Harish Kumar

Harish KumarSep 11, 2026

🚨 This AI story honestly made me stop for a moment. A former OpenAI and Anthropic researcher, Jacob Coxon, has publicly raised concerns about where AI development is heading. His worry is simple: AI companies may be moving toward self-improving AI faster than we are solving the safety problem. What caught my attention even more was the response from Anthropic alignment researcher Evan Hubinger. He said he personally puts the probability of AI causing human extinction within the next decade at more than 10%. Now, I don't know whether that prediction will ever come true. And I don't think we should automatically believe every extreme AI prediction. But when people who actually work on these systems start publicly raising concerns, I think we should at least listen and ask questions. Because AI capability is moving incredibly fast. The bigger question is: Are our safety systems moving fast enough too? We spend so much time talking about: → New AI models → AI agents → AI coding → AI automation → AI productivity But maybe we should spend a little more time talking about: How do we keep increasingly powerful AI under human control? AI doesn't scare me. Building something extremely powerful without fully understanding how to control it does. What do you think? Are AI companies moving too fast, or is this concern being exaggerated? ♻️ Repost if you think AI safety deserves more attention. Follow Harish Kumar for more AI & technology insights. #Claude Claude

Harish KumarSep 2, 2026

🚨 Most companies think AI governance starts with compliance. It doesn’t. Compliance is the final 10%. The first 90% is everything that makes compliance actually work. I’ve shipped production AI in fintech. Credit models. Payment rails. Agentic workflows. And here’s what I learned: The checklist was never the hard part. The hard part was building the foundation underneath it. Here are the 6 layers of AI governance, in the order they should be built 👇 1️⃣ AI INVENTORY You can’t govern what you can’t see. Document every model, copilot, chatbot, scorer, and AI agent your team actually uses. One team thought they had 4 AI systems. We counted 11. That discovery alone changed the conversation. 2️⃣ DATA FOUNDATION Before worrying about AI outputs, understand the data feeding them. → Lineage → Quality → Freshness → Bias checks Bad data can quietly produce bad decisions at scale. 3️⃣ DATA SECURITY & ACCESS Not everyone needs access to everything. → Encryption → Anonymization → Least privilege → Access controls AI governance without data security is an unfinished system. 4️⃣ MODEL ASSURANCE A model that passed testing last quarter may behave differently today. You need: → Benchmarks → Model cards → Fairness testing → Red teaming → Drift monitoring Production is where assumptions meet reality. 5️⃣ HUMAN OVERSIGHT AI should have boundaries. People need: → Review paths → Override rights → Clear accountability → The ability to stop the system Someone must be responsible when the AI gets it wrong. 6️⃣ COMPLIANCE & AUDIT Now comes the checklist. Map every AI system to the regulations and policies that apply. Log decisions. Maintain evidence. Make the system auditable. Because if you can’t explain what your AI did, you have a governance problem. And this is where most companies make the mistake: They start at Layer 6. They write policies. They create checklists. They schedule audits. But the first five layers are missing. A policy document sitting on top of broken foundations isn’t AI governance. It’s paperwork. Build the 90% first. Start with Layer 1: Write down every AI system your team actually uses. Every chatbot. Every copilot. Every scorer. Every agent. You might be surprised by what you find. Which layer is weakest in your organization today? ♻️ Repost this to help someone building AI responsibly. ➕ Follow Harish Kumar for more AI, technology & career insights.

Harish KumarSep 2, 2026

🚨 2025 → 2026: Your AI workflow needs an upgrade. Most people are still using AI like they did last year. But the real advantage in 2026 isn’t learning more AI tools. It’s knowing which tool to use for the right task. ⚡ 🔎 Google Search → Perplexity for research 💬 ChatGPT → Claude for deeper writing & reasoning 📄 Reading PDFs → NotebookLM for understanding documents 🎨 Illustrator → Ideogram for AI visuals 🖼️ Photoshop → Nano Banana for AI editing 📊 PowerPoint → Gamma for presentations ⌨️ Keyboard → Wispr Flow for voice-first work 📈 Excel → Julius AI for data analysis 🌐 Chrome → Arc for a different browsing workflow 🎬 Premiere → OpusClip for repurposing videos 💻 GitHub Copilot → Windsurf for AI-assisted coding The future isn’t about replacing every app. It’s about upgrading the workflow. The people who adapt early will have a serious productivity advantage. 🚀 💬 Now I want to know: If you could keep ONLY ONE AI tool for 2026, which one would you choose and why? 👇 Drop your answer below 👇 Save this. You’ll want it later. 🔖 Follow Harish Kumar for more AI + Career content. #AI #AITools #ArtificialIntelligence #FutureOfWork #Productivity #CareerGrowth #AI2026 #TechTrends