fail to regain their mid-year peaks. The hype around new technology has historically led to unsustainable heights in stock values, only to see a sell-off when investors get cold feet. Political uncertainty, particularly around the U.S. election and Donald Trump’s views on Big Tech companies and semiconductor production in Taiwan, adds to the speculation.
The long-term value of AI is being questioned, with comparisons to the internet boom and bust in the late 1990s and early 2000s. Skeptics claim that AI progress is about to hit a wall, as models like GPT-4 and Gemini have already used up most of the available data for training. However, history suggests that doubters may end up in the same position as those who underestimated the potential of companies like Amazon in the early 2000s.
The generative AI revolution has revived the idea that “data is the new oil,” but the comparison goes beyond just being the defining resource of a technological era. Ray Kurzweil notes that both data and oil vary greatly in terms of their impact and potential. While data is crucial for AI development, it is not just about quantity but also quality and how it is utilized in training models.
Despite concerns about AI progress slowing down, there is evidence to suggest that advancements will continue at a rapid pace. The hoarding of data by current models like GPT-4 and Gemini may not be a significant hindrance, as new approaches and technologies are constantly being developed to push the boundaries of AI capabilities. Just as past naysayers were proven wrong about the internet’s potential, it is possible that those casting doubts on AI’s future may also be missing the bigger picture.
In conclusion, while there are valid concerns about the sustainability of the AI market and the potential for a bubble burst, history and ongoing technological developments suggest that AI progress is unlikely to slow down significantly. Investors and analysts should consider the broader context of AI development, beyond short-term fluctuations in stock prices, to make informed decisions about the future of the industry.
