Misunderstandings Surrounding Artificial Intelligence

How do you know what you know? Now, ask the same question about AI. We assume AI "knows" things because it generates convincing responses. But what if the real issue isn’t just what AI knows, but what we think it knows? A recent study on Large Language Models (LLMs) exposes two major gaps in human-AI interaction: 1. The Calibration Gap – Humans often overestimate how accurate AI is, especially when responses are well-written or detailed. Even when AI is uncertain, people misread fluency as correctness. 2. The Discrimination Gap – AI is surprisingly good at distinguishing between correct and incorrect answers—better than humans in many cases. But here’s the problem: we don’t recognize when AI is unsure, and AI doesn’t always tell us. One of the most fascinating findings? More detailed AI explanations make people more confident in its answers, even when those answers are wrong. The illusion of knowledge is just as dangerous as actual misinformation. So what does this mean for AI adoption in business, research, and decision-making? ➡️ LLMs don’t just need to be accurate—they need to communicate uncertainty effectively. ➡️Users, even experts, need better mental models for AI’s capabilities and limitations. ➡️More isn’t always better—longer explanations can mislead users into a false sense of confidence. ➡️We need to build trust calibration mechanisms so AI isn't just convincing, but transparently reliable. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. We need to design AI systems that don't just provide answers, but also show their level of confidence -- whether that’s through probabilities, disclaimers, or uncertainty indicators. Imagine an AI-powered assistant in finance, law, or medicine. Would you trust its output blindly? Or should AI flag when and why it might be wrong? 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬—𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐮𝐬 𝐚𝐬𝐤 𝐛𝐞𝐭𝐭𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. What do you think: should AI always communicate uncertainty? And how do we train users to recognize when AI might be confidently wrong? #AI #LLM #ArtificialIntelligence

The Agentic AI Reality Check: 10 Myths Derailing Your Strategy Time for straight talk on agentic AI. After working with dozens of implementation teams, here are the misconceptions causing costly missteps: 1.    "Agentic AI" ≠ "AI Agents" -Most "agents" today follow narrow instructions with little true agency. Know the difference. 2.    Adding More Agents Isn't Linear Scaling- Agent interactions grow combinatorially, not linearly, explaining why multi-agent systems often fail in production. 3.    It Won't Run Your Business Autonomously- Current systems require significant human oversight—they're augmenting knowledge workers, not replacing them. 4.    Scaling Laws Are Hitting Limits- The "just make it bigger" approach is showing diminishing returns as quality data becomes scarce. 5.    Synthetic Data Isn't a Silver Bullet -You can't bootstrap wisdom by endlessly remixing the same information. 6.    Memory Remains a Fundamental Limitation- Most systems still forget critical details across extended interactions. 7.    Emotional, High-Stakes Tasks Need Humans- AI lacks the empathy and judgment needed for your most valuable use cases. 8.    Scaling Is Organizational, Not Just Technical- The hardest problems involve cross-functional coordination and process redesign, not just better tech. 9.    It's Not "Almost Conscious"- These are pattern-matching systems—nothing more, nothing less. 10. Smaller Models Often Outperform Giants- The future is the right model for the right job, not one massive model for everything. The next wave of innovation will come from those who see past these myths and focus on thoughtful integration with human workflows. What Agentic AI misconceptions have you encountered? Share below. #AgenticAI #AIStrategy #AIMyths #FutureOfWork Venkatesh G. Rao Bo ZhangWinnie Cheng Ananth R. Stuart Henderson Laura Gurski

Science Has Figured Out What Businesses Can't They're the grownups in the GenAI world, while business sits at the kiddie table The scientific community has been remarkably clear on what GenAI is, and what it isn’t, for years. They are solving problems and using GenAI as the ultimate augmentation partner. It's breathtaking to see what they've accomplished. But business leaders continue to put on a theatrical performance with their management consulting producers Here’s what science has known for a while: It doesn’t reason. It predicts. It can't replace anyone, it's not qualified. It doesn’t understand. It correlates. It needs to be managed as it can’t be trusted It doesn’t “know” truth. It reflects training data. It doesn’t have intent, memory, or values. The confusion in business surrounding GenAI leads to: Misguided Investments and Lazy Analogies Companies imagining GenAI as a “smart intern” waste money on unrealistic expectations. Failed Pilots When treated like a “Chief Productivity Officer,” GenAI is tested with human-like productivity assumptions, resulting in failure rates as high as 70%.⁵ Inflated Expectations The hype about “automating everything” leads to disillusionment when GenAI doesn’t deliver on promises. There is no Hype-as-a-Service (HaaS) in science. In science, GenAI is used with clear, grounded expectations, augmentation, not magic. In business, HaaS rules, where the focus is more on selling the promise of AI, not the reality. Instead of chasing efficiency at all costs, we need to ask the right questions: How can GenAI elevate human judgment? How can it augment creativity, not automate routine tasks? How can it help us see patterns we couldn’t see before? It’s a mirror, not a mind. The Path Forward: First Principles, Not Metaphors Think about the core capabilities of GenAI and how it fits into your business. Clear Thinking, Not Brand Buzzwords Use clear, accurate language to understand GenAI’s real potential, and avoid marketing speak that clouds judgment. Augmentation, Not Automation Embrace GenAI as a tool to enhance and elevate human capabilities, not replace them. ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder & CEO of Curiouser.AI, the world’s first values-based AI platform, strategic coach, and advisory. He also teaches AI strategy and ethics at UC Berkeley. To learn more visit curiouser.ai or connect on hubble at https://lnkd.in/gphSPv_e Footnotes: Radford et al., GPT: Improving Language Understanding by Generative Pre-Training, OpenAI, 2018 Bender et al., On the Dangers of Stochastic Parrots, FAccT, 2021 Weidinger et al., Taxonomy of Risks Posed by Language Models, DeepMind, 2022 Mitchell, M., Artificial Intelligence: A Guide for Thinking Humans, 2021 McKinsey, The State of AI in 2024, 2024