NYTimes: “[Sweden] has a roster of high-tech entrepreneurs that is the envy of its neighbors. Spotify and Skype are globally recognized brand names. Klarna, a financial tech firm, and King Digital Entertainment, the maker of the video game juggernaut Candy Crush, are other examples of homegrown tech powerhouses. “They have something — particularly in the tech sector — which other European countries do not really have to the same extent,” said Jacob Kirkegaard, a senior fellow at the German Marshall Fund. That entrepreneurial track record has been attracting renewed attention at a time when anxieties are mounting about Europe’s ability to compete with American and Chinese advancements in high technology.”
Niranjan Rrajadhyaksha: “A country can move from low-income to lower middle-income status with minimal changes in its employment structure. People can climb the income ladder by being slightly more productive in their existing jobs. The shift from lower middle-income to upper-middle income status involves a major shift in the employment structure, or the reallocation of labour on a large scale. The best vehicle for this is young and dynamic firms that are keen to take on more workers. Firm dynamism is an underrated issue in the ongoing discussion on how the Indian economy can provide jobs for a growing workforce that aspires to move ahead in life.”
Naushad Forbes: “Three manufacturing sectors can create jobs by the million: Apparel, food, and electronic assembly. Our apparel sector has long languished with little attention paid to it. Let’s talk to them and ask them what it would take to scale by a factor of ten. Just as we have attracted Foxconn to India, let’s try to do the same with Li and Fung, the world’s largest apparel company that indirectly employed 1 million people in China. A large garment factory in Bangladesh employs 30,000 – 50,000 people; in India, it is only 3,000 – 5,000. What would help them grow? Design and technology? Skills on the shopfloor? Tariff-free access to markets through free-trade agreements? Labour reform? Hiring seasonal labour more easily? In food processing, we are still a small player by international standards. We grow over 20 per cent of the fruit and vegetables in the world but process, Deloitte tells us, 4.5 per cent of our fruit and 2.7 per cent of our vegetables. We could dominate world markets for both. Electronic assembly is a new success story. We are finally seeing large labour-intensive factories being set up by Tata Electronics, Foxconn and Pegatron. Set up under the production linked incentive (PLI) scheme, electronics assembly is the only one of the 14 PLI sectors that is labour-intensive…An effective jobs strategy demands a sectoral focus on labour-intensive industries.”
Karen Lynch: “Our job as managers or leaders is to really coach people to their top performance. Think about athletes. Athletes have coaches for everything to sharpen their skills. That’s how we have to think about our colleagues. Giving them coaching feedback is really intended to sharpen their skills and make them even better. It’s not to be hurtful, and it’s not to give negative feedback. It’s really to have them sharpen what they’re trying to do. Now, the flip side, which I also talk about in the book: feedback is a gift. I always tell people that feedback’s a gift; coaching is a gift. You can choose whatever you want to do with it. Try it on, see if it fits. If it doesn’t, you don’t have to do it. But our job as leaders and managers is to help people achieve their goals, be it the next promotion or being the best individual contributor they can be. And it is their choice on which direction they want to go. Our job is to help coach and guide them to be the best performers they can be.”
Jaspreet Bindra: “Let us look at the Generative AI ‘stack,’ or the layers that make up this technology…The lowest layer of the stack (let’s call it Layer 1) is formed by AI infrastructure. Infra providers include companies like Nvidia and AMD, which make the powerful graphics processing units (GPUs), which in turn make the Large Language Models (LLMs) of GenAI work…The Layer 2 above the infrastructural base is the model layer, the one grabbing all the eyeballs right now. This comprises OpenAI, Anthropic, Google, Meta and others, companies which make the LLMs that seem to do magical things for us—ChatGPT, Claude, Llama, Gemini, etc. If the infra layer spouts out money, the model layer consumes most of it. Layer 3 above those models comprises development platforms, which are used by developers to build applications on top of LLMs. Usually nestled in large clouds like Azure and AWS, this is the investment that Big Tech firms make to get applications built on top of them. Layer 4 is the application layer. This is where innovation abounds, with startups and big companies building their own focused GenAI apps on top of powerful LLMs created by companies like Meta and OpenAI.”
