{"id":924230,"date":"2026-08-02T05:13:19","date_gmt":"2026-08-02T10:13:19","guid":{"rendered":"https:\/\/newsycanuse.com\/index.php\/2026\/08\/02\/four-time-scales-for-technology-development-and-deployment\/"},"modified":"2026-08-02T05:13:19","modified_gmt":"2026-08-02T10:13:19","slug":"four-time-scales-for-technology-development-and-deployment","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2026\/08\/02\/four-time-scales-for-technology-development-and-deployment\/","title":{"rendered":"Four Time Scales for Technology Development and Deployment"},"content":{"rendered":"
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I have come to understand four very different time scales for development of technologies and their deployments. \u00a0And 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.<\/p>\n

Time scale 1. New Research Ideas<\/em><\/span><\/p>\n

New research ideas take ten to twenty years to form before there is an understanding to bring them to really solid lab demonstrations. \u00a0Some things take much longer as there are many,\u00a0 many false starts, or there is a really hard step which takes decades to crack.<\/p>\n

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.<\/p>\n

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The first \u201ccomputational\u201d models of neurons were published in 1943 (McCulloch and Pitts), but it wasn\u2019t 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 \u201cneurons\u201d of today\u2019s neural networks. Then years more work, were necessary to get to (1) good convolutional networks with (2) back propagation, allow for learning2<\/sup> about objects anywhere1<\/sup> in an image. And then it was twenty years until in 2012 (Hinton) the larger structure, the \u201cdeep\u201d in deep learning, let trained neural network image labelling take over from conventional non-neural vision algorithms. Another decade on we got to today\u2019s LLMs (Large Language Models), the thing that is getting the whole world in a tither. \u00a0So 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.<\/p>\n

Time scale 2. Hype generation<\/em><\/span><\/p>\n

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. \u00a0Just look at how quickly \u201cAI agents\u201d went from nothing to decorating the sides of busses on the streets of San Francisco. \u00a0None in mid 2025, and now today it is hard to find a bus that has any sort of \u00a0AI ads on it that are not about agents. \u00a0And they all have AI ads on them.<\/p>\n

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Then the hype dies down as new hype comes along. Above I\u2019ve named a few. If you are 30 years old you may remember block chain and also the metaverse. \u00a0Pretty much gone now. \u00a0Computers are not heating up the world working the blockchain algorithm for bitcoin mining. Instead it is data centers for training LLMs \u2014 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). \u00a0And 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.<\/p>\n

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. \u00a0Which it only very rarely ends up doing. \u00a0Additionally, there are a lot of \u00a0delusional people who really believe things that they say, but which are impossible due to such little problems like fundamental physics. \u00a0The ratio of extraordinary hype events to actual extraordinary technologies is way too high.<\/p>\n

Time scale 3. At scale deployment<\/em><\/span><\/p>\n

The next time scale is driven by how long it takes to go from really solidly engineered product to mass adoption.<\/p>\n

Software has zero marginal cost to manufacture more copies. You don\u2019t 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 machines, if they already exist.<\/p>\n

But even so, software typically takes 20 years or more to scale up. Just because some interesting software exists it doesn\u2019t mean that everyone is going to jump in and re-engineer their business to use it right now. Some people wait to see how well it works out for others. And other people just don\u2019t want to change their existing business practices to adopt the new software.<\/p>\n

Unix was developed at Bell Labs starting in 1969. Commercial versions of it were shipping 15 years later, but the dominant operating system was Microsoft Windows. Then a free open source version of Unix was developed starting in 1991, known as Linux. Every computer science graduate student had heard of Linux within five years of it being established, \u00a0but it wasn\u2019t until 2012 that it was adopted by Microsoft. \u00a0Now most backend systems and billions of mobile devices run on Linux.<\/p>\n

Hardware based systems take even longer to adopt at scale. I first sat through a talk showing a self-driving car running on a freeway outside of Munich back in 1987 (Dickmanns). \u00a0It wasn\u2019t until the DARPA Urban Challenge of 2007 that the idea of such cars being practical got into people\u2019s consciousness. \u00a0I first rode in a Waymo predecessor (when it was still at Google X) in 2012, out onto highway 101 and safely back to the office. Last night I rode in a Waymo in San Francisco. \u00a0They are now licensed to operate about 4,000 vehicles in the city and they are the clear leaders in the US market. But the scale is tiny compared to the number of cars in San Francisco, let alone the whole of the US. \u00a0Oh, and despite promising in the app to take to my house the Waymo didn\u2019t \u2014 it dropped me off somewhere else despite me being on the line with \u201ccustomer support\u201d for over 20 minutes.<\/p>\n

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Getting things to work at scale is orders of magnitude harder than getting them to work at first and having your first few dozen satisfied customers. \u00a0Scale has always taken Herculean effort.<\/p>\n

However, people make the mistake of thinking adoption and scaling up the supply chains, the deployments, and the customer support will just happen. \u00a0It doesn\u2019t.<\/p>\n

Time scale 4. Reshape the economy<\/em><\/span><\/p>\n

The themes of the two biggest hype concentrations right now are the same. \u00a0Replacing massive swaths of human labor with AI (LLMs to replace white collar labor) and robotics (humanoid robots to replace blue collar labor). \u00a0Then everyone will somehow, magically, be so rich that the world will be wonderful. \u00a0A new world economy. A new world order. \u00a0A reshaping of the economy.<\/p>\n

Many, many, many technologies have reshaped the world\u2019s economy over the last few millennia, from domesticated animals to sailing ships in the ancient world, from domestic electrification to commercial air transportation to shipping containerization in the 20th century. \u00a0But each of these things took over 50 years of continuous at scale deployment.<\/p>\n

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People think that when they hear about a new research result it is going to change everything. \u00a0And then they believe the hype about how soon it will do so, as hype-notists manipulate capital markets thought their promises. Some things fail completely in the commercial world despite high hype levels (all the companies formed to commercialize Hyperloop have now shut down, and the Hyper-hype has quietly disappeared). And then, people think that it can change the world economy in one or two or ten years. It really does take decades, essentially a human lifetime (and there is a causal correlation), to deploy a technology at large enough scale that it reshapes the world\u2019s economy.<\/p>\n<\/p><\/div>\n

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\n Luz Ramage<\/p>\n","protected":false},"excerpt":{"rendered":"

I have come to understand four very different time scales for development of technologies and their deployments. \u00a0And 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<\/p>\n","protected":false},"author":1,"featured_media":924231,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[40559,46],"tags":[],"class_list":["post-924230","post","type-post","status-publish","format-standard","has-post-thumbnail","category-scales","category-technology"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/924230","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=924230"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/924230\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/924231"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=924230"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=924230"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=924230"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}