 I have been studying technology diffusion for the last 10 years. Technology diffusion is potentially one of the most significant drivers, if not the most significant driver of cross-country difference in productivity. Over this time I have tried both to understand the reasons why countries adopt technologies at different rates and the consequences that this has for productivity and for productivity growth. In this paper we study how the process of technology diffusion has evolved over time in a wide range of countries, approximately 150 countries, for 25 technologies that have been invented over the last 200 years. We think about technology diffusion as having two components. One is when technologies arrive to countries and the second component is how intensively they penetrate once they have arrived to countries. For each of these 25 technologies and for each country we measure each of these two components. We try to understand how these components have evolved over time in our cross-section. What we find is that adoption lacks have converged over the last 200 years so that these differences in the last week with technologies arrive to countries have declined dramatically. In contrast, the intensity with which technologies have penetrated that has diverged over the last 200 years. That means that the gap in penetration rates between rich and poor countries used to be very small and now they are much wider. So after understanding these key facts about the process of technology diffusion over the last 200 years, what we do is we try to explore the implications that this has had for the dynamics of productivity growth, that is for the great divergence, for why productivity cross-country differences have exploded over the last 200 years. And we do that by plugging those estimates of the dynamics of technology diffusion in a very, very simple standard model of growth. And what we find is that basically the divergence of the intensity of use of technologies generates divergence in productivity growth that not only overcomes the convergence in productivity associated with the convergence adoption lacks, but is capable of explaining approximately 80% of the great divergence. So that's basically the key finding of the paper. Well, of course all this research was done extremely carefully using state-of-the-art tools both in the estimation of adoption lacks and intensive margins for specific technologies and using some state-of-the-art techniques to model technology diffusion and productivity growth. Of course, it was critical the use of our data set, the chat data set that I've put together with various co-authors including Bar-Hobain from the San Francisco Fed over the last decade or so and this data set covers comprehensively the diffusion of approximately 100 technologies, 104 technologies in 154 countries over the last 200 years. There are many, many, many, many questions that I would like to continue doing in the future, but one that is unavoidable is to understand better what has been driving these divergence in the intensity of use of technologies. That seems like one of those questions that once you start thinking about it's impossible to think about anything else. I think that once I answer the question, once I understand why the intensity of use of technologies has diverged over time, I will be able to draw policy implications that can help us redirect that trend and that may help us understand better or may help us poor countries grow at faster rates and converge eventually to the rates of rich countries. Probably, those policies will be targeted to increase in the technological knowledge that people and companies have in developing countries. But for this, first I need to prove that this is actually what is driving the dynamics of the intensity of use of technologies in poor countries. And second, we need to find effective ways to impact the technological know-how of people and companies in developing countries.