Four Time Scales for Technology Development and Deployment

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 learning2 about objects anywhere1 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. And it was declared dead many times along the way, but a few brave, or stubborn, souls persisted. 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.  And they all have AI ads on them. Then the hype dies down as new hype comes along. Above I’ve named a few. If you are 30 years old you may remember block chain and also the metaverse.  Pretty much gone now.  Computers are not heating up the world working the blockchain algorithm for bitcoin mining. Instead it is data centers for training LLMs — itself a new subject of hype, AI training. If you are a bit older you may well remember IBM Watson and even nanotechnology molecular machines. I remember when a maker of chinos had TV ads touting the nanotechnology that they had put in their pants (the ones there were selling).  And if you are old enough to get social security payments you may remember expert systems which were going to capture all the knowledge of experts and let companies lay off their workers. The problem is that many people not steeped in technology understanding may get confused between ongoing research and the hype about how it is going to change everything.  Which it only very rarely ends up doing.  Additionally, there are a lot of  delusional people who really believe things that they say, but which are impossible due to such little problems like fundamental physics.  The ratio of extraordinary hype events to actual extraordinary technologies is way too high. 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. You don’t really need much in the way of supply chains and raw materials to go from one copy of software running on one machine to having it run on thousands of