Ways to Stay Ahead in AI Innovation Trends

Many ask me how I stay updated with all the AI news and announcements. Here are my strategies: 1. Poscasts I Listen to Weekly - All In Podcast: podcast not solely focused on AI; it covers a wide range of topics including economics, technology, politics, social issues, and poker. The hosts—Chamath, Jason, Sacks, and Friedberg—are successful tech veterans. - The AI Podcast is produced by Nvidia. Each episode focuses on one person, one interview, and one story. I like it because it emphasizes AI's impact on our world. - Latent Space: the most technical, from AI Engineers to AI Engineers. It covers in-depth new AI technology with a big focus on Open Source. I also watch all the videos on YouTube from Matthew Berman. He conducts technical deep dives. 2. Social Media Accounts I Follow On X: - Bindu Reddy: CEO of Abacus AI, using Gen AI to build Applied AI and LLM agents and systems at scale, ex-AWS / Google. Her tweets are technical, provocative, and fun. - Jerry Liu, CEO and Founder of LlamaIndex, will provide many updates on their project and examples of famous use cases. - Andrew Ng: Stanford professor and AI luminary, co-founder of Google Brain and Coursera, shares insights on AI research and applications through his active X presence. - Yann LeCun: French computer scientist known for his pioneering work in deep learning and convolutional neural networks, and he is the founding Director of Facebook AI Research. - Rowan Cheung: founder of The Rundown AI newsletter, shares the latest developments in artificial intelligence. - Jim Fan: Researcher at Nvidia, he explains advanced AI innovations and is now focused on Models for Humanoid Robots. On Linkedin: - Philipp Schmid: Technical Lead at HuggingFace, publishes a lot about new Open Source releases and innovations. - Allie Miller: we worked together at IBM; she previously worked as Head of Business Development for Startups at Amazon. She shares a lot of updates and tips on how to apply AI for Business. - Aishwarya Srinivasan: great friend and ex-colleague of IBM. She worked at Google and is now an AI Advisor at Microsoft AI. She posts a lot of educational content. - Bojan Tunguz: senior Systems Software Engineer at Nvidia. He posts insightful content about AI (and xgboost!). 3. Newsletters I Am Subscribed To - The Algorithm: by MIT Technology Review, great to explore and clarify AI breakthroughs weekly and discuss unexpected impacts. - The Rundown AI: My favorite one to get the latest news in AI every day. - The Augmented Advantage: I enjoyed Tobias’ newsletter because it explained the practical application of AI in business. - Alpha Signal: too many papers, too little time to read them all. Get a weekly summary of the top innovations from the researcher community.

Many students ask how to keep up with AI progress. So here’s the approach that works for me, broken into three levels: 1. Short-term trends (~1 hour/day): I follow real-time discussions on X, Reddit, and LinkedIn, focusing on a curated list of accounts. This keeps me updated on the latest news, debates, and announcements. 2. Mid-term trends (~5 hours/week): I read publications from ML conferences like NeurIPS, ICLR, and ICML, journals like Nature and Science, blogs from AI labs in universities or companies, newsletters (like The Batch from DeepLearning.AI), and podcasts from the investment community. These provide insights into emerging tech and the next wave of AI developments. 3. Long-term trends (~5 hours/month): I have in-depth conversations with experts and thought leaders in my network. These discussions help me connect the dots and understand the broader direction of AI. This approach is structured and helps me make informed decisions at work, such as determining what to build and what not to build, given the roadmaps of model and infrastructure providers. If you're able to share your learning strategy or some of your favorite sources, please do!

In a world where AI announcements seem to drop every 15 minutes (seriously, it’s so hard to keep up), I've been reflecting on what actually matters beyond the hype. As a people leader navigating this landscape, I've learned that the challenge isn't just adopting AI tools quickly—it's adopting them thoughtfully. This is especially important at HubSpot, where helping our employees move faster helps our customers win faster. I'm seeing AI reshape not just what we do, but how we make decisions and prioritize our people. Here are some approaches that have worked well for us as we continue to test and learn: 1. Expedite access to AI tools and encourage experimentation. We're experimenting with the latest versions of Claude, Gemini, ChatGPT, and more—providing teams access within hours of new releases, not weeks. This creates a culture of experimentation and keeps us ahead of the curve. 2. Foster knowledge-sharing. We've created dedicated channels where employees share their AI wins and habits. Our People team sends a weekly "MondAI" digest featuring different employee use cases that inspire others across the organization. 3. Prioritize leader enablement. We've built AI-first resources, starting with People Leaders who then cascade knowledge to their teams. This isn't just about tools—it's about developing judgment for when AI enhances human work and when human expertise should lead. 4. Seek external expertise. We regularly bring in experts from companies like Anthropic and Google to share insights with our teams. We've cultivated a culture of learn-it-alls, not know-it-alls. 5. Integrate AI into existing workflows. We're incorporating AI tools directly into team processes, focusing on high-impact, repetitive tasks first. Our AI support bot now handles over 35% of tickets while maintaining high customer satisfaction. The most exciting part? Watching our teams develop the discernment to make AI work harder for them, not the other way around. When people and technology make each other stronger—that's the sweet spot. Fellow people leaders: How are you balancing rapid AI adoption with thoughtful implementation that truly empowers your people? Other insights we can learn from?

Want to keep up with AI tools? We're in the Red Queen's race now. We have to run twice as fast just to stay in the same place. Yesterday, Anthropic announced Claude Artifacts which lets users build full apps directly inside of Claude. This brings most of Bolt/Lovable/v0's value proposition directly into Claude's product and we can see how "most" will become "all" in the near future. This is no longer about one tool beating another. It's about the pace of change itself: ↳ Three months ago, you learned Cursor for AI coding ↳ Two months ago, you mastered v0 for prototyping ↳ Last month, you figured out Bolt for full-stack apps ↳ This week, Claude just absorbed Bolt's core value into their main product The old strategy for staying on top of the latest tools was: pick a tool, master it, ride that expertise for years. The new reality is: the key skill isn't tool mastery anymore. It's tool tinkering. If you want to stay relevant, you must remain paranoid and avoid complacency: ↳ Always be testing new tools ↳ Be willing to completely change your workflow every 2-3 months ↳ Learn tools for concepts, not specific features ↳ Treat every breakthrough as a 2-week experiment, not a 2-year investment The moment you stop tinkering, you're already behind. The Red Queen from Alice in Wonderland was right: "It takes all the running you can do, to keep in the same place." To remain relevant in the era of AI tools, always be tinkering!