Critical Thinking in the Age of AI

In this episode, I walk through the cultural reckoning that's already begun:A Japanese author wins a prestigious literary prize, then reveals 5% was ChatGPT-generated, sparking a crisis about what "using AI" even meansSam Altman (OpenAI's CEO) is building iris-scanning technology to verify human identity because he knows what's comingThe Authors Guild is racing to create "human-made" labels for booksBut here's the problem: How do we define "human-made" when AI is in our spell-checkers, our research tools, our editing software, our creative process?At what percentage does something stop being yours? At what point in the process does AI involvement "count"? And who gets to decide?These aren't hypothetical questions. These are the standards being set right now—in awards committees, publishing houses, galleries, and platforms. The defaults being established today will shape what gets valued, compensated, and trusted for decades.This episode isn't about being pro-AI or anti-AI, especially now that AI is everywhere and embedded. It's about being intentional. Because this shift is happening whether we're ready or not, and we get to decide what we value in the transition.I want to hear your thoughts: Should human-made content carry a premium? Would you pay attention to a "human-made" label? If you discovered something you loved was AI-generated, would that change how you feel about it?Let's figure this out together.Follow me onInsta: https://www.instagram.com/iamshaeo/ YouTube: https://www.youtube.com/@iamshaeo Subscribe to my Newsletter: https://shaeomonijo.substack.com/ Check out my Stan store: https://stan.store/iamshaeo 

Learn the fundamental building blocks of critical thinking in this foundational episode. I break down what critical thinking actually means in practice and give you the tools to evaluate claims, spot weak arguments, and avoid cognitive biases.In this episode, you'll learn:What critical thinking really is (and why it's not just being smart)The difference between beliefs, claims, and issuesHow to identify premises and conclusions in argumentsConfirmation bias: why we notice information that confirms our beliefsBelief bias: how our pre-existing beliefs affect our evaluation of logicWhy these skills are essential for navigating AI and information overloadThis is a reference episode, so please bookmark it and return whenever you need a reminder of what clear thinking looks like.Follow me onInsta: https://www.instagram.com/iamshaeo/ YouTube: https://www.youtube.com/@iamshaeo Subscribe to my Newsletter: https://shaeomonijo.substack.com/ 

California just implemented one of the most comprehensive AI transparency laws in the country and it's already facing a constitutional challenge. In this episode, I break down California Assembly Bill 2013, the AI Data Transparency Act that went into effect January 1, 2026, and xAI's lawsuit filed just days before.What is AB 2013?The law requires AI companies doing business in California to publicly disclose detailed information about the datasets used to train their generative AI systems including sources, whether copyrighted material was used, cleaning processes, and more.Why is xAI suing?xAI argues the law forces them to reveal valuable trade secrets, violates the First Amendment by compelling speech, and is unconstitutionally vague. Their central claim: this information helps competitors replicate their models but doesn't actually help consumers make informed decisions.The bigger questions:This lawsuit raises critical questions about AI governance: How do we audit black-box systems? Who decides what "aligned AI" means? What happens when critical infrastructure is completely opaque? And how do we balance innovation incentives with democratic oversight?Topics covered:California AB 2013 requirements xAI's constitutional arguments The epistemological stakes of AI transparencyThe AI data scarcity crisis and industry responsesTrade secrets vs. public right to knowWhat this means for the future of AI regulationKey questions for you:What level of transparency should we demand from AI systems that make decisions about our lives? Should training data be considered proprietary trade secrets? How do we create accountability for systems we can't fully understand?This is the second episode of Critical Thinking in the Age of AI, a podcast about AI's intersection with culture, society, and what it means to be human. If you care about thinking critically about our collective AI future, subscribe and join the conversation.Resources mentioned:California AB 2013: https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202320240AB2013xAI v. Bonta Lawsuit (Case No. 2:25-cv-12295): PDF linked in this article: https://chatgptiseatingtheworld.com/2025/12/31/elon-musks-xai-represented-by-clement-murphy-firm-sues-california-law-ab-2013-the-generative-ai-training-data-transparency-act/ Follow me onInsta: https://www.instagram.com/iamshaeo/ YouTube: https://www.youtube.com/@iamshaeo Subscribe to my Newsletter: https://shaeomonijo.substack.com/ 

2026 won't be defined by what AI can do, but how we respond to the way it's already been impacting our lives.In this episode, I share my 8 predictions for 2026: from the cultural reckoning coming when AI-made/assisted art wins major awards, to the rise of explainable AI, to a philosophical rebellion rooted in our deep human need for recognition.We get into Alexandre Kojève's philosophy of recognition, Ubuntu philosophy ("I am because you are"), why privacy-first AI is about to go mainstream, and why I think AI policy will create a new kind of single-issue voter.Subscribe to my newsletter: https://shaeomonijo.substack.comHere is the post on the 5 different kinds of data: https://open.substack.com/pub/shaeomonijo/p/what-is-the-most-valuable-kind-of?r=2ptp7i&utm_campaign=post&utm_medium=web