Societe Generale Corporate and Investment Banking - SGCIB’s Post

Stop asking if AI will replace your people.Ask if your learning culture is ready for 2026. I just listened to a fascinating podcast by Kokou Agbo-Bloua with Dr. Barbara Oakley, and it completely reframed how I think about talent development in an AI world. The verdict? Biological Intelligence (BI) + Artificial Intelligence (AI) is the new superpower. But only if you build the right environment. 4 lessons for HR and L&D leaders: 1) Your people run on 20 watts The human brain outperforms massive AI models on creativity, imagination, and ethical judgment. Your job? Protect and invest in these irreplaceable capabilities. 2) Deep learning isn't grind culture Real expertise forms in "alpha" states—focused, calm, uninterrupted. Not frantic "beta" chaos. Are you giving your people space to actually think? 3)Build centaur teams The magic isn't human or AI. It's both. Teach your people to use AI for speed (search, synthesis, simulation). They bring the strategy, empathy, and creative spark. 4) Discomfort is a feature, not a bug Offload critical thinking to AI and cognitive atrophy follows. Real growth requires struggle. How do you reward people who lean into challenge vs. take the easy path? The bottom line? AI is the ultimate productivity tool. But it should stretch your workforce, not shrink their capabilities. The organisations that win in 2026 won't have the most AI. They'll have the best human-AI collaboration. https://lnkd.in/gMadBdvg #HR #LearningAndDevelopment #FutureOfWork #TalentStrategy #Leadership #AI

Why do people resist AI—even when it performs well? Last week, our colleague Prof. Dr. Elisa Konya-Baumbach (Professor of Consumer Psychology & Behavior at BFH Wirtschaft and Co-Founder of humest GmbH) joined Valeriya Pilkevich on the podcast AI Made Simple to discuss the psychology behind AI adoption. In the conversation, they explore why AI adoption often fails not because of technology, but because of human reactions - such as lack of trust, perceived threats, and the way organizations communicate about AI internally. Even how AI is framed (for example, “AI” vs. “smart technologies”) can influence whether employees engage with it or resist it. 📹 Watch the conversation below: https://lnkd.in/ew_s3i_4 🎙️ The episode is also available on podcast platforms.

Really enjoyed Dr. Stephen Cave on The Good Robot Podcast on the origin myths of AI. Episode blurb: Why do we call it "Artificial Intelligence"? It wasn't the only name on the table in 1956, but it’s the one that stuck, and according to Dr. Stephen Cave, that’s because it tapped into 3,000 years of deep-seated mythology. In this episode, Kerry McInerney sits down with Dr. Stephen Cave to discuss his new book (co-authored with Dr. Kanta Dihal) on the origin myths of AI. They dive into the "value-laden" history of technology, exploring how the binary of the artificial vs. the natural was used to justify colonial conquest, and how the very concept of intelligence was forged by card-carrying eugenicists to structure society into hierarchies of "fitness". From the labs of Silicon Valley to the history of SATs (Standard Assessment Tests), Dr. Cave illustrates how these old stories continue to shape who gets to build our future and whose voices are sidelined. https://lnkd.in/eAXd4dD4

AI is a foundational technology that is supercharging other scientific fields and transforming society, economics, and politics around the world. Andy Dumbell highlights the 2026 Stanford Emerging Technology Review on The AI Breakdown podcast, examining the current capabilities of AI agents, the need to invest in scientific research, and more: https://lnkd.in/gZGhN-tJ

Honoured to get this shout out from the Hoover Institution, Stanford University. In this episode I dig into the AI chapter of the Stanford Emerging Technology Review 2026. Co-chaired by Condoleezza Rice, Jennifer Widom, and Amy Zegart. Produced across Stanford’s School of Engineering, the Hoover Institution, and the Institute for Human-Centered AI. What I really like about this report is, it isn’t a hype piece. It’s deliberately trying to inform rather than advocate. And that’s exactly why I wanted to cover it. I pull out the parts I think matter most for technology leaders. The gap between AI’s foundational promise and its current operational fragility. The overhyped narrative around agents. The hollowing out of the public research pipeline. And the governance and legal shifts that will impact us all. Here’s their full report: https://setr.stanford.edu/

AI advancements accelerate as new transformer discoveries emerge. AlphaEvolve's relevance questioned as AI-driven breakthroughs surface, including a recent paper on arXiv, signaling rapid evolution in the field. https://lnkd.in/g6cihZWN

From using ferret neural nets to diagnose trench foot in soldiers, to driving forward enterprise AI adoption, James Kuht MBE hasn’t had your typical journey through AI.  With PAIR, he's solving the problem he first spotted in the halls of No. 10: moving organisations from random, scattered AI experimentation to measurable results. Listen to Carlos's interview with James on This Much I Know: The Seedcamp Podcast, at the link in comments 🔗

Are you using AI to not have to learn? The other day I listened to a Huberman Lab podcast episode which featured Dr. David Eagleman, a neuroscientist at Stanford. He shared that brain plasticity happens when you have the right "neurotransmitter cocktail," which maps directly onto curiosity. It made me rethink how I've been using AI. I was letting it do the learning for me. Asking for answers before I'd sat with the question long enough to actually care or be curious about it. Since then I've been trying three things instead: Socratic Interrogation, where I ask Claude to question my reasoning and logic rather than confirm it. The Feynman Challenge, where I explain a harder concept back in plain language and have AI tell me where there are gaps in my understanding. Premise Testing, where I state what I know so far, ask it to push back (and counterargue), and then I defend my understanding. (Wispr Flow has been incredibly helpful with all of these) I'm still figuring out the right balance. But I'm retaining more than I was a month ago. What's one way that you've been using AI to learn better?

AI is evolving fast, but do we really agree on what “general intelligence” means? In the new episode of Reply’s AI Insights podcast, we talk with Professor Pei Wang, AGI pioneer and creator of the Non‑Axiomatic Reasoning System (NARS), to rethink some of today’s core assumptions about AI and AGI. In this deep‑dive conversation, Professor Wang: ■ retraces his journey from early AI research in Beijing and Indiana to coining and shaping the modern field of AGI ■ explains why intelligence should be treated as one unified problem, not a collection of isolated subfields ■ discusses the limits of current LLMs, and where they fit (and do not fit) in the broader AGI landscape ■ argues that AGI is not a single technology, but a family of approaches with very different goals, constraints and assumptions If you are interested in what comes after today’s LLM‑centric view of AI, this episode offers a rare perspective from one of the field’s foundational thinkers. 👉 Listen to the latest AI Insights episode: “Beyond the Hype: Pei Wang on AGI, NARS, and the Future of Thinking Machines.” and follow us on Youtube to stay up to date: https://lnkd.in/eaQjZh6j