Nabil Laoudji

Some key takeways on this podcast from Bob Sutton and Huggy Rao (Stanford) below: Key Takeaway 1: Friction in organizations increases as they grow larger and more complex, making it difficult to get things done efficiently. This is due to added processes, specialization, and hierarchy that are necessary but can hinder productivity if not managed well. Key Takeaway 2: Leaders should strive to be "elephants" who listen and ask helpful questions, rather than "hippopotamuses" who constantly talk and make statements. Encouraging open communication and collaboration while respecting others' time is crucial for reducing friction. Key Takeaway 3: Speed is important for startups, but it must be balanced with the ability to pivot when needed. Temporarily halting hiring or product development can allow for necessary adjustments and prevent friction from building up. >> This is the top reason startups fail, don't pivot/iterate fast enough. Key Takeaway 4: Founders should be mindful when allocating equity and hiring early on. Keeping some equity in reserve and staggering hiring can help maintain flexibility and prevent friction caused by misaligned incentives or onboarding challenges. Key Takeaway 5: Reducing organizational friction requires regularly "mowing the lawn" by removing unnecessary processes, meetings, and bureaucracy. This is an ongoing effort, not a one-time event, and is essential for maintaining efficiency as organizations grow. https://lnkd.in/e5SfQwCU

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For those of us marketing research practitioners in the space, how many of these tools are you confident using in your practice? • The difference-in-differences (DID) method • Synthetic control and related methods • Selection on observables (and consequently, on unobservables) This is a must read as for those of us interested in #CausalInference (who isn't?) and need to use #QuasiExperimentalData (which happens often in practice). Many thanks to the marketing professors for addressing the breadth of the methodological toolkit in an accessible and rigorous way: Kathy Li, Lan Luo, Adithya Pattabhi #research #data #experimentaldata #marketingresearch #causalinference #marketing #experiments #researchmodels

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New insights on AI reveal that Generative Ai hype is prevalent across industries. What may also he worrying is that over inflated expectations, hyped up productivity possibilities and raising future outlook based on perceived adoption by industry can lead to a messy future. Read the new article by Ash Center at Harvard to understand better. https://lnkd.in/gKRUH-fd #harvard #genai #ai #aiproductivity #aitools #aiOutlook #futureai #airegulations

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There’s been a lot of chatter about the “end of LLMs” or “LLMs starting to fail.” Those are only headlines – clickbait, really. If you dig a bit deeper, you’ll read that there are several schools of thought regarding how to efficiently scale the foundational models. If the goal is AGI (artificial general intelligence, a term with no agreed-upon definition), then just adding compute power to pre-training may not be the best path to follow. Instead, researchers are exploring an alternative called “inference scaling” to achieve smarter AI. Inference, the process when an AI model generates outputs and answers, can be optimized by having models “think” through multiple possibilities before settling on a response. This approach enables complex reasoning during real-time use without increasing model size. OpenAI’s recently launched o1 model is a good example. By enhancing inference, o1 can tackle tasks that demand layered decision-making, such as coding or problem-solving, in ways similar to human thought. “Test-time compute” techniques make this possible, allowing models to dedicate more processing to challenging queries as needed. A move toward inference-focused, distributed, cloud-based servers (instead of large, centralized training clusters) might create a more competitive chip landscape. While NVIDIA is the go-to chipmaker for pre-training hardware, there are a bunch of chipmakers (AMD, Intel, etc.) that make hardware suitable for this new method of inference scaling. The key takeaway is simple: LLMs are not failing, they are evolving. Sensationalist headlines aside, this is how product development works. -s

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Many organizations still struggle to move from #AI experimentation to AI operationalization. Pilots fail to scale. Or flat-out don't deliver any meaningful impact. In our work with CognitivePath Research, Inc. clients, we've found that the challenges often stem from an inability to identify, evaluate, and prioritize the best high-potential use cases. So, we’ve worked with our clients to develop an effective AI use case scoring methodology that ensures every AI application is both a fit (it’s strategically aligned and has clear, measurable outcomes) and feasible (the organization can implement it). This week, we’re sharing our framework. 👉 Check it out. Download the visual. If you’re implementing AI for your organization and want to know how this can work for you, let’s talk.

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I refer to Sarah Tavel's "selling the work" thoughts on a regular basis when it comes to what kind of models will be built in our AI driven future. (https://lnkd.in/etBvUeQ9) Here's one snippet I rediscovered talking about the post to Ilya Vakhutinsky from Careswitch--the future of homecare work: "𝙰𝚗 "𝚎𝚊𝚛𝚗𝚎𝚍 𝚜𝚎𝚌𝚛𝚎𝚝" 𝚎𝚟𝚎𝚗 𝚖𝚘𝚛𝚎 𝚒𝚖𝚙𝚘𝚛𝚝𝚊𝚗𝚝 𝚏𝚘𝚛 𝚜𝚎𝚕𝚕𝚒𝚗𝚐 𝚠𝚘𝚛𝚔. 𝙷𝚊𝚛𝚍 𝚝𝚘 𝚜𝚎𝚎 𝚝𝚑𝚎 𝚘𝚙𝚙𝚘𝚛𝚝𝚞𝚗𝚒𝚝𝚢 𝚒𝚏 𝚢𝚘𝚞 𝚊𝚛𝚎𝚗'𝚝 𝚏𝚛𝚘𝚖 𝚝𝚑𝚎 𝚟𝚎𝚛𝚝𝚒𝚌𝚊𝚕. 𝙸'𝚍 𝚐𝚞𝚎𝚜𝚜 𝚝𝚑𝚊𝚝 𝚝𝚑𝚎 𝚏𝚘𝚞𝚗𝚍𝚒𝚗𝚐 𝚝𝚎𝚊𝚖𝚜 𝚐𝚘𝚒𝚗𝚐 𝚊𝚏𝚝𝚎𝚛 "𝚠𝚘𝚛𝚔" 𝚊𝚛𝚎 𝚐𝚘𝚒𝚗𝚐 𝚝𝚘 𝚕𝚘𝚘𝚔 𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝 𝚝𝚑𝚊𝚗 𝚌𝚕𝚒𝚌𝚑𝚎 "𝚌𝚘𝚕𝚕𝚎𝚐𝚎 𝚍𝚛𝚘𝚙-𝚘𝚞𝚝" 𝚙𝚛𝚘𝚏𝚒𝚕𝚎. 𝚃𝚑𝚎𝚢 𝚊𝚛𝚎 𝚐𝚘𝚒𝚗𝚐 𝚝𝚘 𝚋𝚎 𝚝𝚎𝚊𝚖𝚜 𝚝𝚑𝚊𝚝 𝚑𝚊𝚟𝚎 𝚕𝚒𝚟𝚎𝚍 𝚊𝚗𝚍 𝚋𝚛𝚎𝚊𝚝𝚑𝚎𝚍 𝚊 𝚙𝚛𝚘𝚋𝚕𝚎𝚖 𝚒𝚗 𝚊 𝚟𝚎𝚛𝚝𝚒𝚌𝚊𝚕, 𝚊𝚗𝚍 𝚜𝚎𝚎 𝚝𝚑𝚎 𝚠𝚎𝚍𝚐𝚎 𝚒𝚗 𝚝𝚑𝚎 𝚏𝚘𝚛𝚖 𝚘𝚏 𝚝𝚑𝚎 𝚏𝚒𝚛𝚜𝚝 𝚝𝚊𝚜𝚔 𝚝𝚘 𝚐𝚘 𝚊𝚏𝚝𝚎𝚛." Ilya's a perfect example of the kind of founder that is incredibly well positioned to build AI into the work experience of the stakeholders in this industry--caregivers, agencies, and patients alike. He not only experienced the lifestyle of a homecare worker through his mom, but he got certified to become one himself. AI Founders: What's your **getting certified as a home healthcare worker** to understand the work firsthand?

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Hopefully you all know by now that at Forerunner, we obsess over spotting shifts in consumer needs & behavior that when combined with step-change tech advances, provide a rare moment to reshape entire markets and build category-defining businesses. Right now, AI’s rapid ascension paired with consumers' increasing demand for more seamless, affordable assistance throughout all aspect of life marks an unbelievable opportunity for every category, sector, and company to be reimagined. This comes off of over a decade of growth where many thought software alone could drive outsized value creation. But now, with AI automating everything that isn’t subjective, software is increasingly becoming a commodity vs. the star. Instead, cultural resonance and consumer connection is what will differentiate AI/tech-driven experiences. ‍‍ Foresight into human adaptation is now essential for success, as the opportunity is much more about how tech can be manipulated to suit personal needs to enable frictionless movement through life. While technical prowess long reigned supreme in Silicon Valley, human understanding and context is now emerging as the ultimate competitive edge. Today, we’re announcing our new $500m early-stage Fund VII, which is dedicated to this time of re-imagination that will ultimately bridge what humans want with what AI can actually do. Reach out if you’re a passionate founder building a life-changing experience within #ConsumerAI and you're looking for an investment partner who has a track record of understanding and addressing evolving consumer needs. https://lnkd.in/gzuXNDqt

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Kudos to the Massachusetts Institute of Technology for convening one of the largest interdisciplinary gatherings of #AI leaders, entrepreneurs, researchers and academics. Thank you to John Werner, Sandy Pentland and Randall Lane for bringing us together. Together with fellow panelists Jeffrey Saviano, Dr. Sasha Luccioni and Ra'ad Siraj we excavated AI Governance, Sustainability, and Scale, and the imperatives of ethics, corporate values and responsible business with a principled-based approach to #AI. As with driving, like business, brakes let you go faster. It feels counter-intuitive, but without brakes we would drive really slowly. A great moment for advancing #AI, with the MIT Media Lab hosting 2000+ attendees, 180 speakers, 80 AI startups, 67 "lightning talks", 36 panels, and 35 technical talks, spread over 4 tracks. #artificalintelligence #startups #entrepreneurship #mit #tech #esg #sustainability #LLM #generativeAI

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OpenAI is undergoing significant restructuring and internal changes related to status and sparking an exodus of high-profile executives. #datascience #AI #artificialintelligence https://hubs.li/Q02ShsZy0 If you enjoyed this post, follow me for more insights on AI and data science!

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Excellent blog post with great advice, Christian Barnard! I know that at KUNGFU.AI we've definitely seen all these pain points play out unnecessarily. Sean Berg Paco Nathan Andy Palmer Ben Lorica 罗瑞卡, I thought you'd enjoy this new blog post. (Ben and Paco, thanks again for the terrific AI Conference two weeks ago!)

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Everyone loves the 'picks and shovels' thesis—but the real untapped goldmine? Vertical agents and applications. 💡 As the underlying foundation models continue to advance, there won't be much space to build unicorn businesses that sit between the models on one hand and agentic applications on the other. (Don’t be surprised if GPT-5 is natively agentic in its architecture and capabilities.) The real opportunity lies in massive end markets ripe for "agentization": industries centered on structured, repeatable activities. Industries • Data Management • Customer Support • E-commerce Operations • Finance and Accounting • Marketing • Human Resources • Healthcare Administration • Supply Chain and Logistics • IT and Software Support • Legal and Compliance Digital Jobs • Virtual Assistants • Content Moderation • Market Research • Quality Assurance • Online Tutoring • Social Media Management • Transcription Services • Digital Advertising • Graphic Design • Software Development Support At Mucker Capital, we've built over a hundred companies in vertical SaaS. We see vertical agents as it's next generation.

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Most people just pretend to know what AI is and how LLMs work, and no one questions it. Learn from people who are building products with LLMs or training foundational models. Of all the things I thought I understood about LLMs in late 2022 and early 2023, about half of them have been proven wrong. I spent months feeling like an imbecile because I couldn’t get some basic things to work. Then, I spent time with product teams and realized that many early proclamations and promises were hype. Several LLM myths are still going strong, and myth peddlers are as prolific as ever. Follow the most qualified, not the most vocal. #ArtificialIntelligence #LLMs #GenAI

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"For innovators and product builders, "Be Intelligent" isn't just about identifying a pattern in the data; it's about deeply understanding it. This means moving from surface-level definitions, which can be misleading or superficial, to deeper, explanatory insights that transform our understanding. We’ll explore this in the context of generative AI, discussing the difference between nominal and explanatory definitions and why this distinction matters for building truly insightful products." https://lnkd.in/gvkZGhNi

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NEWS - Validation and Evaluation for Trustworthy Artificial Intelligence (VET AI) Act Introduced U.S. Senators John Hickenlooper and Shelley Moore Capito introduced the bipartisan Validation and Evaluation for Trustworthy Artificial Intelligence (VET AI) Act (details below). I'm excited about this bill because it builds on recommendations we--as the IEEE USA AI Policy committee--made in a position statement released last February (https://lnkd.in/e9sETwNM). It was a pleasure to co-lead this effort with Dr. Cari Miller. I'd also like to thank Sen. Hickenlooper's staff for being receptive to feedback that I, and colleagues, provided on this bill. Details: ➡ Directs NIST, in coordination with the Department of Energy and National Science Foundation, to develop voluntary specifications and guidelines for developers and deployers of AI systems to conduct internal assurance and work with third parties on external assurance regarding the verification and red-teaming of AI systems. ➡ Such specifications require considerations for data privacy protections, mitigations against potential harms to individuals from an AI system, dataset quality, and governance and communications processes of a developer or deployer throughout the AI systems’ development lifecycles. ➡ Establish a collaborative Advisory Committee to review and recommend criteria for individuals or organizations seeking to obtain certification of their ability to conduct internal or external assurance for AI systems. ➡ Require NIST to conduct a study examining various aspects of the ecosystem of AI assurance, including the current capabilities and methodologies used, facilities or resources needed, and overall market demand for internal and external AI assurance.

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Everyone's talking about #AI agents. But talk is cheap. My partner at CognitivePath Research, Inc., Geoff Livingston, provides a nice primer -- and a smart POV about the current reality and future potential of agentic AI. The tl;dr is: The potential of agents is real, but you're probably not ready. If the state of your organization's data is holding you back from making the most of traditional or generative AI, you're really gonna struggle with agentic AI. But go ahead and read the whole thing. Heads up that part of the article is gated for subscribers only, but you can sign up for a free trial to see the whole thing.

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A brilliant read for anyone wondering if AI development is plateauing. It’s not. But it might not be something to worry about. This piece helped me understand why. Of course it raises new questions also.. but for now here’s where we are.

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These are subjects that are vital for the future of anyone in the technical content world.

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Why should we view generative AI as a “steam engine of the mind”? 🫶🏽🎧 A very insightful podcast from Lareina Yee (McKinsey & Company) and Reid Hoffman (aka mister LinkedIn). Especially the part 'Manage the inevitable workforce transition with compassion', I really like 👀⤵️ https://lnkd.in/een7dgpa #firstdegree  #founders  #investors #alwaysbelearning  #sharingiscaring #changethewaywework 

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