Dream11

Bukhar mein bhi kaam karo, tabhi 'dedicated employee' kehlaoge. An employee took a sick leave today. Stomach issues. Weakness. Body gave up. The boss replied at 12:29. "Why do you take leaves during pressure time." "Check the structure supervisors if possible. We need that urgently." The employee picked up the phone. Not because they felt better. But because they felt guilty. And nobody taught them that guilt. The workplace did. Until rest felt like a crime. Until sick leave needed an apology. This is not dedication. This is damage. Your salary buys your time. Not your health. Not your life. Your company will post a new job opening within 2 weeks of your funeral. Rest. You are not the business. #ToxicWorkCulture #EmployeeWellbeing #MentalHealthAtWork #WorkLifeBalance #CorporateLife

Harsh Agrawal , CTO in Residence at Elevation Capital , spent time with the Seekho team this week. The most important point from the session was straightforward: The real AI gap in most organizations is not model access. It is operating discipline. Harsh broke the process down into the right sequence: 1. Clarify the problem 2. Sharpen the context 3. Plan the work 4. Execute in loops 5. Refine and repeat That sounds basic. It is not. Most teams still approach AI as a layer on top of existing habits. That is why they get activity, but not transformation. The shift only becomes meaningful when teams start working differently: 1. Better framing before execution. 2. Faster research before commitment. 3. Tighter review loops. 4. More structured workflows across non-technical and cross-functional teams. That is why I do not think org AI transformation is primarily a tools question. It is a workflow question. It is a capability question. It is a leadership question. Appreciate Harsh Agrawal for bringing clarity to that. And thanks to Yash Banwani for helping put together the session. The real unlock is not teaching people how to prompt better. It is teaching teams how to operate better. #AI #ProductManagement #FutureOfWork

We have officially reached a strange point in the AI cycle. Teams are using AI to help with hiring. Job descriptions. Screening questions. Candidate summaries. Interview notes. Scorecards. Follow-up emails. At this rate, the only human left in the process will be the person saying, “let’s circle back after the budget is approved.” Jokes aside, this is where product judgment matters. Using AI to remove admin friction is smart. Using AI to outsource actual judgment is where things get weird. The goal is not to automate hiring. The goal is to automate the low-value work around hiring. That distinction is going to matter a lot more over the next 12 months. #AI #ProductManagement #FutureOfWork

Some conversations don’t happen in boardrooms. They happen on quiet morning walks. Last Saturday at 7 AM, I joined the Agara Walks & Filter Kaapi not fully expecting what showed up. Over 50 leaders. Fully present. Energetic. Curious. Smiling. What followed was more than just a walk around the lake. It was a series of honest, unfiltered conversations about what keeps us moving, how we motivate our teams, and just as importantly, how we unwind. Post the walk, over breakfast and filter coffee, we had a “stand-up panel” on Vision 2030: The Future of GCCs. The discussion spanned: - The evolution of GCCs from cost centers to strategic drivers - The real impact (and noise) around AI - What the next decade demands from leaders building at scale What stood out wasn’t just the insights but the openness. No slides. No scripts. Just practitioners sharing what’s working (and what isn’t). Great to talk to and hear from Saraswathi Ramachandra , Karthik Subramanian , Suchint Majmudar , Soumya Datta , Suchitra Karumanchi , Santhosh Kumar R , Sumedh Waikar , Abhinav Srivastava Kudos to Srikanth Prabhu for bringing this together, along with Anuj Agrawal from Zyoin Group and Amal Mishra from Urban Vault . More such spaces are needed where leaders can step out of roles and into real conversations. 👉 Curious, what’s one shift you believe GCCs must make before 2030 to stay relevant? Would love to hear your thoughts in the comments. #GCC #Leadership #FutureOfWork #AI #Bangalore #Learning #Networking

$1.8B in projected revenue. 2 employees. Let that sink in. The company is Medvi. The founder, Matthew Gallagher, reportedly launched it with about $20K, a tiny team, and a stack of AI tools. That is the headline. But the real lesson is more important than the headline. This is not just an AI story. It is an execution story. AI handled parts of the work that used to require a much larger team: code creative production customer support analysis marketing operations But the leverage did not come from AI alone. It came from combining: a clear customer need fast distribution thin infrastructure aggressive execution good enough tooling across the stack That is the shift product people should pay attention to. The question is no longer: How big does the team need to be to launch this? The better question is: What is the minimum team needed if AI handles the repetitive work and humans keep the judgment? That does not mean teams do not matter. It means team design matters more than ever. The winners will not be the companies with the most people. They will be the ones that know exactly where humans create leverage, and where AI should take the load. That is what Use-Case-First AI looks like in practice. If you are building products right now, what part of your workflow still has too many humans doing machine work? #AI #ProductManagement #Startups #Founders #FutureOfWork

I came across BADAS 2.0 by Nexar recently, and what stood out to me is that it doesn’t just react, it actually tries to anticipate what’s about to happen. It’s trained on real-world driving data and built on a world-model style architecture, which means it’s not just recognizing objects on the road, but learning how situations evolve over time. That feels especially important because real-world environments are messy, unpredictable, and full of edge cases that simulations often miss. What makes this relevant right now is where AI is heading. We’re moving beyond models that just analyze text or images, into systems that need to operate in real environments — whether that’s autonomous driving, robotics, or safety systems. In that context, predicting risk before it unfolds is a meaningful step forward. It also highlights something that often gets overlooked, grounding models in real-world data and behavior can matter more than just scaling up parameters. If systems like this continue to improve and prove reliable, the impact could be significant, especially in areas like road safety where even small improvements can save lives. Curious to see how this space evolves from here.

I was in a room full of senior product managers last week. Someone asked a question that killed the conversation: Can anyone here build an agentic AI tool? Silence. Not because the room lacked smart people. Not because people were not paying attention to AI. Because most of us have spent the last year consuming AI. Very few have spent it building with AI. That gap matters. In 2026, understanding AI is not enough for product leaders. You do not need to become an ML engineer. But you do need to know how to: Identify a real use case. Prototype fast. Test workflows. Understand what breaks in practice. Otherwise you end up managing a shift you cannot really evaluate. The good news is that this gap is still very fixable. I found a few free, structured courses that do a good job of helping product people move from “I get AI” to “I can actually build with it.” If you want them, comment AI and I’ll send the links. #ProductManagement #AI #AgenticAI #Learning #Upskilling

Becoming a father changed how I think about product quality. Not because my kids give me brilliant UX feedback. They do not. It changed my thinking because parenthood changed how I use products. You are tired. Distracted. Interrupted every few minutes. Often using a phone with one hand. Usually trying to do something quickly. In that state, every extra step feels heavier. Every delay feels longer. Every confusing screen feels worse. A lot of product decisions look fine in a calm meeting room. They look very different in real life, when the user has low time, low attention, and zero patience. That is the bar I have learned to care about more. The question is not just, “Does this work?” It is, “Does this still work when life is chaotic?” The best products do. #ProductManagement #Parenting #UserExperience