The author of this post really needs to read Thomas Kuhn’s The Theory of Scientific Revolutions and some Marshall McLuhan (especially on tetrads).
What he’s describing here is to do with mindsets, the attempt we make to try and fit ‘the phenomena’ into our existing mental models. When that doesn’t work, there’s a crisis, and we have to come up with new paradigms.
But, more than that, to use McLuhan’s phrase, we “march backwards into the future” always looking to the past to make sense of the present — and future.
I have a theory that technological cycles are like the stages of Squid Game: Each one is almost entirely disconnected from the last, and you never know what the next game is going to be until you’re in the arena.
For example, some new technology, like the automobile, the internet, or mobile computing, gets introduced. We first try to fit it into the world as it currently exists: The car is a mechanical horse; the mobile internet is the desktop internet on a smaller screen. But we very quickly figure out that this new technology enables some completely new way of living. The geography of lives can be completely different; we can design an internet that is exclusively built for our phones. Before the technology arrived, we wanted improvements on what we had, like the proverbial faster horse. After, we invent things that were unimaginable before—how would you explain everything about TikTok to someone from the eighties? Each new breakthrough is a discontinuity, and teleports us to a new world—and, for companies, into a new competitive game—that would’ve been nearly impossible to anticipate from our current world.
Artificial intelligence, it seems, will be the next discontinuity. That means it won’t tack itself onto our lives as they are today, and tweak them around the edges; it will yank us towards something that is entirely different and unfamiliar.
AI will have the same effect on the data ecosystem. We’ll initially try to insert LLMs into the game we’re currently playing, by using them to help us write SQL, create documentation, find old dashboards, or summarize queries.
But these changes will be short-lived. Over time, we’ll find novel things to do with AI, just as we did with the cloud and cloud data warehouses. Our data models won’t be augmented by LLMs; they’ll be built for LLMs. We won’t glue natural language inputs on top of our existing interfaces; natural language will become the default way we interact with computers. If a bot can write data documentation on demand for us, what’s the point of writing it down at all? And we’re finally going to deliver on the promise of self-serve BI in ways that are profoundly different than what we’ve tried in the past.
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