Super thoughtful framing of AI within the recursive improvement lineage. The compiler comparison is especially useful since that was arguably the first moment machines could write software for themselves, and most people dunno that history. I've been thinking about this same continuity angle after watching enterprise AI deployments quietly compound gains while consumer chatbots plateau. The Mokyr micro-inventions framwork connects it all nicely.
The compiler history is fascinating here (and brings back memories of the dragon book). But one key difference: compilers showed immediate, tangible benefits even when costly to deploy—faster execution, portability, fewer manual errors. Early adopters paid high integration costs because the value was obvious.
With enterprise AI, you mention “quietly compound gains” but the deployment data tells a different story: 89% haven’t scaled agents, ~90%of enterprise pilots fail to reach production. That’s not compounding—that’s selection effects where the 6% scaling 3x faster have found narrow niches where the value/cost ratio actually works. Most importantly the wage premium and job growth is going to people using the tools, not those making them.
The question is whether we’re seeing early-stage adoption of a broadly transformative technology, or whether we’re watching the market correctly identify the limited contexts where these tools are actually cost-effective. The expert systems era followed a similar pattern: impressive capabilities, real value in narrow domains, but the “quiet compounding” never became the revolution.
The hockey-stick framing assumes there’s a viable product waiting for its friction-reduction moment. But historically, technologies that showed real value (electricity, cars, phones) saw aggressive early adoption despite massive installation costs—because the benefit was obviously worth it.
Current AI adoption patterns look different: high friction costs and unclear value proposition. That’s not a pre-hockey-stick moment—it’s the expert systems trajectory, where impressive benchmarks don’t translate to sustained business value in deployment.
The compounding innovation narrative mistakes what drove past transformations. It wasn’t marginal improvements accumulating—it was infrastructure and economic barriers collapsing once the core value was proven.
This piece really made me think, your distincion between consumer-facing AI hype and the real, deep value in back-end infrastructure is just spot on, like you peeled back the layers to the true kernel. It makes me wonder though, if that initial 'spectacle' wasnt also a necessary evil, maybe even a good thing, for sparking the broader public's imagination, even if it meant some fleeting disappointment later.
Super thoughtful framing of AI within the recursive improvement lineage. The compiler comparison is especially useful since that was arguably the first moment machines could write software for themselves, and most people dunno that history. I've been thinking about this same continuity angle after watching enterprise AI deployments quietly compound gains while consumer chatbots plateau. The Mokyr micro-inventions framwork connects it all nicely.
The compiler history is fascinating here (and brings back memories of the dragon book). But one key difference: compilers showed immediate, tangible benefits even when costly to deploy—faster execution, portability, fewer manual errors. Early adopters paid high integration costs because the value was obvious.
With enterprise AI, you mention “quietly compound gains” but the deployment data tells a different story: 89% haven’t scaled agents, ~90%of enterprise pilots fail to reach production. That’s not compounding—that’s selection effects where the 6% scaling 3x faster have found narrow niches where the value/cost ratio actually works. Most importantly the wage premium and job growth is going to people using the tools, not those making them.
The question is whether we’re seeing early-stage adoption of a broadly transformative technology, or whether we’re watching the market correctly identify the limited contexts where these tools are actually cost-effective. The expert systems era followed a similar pattern: impressive capabilities, real value in narrow domains, but the “quiet compounding” never became the revolution.
The hockey-stick framing assumes there’s a viable product waiting for its friction-reduction moment. But historically, technologies that showed real value (electricity, cars, phones) saw aggressive early adoption despite massive installation costs—because the benefit was obviously worth it.
Current AI adoption patterns look different: high friction costs and unclear value proposition. That’s not a pre-hockey-stick moment—it’s the expert systems trajectory, where impressive benchmarks don’t translate to sustained business value in deployment.
The compounding innovation narrative mistakes what drove past transformations. It wasn’t marginal improvements accumulating—it was infrastructure and economic barriers collapsing once the core value was proven.
This piece really made me think, your distincion between consumer-facing AI hype and the real, deep value in back-end infrastructure is just spot on, like you peeled back the layers to the true kernel. It makes me wonder though, if that initial 'spectacle' wasnt also a necessary evil, maybe even a good thing, for sparking the broader public's imagination, even if it meant some fleeting disappointment later.