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New products like ChatGPT have captivated the public, but what will the actual money-making applications be? Will they offer sporadic business success stories lost in a sea of noise, or are we at the start of a true paradigm shift? What will it take to develop AI systems that are actually workable?
To chart AI’s future, we can draw valuable lessons from the preceding step-change advance in technology: the Big Data era.
2003–2020: The Big Data Era
The rapid adoption and commercialization of the internet in the late 1990s and early 2000s built and lost fortunes, laid the foundations of corporate empires and fueled exponential growth in web traffic. This traffic generated logs, which turned out to be an immensely useful record of online actions. We quickly learned that logs help us understand why software breaks and which combination of behaviors leads to desirable actions, like purchasing a product.
As log files grew exponentially with the rise of the internet, most of us sensed we were onto something enormously valuable, and the hype machine turned up to 11. But it remained to be seen whether we could actually analyze that data and turn it into sustainable value, especially when the data was spread across many different ecosystems.
