Unlike with a technology designed to make a particular process better, which can often have immediate positive productivity impacts, it often takes considerable time for people to figure out how best to deploy a general purpose technology. During this “figuring it out” period, productivity can actually fall rather than increase. This is because companies need to spend time and money experimenting with how to use the new technology, often without seeing a positive bottom line impact. Only later, once people figure out the optimal ways to redesign business processes around the new tech, does productivity experience a sudden acceleration.
The classic example of this that Azhar goes into some depth on is the invention of electricity and its impact on manufacturing. The first thing factories did with electricity was to replace gas lighting with electric lighting. That was a cost savings, but didn’t really change much in terms of the firm’s output. (And there was some cost in installing the lights and wiring the factory, which even muted those savings.) The physics of steam meant that pre-electric factories were built with a central engine that powered many, or even all, of the factory’s equipment off a single drive shaft. So, the second thing factories did was replace the large central steam engine with large electric motors, which they still used to run clusters of machines off central drive shafts. This was cheaper than trying to reconfigure the whole factory. But it turned out to not be very efficient or operationally cost-effective. Productivity gains in one part of the production floor often simply caused bottlenecks elsewhere on the assembly line, and overall the factory saw little gain. It was only when companies began electrifying individual machines and reorganizing the entire layout of factories, that firms saw big productivity boosts.
Azhar predicts that the same thing will happen with AI, but that most firms are sort of stuck in stage one or stage two of this evolution. I think he’s probably right. Tokenmaxxing is easy. Redesigning workflows is hard. Harder still—and something which Azhar doesn’t talk about—is rethinking entire business lines, i.e. what products or services the firm sells, and even business models. This gets at the fundamental purpose of the company. This is where the really big value from AI is. It’s about reinvention, not redesign. But most companies are still not thinking big enough.
Because most existing businesses are being too small minded about how they use AI, AI-native firms have a great opportunity right now. They will be able to move faster and to steal significant market share from incumbents before the legacy companies can effectively respond. It’s much easier to invent a new business from the ground up than it is to try to gut-renovate an existing one. (This is also why it may be more difficult than many private equity firms hope to simply add a dash of AI to their portfolio investments and hope to flip the businesses at higher valuations.)
Ok, with that, here’s more AI news.
