Just wanted to share that from my recent talks in board meetings and with executives globally, the consensus is that companies are indeed laying off, and these numbers are accurate that I share below and maybe you have seen also around . You can verify them yourself. While they largely represent cumulative cuts and aggressive restructuring from 2023 through early 2026, the direct job eliminations are very real. I checked some of the biggest tech and legacy players: - Amazon: 30,000 (Cumulative cuts across corporate and managerial roles) - Intel: 25,000 (Aggressive restructuring to pivot toward the AI market) - Microsoft: 15,000 (Accumulated over several rounds of tech-sector corrections) - Dell: 12,000 (Massive sales team reorganization) - Salesforce: 4,000 (Role eliminations to reallocate funds toward AI development) - GM (General Motors): 3,300 (Cuts across autonomous vehicle divisions, software, and factory shifts) In the energy industry, we are going through the exact same ruthless transition, and the ones losing their jobs are the ones who have completely failed to adapt to new technologies and who rely on an old mindset. The industry is rapidly pivoting into professionals who understand / explain/ can storytell machine learning models, AI integration, optimization etc. The professionals who are aggressively upskilling and learning how to integrate data science into traditional energy workflows are the ones keeping their seats . If you think that energy is "safe" and all I have to do is "just go to meetings, and do coffee breaks" this is not the truth! "Oh I studied 5 years. I am now going to have a relaxed professional life". No! Constantly improve your skills. Never settle. Those who think 'energy is just writing reports and go to meetings" may get anytime a phone call "My friend, I love you. But I am sorry to say, you are among those who are going to be laid off. I am truly sorry. You know how much I care but I am sorry. It is a company policy". And that's it.

In this course, we are developing a model in Python that uses Reinforcement Learning to minimize the operating costs of a Smart Building (also known as an 'energy hub'). The course is In the 'Classroom' at the very end (numbered 117). The full Python code is available for download. Definitely watch the videos as they explain all details necessary. Below is an explanation (using simple language with all jargon explained). Also you can download a slide in PDF (the slides in PowerPoint format are inside the course). I believe the best way to truly understand Machine Learning and Reinforcement Learning is by diving straight into the code. The code in these videos is based on a real-world application where I tested various methodologies to determine the most cost-effective way to operate a smart building. This building is 'smart' because of how it manages energy. It is equipped with its own energy storage unit (a battery), a solar photovoltaic (PV) unit, and a central computer system. Using advanced algorithms, this system autonomously decides: - When and how much to charge or discharge the battery. - Whether to curtail (reject) excess energy output from the solar PV unit. - When and how much electricity to purchase from the main grid. Over time, we have explored different algorithms for this central computer system. We covered Monte Carlo methods in Course 115 and Stochastic Optimization in Course 116. This new course (Course 117) focuses specifically on Reinforcement Learning. Reinforcement Learning is a subfield of Machine Learning. While traditional Machine Learning models learn by studying historical datasets, Reinforcement Learning trains an algorithm to learn through trial and error—interacting with an environment to figure out the best sequence of actions to achieve a goal. Machine Learning is a subfield of AI, and Reinforcement Learning is a specific branch of Machine Learning. Machine Learning models learn by studying datasets. It's called Reinforcement Learning similar to behavioral psycholog which says that we are learning through rewards and penalties. When the AI (the "agent") takes an action that gets it closer to its goal (in our case: saving money in the smart building) it receives a mathematical "reward." This positive feedback reinforces that specific behavior of the algorithm. Reinforcement Learning trains an algorithm (which is known as Agent) to learn through trial and error.

Space based solar power means to deploy large solar photovoltaics panels on satellites to collect sunlight and then beam the energy to Earth in the form of microwaves. On earth, huge ground receivers would convert it into electricity for the grid. It could help provide steady low carbon power and might become cost competitive by 2040, but early systems would be very expensive and face major technical, safety, and space security risks. See the attached screenshot for how it will work. A new report about this topic has been published in 'Classroom' , at the very end In the section "Energy Industry Support" (a special section with reports that explain the current status and trends in the energy sector). It is written in simple, easy-to-understand language with every terminology/jargon explained. It has been written by compiling data from official sources (Financial Times, Bloomberg, Wall Street Journal, the Economist, Forbes, Investors Chronicle etc). Feel free to use this report in your projects, work, or studies. Reading these reports can help with interviews, meetings, presentations, networking, and public speaking, so it is strongly recommended. “

Just a little reminder ! I was in an energy conference in Australia travelling, and it was about energy jobs. Here are some points from top recruiters (eg director of HR in Chevron, director of HR in Total etc): --> energy is a global marketplace. Companies in Australia/Europe/Asia/Africa/America hiring people from all over the world And they sponsor VISAs. And no lay offs! No AI threat. Energy is considered TOO critical and humans are needed. Also be careful with CVs!!! dont just send randomly ! Many people apply to 20, 50, even 100 jobs…And still get no response. Why? Because job application today is not about volume it’s about alignment. Are you: • Applying to roles that truly match your experience? • Reading the job description carefully? • Positioning yourself based on what the employer is actually asking for? • Following application instructions properly? One small mistake in your application can cost you an interview. So you must: • Apply strategically instead of randomly • Align your experience with job requirements • Structure strong application responses • Navigate remote, hybrid, and on-site roles confidently Job searching is competitive, but with the right approach, you can stand out. I strongly recommend you use the service of CV feedback here. Everytime you apply for a job! Don't just send a CV. Same for interview! And last: BELIEVE THAT YOU CAN GET THE JOB. Stop thinking negatively. Tell yourself "I DESERVE THIS JOB."