The AI Longread

In his now famous piece still relevant piece from 2019 Sutton teaches us that the critical lesson from decades of AI research:leveraging computation through general methods is far more effective than incorporating human knowledge into AI systems.Original article here.

Today we're covering our first scientific paper. The authors present "Agentic Web," a comprehensive conceptual framework for the next phase of the internet, where autonomous AI agents act as primary intermediaries.Let us know how you like this format by emailing us, details at the end of the podcast!Article is here: Agentic Web: Weaving the Next Web with AI AgentsResearch paper is here: Arxiv research link

Ines Lee, a former economics professor and content creator for productivity expert Ali Abdaal, shares insights on thriving in an AI-driven job market. She emphasizes the importance of becoming a T-shaped professional—mastering one area while acquiring broad skills in related fields. Ines introduces a three-phase framework to identify where humans can add unique value, proving relevance trumps traditional credentials. Plus, she outlines five actionable steps to start building your T shape for a resilient career.

In this article, Leon explains why he supports the right of educators and academics to refuse the use of artificial intelligence and why he believe some of the criticisms levelled against resistors are flawed.Original article here: The Right to ResistReferences from this article The Myth of Inevitable AI Publishers' Open Letter Against AI Artists and Writers Open Letter on AI Copyright Netherlands Academics Open Letter on AI Australian/US Educators Open Letter Refusing GenAI AI Ethics - Technology Flaws Network Effects and Social Media Companies Instructure and OpenAI Partnership Environmental Costs of AI AI Copyright Issues AI Bias Issues AI Privacy Concerns AI Data Collection Issues Microsoft Research on GenAI Impact on Critical Thinking Rethinking Assessment for GenAI Ebook The Myth of Inevitable AI Podcast Episode It's Uncomfortable on the Fence But At Least the View is Nice

The discussion highlights the urgent need for educational reform to equip students for an AI-driven future. It emphasizes the importance of developing curiosity and resilience, along with essential skills like pattern recognition and critical thinking. Strategies for fostering adaptability through self-assessment and collaboration are explored. Additionally, the podcast delves into transforming education from passive to active learning, advocating for systemic changes that promote deeper understanding and engagement in the classroom.

Ethan Mollick shares examples about what GPT-5 gets right and what it just decides it's going to do without being asked. Despite its advancements, GPT-5 still requires human oversight to correct errors and validate results, emphasizing the need for collaborative interaction with AI.Original article: GPT-5: It Just Does StuffReferences from this article Google Gemini 2.5 with Deep Think achieving gold medal at the International Math Olympiad 3D city builder simulator created with GPT-5

Timothy B. Lee, a writer for the Understanding AI website, offers a refreshing perspective on AI management. He argues that fears about losing control over AI are exaggerated. Instead of viewing AI as a threat, Lee suggests practical management strategies focused on human oversight. He emphasizes the importance of robust review processes and the principle of least privilege to integrate AI safely and effectively into organizations. His insights inspire a balanced approach to harness AI's potential without succumbing to panic.

The podcast dives into the rapid rise of generative AI and its potential to revolutionize organizational practices. It contrasts this with the slow adoption of automation, revealing the complexities facing companies. A case study highlights complacency in healthcare, where older systems persist despite new technologies. Historical insights uncover how past innovations were sluggish compared to today's fast-paced advancements. Ultimately, it discusses how organizations must adapt to navigate the evolving landscape of AI and automation effectively.

Organizations are messy “garbage cans,” and our instinct is to tame that mess with handcrafted process and bespoke agents like Manus. The Bitter Lesson says the opposite often wins—systems trained for outcomes, scaled with compute, tend to outpace careful craft.References from this article Moving off the Map: How Knowledge of Organizational Operations Empowers and Alienates by Ruthanne Huising The Garbage Can Model 43% of American workers have used AI at work (research paper) The Bitter Lesson by Richard Sutton (2019 essay) Our first AI-powered teaching games AI ability to work autonomously is increasing very rapidly Manus Tips for building agents from the Manus team ChatGPT agent OpenAI used reinforcement learning to train their AI Atlantic article on chess ratings Original article link

This study challenges the common assumption that better AI models alone will yield superior results, emphasizing the critical role of the human that's in the loop. References from this article: In a large-scale experiment MIT Initiative on the Digital Economy Leading the AI-Driven Organization courseOriginal article