Alex Wang's Linkedin Analytics

By 2030, we’ll see 170 million jobs created — and 92 million jobs lost. 39% of current skills will be outdated. And 59% of workers will need reskilling — but 1 in 5 may not get it. According to the new Future of Jobs 2025 report from the World Economic Forum, AI and information processing tech are expected to transform 86% of businesses — more than any other technology. We’re not in a “future of work” moment anymore. We’re in a present of rapid transformation — and AI is at the center of it. These aren’t just stats. They’re a snapshot of the pressure — and potential — we’re all navigating. 💡 The AI-native workforce isn’t just technical. Yes, AI and big data top the list of fastest-growing skills. But so do: 🔹 Resilience & agility 🔹 Creative thinking 🔹 Curiosity & lifelong learning 🔹 Leadership & influence 🔹 Systems thinking The world doesn’t just need more AI experts. It needs more adaptive leaders who can think with AI — strategically, creatively, and ethically. That’s the mindset we’re helping build in our Executive AI course — for leaders who want to stay ahead of the curve, not chase it. We dive deep into skills like agentic AI, enterprise data strategy, and predictive decision-making — exactly the capabilities WEF highlights as essential for the future of work. Enroll / More info here ➡️https://bit.ly/3C3GSgF Curious — which of these shifts are you feeling most in your work right now? __________ For more on AI and news, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #leadership #ai #technology #innovation #data

We talk a lot about the jobs AI might replace. But not enough about the ones it’s creating. According to WEF's latest report, we’re heading toward a global churn of 22% of current jobs by 2030. That includes:  🔹 170 million new jobs created 🔹 92 million jobs lost And many of those new jobs? They’re being powered and shaped by AI. Some of the fastest-growing roles globally, driven directly by AI adoption, include: - AI & Machine Learning Specialists - Big Data Analysts - Fintech Engineers - Data Warehousing Experts - AI-augmented UX Designers - Information Security Analysts - Process Automation Specialists ... What’s interesting is that many of these roles didn’t even exist at scale just a few years ago. And they’re not all technical. We’re also seeing the rise of roles like AI ethics leads, prompt engineers, and AI product strategists — cutting across industries. The visual from Gartner also shows a growing spectrum of new AI roles, some emerging, some already essential. It’s no longer just about hiring data scientists and ML engineers. Organizations now need: - AI risk and governance specialists - AI product managers - AI ethicists ... Even decision engineers — people who design workflows where humans and machines make decisions together. And these roles are not one-size-fits-all. Some are deeply technical. Others sit at the intersection of  𝗔𝗜 + 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝗔𝗜 + 𝗱𝗲𝘀𝗶𝗴𝗻, or 𝗔𝗜 + 𝗲𝘁𝗵𝗶𝗰𝘀. This shift reflects something I keep seeing in business: We don’t just need more AI talent. We need more adaptive leaders who can 𝘁𝗵𝗶𝗻𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜 — strategically, creatively, and responsibly. That’s exactly the mindset we’re building in our Executive AI course — for leaders who want to stay ahead of the curve, not chase it. 𝟭𝟬 𝘄𝗲𝗲𝗸𝘀. 𝟳 𝗺𝗼𝗱𝘂𝗹𝗲𝘀. 𝟭𝟬 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿𝘀. 𝗔𝗹𝗹 𝗹𝗶𝘃𝗲.  Enroll / More info here ➡️https://bit.ly/3C3GSgF __________ For more on AI and news, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #leadership #data #technology #innovation #ai

What is MCP? And what do you need to know about it? (GitHub links at the end, with open-source code) MCP, stands for Model-Context Protocol. Sounds formal, but here’s the deal: It’s like a shared language between apps and LLMs, so they can communicate not just through prompts but through structured context! Remember how ChatGPT kind of "figures out" who we are based on our previous messages? MCP makes that understanding 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁. Instead of relying only on natural language, apps can pass structured information like: - who the user is - what tools or APIs are available - what documents or data are relevant - what rules or guardrails should be followed - what the LLM should remember during this interaction This turns LLMs from clever text predictors into something much more powerful: 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲, 𝗺𝗲𝗺𝗼𝗿𝘆-𝗿𝗶𝗰𝗵, 𝘁𝗼𝗼𝗹-𝘂𝘀𝗶𝗻𝗴 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀. It’s still early days, but there are already some great MCP projects on GitHub to explore: 📍Bonus- Upcoming live webinar: Intro to Zapier MCP: Best Use Cases ➡️https://bit.ly/4iasDFW *30 minutes, free to join, recording will be available afterward 1️⃣ MCP GitHub (Official) https://lnkd.in/gCFAaRrd Docs, spec, SDKs, and examples. Everything you need to start building with structured context. This is the best place to begin. 2️⃣ MCP Server for GitHub https://lnkd.in/g8rqYeKb Build AI-powered tools that interact with GitHub repos, issues, pull requests, and workflows using structured MCP context 3️⃣ Dive AI Agent (MCP Desktop App) https://lnkd.in/gErGUHgi An open-source desktop app that runs locally as an MCP host. It supports ChatGPT, Claude, Gemini, Ollama, and more Perfect for testing MCP integrations across platforms MCP moves us toward deeply integrated, multi-modal, memory-aware agents.💡 It unlocks much more sophisticated agent behaviors, and helps standardize how LLMs are integrated into real-world systems. Visual credits: GitHub & CobusGreylingAI __________ For more on AI & learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #aiagents #mcp #generativeai #aitools #opensource