Why AI can't replace your story: embracing FOBO

I see the recent warnings from both Sam Altman and Mark Zuckerberg about a potential AI bubble as a wake-up call. The excitement around AI innovation is wholly justified — there’s no doubt it’s transformative — but when valuations outpace actual capability, when hype overshadows risk, that’s when things get fragile. We must double down on responsible innovation: strong guardrails, realistic expectations, ethical frameworks, and robust governance. Leading transformation isn’t just about pushing the edge of what’s possible — it’s about ensuring stability, trust, and long-term impact. Deutsche Bank called it “the summer AI turned ugly.” #AI #DigitalTransformation #Leadership #CIO #ResponsibleAI #Innovation #TechGovernance #FutureOfWork https://lnkd.in/eBGPyhCw

Interesting read. Agree that everyone’s crowding upstream: NVIDIA, foundation models, the obvious infrastructure plays. Feels late, upside is capped, and will likely be a race to the bottom for many players who don’t get out early. The earlier opportunity? Fishing downstream (or at least anywhere other than upstream). Integrating AI into every workflow, business and personal. That’s going to take years of iteration and create wave after wave of new value. I'm personally betting on value creation for marketers, now that ideas are easier than ever to build into personalized customer experiences. One nit with the article: Most of the article focuses the value that investors can capture. That seems narrow. AI can (and should) create broad value for the world. The best companies will do both, but even if some just make work and life better without minting billionaires, that’s still a win.

We’re always moving, working, and thinking about one thing: we want to speak better “you”- and make it loud. Today, we’re showing you how to turn a single branded image into a stunning video. Which model should you use: Kling, VEO, GPT? Who cares? Let iGentity choose automatically for the best results. Can’t wait to see your feed!

"Overall, the performance of our 'synthetic sample' is too poor to be useful for all of our research questions...Further, we have demonstrated synthetic samples generate such high errors at the subgroup level that we do not trust them at all to represent key groups in the population." Ell. Oh. Ell. https://lnkd.in/ebajB7Hq

𝗕𝗶𝗮𝘀 𝗶𝗻 𝗔𝗜: 𝗪𝗵𝘆 𝗜𝘁’𝘀 𝗠𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱 𝗧𝗵𝗮𝗻 𝗜𝘁 𝗟𝗼𝗼𝗸𝘀 (𝗮𝗻𝗱 𝗛𝗼𝘄 𝗮 𝗠𝗶𝘅𝘁𝘂𝗿𝗲 𝗼𝗳 𝗩𝗼𝗶𝗰𝗲𝘀 𝗖𝗼𝘂𝗹𝗱 𝗛𝗲𝗹𝗽) When people talk about “bias in AI,” it often gets framed like a bug: find it, patch it, done. But bias doesn’t work that way. It’s layered, contextual, and often subjective. Some phrases are clearly sensitive. Others are “dog whistles”: ordinary-looking language that carries hidden meaning in certain contexts. Researchers like Kruk et al. (2024) have cataloged thousands of examples, while Sasse et al. (2024) showed how new dog whistles emerge in online spaces faster than lexicons can keep up. Add fuzzy semantic matching — where embedding models collapse distinctions between “close enough” queries — and the problem gets trickier still. The harder question is: what do we even mean by bias? For one person, objectivity means sticking to bare facts. For another, it means balancing perspectives. For someone else, it might mean optimizing for creativity or contrarian analysis. When we say we want AI to be “unbiased,” we’re usually asking it to reflect our own preferences. Classic work like Caliskan, Bryson & Narayanan’s WEAT study (2017) showed that even broad word embeddings replicate human stereotypes. Ferrara’s 2023 survey catalogs how bias arises not just from data but also from design and deployment. Even model architectures matter: Chung et al. (2024) found that gating in Mixture of Experts (MoE) models embeds its own biases. So maybe the real challenge isn’t eliminating bias — it’s making it visible and navigable. That’s the motivation behind a project I’ve been working on: Mixture of Voices. Instead of pretending one model can be the “neutral” voice, it routes across multiple AI systems (Claude, ChatGPT, Grok, DeepSeek, etc.) and explains why decisions are made. If a safety rule triggers, you see it. If a model is chosen for math, it tells you. The system surfaces trade-offs (safety vs. performance, precision vs. recall) and lets users steer according to their own definition of objectivity. Bias isn’t a bug to squash. It’s a set of editorial decisions that should be transparent and user-configurable. So I’ll leave you with a question: would you rather use an AI that claims to be neutral, or one that admits its biases and gives you the steering wheel? Want to see what I have been up to regarding optimizing AI model selection (and helping address bias and transparency along the way)? See my open source Mixture of Voices project at https://lnkd.in/e2j7cyJn A very quick demo can be see at: https://lnkd.in/eY7z73rN #ai, #artificialintelligence,#openai,#claude,#grok,#groq, #deepseek,#opensource

You don’t need more content. You need more clarity. Most experts assume visibility means volume. So they write, repost, repeat — until they burn out or vanish. There’s a smarter way. Clone the core of your message — once. Then let AI carry it with consistency and care. That’s what I’ve done. Not to replace Ron. But to protect his voice while he lives the rest of his life. Strategy scales. Soul doesn’t have to suffer. Want to build authority without burning out? Start by clarifying what must be preserved. Then turn it into a system. Follow for more on how to scale your message — with clarity. Something big coming very shortly!!

“Part of every employee’s job in your agency is to build trust in your community.” - Judy P., discussing ways to help combat AI-generated content at the NIOA Conference. #NIOA2025 #10-8Communications

I am very pleased to see my name alongside Steve Powell and Gabriele Caldas, in the recent Special Edition of #Evaluation. 🎉 Our article 'A workflow for collecting and understanding stories at scale' explores the opportunities and challenges of AI-assisted interviewing and casual mapping. A preprint of our article is available here: https://lnkd.in/eA_BqgrP

So what’s really going on? This week, Jake dives deep into decoding what prospects really feel beyond their words… tuning into posture, tone, cadence, and microexpressions. Learn how to read digital and in-person body language, spot urgency and excitement, and adjust your messaging for real connection. Discover why listening with your eyes gives you an edge over scripts and AI alone. If you want to connect on a deeper level and close more, this episode is a must - check out the link in the comments!

Cut bias in your prompts. Ask the model to: 1. Find loaded terms and hidden assumptions. 2. Rewrite your ask in neutral words. 3. Answer the neutral version with multiple views and evidence. Use this for hiring, strategy, and market research—anywhere stakes or bias run high What changes when you run this on your last request? #PromptBias #BiasInAI #ResponsibleAI #FairAI #PromptEngineering #InclusiveLanguage #AIethics #NeutralPrompts #EvidenceBased #DiversePerspectives