LinkedIn thought leader ad examples

We recently partnered with Sabadell Seguros to help them scale a GenAI assistant to 6,000 sales agents. A bit of context: SofIA is their generative AI assistant for Home, Life, and Income insurance. Before scaling to 6,000 agents, they wanted hard proof it was ready. These are the final numbers: 8% uplift in accuracy. 20% fewer refusals. Not everybody insists on this level of rigor before scaling. The team led by Joan Franco did, and it shows. You can read the full story here → https://lnkd.in/efQ7WUnR

How will AI shape value creation strategies? Backwell will host a private presentation in Paris and bring together a selected group of investment and industry professionals to discuss how AI can be applied to established businesses to improve operational performance, productivity, and long-term value creation. The event will include a presentation followed by networking and refreshments. If you want to join, please register to request a slot via the link: https://lnkd.in/ddjHefVC

Predict what matters. Learn more at: https://lnkd.in/dCWZAM9M

Can't wait to be in SF with Pierre-Louis next week for OpenAI DevDay! We're putting together a pre-event party with our dear friends at Hexa for builders to meet each other before this awesome day. Beer, pizzas, good conversation and ski goggles, as always! Link in first comment 👇

GEO won't make you discoverable in ChatGPT. Plugins will. OpenAI juste announced the dynamic discovery of Plugins in ChatGPT 👀

My little creator business is on pace to make $300K this year. And the craziest part is that I’ve barely opened my laptop for the last 3 weeks. Yep, instead of working, I’ve spent most of July traveling around Canada and the US, seeing friends and family. Now, normally after a trip like this, I would be in PANIC MODE. Frantically replying to emails. Putting out fires. Apologizing to sponsors for going dark. But that didn't happen this time (yay!). I’m not writing this to brag, but rather to show what’s possible when you have the right systems in place. And for me, Hyperagent has made creating those systems wayyyy easier. Hyperagent lets you build and deploy AI teammates that connect to your tools, learn how your business works, and handle complex jobs on demand or automatically. Before leaving for my trip I set up THREE agents (and yes, I named them): 1. Otis (my triage agent): Checks my inbox every morning and lets me know if anything urgently needs my attention, so I'm not spending 30 minutes combing through it myself. 2. Scout (my sponsor agent): Reads every message that hits my sponsorship address, researches the company, and sends me one short Slack message with what they want and what I'd need to decide. 3. Carmichael (my post-drafting agent): I record voice memos in the car. Carmichael turns them into first-pass posts in my voice. There were 9 drafts sitting in the folder when I landed. A few small tweaks and they’re ready-to-publish posts. It felt like having a full team supporting me while I was on vacation. Which meant I could climb mountains and eat crawfish boils without worrying that my business was going to fall apart. Hyperagent is giving away $500 in credits to the first 500 people to sign up. If you want to try it yourself, my link below gets you credits to build your own agents. https://lnkd.in/gFydZsh8 #HyperagentPartner

Not unemployed. Just found a loophole! I haven't touched my email in 4 days, yet I still made money this week. Turns out I don't need to lose your business to leave my desk. I just need Bob! Meet Bob. Every morning he sends me the 3 things that actually need me, not the 200 that don't, and drafts my replies so all I do is approve. He's apparently also started hiring. I didn't ask for a team. I got one anyway. If you've been telling yourself you need to be unemployed to have a life, there's an easier way. Built with Hyperagent. Hyperagent is giving away $500 in credits to the first 500 people to sign up. #HyperagentPartner

AI is smart. But is it smart enough on its own? 🤔 Look, I love AI—it’s fast, efficient, and never takes a sick day. But let’s be real… we’ve all had those moments where AI totally misses the mark, leaving us yelling “REPRESENTATIVE!!” into the void. That’s why the best customer experiences don’t rely on AI alone. They combine AI’s speed with human intelligence, creating seamless, frustration-free interactions that actually help people. And for businesses, that’s not just good CX—it’s a massive opportunity. Partners who bring true Conversational AI + human understanding into their offerings don’t just improve customer experiences – they stand out, drive more revenue, and build stronger relationships in the process. What’s the worst AI customer service fail you’ve experienced? Drop your stories in the comments. Let’s stop bad customer service in its tracks— you in? ⬇️

I’m sure this experience will not leave a bad taste in your mouth… The Master Chef of Customer Experience: Why Experience Trumps Experimentation Imagine you're planning a gourmet meal for a huge, high-stakes event. Your reputation is on the line. Do you call a Master Chef who has spent two decades running successful five-star restaurants, serving thousands of discerning customers daily with a perfectly synchronized team of sous chefs? Or do you gamble on someone who just opened their first small cafe last year, cooks well for their family, and is still learning the ropes of catering large-scale events? It’s an obvious choice, right? The master chef knows how to handle unexpected dietary restrictions, last-minute changes, and ensure every dish is perfect, even under immense pressure. They've refined their processes, their team is a well-oiled machine, and they've learned from every past challenge. The home cook, while well-intentioned, is more likely to be overwhelmed, make mistakes, and lack the sophisticated solutions for complex problems that inevitably arise. This isn't just about food; it's about effortless customer experience. Many companies today are entrusting their most critical customer interactions to providers still learning to boil water. At Interactions LLC, we are the Master Chefs of Conversational AI. With 20+ years of experience perfecting Intelligent Virtual Agent (IVA) solutions for global brands, we've navigated every "dietary restriction" and "last-minute change" imaginable. Our seasoned team of designers, developers, and data scientists aren't just good cooks; they are Michelin-star caliber experts who've mastered the art of delivering effortless, scalable, and continuously improving customer journeys at immense scale. Don't settle for a home cook when your customer experience demands a master chef. Partner with Interactions and taste the difference a good experience makes. #CustomerExperience #AI #ConversationalAI #IVA #Interactions #DigitalTransformation #CXO

Mark your calendar: we are closing the dotAI Conference with a party on Sep. 17🕺🥳 What would be a conference without any side event? And even more without a good closing party? H Company is teaming up with dotConferences – AI, JS & Tech Events and Eugenia School to cook you a chill and viby night after a day of talks! What's on the menu: drinks, food, DJ, informal chats with your peers (and also heard there'll be surprises 👀) Warning: there won't be any slides or technical talks - just vibes ⚠️ Limited spots, better be quick 📅 Sep. 17 🎟️ Link in comment Big up to the folks that are helping putting this together: Stéphane Velasquez, Nolwenn Guiban, Mathieu Diaz, Matthieu Saint-Raymond and Anne-Sophie Norca 🥳

Got plans on the 17th? Come party with us: we're closing out dotAI Conference in style 💃 🪩 H Company is teaming up with dotConferences – AI, JS & Tech Events and Eugenia School to throw a chill, viby night after a full day of talks. No slides, we promise, only drinks, food, a DJ, and good conversations with your peers. (We also heard there might be a few surprises 👀) ⚠️ Spots are limited 📅 Sep. 17 🎟️ Link in comments Huge thanks to the crew making this happen: Stéphane Velasquez, Jules Belveze, Mathieu Diaz, Matthieu Saint-Raymond and Anne-Sophie Norca

Sovereign AI doesn't mean building your own foundation model. It means not being held hostage by your vendors. The term gets thrown around a lot, but for most organizations, it's really about maintaining control over the parts that matter. Where does your data live? Can you choose which model to use, or switch providers without rebuilding everything? Can your systems continue operating if one vendor changes pricing, access, or strategy? Those questions have much more impact on long-term resilience than whether you're using the latest benchmark leader. The conversation shouldn't start with "Which model should we use?" It should start with "Which parts of our AI stack do we actually control?" That's what sovereignty looks like in practice. What's the hardest part of your AI stack to keep control over right now?

Europe doesn't have an AI talent problem. It has a deployment problem. I'm actually optimistic about companies like Mistral. European AI models, sovereign infrastructure and on-device AI all matter. Especially in healthcare. But we shouldn't confuse good models with production-ready healthcare systems. The difficult part isn't training another model. It's integrating that model into hospitals, clinical workflows and existing infrastructure. Healthcare AI won't be won by whoever has the biggest model. It will be won by whoever earns the most trust.

Abacus raised $5 million in Series A funding. Honestly, it feels surreal to write that. I’m incredibly grateful to Jay Eum and the team at GFT Ventures for leading this round, and to Carey Ransom at BankTech Ventures and the LAUNCH Fund for believing in what we’re building at Abacus. For anyone who doesn’t know our story: my co-founder Lisa Gillespie was my accounting professor at Loyola University Chicago. (Yes, really.) People ask us all the time how that’s worked out. She was tough then, and she’s still tough now, but in the best way. The rigor, precision, and accountability she demanded in the classroom is exactly what we’ve built into Abacus from day one. Here’s what we’re solving: Regulated industries – banks, credit unions, healthcare, insurance – can't afford black-box models or data leaks. They need AI they can actually trust. That's Abacus. Abacus is on-prem AI infrastructure that keeps your data within your control. The demand from banks and credit unions has been overwhelming (in the best way possible) and now we’ll be able to meet that demand. This round lets us grow our team, deepen our platform, and keep pushing on what we believe in: AI assurance. Excited to see where this journey takes us.

🚨 GenAI projects are not stuck in pilot mode - YOUR GenAI project is stuck in pilot mode🚨 Anyone talking about “GenAI hype” and “being stuck in PoCs” is a new red-flag for me. Clients of Great Wave AI like PXC, The Insolvency Service and Warwickshire Police are not stuck in pilot mode. They have a regular cadence of production-ready GenAI use cases going live and delivering real business value. Just check out the case-studies and video interviews on our website. (Links in comments). So why are they doing so well? Right from the start, they established the need for an agent orchestration layer. This allows: ⚙️ Lots of precision agents to be built and connected quickly 📣 Collection of business user feedback 🔁 Fast iterations until quality is achieved 🧾 Audit and logging delivered out of the box 📊 A single pane of glass through which all agents can be monitored and managed In short, it is business-focussed, scalable, secure and easy for leadership to understand and sign-off. What’s more, it does all this while keeping LLMs where they should be – out of the organisation’s businesses processes and down in the commodity infrastructure layer where they can be selected in and out when things like price or performance change. With the single addition of an effective orchestration layer, everything else moves quickly - outcomes become clear, costs become predictable, resistance drops, audits succeed, and tangible public value emerges in weeks, not years. If you'd like to understand more, please get in touch.

Wondering what the Great Wave AI Platform actually does? Here are the answers: 1. De-risks and accelerates GenAI product delivery 2. Provides a single pane of glass through which to observe and manage agents 3. Delivers governance, risk management and assurance out of the box Don’t believe me …? Check out what Phill Magill Chief Engineer at PXC - the UK’s fastest growing independent wholesale telecoms platform - says about how our platform has turbo-charged their GenAI journey. Link to write-up and full video in comments 👇

Many advances in AI are really part of the same story: finding ways to make deeper models work. Sander Dieleman discusses this in his latest conversation with Marta Garnelo. Full interview coming soon

Japan's largest bank is betting on Fundamental. Today, MUFG, one of the world's largest financial institutions, announced a strategic partnership with Fundamental 🤝 Full blog in the comments

Over a decade of working with organisations where getting decisions right genuinely matters, at scale and under pressure, has left me with one consistent observation. The sectors look very different from the outside, and this holds regardless of industry: financial services, energy, pharma, national infrastructure. But the underlying challenge is remarkably similar. The number of decisions organisations must make has multiplied. The stakes of getting them wrong have increased. The operating model for making them has barely changed. When we founded Faculty, our mission was the safe and widespread adoption of AI. What's become increasingly clear to me is that "widespread" won't come from better models alone. It will come when organisations are willing to rethink how decisions are made, coordinated, executed, and learned from over time. That is the shift most enterprises are still yet to make. I've tried to put some of my thinking on what it actually requires here: https://lnkd.in/eF-UVzzB

Satya Nadella just made the case for everything we've built at Empromptu: "There should be as many models in the world as firms in the world. Because after all, what is a firm? A firm is a learning system." The whole article reads like a blueprint for what we ship. Line by line, we check all the boxes: "I don't want to be locked into any one model" ✅ "I want to use my own context, my own data" ✅ "...cost-efficient model" ✅ "...fine-tuned model" ✅ And the line that puts it into a sharper perspective: "If you outsource your learning, then why exist?" Exactly. So we turn every correction, edge case, and real-world use into training data that builds custom AI your company owns, not rents. Our Self-Managing Context tech holds up to 98% accuracy in production. In the wild, that's 50,000+ AI requests a day across 1,600+ store locations, and 20,000+ creators managed with zero AI engineers on the client side. You own your context. You own your learnings. Take it from Satya. This is the future. Source in the comments. Want to see it work on your data? 👇

It's always energizing when one of our founders helps move an industry conversation forward. This week, TechCrunch's Russell Brandom dug into one of the most-hyped (and least-understood) ideas in AI right now: recursive self-improvement (RSI), the point at which AI systems can meaningfully improve themselves. For the piece, he turned to Doris Xin, Co-Founder & CEO of .406 portfolio company Disarray. Doris doesn't see #RSI the way most of the field does. The common view treats it as a problem of intelligence, that once models get smart enough, they'll start improving themselves. Her argument is that the intelligence is largely here already, and what's missing is the infrastructure to put it to work: "Given infinite compute and infinite time horizon, we are already there. I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering." It's a characteristically clear-eyed take from someone who builds rather than speculates. She demonstrated the value of infrastructure to activate the current model intelligence in her recent 24-hour run, where on a single A100, Disarray's MLE agent earned 28 Kaggle medals, with nine top-10 finishes and a #1 ranking ahead of every human team. Congratulations to Doris and the team on the well-deserved recognition. You can read the full article below. https://lnkd.in/eEt3fVk2 Moustafa AbdelBaky, Kevin C. Wang

In a TechCrunch article on RSI, I was quoted saying “[RSI] is not a creative endeavor.” What makes me say this? To oversimplify, RSI is about finding configurations that lead to AI improvement from a vast, uncertain space. With infinite resources, you can sample randomly until something works. Without infinite resources, progress depends on testing high-quality hypotheses. That is where “creativity” comes in. A creative breakthrough often means sampling from a subspace no one had thought to search before. AI can test far more hypotheses than humans, but it can’t generate good, novel hypotheses effectively if it doesn’t understand the entire search space and can’t leverage existing knowledge to prune the space for efficiency. The "meat and potatoes engineering" I was referring to is about building specific mechanisms to overcome these limitations: A context graph tells an agent what has already been tried, which evidence is trustworthy, which assumptions are stale, and where adjacent opportunities may exist. A harness keeps the search process coherent over time, so the agent does not lose state, repeat failed work, overtrust its own judgment, or keep pursuing a bad trajectory. These are the hard engineering challenges we need to solve to meaningfully move towards RSI today. The full TechCrunch article by Russell Brandom offers diverse perspectives on the topic from luminaries in the field and a sharp synthesis: https://lnkd.in/gWy6qqas

Why is gas suddenly everywhere in AI infrastructure? It comes down to speed-to-token due to constraints on the electrical grids in regions where gas popularity has grown. It's just the fastest way to power the GPUs generating the tokens customers actually want, and right now, speed wins. But sustainability isn't going anywhere. Every operator I talk to is signed up to ESG goals, SDGs, Scope 3 reporting especially in Europe. That stuff is shaping how people are thinking long-term, even if it's not driving today's decisions. Which is why fuel cells like Bloom Energy are starting to get really interesting. Same gas, but you're running it through an electrochemical process instead of burning it, so you strip out the sulphites and nitrogen oxides. You're still emitting CO2 unless you go hydrogen, but it's a real step forward. There's a quiet hand-off happening. Speed-to-token is calling the shots today, but the cleaner pathways are catching up faster than most people realise. Really enjoyed being part of the conversation at SES Engineering Services roundtable. Check out a clip from the day below.

Concentrate AI is now the only independently owned, at scale, AI Gateway left and the only one where the sole purpose is to be the best gateway for companies https://lnkd.in/gZ9-eH-j