OVERHEARD AT WEF, DAVOS (1/∞) “Single-digit billion companies are the new little guys.” #WEF2026 #WEF #Davos

Not bad for now

NVIDIA CEO Jensen Huang’s best career advice: “Passion isn’t enough, you’ve got to endure.”

I’m teaching a course on Claude Code for absolute beginners If you’ve never coded or used the terminal before—this is a 1-day course where I’ll take you step by step on everything you need to know Course is November 19th, we already have 110 students signed up. Learn more here: claude101.every.to

That’s quite in line with my current usage.

Anthropic has overtaken OpenAI in enterprise LLM API market share. OpenAI fell from 50% in late 2023 to 25% by mid-2025, which shows that brand alone does not hold share once real workloads start. Anthropic now leads enterprise LLM API usage with 32%, while OpenAI has 25%, pointing to a real shift in how companies pick vendors. Enterprise LLM API spend hit $8.4B in the first half of 2025. Anthropic’s push on data controls, compliance, and clean integration with existing systems won trust, and that trust tends to decide renewals and expansions. Claude’s recent lines, including stronger reasoning and coding, helped too, with developer code-gen share around 42% for Anthropic vs 21% for OpenAI. Usage is shifting to inference at scale, so uptime, latency, and incident response matter more than raw benchmark wins. Vendor switching stayed low at 11%, and 66% of teams just upgraded within the same vendor, so any share gain here is hard won. Google sits near 20% and Meta near 9%, so this is not a 2-player market, and strengths differ by use case like agents, code, or retrieval. Buyers now weigh cost per token, data residency, auditability, SOC reports, and fine-grained controls as much as model quality. Multi-vendor setups are rising because they reduce lock-in and let teams route tasks to the best model for that job.

The EIC Board welcomes the proposed expansion of the EIC under the next EU Framework Programme (FP10). This marks a big step towards strengthening Europe’s leadership in innovation, giving entrepreneurs, researchers, & startups the tools to scale. 👉 link.europa.eu/fXYqGd

I got rejected by 144 investors before raising $150M for my $200M rev/year startup. After 144 rejections, I started questioning our approach. Were we solving the right problem? What were we doing wrong? Why weren’t investors seeing what we were seeing? Were we the right team to build this? We tried everything: different pitch angles, new deck structures, and reframing the problem. Then came the 145th meeting, where we closed our first growth round. That yes made everything worth it. But getting there took years of mistakes and hard work. We went through a lot of trial and error just to figure out what resonates with investors. We tried dozens of approaches to figure out what made investors engage. Some landed, most didn't. But each iteration taught us something about what builds conviction versus what just sounds good on paper. And once we cracked that code, our Series C closed faster than expected. And today, I see so many founders in the exact same position I was in 10 years ago: grinding through rejections, questioning everything, and trying to figure out what works. So today I want to give you the resource I wish I had back then: Something that shows you exactly how to structure these conversations and navigate the entire process (because the fundraising cycle can be a big distraction and take a toll on you as a founder). So I've partnered with Notion's Startups Team to create the essential fundraising resource that helps you avoid the mistakes that cost me years. Here's what you are getting: • The actual decks I used to raise $150M for Super[.]com (Series B, C) • 50 real examples from funded startups like Eleven Labs and Artisan AI • A searchable database of 10,000 investors - angels, VCs, and accelerators you can reach out to immediately (this alone would take months to build manually) • An AI-powered fundraising agent built into Notion with step-by-step prompts (no separate ChatGPT needed) Want access? • Like and share this post • Comment "FUNDRAISE" • Follow me so I can DM you the link I'll send it over ASAP. P.S.: If you are serious about fundraising (now or in the future), you should grab it right away.

What is an AI Agent? 🤖📘 AI Agents are the future of automation — they think, act & learn like humans ⚡ Also I’ve compiled 1000 Materials — including AI Agents, LLMs, Prompting, SQL & Automation Guides 🚀 To get it 👇 1️⃣ Follow me (@daievolutionhub) so I can DM you 2️⃣ Repost this post 🔁 3️⃣ Comment “AI” 💬 #AI #AIAgent #LLM #MachineLearning #Automation #Tech

Joining forces with leading 🇪🇺 investors to set up the Scaleup Europe Fund, a multi-billion scaleup & late-stage fund to invest in Europe's most promising strategic tech companies. We reinforce the EU as a hub for innovation & growth! ec.europa.eu/commission/pres…

I finally understand the fundamentals of building real AI agents. This new paper “Fundamentals of Building Autonomous LLM Agents” breaks it down so clearly it feels like a blueprint for digital minds. Turns out, true autonomy isn’t about bigger models. It’s about giving an LLM the 4 pillars of cognition: • Perception: Seeing and understanding its environment. • Reasoning: Planning, reflecting, and adapting. • Memory: Remembering wins, failures, and context over time. • Action: Executing real tasks through APIs, tools, and GUIs. Once you connect these systems, an agent stops being reactive it starts thinking. Comment "Paper" and I'll DM you the link.

🤖 I finally understand the fundamentals of building real AI agents. This new paper “Fundamentals of Building Autonomous LLM Agents” breaks it down so clearly it feels like a blueprint for digital minds. Turns out, true autonomy isn’t about bigger models. It’s about giving an LLM the 4 pillars of cognition: • Perception: Seeing and understanding its environment. • Reasoning: Planning, reflecting, and adapting. • Memory: Remembering wins, failures, and context over time. • Action: Executing real tasks through APIs, tools, and GUIs. Once you connect these systems, an agent stops being reactive it starts thinking. Full thread 🧵 Paper: arxiv. org/abs/2510.09244

This Instagram Reels AI agent is absolutely wild 🤯 It scrapes trending Reels in your niche, analyzes them with AI, and extracts every creative insight you need. All inside n8n Airtable. Perfect for DTC brands & agencies who need to know what's working on Instagram before they create content. Here's the problem: Manual Instagram research takes forever. You're scrolling for hours, screenshotting videos, manually noting hooks, trying to remember what worked. And by the time you act on it, the trend is dead. This n8n automation solves it: → Enter a keyword (e.g., "skincare", "fitness", "productivity") → AI scrapes trending Instagram Reels automatically → Writes all videos to Airtable with views, likes, comments → Click "Analyze Video" button in Airtable → Gemini watches each video and extracts: Hook, Proof Point, Theme → Click "Analyze Comments" for instant comment insights No manual scrolling. No spreadsheets. No missing trends. What you get in Airtable: → Video URL, creator handle, performance metrics → AI-extracted hooks (what stopped the scroll) → Proof points (what built credibility) → Creative themes (the narrative structure) → Comment insights (what the audience is asking) Built 100% in n8n. Want the complete n8n template Airtable base? > Comment "REELS" > Like this post And I'll send it over (must be following so I can DM)

algebra isn’t boring; it’s poetry in symbols. > this book turns math’s most hated subject into a story of patterns, logic, and hidden elegance. > for anyone who ever thought they ‘just weren’t a math person

Gemaskerde groepen die openbare gebouwen, privé bezittingen en politie viseren en vandaliseren... Hoe is het mogelijk dat men dit relativeert en tolereert? Brave burgers betalen braaf GAS-boetes, gemaskerde bendes mogen ongemoeid miljoenenschade berokkenen. #Antifa

10 of the world’s biggest banks are teaming up to launch a G7-backed stablecoin network. #Crypto #Bitcoin

What the fuck just happened 🤯 Stanford just made fine-tuning irrelevant with a single paper. It’s called Agentic Context Engineering (ACE) and it proves you can make models smarter without touching a single weight. Instead of retraining, ACE evolves the context itself. The model writes, reflects, and rewrites its own prompt over and over until it becomes a self-improving system. Think of it like the model keeping a living notebook. Every failure becomes a lesson. Every success becomes a rule. And the results are absurd: 10.6% better than GPT-4–powered agents on AppWorld 8.6% on financial reasoning 86.9% lower cost and latency No labels. Just feedback. Everyone’s obsessed with “short, clean” prompts. ACE flips that. It builds dense, evolving playbooks that compound over time and never forget. Because LLMs don’t crave simplicity. They crave context density. If this scales, the next generation of AI won’t be fine-tuned. It’ll be self-tuned. We’re entering the era of living prompts.

Holy shit...Stanford just built a system that converts research papers into working AI agents. It’s called Paper2Agent, and it literally: • Recreates the method in the paper • Applies it to your own dataset • Answers questions like the author This changes how we do science forever. Let me explain ↓