Technology is changing how we work and solve problems across industries and new tools driven by Artificial Intelligence are reshaping the way experts approach their everyday challenges. But what are the real opportunities AI has to offer to industry? And why do some organizations succeed in creating real value from it while others do not? Go to the link in the comments and discover some insights into what it takes to make AI really work in manufacturing. 👇 #IndustrialAI #Engineering #DigitalTransformation #PeopleDriveTechnology

Today, Sidhant Gupta, Ph.D. and I are deeply proud to introduce Material. Material is an AI company, but it is also a declaration of what we believe. Technology should create meaningful, measurable change. Not noise, theater, or complexity for complexity’s sake. How we build matters. The best applications of AI will pair extraordinary technology with human judgment, courage, creativity, and care. When the last chapter of my professional life ended, I knew I would build again. I just didn’t know what I wanted it to feel like. I didn’t want to recreate something simply because I knew how. I wanted to take what I had learned from the wins, mistakes, pressure, and growth and build something more honest. More disciplined. More human. That vision became Material. And there is no one I would rather build it with than Sidhant. Sidhant is one of the most brilliant technical minds I know. He sees around corners and understands what is possible long before most people do. Just as importantly, he knows what is useful, responsible, and worth building. But brilliance alone is not why I chose him. I chose Sidhant because of his character. He is principled when no one is watching. He is steady where I am kinetic. He challenges me without diminishing me and believes in me without simply agreeing with me. He cares deeply about the work, the people affected by it, and the commitments we make. And we have fun. We laugh, dream, and push one another. We take the work seriously without taking ourselves too seriously. We are also fiercely committed to respecting one another’s lives. We will not build a company that requires anyone to sacrifice being whole in order to make it successful. Together, we created Material to help leaders move beyond experimentation and put AI to work against what actually matters to their businesses. We will tell the truth, even when AI is not the answer. We will measure success by outcomes, not activity. We will build responsibly and remain accountable for our work. We will choose depth over scale for its own sake. We are not chasing awards, headlines, or an exit. This is not a hobby or temporary chapter. We are building a durable, meaningful company that we intend to spend our lives creating. Our ambition is not simply success. It is to leave people better because they encountered Material. More capable, supported, inspired, and, yes, even happier and healthier. We are beginning exactly as we intend to continue: thoughtfully, selectively, joyfully, and with enormous ambition. If you are leading an organization through meaningful change, want to build or partner with us, or simply want to be part of the community cheering us on, we welcome all of it. Every person who becomes part of this story is Material to us. This is Material. And we are ready to build what matters.

🚀 AI Is Changing the Way Products Are Designed! Generative AI is helping engineers explore design alternatives, optimize performance, and accelerate product development. From generating new concepts to improving existing designs, AI is becoming a powerful tool in modern engineering. 💡 The goal isn’t to replace engineers—it’s to help them design smarter and innovate faster. 💬 Would you trust AI to help design your next product? #ZeroToInfinit #AI #GenerativeAI #ProductDesign #Engineering #Innovation #FutureOfTechnology

You'll struggle building AI agents for development until you understand how AI harnesses are built and function - they are the major unlock to efficiency and organization. I would be willing to bet 99% of engineers that have issues with agents don't understand them.

Production AI benefits from both research discipline and engineering urgency. Working in industry has taught me to ship useful systems, monitor their behavior, and respond to failures that appear under real operating conditions. Doctoral research has taught me to define the question before optimizing the answer. What exactly are we measuring? What is the baseline? What evidence would demonstrate that a change is genuinely better? I bring both perspectives to AI evaluation. Before changing a prompt, model, retrieval method, or workflow, I want to understand the current behavior and define the expected improvement. I also want to separate model quality from the surrounding system. Did the model improve, or did it receive better context? Did accuracy increase, or did the evaluation overlook difficult cases? Did the output look better, or did the workflow become more dependable? Production systems add questions that an offline benchmark may miss. Can the workflow recover from partial failure? Are uncertain results visible? Can we determine why a decision was made? Does the improvement remain meaningful after deployment? Research discipline and practical engineering are not competing priorities. Together, they make experimentation easier to interpret and the resulting systems easier to trust. What evaluation practice has prevented your team from shipping the wrong improvement? #AppliedAI #AIEngineering #MachineLearning #MLOps #Research

Every failed part holds a lesson. AI can help us connect it to the lessons we’ve already learned. In failure analysis, valuable knowledge is often scattered across reports, images, process records, and people’s experience. AI offers an opportunity to bring that knowledge together, helping us find similar cases, explore hypotheses, and focus our next investigation. The responsibility remains ours: question the output, test the hypothesis, and validate the root cause with physical evidence. I believe we should see AI as an opportunity rather than a threat. Reducing repetitive searching creates more room for what engineers bring: curiosity, critical thinking, creativity, and judgment. AI can help us find connections. Engineering turns those connections into understanding. #FailureAnalysis #RootCauseAnalysis #ArtificialIntelligence

AI can generate reports in seconds, but human attention and judgment remain limited. As documents multiply, the bottleneck shifts from producing information to reviewing evidence and making decisions. Faster drafting may transfer work to readers, while verification remains costly. In engineering and other complex fields, AI systems should organize results around decision points: what matters, what evidence supports it, what remains uncertain, and what action requires approval. Full reports still provide traceability and support review, but they should not be the primary measure of usefulness. The real advance is helping people make better decisions, rather than giving them more pages to read.

Have We Entered the New Industrial Age of AI? The first Industrial Revolution didn't happen overnight. Steam engines were around for decades before factories reorganized around them. The real shift came when people stopped asking What can this machine do? and started asking How should we rebuild work around it? I think we're at that moment with AI. Look at what's changed in just a few years: 🔹 Tasks that took hours, like drafting, analyzing, coding, and summarizing, now take minutes. 🔹 AI is moving from a tool we use to a collaborator that acts. 🔹 Entire workflows are being redesigned, not just sped up. The first industrial age extended human muscle. This one extends human cognition. That's a bigger shift, and it touches nearly every profession. But history offers a warning too. The winners weren't the ones who bought the first machines. They were the ones who retrained their people, rethought their processes, and built the skills to use the technology well. So the question isn't whether we've entered a new industrial age. It's whether we're ready for it: ✔ Are we upskilling our teams? ✔ Are we putting guardrails around security, ethics, and accuracy? ✔ Are we redesigning work, or just bolting AI onto the old way? The technology is arriving either way. The advantage goes to those who adapt first and adapt thoughtfully. What do you think? Are we at the start of a new industrial age, or still in the hype phase? #AI #FutureOfWork #ArtificialIntelligence #Innovation #Leadership

Prompt engineering tells AI what to do. Context engineering gives it the information it needs. Harness engineering controls how it executes tasks. But what happens after the AI gets a result? That’s where Loop Engineering comes in - Act → Check → Decide → Repeat The AI observes the current state, takes action, verifies the result, and decides what to do next — repeating until the task is genuinely complete. Before building an AI loop, ask four critical questions: 1.When should it stop? 2.When should it verify? 3.When should it ask a human for help? 4.What evidence proves the task is complete? The real goal isn't just to make AI follow instructions. Its to make AI act, verify, adapt, and finish tasks reliably.That’s the difference between an AI that generates answers and an AI agent that gets things done. #AI #PromptEngineering #ContextEngineering #AgenticAI #LoopEngineering

If AI can perform tasks faster and at a lower cost, where does the value of human expertise lie? AI is reshaping not so much entire professions as the tasks within them. Data analysis, document preparation, and information gathering are increasingly being automated. But this doesn't mean professionals are becoming obsolete. Rather, the nature of their work is changing. The focus is shifting toward capabilities that are harder to automate: defining the right problems, evaluating outcomes, making decisions under uncertainty, and taking responsibility for results. For businesses, this raises a broader question: How should organizations redesign workflows and develop talent as AI takes over an increasing share of traditional tasks? In the latest article, Marina Dorokhova, Director of the AI & High Tech Practice, explores how AI is transforming career paths, the nature of expertise, and the way professionals create value. Read the full article on Telegraph (https://lnkd.in/dugngebj).