# What Can Economic History Teach Us About the AI Moment? — Clemens Rawert — session 2026-09-23T15:50:00.000Z → 2026-09-23T16:08:48.000Z _26 transcript lines · 45 slides · source: full recording_ ## Transcript **[00:00:00 · 0]** Labor to the economy because we can add capital to the workers that are in the economy or because we become better at turning this labor and capital into economic output. And that's what we would call productivity increases in economics. And in the long run, the main driver of economic growth are these productivity advances and those in turn are basically improvements in technology and also in organizational progress which is closely related. And This is where I want to start in to bring an actual history to draw parallels to the AI productivity puzzle that I was alluding to at the start. We can, for example, I think very well compare AI to the prime example of a technological revolution in history which is the Industrial Revolution. **[00:00:42 · 0]** The industrial revolution as I mentioned kick started modern economic growth yet we observe a somewhat similar and interesting pattern for it too. The steam engine, its main underlying technology contributed close to nothing to productivity gains for many, many decades after it was invented and it took actually over a century until we see productivity and economic growth showing up in the data after the invention happened. Alright, a brief slide on technologies. In economic history, there's generally a class of technologies that's described as general purpose technologies, which are high in economic history funnily enough referred to as GPTs. And nice coincidence for this talk but general purpose technologies generally have three attributes. **[00:01:27 · 0]** They are pervasive. That means they apply across basically all sectors of the economy. The technology can be improved over a long period of time. And the third point is pretty interesting because it means that they have complementarities. And that means the technology raises the returns in its applications as they improve and as the applications improve that in turn raises the value of the initial technology that helped bring them to life. **[00:01:52 · 0]** In other words, general purpose technologies can keep on giving for a really long time for the economy and an important question that history or future will tell is whether our AI engineering GPT turns out to be a GPT that can achieve this. While there's no official list, technologies that are often considered to be general purpose technologies are for example the water wheel, steam engine, the railways, electricity, or the computer. And there's a second idea that I just want to briefly mention and keep in the back of your mind and that's that there's other kinds of technologies that are invented that help us improve how we do research and development going forward. So these innovations can basically help us drive productivity growth going forward. And among these are for example innovations such as the printing press, a microscope, the invention of the corporate or the university research lab, which we also obviously see in AI a lot right now, or machine learning technology. **[00:02:52 · 0]** I think AI has a good claim to be both of these kinds of technologies at the same time. Both a pervasive technology that is unlikely to saturate quickly and can add economic growth for a long time, but also a technology that can be used to improve the process of invention itself. And I think over recent weeks especially we've seen that in mathematics, for example. And I think that's very exciting for what it can do for living standards because as you can see on this graph on the slide, for the past one hundred and fifty years, The US economy has basically grown at a stable 2% per year and per capita terms you see the trend line fits very well. There's periods where growth is faster, there's periods where growth is slower, booms and busts etc. **[00:03:34 · 0]** But on average over one hundred and fifty years pretty remarkably stable 2%. This is a lot. Yeah, that y axis is a log so every linear change basically means a doubling. 2% a year growth doesn't sound like a lot. If you were going to invest your money and get 2% a year you'd probably not be terribly excited but it does a lot when it compounds over a long time. **[00:03:59 · 0]** So over the one hundred and fifty years on this graph, one hundred and forty years on this graph, we roughly see income per person adjusted for inflation etc. Grow by a factor of 10. To me it's pretty astounding what it takes to achieve 2% of growth. This was an era, 1880 to 2020, of amazing innovation. Basically all modern technologies that we marvel at today were invented then: the automobile, aviation, space travel, biotechnologies, the Internet, just to name a few and I think you can fill in the gaps. **[00:04:30 · 0]** In the end, all of this comes together to giving us an average growth in the economy, in The US economy, of 2% per year. Now AI might do two things. Firstly, it might support this trend so that it can keep going for a longer time. I think that's already a great achievement because we can see what level of technological innovation it takes to just sustain 2% per year. But, and excitingly, it might also accelerate this. **[00:04:56 · 0]** There is not really consensus right now in economists of the magnitude of what we might expect this graph to look like going forward and estimates range from as little as a 0.1 percentage point increase of additional GBT above trend per year. But there's also models of economists that take into account recursive self improvement, some sort of intelligence explosion in AI that arrive at the possibility of high single digit percentage point increases to what you can see on this chart. And as living standards compound over time as you can see here what new regime of growth we end up in over the next couple of years is really going to make an incredible difference to living standards. Alright, now let's turn back into the history and start with the Industrial Revolution where it all has to start when talking about technology. The steam engine is the canonical technological innovation of the Industrial Revolution. **[00:05:56 · 0]** In principle, it helped divorce the creation of mechanical power from mussels and rivers and made it possible to provide power at some scale and also much more location independent than we were able to do so before. It was first invented by Thomas Newcomen in 1712 and then materially improved by James Watt whose name at least everyone knows later in the century. But the technology really took well into the nineteenth century to continue to improve before it was widely adopted in the economy and industry and factories. And only when high pressure steam engines spread and the price of power came down by many orders of magnitude we can actually see productivity and GDP take up in the statistics. So the steam engine really had to become a radically better technology for a long, long period of time because we could start to deploy it at any scale. **[00:06:48 · 0]** Then it took the invention and also deployment of its complements new machine tools, railways, steamships, a newly skilled labor force until an entire economic system could basically develop around it and the Industrial Revolution as we know it and the transformative change that we see in the numbers starts really humming and making a difference for people. Right. I think this carries an interesting observation. A technology can arrive a very, very long time before it diffuses and an economic system develops around it. In the case of the steam engine, first event in 1712, productivity advances really showing in the 1830s at the earliest so more than one hundred years really until we see that show up. **[00:07:31 · 0]** Another example is the electrification of The United States. Thomas Edison installed the first power plant in New York, Pearl Street Station, in 1812. But it took about thirty-forty years for us to see any meaningful electrification of factories in The United States. This time it wasn't the technology that needed to improve but it was really a redesign of the factory that was required as the place where power is consumed and not created. Factories have previously run on steam engines and were laid out to use one power source. **[00:08:09 · 0]** You can kind of see that on the picture on the bottom right that I made with GrokBot actually. There was one power source that basically ran all machines in the factory. So one power source connected with belts and sharps to all machines in the factory, which basically meant that you had to lay out the factory in a way to accord with how you transmit power in it and also factories were often laid out vertically over multiple stories. While you could really quickly actually replace the steam engine with an electrical motor, this wasn't really the best way to set up a factory that had electricity available to it. Electricity really unfolded its biggest gains when you could use its unique advantage and the unique advantage of electricity is that you can generate it at a different point than the place where you consume it. **[00:09:06 · 0]** This allowed for the so called unit drive system to take hold which basically meant every machine in a factory has one power source. I think the advantages become clear very quickly because now if you you can turn off just one machine in the group drive system, if you turn off the steam engine the entire factory starts running, if you turn off one motor electrical motor for a machine only this one machine needs to needs to be turned off. So you can start laying out the factory according to your workflow and not according to where the belts and crafts need to go. You can expand it easily, you can retool it, you can start producing something different, but this is a slow and expensive process, requires retooling of the entire factory and not Consumed in factories and after fifty years 80% of power consumed in factories came from electricity. We'll move a bit closer to our own time right now. **[00:10:26 · 0]** And starting with a famous quote from the economist Robert Solow where he wrote that you can see the computer age everywhere but in the productivity statistics. And he said that in 1987 where personal computers were already all over the place and computing quite widely adopted in the economy. And I think in general very well known, second half of the twentieth century, we saw really amazing innovation in computing, personal computing, internet, I think altogether information technology that drove down the cost of processing, storing, communicating information precipitously. But it took until the mid to late 1990s for productivity to actually respond here. Again, the economy needed some changes and this time these were mostly organizational. **[00:11:12 · 0]** Firms and workers need to change their processes, develop the skills, adopt the right complements to really gain from computer technology. This time around that was things like developing... Well other firms had to develop enterprise software, database technology, supply chains needed to be redesigned to actually work with digital ways of accounting for goods. And of course also it meant the adoption and rollout of many many many individual pieces of hardware and software to workers in the economy. And this is actually a much slower transformation of the organization that was required rather than the invention just of the computer technology that ultimately helped us take advantage of this technology in a really meaningful way for the economy. **[00:11:57 · 0]** And This relationship or this kind of development that we saw there sometimes described as a j curve relationship where the investment required initially to adopt the technology to start learning new processes, buy new hardware, etcetera, etcetera, actually slows down productivity growth before it enables it to accelerate later in the process. And I wanna very briefly touch on another place where technology can be out of sync with another factor and that's in capital markets. In history, the real promise of groundbreaking technologies has always been able to marshal significant money and resources early on before we actually know how and when a technology is going to produce growth in the economy. I want to put two historical episodes next to AI's build out and compare it a little bit at least in magnitude. In the 1840s in Britain, parliament authorized hundreds of new railway lines that they could be built. **[00:12:59 · 0]** This has basically happened all at once and a sort of mad rush of cheap capital, rising demand for freight, especially coal and the belief that every town and everywhere in the country needs to have a railway connection to participate in the economy led to a crazy investment boom that has not many many comparables in history. It's one of the largest of all time and railway investment reached five to 7% of GDP, in a single year in the 1840s. And another example, closer to digital technologies from the late 1990s, The computer and the internet in the late 1990s commercial internet takes off and a race to deploy long haul fiber started to basically enable people broadband access. There was a lot of deregulation in The US New carriers, new companies that were laying fiber and expectations that demand and transmission of data was going to to grow insatiably led to basically a way overbuilt out of fiber technology. This boom was around one to 1.5% of GDP in 1999. **[00:14:07 · 0]** And today, if we compare these magnitudes to AI, AI infrastructure investment is already at about 1.8% of US GDP this year. So already bigger than fiber and forecasted to to reach about 3% of US GDP by 2028. So in other words, AI infrastructure has already up there with, some of the largest investment booms that we've seen in history of all time. I'm not gonna really be drawn out to to comment on whether it's a bubble or not. I really don't know. **[00:14:33 · 0]** But I think there's an interesting observation from history that no matter what the the private returns for investors end up being from investment in this infrastructure, the infrastructure that's built out can outlast and turn out to be productive later on even if there's a bubble. So this is the case with, railways, surplus railway tracks that were late and overbuilt or it's also called dark fiber, in the .com era that later enabled much cheaper transportation and things like streaming, the mobile era to to happen with much, much cheaper infrastructure available to the economy. Alright. A strong pattern that I've hopefully driven home, is that technology can move at two clocks. One is the faster clock of technological innovation and prototypes when a transformative technology emerges. **[00:15:24 · 0]** And the second is the slower clock of actually deploying it when a technology gets deeply integrated, embroidered into the fabric of the firm and and the economy as a whole. For steam, that was additional technological innovation in the core technology. And for electricity, that was a new factory that needed to be designed to complement the new technology and for computers it was basically large scale organizational change, new skills, new ways of working and also the deployment of course of hardware and software. We're now four years into the generative AI era if you will and I would like to invite you to think about what it'll take for AI to ultimately start to raise productivity and economic growth for everyone. Is that the core technology improvements in foundation models? **[00:16:17 · 0]** Is it adoption across other sectors of the economy that are currently not really touched? Is it complements the application layer? Is it reskilling the workforce? I don't really have the answer with me but I'm excited to speak with everyone that wants to approach me over the next two days about this. Alright, to wrap up, I hope I've made the point that invention creates possibility as a start, but to turn this possibility into growth, more is needed. **[00:16:46 · 0]** Diffusion, investment and complements, organizational change can all determine how much of this really turns out into productivity which turns into improved living standards for everyone. Bringing AI into firms, rewiring how work gets done, training and transforming how other people work, and starting new companies that do this from scratch with new ideas is I think very much how this technology gets on the road. And that feels like a very encouraging message to to end on for this room because I think most of us spend all of their days working into rewiring the economy for this new technology. Thank you so much for your time and have a great conference. **[00:17:36 · 1]** Let's give it up one more time for Clemens, please. Wow. I always like to see how we can, you know, extrapolate what happens in the past to kinda try to predict the future. And, Clemens spoke a little bit about how we should feel about today. And our next speaker is gonna talk to us about the future. **[00:17:57 · 1]** Actually, our next two speakers are going to talk to us about the future. And, our next speaker is somebody who doesn't need any introduction. If you've been on social media, if you've been on X, you probably have seen his hot takes. He calls himself a master tinker. I still don't know what that is, but we'll go with that. **[00:18:16 · 1]** Okay? Alright. So please, let's give it up for the pizza. This never works, but let's try to make it work. ## Slides ### 00:00:00–00:00:20 # Long-run growth is dependent on technological progress ## Why care about growth? - **Output per person** -> **living standards** (leisure, room to do and consume "more") ## What drives growth? - **Output per person** -> **Labor** + **Capital** + **Productivity** - **Productivity**: **technology** + **organization** ### 00:00:20–00:00:40 # Long-run growth is dependent on technological progress ### Why care about growth? - Output per person → living standards (leisure, room to do and consume “more”) ### What drives growth? - Output per person → Labor + Capital + Productivity Productivity: technology + organization ### 00:00:40–00:01:00 # Long-run growth is dependent on technological progress ### Why care about growth? - Output per person -> **living standards** (leisure, room to do and consume "more") ### What drives growth? - Output per person -> **Labor + Capital + Productivity** - **Productivity**: technology + organization ### 00:01:00–00:01:20 # Long-run growth is dependent on technological progress ### Why care about growth? - **Output per person** -> living standards (leisure, room to do and consume "more") ### What drives growth? - **Output per person** -> Labor + Capital + Productivity - Productivity: technology + organization ### 00:01:40–00:02:00 # Gen AI looks a lot like a general-purpose technology - AI Engineer: GPT → **Generative pre-trained transformer** - Economics: GPT → **General-purpose technology** - **Pervasive**: usable across tasks and sectors - **Improving**: technology improves over time - **Complementary innovation**: applications get better through GPT innovation and GPT becomes more valuable through applications - AI potentially a method of invention itself ### 00:02:00–00:02:20 # Gen AI looks a lot like a general-purpose technology - AI Engineer: GPT -> Generative pre-trained transformer - Economics: GPT -> **General-purpose technology** - Pervasive: usable across tasks and sectors - Improving: technology improves over time - Complementary innovation: applications get better through GPT innovation and GPT becomes more valuable through applications - AI potentially a method of invention itself ### 00:02:40–00:03:00 ## Gen AI looks a lot like a general-purpose technology - AI Engineer: GPT -> **Generative pre-trained transformer** - Economics: GPT -> **General-purpose technology** - Pervasive: usable across tasks and sectors - Improving: technology improves over time - Complementary innovation: applications get better through GPT innovation and GPT becomes more valuable through applications - AI potentially a method of invention itself ### 00:03:00–00:03:20 ### Figure 1 Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. Jones (2026), JEP, AI & our economic future, p.7 [Line graph showing average US income per person in 2023 US dollars from 1880 to 2020, with an annotated trend line indicating 2% per year growth] ### 00:03:20–00:03:40 # Figure 1 ## Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. [Line graph showing average US income per person from 1880 to 2020, with a 2% per year trend line.] ### 00:03:40–00:04:00 # Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. Jones (2026), JEP, AI & our economic future, p.7 [Line graph showing average US income per person in 2025 US dollars from 1880 to 2020, with a trend line indicating 2% growth per year.] ### 00:04:20–00:04:40 ### Figure 1 Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. Jones (2026), JEP, AI & our economic future, p.7 [Line graph showing average US income per person (in 2025 US dollars) from 1880 to 2020, with a green line representing actual income and a dashed orange line indicating a 2% per year growth trend.] ### 00:04:40–00:05:00 # Figure 1 Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. Jones (2026) [Line graph showing average US income per person in 2025 US dollars (log scale) from 1870 to 2020, with a trend line indicating 2% growth per year.] ### 00:05:20–00:05:40 # Figure 1 ## Average US Income per Person Source: Data are from US Bureau of Economic Analysis (2025) since 1929 and from Barro and Ursúa (2010) for 1870 to 1928. Note: Average income per person in the United States rose at a remarkably steady rate of 2 percent per year for 150 years even while amazing technologies such as electricity and information technology transformed economic activity. Jones (2026), ... econon [Line graph showing average US income per person in 2025 US dollars from 1880 to 2020, with a dashed line indicating a 2% per year growth rate.] ### 00:06:00–00:06:20 ## Steam: the engine improved over time - Steam divorces mechanical power from muscle and waterwheels - 1712 Newcomen → 1760s/70s Watt → 1830s+ large productivity contribution - **Peak productivity impact >100 years after Newcomen** [Illustration of an early steam engine] ### 00:06:20–00:06:40 ## Steam: the engine improved over time - Steam divorces mechanical power from muscle and waterwheels - 1712 Newcomen → 1760s/70s Watt → 1830s+ large productivity contribution - Peak productivity impact >100 years after Newcomen [Illustration of an early steam engine] ### 00:07:00–00:07:20 ## Steam: the engine improved over time - Steam divorces mechanical power from muscle and waterwheels - 1712 Newcomen → 1760s/70s Watt → 1830s+ large productivity contribution - **Peak productivity impact >100 years after Newcomen** [Illustration of an early steam engine] ### 00:07:40–00:08:00 # Electrifying America in the late 19th century - Electric power from the 1880s (Pearl St. Station, 1882); motors can drive machines - First response often "group drive": one motor, long shafts and belts to machine (as before) - Big gains with "unit drive": one motor per machine - Requires redesign of the factory and workflows - Free floors, no stories, new workflows - **Electric Drive penetration is slow** - 1899: <5% → 1919: ~50% → 1929: ~75% [Illustration of a factory floor with industrial machinery connected by a system of belts and shafts] ### 00:08:00–00:08:20 # Electrifying America in the late 19th century - Electric power from the 1880s (Pearl St. Station, 1882); motors can drive machines - First response often **"group drive"**: one motor, long shafts and belts to machine (as before) - Big gains with **"unit drive"**: one motor per machine - Requires redesign of the factory and workflows - Free floors, no stories, new workflows - Electric Drive penetration is slow - 1899: <5% → 1919: ~50% → 1929: ~75% [Illustration of a factory interior with machinery driven by overhead shafts and belts] ### 00:08:20–00:08:40 # Electrifying America in the late 19th century - Electric power from the 1880s (Pearl St. Station, 1882); motors can drive machines - First response often “group drive”: one motor, long shafts and belts to machine (as before) - Big gains with “unit drive”: one motor per machine - Requires redesign of the factory and workflows - Free floors, no stories, new workflows - **Electric Drive penetration is slow** - 1899: <5% → 1919: ~50% → 1929: ~75% [Illustration of a factory floor with multiple machines connected by overhead shafts and belts] ### 00:08:40–00:09:00 # Electrifying America in the late 19th century - Electric power from the 1880s (Pearl St. Station, 1882); motors can drive machines - First response often "group drive": one motor, long shafts and belts to machine (as before) - Big gains with "unit drive": one motor per machine - Requires redesign of the factory and workflows - Free floors, no stories, new workflows - **Electric Drive penetration is slow** - 1899: <5% → 1919: ~50% → 1929: ~75% [Illustration of a factory floor with multiple machines connected by a system of overhead shafts and belts] ### 00:09:00–00:09:20 # Elec [Several circular outlines on the slide] ### 00:09:20–00:09:40 # Elec [Abstract circular shapes on a light green background] ### 00:09:40–00:10:00 ## Electrifying America in the late 19th century - Electric power from the 1880s (Pearl St. Station, 1882); motors can drive machines - First response often "group drive": one motor, long shafts and belts to machine (as before) - Big gains with "unit drive": one motor per machine - Requires redesign of the factory and workflows - Free floors, no stories, new workflows - Electric Drive penetration is slow - 1899: <5% → 1919: ~50% → 1929: ~75% [Illustration of an old factory floor with machinery driven by belts and shafts] ### 00:10:20–00:10:40 # Elec [Diagram showing three circular outlines] ### 00:10:40–00:11:00 # Computers & productivity paradox - Solow, 1987: "You can see the computer age everywhere but in the productivity statistics" (productivity paradox) - Then: US productivity accelerates in mid-to-late 90s - Software, skills, processes, organizational capital needed to unlock productivity [Illustration of a vintage desktop computer setup with a CRT monitor, tower, and keyboard] ### 00:11:00–00:11:20 ## Computers & productivity paradox - Solow, 1987: "You can see the computer age everywhere but in the *productivity statistics*" (productivity paradox) - Then: US productivity accelerates in mid-to-late 90s - Software, skills, processes, organizational capital needed to unlock productivity [Illustration of a vintage desktop computer with a CRT monitor, tower, and keyboard] ### 00:11:20–00:11:40 # Computers & productivity paradox - Solow, 1987: "You can see the computer age everywhere but in the productivity statistics" (productivity paradox) - Then: US productivity accelerates in mid-to-late 90s - Software, skills, processes, organizational capital needed to unlock productivity [Illustration of a vintage computer setup with a CRT monitor, desktop tower, and keyboard] ### 00:12:20–00:12:40 # Computers & productivity paradox - Solow, 1987: "You can see the computer age everywhere but in the productivity statistics" (productivity paradox) - Then: US productivity accelerates in mid-to-late 90s - Software, skills, processes, organizational capital needed to unlock productivity [Illustration of a vintage computer setup with a CRT monitor, desktop unit, keyboard, and mouse] ### 00:12:40–00:13:00 # Transformative tech & finance travel together - Real technologies attract enormous amounts of investment - Railways (1840s Britain) - ~5–7% of GDP in peak years - Telco investment (1999) - ~1–1.5% of GDP - AI infrastructure (now) - ~1.8% today, 2.5% - 3.0% in 2028 - Private returns ≠ social returns to the infrastructure [Illustration of a steam train on a stone viaduct] [Illustration of a bundle of cables fanning out into individual wires with connectors] ### 00:13:00–00:13:20 - ~1.8% today, 2... ## Private returns ≠ social infrastructure ### 00:13:20–00:13:40 ## Transformative tech & finance travel together - Real technologies attract enormous amounts of investment - Railways (1840s Britain) - ~5-7% of GDP in peak years - Telco investment (1999) - ~1-1.5% of GDP - AI infrastructure (now) - ~1.8% today, 2.5% - 3.0% in 2028 - Private returns ≠ social returns to the infrastructure [Illustration of a steam train on a stone viaduct] [Illustration of a bundle of fiber optic cables fanning out] ### 00:13:40–00:14:00 ## Transformative tech & finance travel together - Real technologies attract enormous amounts of investment - Railways (1840s Britain) - ~5-7% of GDP in peak years - Telco investment (1999) - ~1-1.5% of GDP - AI infrastructure (now) - ~1.8% today, 2.5% - 3.0% in 2028 - Private returns ≠ social returns to the infrastructure [Illustration of a steam train on a multi-arch bridge] [Illustration of a bundle of fiber optic cables] ### 00:14:00–00:14:20 - Priv... - infra... ### 00:14:20–00:14:40 - ~1.8% today, 2 ## Private returns ≠ so ## infrastructure ### 00:14:40–00:15:00 # Priv... # infra... ### 00:15:00–00:15:20 # Transformative tech & finance travel together - Real technologies attract enormous amounts of investment - Railways (1840s Britain) - ~5-7% of GDP in peak years - Telco investment (1999) - ~1-1.5% of GDP - AI infrastructure (now) - ~1.8% today, 2.5% - 3.0% in 2028 - Private returns ≠ social returns to the infrastructure [Illustration of a steam train on a stone viaduct] [Illustration of a fiber optic cable with strands fanning out] ### 00:15:20–00:16:00 # Technology moves on two clocks - **Faster clock**: Capability, cost, improvement - What can it do? - **Slower clock**: Diffusion, Complements, Organization - Can the economy use it? --- - Steam -> better technology - Electricity -> new factories - Computing -> new organization ### 00:16:00–00:16:20 # Elec... # Com... ### 00:16:20–00:16:40 ## Elec... ## Com... ### 00:16:40–00:17:00 # Invention creates possibility for deployment - More reading & sources: [rawert.xyz/aie](rawert.xyz/aie) Not in scope today: safety, distribution, political economy ### 00:17:20–00:17:40 # Invention creates possibility for deployment - More reading & sources: rawert.xyz/aie scope today: safety, distribution, political economy ### 00:17:40–00:18:00 ### Companies and Partners - NVIDIA - Cockroach Labs - Mistral - FACTORY - SENTRY - Sonar - Cloudflare - Daytona - ### 00:18:00–00:18:20 - AI Engineer PARIS - Notion - Microsoft - Sonar - cominty [Grid of company logos including AI Engineer Paris, Notion, Microsoft, NVIDIA, Sonar, and Cominty] ### 00:18:20–00:18:40 - Mistral - AI Engineer (Paris) - NVIDIA - langfuse (by ClickHouse) - Sonar - AI Engineer - NVIDIA - minty - descript ### 00:18:40–00:19:00 NVIDIA, AI Engineer Paris, Cockroach Labs, Mistral, FACTORY, AI Engineer Paris, SENTRY, Sonar, CLOUDFLARE, Daytona, AI Engineer Paris