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Vijay Venkatesh
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I build engines that lead markets and the tech stack beneath them<br> <br>As CTO at Bluesight, I've led a 6× ARR growth journey at a PE-backed healthtech company, executing three M&A integrations, a full cloud migration in 8 months, and a FinOps program that raised enterprise value by $50M. My teams ship AI infrastructure powering the modern hospital pharmacy, faster, leaner, and with a fraction of the turnover. We recently shipped a joint publication with Amazon Web Services on agentic AI in regulated healthcare environments<br> <br>My philosophy: clear vision, clear execution. I thrive in change; scaling a core product, expanding into adjacent markets, integrating acquisitions, and turning technical debt into competitive advantage. I've done it across healthtech, edtech, e-commerce, and logistics including from 4 markets to 40, from 10% conversion to 35%, from 30% attrition to under 5%.<br> <br>What I deliver: M&A technical diligence & integration · Cloud-native architecture · Agentic AI in Production · FinOps & COGS optimization · Full Security program and HIPAA/SOC2 compliance · PE & board-level reporting · High-performing engineering cultures · Machine Learning product delivery<br> <br>"2025 was highly productive, not just from effort, but from sheer results. Vijay has built a prolific engineering organization and a fierce engineering leadership."
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Neil Zeghidour
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I spoke with Amplify Partners about Kyutai and Gradium, and more broadly about why voice is the modality where small, disciplined teams routinely outcompete larger organizations despite having far less compute and staff. The post goes into how we approached audio end-to-end, why we insisted early on full-duplex, real-time interaction, and how much of that came from tight iteration loops rather than scale. It also reflects on our culture, competition as a forcing function, and why having fewer people can actually make technical progress faster, not harder. No grand vision statements. Just how things were built, why certain bets were made early, and what it feels like to work at the edge of audio and interaction. If you’re interested in audio, voice, or small teams doing real systems work, this captures it well: https://lnkd.in/e8X_zmiP
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Matthew Chew (FRSA)🔸
Argent Blue Partners • 4K followers
My takeaway inspired by Ksenia 's Turing Post analysis of this conversation between Sutton and Patel on Dwarkesh’s podcast. Sutton wants AI that learns like a baby animal: - No massive pretraining on human text - No supervised learning (no human telling it "this is right, that's wrong") - Just pure exploration and trial-and-error in the real world - Learning driven by curiosity, fun, and making better predictions Think of a baby zebra that can run within minutes of birth - it doesn't need to read "How to Run 101" on the internet first! Here's where it gets tricky. Sutton points to two examples proving his approach could work: - AlphaZero learned Go from scratch → But Go is basically fancy tic-tac-toe, not the messy real world - Animals learn without supervision → But wait... Karpathy’s reflection and this is the coolest part. Karpathy says current LLMs are creating something entirely new: and that Pretraining is our crappy evolution (“Animals aren't blank slates! because evolution already "pretrained" our brain over millions of years. Its DNA encodes a sophisticated initialisation - it's not learning from zero.”). We can't wait for evolution, so we use the internet as a shortcut to give our AI a head start. It's not "pure" in Sutton's sense, but it works. Why This Matters This debate is about two fundamental questions: (1) Should AI learn everything from scratch (pure but slow) or build on human knowledge (practical but biased)? (2) Are we trying to recreate natural intelligence or create something entirely new? Karpathy suggests we might be creating a new form of intelligence - these "ghosts" that are neither human nor animal, but something unique. Like planes that don't flap wings yet still fly, they could still be transformative. These 2 fundamental questions hint at something even bigger: What if intelligence isn't a single thing? Maybe there's: 🦮 Animal intelligence (embodied, experiential) 🙋♂️ Human intelligence (animal + language/culture) 👻 Ghost intelligence (disembodied, textual) And potentially other forms we haven't imagined 🤯 Each might have unique strengths. Ghosts might be better at synthesising human knowledge. Animals might be better at robust real-world interaction. The future might not be about choosing one, but orchestrating all of them. The crossroads is not a problem to solve but a recognition to embrace: We're not failed animal-builders or imperfect brain-replicators. We're successful ghost-summoners, creating something unprecedented. And that's not just okay - it might be exactly what we need. "P.S. - If these questions fascinate you, there's a certain manga from 1989 that explored what happens when 'ghosts' emerge from pure information... 🤖" https://lnkd.in/g8WMK6u6 https://lnkd.in/gtEyuXh8 https://lnkd.in/gfc3NQTW #ArtificialIntelligence #MachineLearning #AIethics #FutureOfAI #AGI
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Alberto Surina
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Founders, stop using AI to generate more. Use AI to consume less. The marginal cost of creation in March 2026 is mathematically zero. Every founder I know is exploiting that. We are drowning in feature bloat, hyper-targeted marketing exhaust, and automated code review paralysis. Altman's $110B Intelligence Utility utility ensures you can generate 10x faster. That's a commodity. The strategic failure of this quarter is confusing creation speed with decision velocity. If your Level 3 Sovereign Agents are building faster than your executive loop can forensically audit the risk, you aren't optimizing productivity. You are increasing your Accountability Latency and overwhelming your own cognitive bandwidth. In 2026, extreme value isn’t derived from adding more intelligence to the stack. Extreme value is derived from enforcing Operational Silence. Stop optimizing the workflow for generation. Optimize the stack for consumption. The mistake you are making today is treating AI as another team of junior analysts (Level 1 Copilots) who just need smarter prompts. You must architect your stack so your Level 3 Agents consume the context so you don’t have to. Stop audits of creation. Start constraint enforcement Stop reading summaries. Start tracing the automated decision tree. Stop managing engaged metrics. Start managing automated performance velocity. Extreme value is the absence of complexity. Your new strategic advantage is the ability to maintain cognitive clarity while your organization operates at infinite transactional speed. Are you building an organization designed for generation, or are you architecting your decision engine for silence? #Startups #Founders #AIStrategy
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Markus Goldstein
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New blog out summarizing the London Working Group on how to evaluate AI for development. Lots of top level lessons not least of which is evaluating AI enabled interventions is different, and requires much more cross-disciplinary collaboration and communication - e.g. a shared theory of change is even more important so everyone understand what is supposed to lead to what. Read more: https://lnkd.in/eVF2keNQ
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Rhonda Coleman Albazie
PRIVILEGE HEALTH ™️ -… • 485 followers
Important nuance (people get this wrong) California does NOT block: • QSBS eligibility • C-corps • Equity issuance But it dramatically increases friction: • Higher state tax drag • Higher audit risk • Franchise tax even on dormant entities
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Will Robinson
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For most people, money is still harder than it should be. If AI is going to matter in finance, it has to make financial products genuinely more helpful, secure, and responsive to real life. I’ve spent the last decade building large-scale data networks, and I’m convinced the next chapter of financial services won’t just be about access to data, it will be about understanding it. Open finance unlocked secure, permissioned connectivity. That foundation enabled a generation of innovation. Now AI is ushering in the next wave of innovation. Intelligent finance means systems that understand context, adapt in real time, and continuously improve. It means stopping fraud without adding unnecessary friction. It means making better decisions across payments, credit, and identity, with the accuracy and trust this industry demands. It also requires infrastructure that is purpose-built and understands how money moves. At Plaid, we’re building intelligence models that learn from patterns across our network and compounds with scale. As part of that work, we’ve developed our first transaction foundation model for finance to deepen how financial activity is understood across products. I believe this shift from open finance to intelligent finance will define the next decade. I wrote more about how we’re approaching it here: https://lnkd.in/gV35NPby
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