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English language Visionary Marketing Podcasts

English language Visionary Marketing Podcasts

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Visionary Marketing Podcasts in EnglishCreative Commons 1995-2022 Visionary Marketing マーケティング マーケティング・セールス 政治・政府 経済学
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  • AI and Creativity: a 2026 update

    AI and Creativity: a 2026 update
    2026/09/10
    It’s back to school and back to the blog too for the 31st time since 1995, and today’s topic will be AI and Creativity. The idea is to deal with the topic of artificial intelligence, not from a technical perspective, but from a more philosophical one, drawing both on my digital and artistic backgrounds. In 2026, the fourth version of my programme entitled “Content Creation in the Age of AI” will be delivered to all the students of the Omnes Education Group. A previous video was recorded in 2024 on that subject, and a serious refresh was needed. I have therefore gathered enough hindsight to learn more about the impact of AI, especially on music and the creative arts. And I decided to focus on just three things, to avoid getting lost in too broad a subject. First, the seeming popularity of AI artists on streaming platforms like Spotify, taking the example of an act called Breaking Rust. Second, the Stanford GSB study on visual arts and the impact of AI on creators. Third, my own thoughts and conclusions based on the above. Has AI Stifled Creativity? A 2024-2026 Perspective AI and Creativity and manufactured popularity, because such things exist as well. In 2024, I asked myself the question, “Will AI stifle all creativity?” as part of my Shift24 programme designed especially for Omnes Education Group. This programme cannot be shared online, but my colleagues at Omnes were kind enough to let me publish this wee piece for the benefit of my readers and followers, and I wish to thank them warmly for this as well as their renewed support these past four years. In this video dedicated to ‘creativity’, drawing on Merriam-Webster, I distinguished innovation from invention and chose to focus on the latter, specifically in music, a topic not yet covered in earlier courses. I then illustrated this with Suno, which could churn out up to 900,000 pieces of music a day as early as 2024 (far outpacing Bach’s 1,128 compositions over a 65-year lifetime). Hence, I shared a song I had generated myself, “The Dying Cyberspace.” I won’t say that I’m proud of this song, but at least I had great fun generating it, and it was instant gratification. All this was raising questions related to the value of musical creation, the risk of creative stagnation, and the thorny issue of intellectual property, since these AIs are trained on centuries of human-created music. So far, nothing new under the sun. Almost every other journalist must have written the same story for the past three years. However, I thought it lacked a bit of perspective. I then tempered the novelty of the phenomenon by pointing to historical precedents: Gershon Kingsley’s “Popcorn” (1969), Zoe Keating’s loop-based compositions (one example amongst many others), and Google’s Project Magenta as far back as 2016. These are mere examples. I decided to keep it short, but there has been no shortage of technology-backed creativity in music these past 50 years or more. Think of the late Klaus Schultze and his pals from Ash Ra Temple, Tangerine Dream, Kraftwerk, and all the so-called Krautrock school of thought to start with, not mentioning the new wave movement from the 1980s. I also cited Wim Mertens, Philip Glass, Laurie Anderson, and German artists like Nils Frahm, who blended electronic and acoustic elements long before GenAI. The real difference today, I argued, is one of scale: such tools are now in everyone’s hands. Be it to create music of the ilk of the Dying Cyberspace or more ambitious AI-produced work like this entire fake Pink Floyd album, which didn’t sell as well as The Dark Side of the Moon or Wish You Were Here. Somewhat courageously, I concluded at the time that AI wouldn’t replace musicians but would become just another instrument in the creative toolkit, with real innovation coming from how humans use these tools. Since music remains fundamentally about human expression and emotion, I thought rather optimistically, if not naively, that AI music didn’t mark the end of musical invention but rather the beginning of a new chapter. Such were my thoughts from two years ago, now is the time to move forward and take a look at what happened in 2025-2026. As exmplained above, I focused this new research on three things. First, the seeming popularity of AI artists on streaming platforms like Spotify, taking the example of an act called Breaking Rust. Second, the Stanford GSB study on visual arts and the impact of AI on creators. Third, my own thoughts and conclusions. Breaking Rust Breaking Rust: a Spotify hit Let’s start with Breaking Rust. Breaking Rust is an AI country act which hit number one on the Billboard Country Digital Song Sales chart in November 2025, on Spotify. The numbers are seemingly enormous: over 2 million Spotify monthly listeners, and over 3 million streams in under a month. The project is credited to Aubierre Rivaldo Taylor, even though very little is known about him. Rivaldo Taylor is ...
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    19 分
  • Agentic E-Commerce, Could AI Become the Shopfront

    Agentic E-Commerce, Could AI Become the Shopfront
    2026/06/04
    Agentic e-commerce is already reshaping how consumers discover and buy products online, yet it still accounts for barely 0.2% of total e-commerce traffic. BASE France is the French arm of Base.com, a Polish-born SaaS scale-up that has spent nearly two decades building operational infrastructure for online retailers. Its CEO, Ben Hamilton, brings a practitioner’s perspective to this emerging model: measured, practical, and refreshingly free of the hype that surrounds most conversations on the topic. Agentic E-Commerce: Could AI Become the Shopfront? Imagine an agentic e-commerce world where e-commerce happens on smartphone screens and robots deliver your purchases. We might be on the brink of this future. This image was created using Midjourney. Commerce as conversation: the oldest model in the book Before there were shops, there was conversation. For thousands of years, trade was oral. A buyer expressed a need, a seller responded with what they had, and the two parties negotiated until a deal was struck. The self-service retail store, born roughly a century ago, was a radical departure from this model. It replaced dialogue with browsing. It handed the customer a trolley and pointed them at the shelves. E-commerce then took that self-service model and, as Ben Hamilton puts it, “multiplied it by about 100,000.” The online shopper today faces a near-infinite array of products across dozens of marketplaces, with no guide, no-one to talk to, and no memory of what they looked at three tabs ago. It is efficient in theory. In practice, it is exhausting. Back to future? The agentic model, Hamilton argues, represents something of a return to origins. Instead of browsing, the consumer talks. An agent listens, asks questions, proposes options, and eventually surfaces an answer to a need that the buyer may not even have been able to articulate clearly at the outset. “back to the future,” Hamilton explains, “that’s what I’m getting at. The agentic model takes us back to something closer to how human beings have traded over thousands of years compared to the last ten, twenty or even a hundred.” My own experience bears this out. I recently found a diagnostician for a property I am selling. As a matter of fact, I didn’t find them through a Google search, but through a conversation with an LLM. I clicked through two or three irrelevant links before landing on exactly the right provider. I then completed the transaction on their website. The research was agentic; the checkout was not. That distinction, as it happens, sits at the heart of what Hamilton believes will define the next phase of e-commerce. Ben Hamilton on agentic e-commerce: “I can totally imagine a portion of that market occurring directly on an LLM”. Agentic E-commerce: Where checkout will and won’t happen One of the more grounded contributions Hamilton makes to this debate is his refusal to conflate two distinct phenomena: AI influence over purchasing decisions, and AI completing the transaction itself. Much of the media discourse collapses the two. Hamilton does not. “I don’t think we’re heading to a world where 20, 50 or 80% of online transactions happen on an LLM,” he says. “I would draw the distinction between where the checkout occurs and how much an agent is involved in the buying process.” For the foreseeable future, he believes, most consumers will continue to research via LLMs and transact on familiar websites and marketplaces. The inertia in human purchasing behaviour is simply too great for the checkout itself to migrate rapidly to a chat interface. This view is supported by the data available. According to research by commercetools, 73% of consumers already use AI somewhere in their shopping journey. Yet only 36% are open to AI agents making purchases on their behalf. In the US, the figure for autonomous AI purchasing drops to 14%. The gap between AI as advisor and AI as buyer is vast, and it will narrow slowly. The risks associated with agentic e-commerce are high The risks of handing uncapped authority to an AI agent are no longer hypothetical. In late May 2026, an AI consultant reported to Axios that one of their enterprise clients had accidentally accumulated a $500 million bill on Anthropic’s Claude in a single month, simply by giving employees unrestricted access to the platform with no usage controls in place. Agentic workflows, which loop through tasks repeatedly, consume tokens at a rate orders of magnitude higher than a standard chat query. The bill was not the result of malicious use or a system failure. It was the predictable outcome of deploying autonomous agents without guardrails. The case is far from isolated: Uber reportedly exhausted its entire 2026 AI budget by April, with per-engineer costs running between $500 and $2,000 monthly. “You’ve got to be bold to give them no upper limit on transactions,” Hamilton observed, and the arithmetic proved him right. [Editor’s note: I...
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    38 分
  • GenAI in Higher Education, Legitimacy and Laziness

    GenAI in Higher Education, Legitimacy and Laziness
    2026/05/21
    Alain Goudey is Associate Dean for Digital Innovation at Neoma Business School and co-author of a peer-reviewed study on GenAI in Higher Education. The survey focused on how students, faculty, and deans perceive the legitimacy of generative AI in French management education. His findings are both reassuring and unsettling. GenAI in Higher Education, Legitimacy and Laziness, and the Exam That No Longer Makes Sense The picture that emerges from a study on GenAI in Higher Education is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. All illustrations in text made with Midjourney When Alain Goudey and his colleagues began surveying French higher education in early 2024, they were not trying to settle the question of whether generative AI was good or bad. They were trying to understand something more precise: why the same tool could be simultaneously valued, feared, accepted, and denounced, sometimes by the same person in the same breath. Their study sits at the heart of what makes GenAI in higher education such a contested terrain. The resulting study, published in the Communications of the Association for Information Systems (CAIS), drew on surveys of 668 students, 204 faculty members, and 29 deans, completed by 22 in-depth interviews with early-adopter professors. The picture that emerges is less a battlefield than a hall of mirrors, where every stakeholder sees a different problem and reaches for a different solution. The starting point is a number that should have settled the debate. Between 80 and 92 per cent of students, depending on the institution surveyed, are already using GenAI tools in their academic work. ChatGPT’s public release produced that figure within roughly 18 months. The tool did not wait for institutional permission. It deployed itself. And higher education is still, in many places, writing the policy. The productivity trap Alain identifies the central tension plainly. Students value GenAI for speed, idea generation, and study support. They also fear, and their institutions fear with them, what the research calls “metacognitive laziness”: the gradual erosion of the cognitive effort that produces real learning. He believes this is not a contradiction to resolve but a course architecture challenge. “The resolution of this problem lies in course design, where we need to deliberately reintroduce cognitive effort and reflection into GenAI as a tool, not as a replacement for human cognition.” The issue, as he puts it, is not the technology but the posture the user brings to it. Someone who submits what he calls a “naive prompt” receives a naive answer, smoothly formatted and perfectly mediocre. The tool is capable of something far more useful, if the user brings enough domain knowledge and critical intent to the conversation. “You have to nurture your own thinking process instead of delegating the whole process to the machine.” This is, as I noted during our conversation, less a matter of prompt engineering than of basic intellectual discipline: the capacity to question the question before asking it, something philosophy departments have been teaching for centuries under less fashionable names. GenAI in Higher Education: faculty should train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. Image made with Midjourney That observation prompted Alain to make a point about AI literacy that differs from what is generally proffered. The debate is not simply about knowing how the tools work technically. It is, equally, about knowing enough about the subject matter to judge whether the output is any good. The observation that AI is most powerful in the hands of people who already know the business resonates here. GenAI does not replace expertise. It amplifies whatever expertise the user already brings. Which raises an uncomfortable question for institutions producing graduates who may never have had the chance to develop that expertise in the first place. At Neoma, the response has been deliberately dual. Faculty train students in GenAI tools and their limitations. They also teach Homer’s Odyssey and Shelley’s Frankenstein as part of the management curriculum. The goal is not cultural enrichment for its own sake. It is to give students mental models for envisioning what leadership looks like, or what happens when creation escapes the intentions of its creator. Alain describes this as “building cognitive infrastructure”: “We need students to be able to envision the world through different models, different kinds of processes and theoretical frameworks, in order to develop genuine critical thinking about what AI generates.” A degree in management that skips that foundation produces graduates who can operate the tool but cannot judge its output. Exams that assessed the wrong thing The ...
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    1 時間 5 分
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