Alright, let’s talk about due diligence—a term that’s moved beyond boardrooms into virtual spaces like Twitter, Discord, and Telegram. Whether you’re evaluating Web3 projects, AI products, or a blend of both, due diligence is essential. But what does that mean, really? Put simply, it’s the homework you do before making decisions. It’s pulling back the curtain to see what’s happening behind the scenes. Think of it as your safety net. In Web3 and AI, where blockchain, NFTs, smart contracts, and machine learning algorithms are redefining the business landscape, due diligence isn’t just a best practice; it’s a necessity. Newsflash: A 15-Minute Google Search or ChatGPT Session Isn’t Due Diligence If you think a quick search or a brief AI-generated summary is enough, let me save you from costly mistakes. Real due diligence is more than scrolling through headlines or attending a quick AMA. It’s about digging deep, understanding the people and technology behind the project, and getting a full picture before you commit. This article will outline a comprehensive due diligence framework for Web3 and AI. At the end, you’ll find three resources to guide your efforts, but before you jump ahead, let’s go through the basics—understanding what many people get wrong and how to avoid their mistakes. Imagine buying a house after a quick walkthrough. You’d be looking at more than the fresh paint—you’d be examining the foundation, plumbing, and neighborhood. Skipping this groundwork in Web3 and AI is like signing up for a roller coaster ride blindfolded. Foundations of a Smart Due Diligence Strategy So, how do you go beyond the basics? Here’s a roadmap to help you avoid the pitfalls of hype and fear of missing out (FOMO). People First: In decentralized spaces like Web3, people still matter. Even in AI, where algorithms do the heavy lifting, human decisions drive outcomes. Vet the team. What’s their track record? Do they stick with projects through tough times, or do they vanish when the going gets tough? Research the founders and developers as rigorously as you’d check a restaurant’s kitchen if you were allergic to peanuts. Understand the Tech: You don’t need to be a developer, but you should grasp the basics. Is the project’s code open-source? Has it been audited? Who can access or modify the AI model? For AI projects, look into data sources, transparency, and ethical practices. If you’re not familiar with the technical jargon, get someone on board who is. Having many eyes on the tech builds security and trust. Ethical and Regulatory Standards: The regulatory landscape for both Web3 and AI is evolving rapidly. Make sure the project adheres to current regulations—and anticipate future changes. Web3’s decentralized ethos sometimes skirts regulation, and AI can face scrutiny for privacy and ethical concerns. Look into how the project aligns with laws in your country and abroad to avoid potential legal headaches. Algorithmic Transparency and Data Integrity: In AI, it’s not just about the tech—it’s about trust in the algorithms and data used. Understand the data behind the model. Does it come from reputable sources? Is the AI model transparent, allowing you or experts to understand how it reaches conclusions? Knowing this can protect you from unexpected biases or inaccuracies. Security and Code Audits: Blockchain technology in Web3 has strengths, but not all projects maintain high-security standards. Ensure the project’s code has undergone thorough audits. AI systems also benefit from security scrutiny—know how the data is handled and stored, especially if it involves sensitive or personal information. A solid audit can expose vulnerabilities before they become issues. Community and Developer Ecosystem: A project’s community often indicates its stability and potential longevity. Check forums, social media, and developer communities. Are developers actively worki