I have come to understand four very different time scales for development of technologies and their deployments. And I think people often jump between them and end up making outrageously wrong, and sometimes damaging, predictions of when in the future a technology is going to be able to do what.

Time scale 1. New Research Ideas

New research ideas take ten to twenty years to form before there is an understanding to bring them to really solid lab demonstrations. Some things take much longer as there are many, many false starts, or there is a really hard step which takes decades to crack. Once things really have been established as a solid laboratory technology there is often a gold rush phase where major new tweaks, on essentially the same idea, come along every six months or so and it feels like the ground is shaking under us. The first "computational" models of neurons were published in 1943 (McCulloch and Pitts), but it wasn't until after a chain other models were tried, that a dominant variety became established in 1960 (Widrow), the linear threshold neurons that are recognizable as the "neurons" of today's neural networks. Then years more work, were necessary to get to (1) good convolutional networks with (2) back propagation, allow for learning about objects anywhere in an image. And then it was twenty years until in 2012 (Hinton) the larger structure, the "deep" in deep learning, let trained neural network image labelling take over from conventional non-neural vision algorithms. Another decade on we got to today's LLMs (Large Language Models), the thing that is getting the whole world in a tither. So this one was sixty years in the research making.

Time scale 2. Hype generation

Often there are incredible hype cycles where we go from all but a small number of people having heard of the idea to it appearing daily in the business press. And all manners of researchers and companies re-market their work and claim that they have been doing it all along. Just look at how quickly "AI agents" went from nothing to decorating the sides of busses on the streets of San Francisco. None in mid 2025, and now today it is hard to find a bus that has any sort of AI ads on it that are not about agents.

Time scale 3. At scale deployment

The next time scale is driven by how long it takes to go from really solidly engineered product to mass adoption. Software has zero marginal cost to manufacture more copies. But even so, software typically takes 20 years or more to scale up. Unix was developed at Bell Labs starting in 1969. Commercial versions shipped 15 years later, but the dominant operating system was Microsoft Windows. A free open source version known as Linux was developed starting in 1991. Every computer science graduate student had heard of Linux within five years, but it wasn't until 2012 that it was adopted by Microsoft.

Time scale 4. Reshape the economy

The themes of the two biggest hype concentrations right now are the same: replacing massive swaths of human labor with AI and robotics. But each reshaping of the world's economy has taken over 50 years of continuous at scale deployment.