The Tyranny of Control by Milton Friedman (Free to Choose)

2nd Chapter - The Tyranny of Controls From Free to Choose - Milton Friedman On this chapter (you can read the book or see the PBS series or both) Friedman present his claim for: 1.      The need for an international free trade framework and its importance for economic development/growth and to avoid the fallacious dichotomy that export are good & Imports are bad. 2.      How tariffs and trade barrier hinder economic development for the country that deploys them and by protecting some national industries are in effecting pushing a significant toll into the overall consumers- picking winners and losers, most of the time motivated by political interests. 3.      The empirical evidence against a centralized economic plan observed in every economy where it was adopted vs. a liberal one where the pursuit of self-interests by the individuals based on a free market and price mechanism allows to a significant economic growth and improved standards of living. 4.      The direct relation between the roll-out of controls by any government and the impacts that it has on any society freedom (economic, political, human rights). Although the book was written in 1979 this topic is as actual as ever, just look what is happening with the Intro on car tariffs (US and EU), the promised tariff policy that will be implemented in case of a 2nd Trump term, the subsidizing of the chip industry and the protection of the steel industry by the current US administration (vs a Japanese take-over), and look, within the European Union, at the commotion that a take-over bid by an Italian bank over a German one is creating, etc, etc… As in all principles and economic policy, the degree of fundamentalism in applying them is as important as the policies in itself. Although, i agree with the laid-out principles I believe that there are some shades/hues that should be considered while implementing them (Friedman is definitely more aggressive that i would be). Each context should be considered for the situation under analysis, however, having always into consideration the key goals that the principles pursue and strive to achieve them in their purest form. In the video, the end-debate, with antagonistic opinions, is also very instructive and illustrative to help you generate your own view on the subject. "... Government planning and detailed control of economic activity lessens productive innovation, and consumer choice. Good, better, best, are replaced by ''approved'' or ''authorized.'' Friedman shows how 'established' industries or methods, seek government protection or subsidization in their attempts to stop or limit product improvements which they don’t control. Friedman visits India, Japan and U.S. Discussion Participants: Robert McKenzie, Moderator; Milton Friedman; Richard Deason, International Brotherhood of Electrical Workers; Donald Rumsfeld, President, G.D. Searle & Company; Helen Hughes, Director of Economic Studies, World Bank; Jagdish Bhagwati, Professor of Economics, MIT. ..." https://youtu.be/CWgNe8v6KFc?si=5nUrVCZX6X5t-muZ

- Pedro

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2nd life for your company - create a digital twin

Imagine you can create a very robust digital image of your industry, company, processes, etc… that would reflect to high degree of accuracy what you a living in the real world…a virtual representation of something That would allow you to simulate so many ideas, concepts, strategies, tactics, process improvements… and see the distribution of its expected results in the digital world before implementing the most sound and promising ones in the real one. The potential application of this concept, imported from Formula 1 industry where it is widely used on the manufacturing domain, is so big and impactful, that just by thinking of it makes me dazzle. The use cases presented in this article are more related with real manufacturing and production problems, but imagine this applied to a investment or pricing domain or even an industry, where you could simulate competition in play, like a multidimensional game theory framework to simulate real life scenarios considering all your value-chain. You could start small, with a simple digital twin of your industry, and grow the complexity step-by-step until you would get to something that could be considered reliable and usable. Think abou it,,,i will surely will. Digital twins are speeding up manufacturing https://www.economist.com/science-and-technology/2024/08/28/digital-twins-are-speeding-up-manufacturing from The Economist

- Pedro

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Should AI models be freely available by Lawrence Lessig

Must confess that until reading this article I did not had an informed opinion on how to balance the benefits of open sourcing AI models and the risks and perils that such decision could have to the society as a all. Lessing’s opinion seems a balanced one, whereby the 4 key components - i) source code, ii) training data, iii) Inference code & iv) model weights - of a AI should be freely shared, but the first 3 should be shared without any constraint, but the fourth (model weights) should be with significant restrictions as it is there that the potential big risks resides, and by freeing up the first 3 it will foster learning, it will underpin the future development of the technology, creating a vibrant ecosystem of future developers, thus fulfilling the open-source objective. The article also brought to my attention the Hugging Face platform, that i will try to explore in the future. “…Hugging Face, an AI community platform, offers over 350,000 AI and machine-learning models, 75,000 datasets and 150,000 demonstration applications, all open-source and publicly available. ..” Will continue to read on the subject, also to have divergent opinions and try to fine-tune my own, but as mentioned before Lessing’s opinion seems a reasonable and balanced one. “…Not all AI models should be freely available, argues a legal scholar https://www.economist.com/by-invitation/2024/07/29/not-all-ai-models-should-be-freely-available-argues-a-legal-scholar from The Economist…”

- Pedro

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Starting a new book! - Value-based Pricing : 12 Lessons to Make Your Transformation Successful by Stephan M. Liozu

Starting a new book! Value-based Pricing : 12 Lessons to Make Your Transformation Successful by Stephan M. Liozu Pricing Book Club - book #1 “…This is Stephan Liozu’s fifteenth book on value and pricing management. It offers a deep dive into value-based pricing methodology, focusing on what it takes to successfully conduct value-based pricing transformations and large-scale initiatives. Stephan shares his extensive knowledge and the lessons he has accumulated over 15 years of work, study, and writing on this topic. Having worked on a dozen value-based pricing transformations, he presents 12 crucial lessons that can help pricing leaders and practitioners design and execute value-based pricing more effectively. This book follows Stephan’s 2016 book Dollarizing Differentiation Value, which provides a more technical and methodological perspective on value-based pricing. Value-based pricing is not suitable for every organization. Some companies may benefit more from improving their cost-based pricing and pricing discipline. Others should focus on building a strong foundation in customer centricity and competitive understanding before embarking on a value-based pricing journey. This book provides a realistic view of what it takes to undertake such a journey. Its purpose is not to advocate for universal adoption of value-based pricing, but to discuss the prerequisites, conditions, and key success factors necessary for pursuing it, without guaranteeing success. This is the challenge. While cost-based pricing can have an immediate impact, investing in value-based pricing requires a higher upfront cost with no clear guarantee of positive results. This presents a conundrum. However, companies that have fully invested in value-based pricing and followed most of these lessons have experienced great success in their transformations. Value-based pricing, when combined with excellence in business strategy and innovation, can help companies achieve unprecedented levels of operating income. These 12 lessons have been tested in several workshops at professional pricing conferences and have resonated with many pricing practitioners. I hope they will resonate with you too. Enjoy the journey! …” https://www.goodreads.com/book/show/217771598-value-based-pricing?ac=1&from_search=true&qid=5giUlRpf6C&rank=2

- Pedro

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These are the two new books you need to read about AI by The Economist

Of >100 AI books just published last year; the Economist recommends these 2 that should be read by all that have an interest on the subject: Feeding the Machine: The Hidden Human Labour Powering AI. By James Muldoon, Mark Graham and Callum Cant - darker side of AI Co-Intelligence: Living and Working with AI. By Ethan Mollick - a practical and more positive way to think about the interaction of people and AI Both were added to my wish list! These are the two new books you need to read about AI https://www.economist.com/culture/2024/08/08/these-are-the-two-new-books-you-need-to-read-about-ai from The Economist

- Pedro

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La palabra saber - Martin Caparrós

“…Quizá hoy Kant tendría que clamar “Dubitare aude!” y convencernos, por fin, de que en principio no sabemos nada y que saber no es un estado sino un recorrido y que cada paso debe ser un riesgo y que hay que darlos con los ojos muy abiertos. Como decía aquel famoso poeta inglés, citado hasta el hartazgo: “Saber o no saber, esa es la cuestión”. Y, aún así, nunca se sabe…” Una pequeña perola de Martin Caparrós! ¡Disfrutad tanto como yo lo hizo! https://elpais.com/eps/2024-09-21/la-palabra-saber.html

- Pedro

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And if you could predict a Tipping Point?

Tipping point definition- (i) the moment of critical mass, the threshold, the boiling point…[where] Ideas and products and messages and behaviors spread like viruses do or (ii) the point at which a series of small changes or incidents becomes significant enough to cause a larger, more important change. I would say that is the moment we stop being at a normal distribution and start seeing Power Laws in play and it is kind the holy grail for the prediction domain and applies from financial markets, economy evolution, book sales…. up to biology, After the fact everyone could see it :-), but no one could detect it before (and the ones who could…were lucky). At least up to now, as it seems some researchers in China were able to proof (study published in the Journal Physical Review X, that with the help of AI they could identify the tipping point ex-ante in complex systems. Unfortunately, as in many AI algorithms on the algorithm knows what specific features and patterns allowed them to identify the tipping points, being currently the work of the team to try to understand what those are. Nonetheless, something to keep your eyes on. AI can predict tipping points before they happen https://www.economist.com/science-and-technology/2024/07/17/ai-can-predict-tipping-points-before-they-happen from The Economist

- Pedro

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