Ben Rigby

Video is a key aspect of an AI-first healthcare ecosystem. There will be a post-AI world soon where the way we interact with technology will fundamentally change. The healthcare ecosystem will evolve to becoming more invisible and assistive, allowing clinicians and patients to interact with each other in a more empathetic way. And technology becomes a true enabler. This will require democratizing access to AI to the healthcare world. And few companies do that as well as Zoom does. We are excited about our strategic partnership with the Zoom team and excited to bring AI features like clinical documentation to the 40% of US healthcare (and global!) that uses Zoom as its Telehealth solution. - Suki Assistant for Epic, Oracle Cerner, Meditech, and Athena systems - Suki Platform for EHRs who want to embed the power of AI inside them - Suki Platform that works with clinical communication companies, medical devices, And now Telehealth Onwards. Let's keep doing this! https://lnkd.in/dWjPwKwJ

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One of the most important concepts we introduced in our recent Healthcare AI roadmap is the notion of “Modality—Business Model—Market fit.” This concept describes how the delivery method and business model of AI in healthcare directly shape its potential to create value. There are many possible combinations of modalities and business models in healthcare. AI can be delivered as software, copilots, agents, services, diagnostics, or therapeutics, supported by business models ranging from usage-based and volumetric pricing to performance-based and shared savings arrangements. Nailing selection of modality and business model is essential for AI companies seeking large market opportunities and profitable operations. As we describe in an example with computer vision-based diabetic retinopathy screening, modality and business model selection can drive a difference in TAM by upwards of 25x, and has serious implications for overall margin structures. Read more here 👉 https://lnkd.in/eK7rjpJz

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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Generally surprised to not see more hardware vendors in healthcare and life sciences partnering with AI-first companies. When the zone of genius of a company is hardware, it's difficult to imagine that same team achieving parity on an AI product – Apple may be the greatest example of success in this realm, and still has secured many AI capabilities via targeted M&A (Siri, Turi, Xnor.ai, Voysis, Inductiv). Given the importance of leveraged distribution models in the ossified industries of healthcare and life sciences, it seems there is untapped opportunity for more hardware manufacturers to partner and co-develop AI products with startups. Control over the way data is captured and processed is critical for building high quality training data sets, which creates an interesting feedback loop between hardware companies and potential AI-first startup partners. Interested to explore this further with folks in healthcare and life sciences hardware!

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Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

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Anthropic introduces Autonomous AI Agents that use a computer mouse and screen to perform complex tasks independently. Currently available for developers only through the Claude 3.5 Sonnet API.

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💥 Finnt is now publicly available! 🌎 Building an AI-native software is a whole new beast - actually that’s why there’s so many great demos but so few amazing experiences in production. The nature of Generative AI makes it difficult to reproduce identical, deterministic outcomes. Where a traditional feature would have two statuses “functional” vs. “bugged”; in the GenAI world, a slightly different prompt or setup can drastically change the output. But this is also what makes AI so powerful and why we are so excited to be building in this space. 🧠 As a reminder, Finnt specializes in extracting crucial data from large, unstructured, and complex documents — including OMs, PPMs, 10-Q/10-K, as well as prospectuses, reports, leases, loans, etc. It then integrates these findings into AI-generated and templated memos, enriching them with real-time market data. 🔑 Key facts: - Ingest any large, complex, and unstructured files - Design custom frameworks tailored towards your specific use cases - Select your preferred LLMs, from closed to open-source options - Customize the goals, structures, and formats of your output reports - Augment your reports with third-party sources and live market data - Ask anything to your files and transform responses into insights - Seamlessly integrate Finnt into your existing workflows 👉 Check the comments to get the link to signup. PS: you might need to get a custom onboarding but we are nice people 😅

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Introducing Ocular AI (YC W24) 2.0 (rebranded and refreshed) and our first-ever Launch Week: https://lnkd.in/gAzPpBJa We stand at the chiasm of a technological renaissance, driven by the symbiotic partnership between humans and machines. This dynamic relationship has ushered in an era where Artificial Intelligence augments human intelligence, creating technologies that are more sophisticated, accurate, and practical—all while preserving human oversight and expertise. AI enhances human capabilities, empowering us to solve complex problems while maintaining agency and control. At the heart of this advancement is real-world data, enriched by human intelligence. Together, they form the foundation for cutting-edge AI that is both impactful and ethical. When Louis Murerwa and I started Ocular AI (YC W24) as an Open-Source Enterprise Search project during Y Combinator, our goal was to make information retrieval more intelligent. Early on, we realized that data quality was the decisive factor in achieving meaningful AI results. Over the past few months, we’ve engaged with hundreds of founders, executives, and engineers from startups, enterprises, research labs, and companies in robotics and self-driving cars. The message is clear: data is the lifeblood of AI. That’s why Ocular AI (YC W24) is building the Data Foundry for the Multi-Modal AI Era. By combining AI scalability with human insight, we’re creating golden datasets that go beyond training models, paving the way for AI breakthroughs. We’re excited to announce Ocular AI (YC W24)’s first-ever Launch Week, from December 2-6! Our team (Vivek Lahole, Louis Murerwa) will unveil two flagship products, Ocular Foundry and Ocular Bolt, which lay the foundation of our vision. Foundry and Bolt will strengthen the AI-human intelligence bond, enabling teams to create, refine, and deploy data with precision, efficiency, and speed. Companies spend huge amounts on training data, but Foundry and Bolt are AI-native tools that will lower costs, reduce manual effort, and accelerate high-quality data collection. We’re replacing outdated, clunky, and expensive data software! 💡 What to Expect: - Daily announcements and product demos - Insights into tackling AI’s biggest challenges - Opportunities to connect with our team 🎯 Be part of the journey: - Explore the Launch Week page: https://lnkd.in/gAzPpBJa If you’re working on AI for: 1. Computer Vision 2. Robotics 3. Self-Driving Cars 4. Foundational Models - Join our Slack community for live updates: https://lnkd.in/gVSeGTC2 - Sign up for our Beta Waitlist: https://lnkd.in/gsgkwsBB We’re already powering data for robotics companies and would love to hear and learn from you!

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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Incredibly excited to share our updated Healthcare AI thesis. This is not a market map – in 4,000 words, we dive deeply into the research, products, business models, and market entry points that are reimagining healthcare with AI. More specifically, we cover: • Translating AI research into real-world products • Mapping modalities (software, copilots, agents, services, diagnostics, therapeutics) to problems in healthcare • Designing AI business models that unlock billion-dollar markets • Closing current gaps in healthcare AI infrastructure • Distinguishing AI platforms from point solutions Let us know what you think ⤵️

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The future of tech is here, and it's wearing glasses 🕶️ Here is an intriguing article about how Snap's CEO, Evan Spiegel, believes his company is leading the charge in AR smart glasses, with Meta and Apple set to follow. As someone who’s been following the AR space closely, I find it exciting to see companies like Snap pushing the boundaries of what smart glasses can do. Spiegel’s vision of using hands and voice to interact with augmented reality is a game-changer. Snap’s Spectacles, now in their 5th generation, are already making waves among developers. The goal is clear: to bring computing back into the real world, away from screens and closer to our daily lives. While Meta’s Ray-Ban Stories and Apple’s Vision Pro are also making significant strides, Snap’s approach to natural interaction and developer-friendly pricing could be the key to mainstream adoption. It’s an exciting time for AR, and I’m eager to see how these innovations shape our future. #AR #SmartGlasses #TechInnovation #FutureOfTech

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(1) Powerful and sophisticated (2) Fast time to value (3) Easy to maintain Generative AI has been a "CHOOSE ONE" world for the past couple of years. With an enterprise generative AI platform like Writer, it's a CHOOSE THREE world. Great example of this from the healthcare space from Sam Julien — a powerful, fast, and easy to scale app built on Palmyra-Med to develop SOAP notes functionality alongside patient / clinician transcripts / live chats.

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Insightful look into bioplatform strategy, and what it takes to raise a Series A from my partner, Omri Amirav-Drory: https://lnkd.in/dFv7GbF6

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The Forbes 2024 Cloud 100 List is live ☁ Congrats to all the honorees – the world's top 100 private cloud companies –including the 3 healthcare and life sciences companies featured this year: • Benchling - biotech R&D platform • Komodo Health - healthcare data analytics • Abridge - AI for clinical documentation For more on how AI is colliding with cloud computing, check out Bessemer Venture Partners's 2024 Cloud 100 report. Personally I'm focused on advancements in multi-modal AI, as applied to biomedical applications (Trend 3!): https://lnkd.in/euzurvQV

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Presenting at the Senate as part of the Coalition for Health AI (CHAI) "Day on the Hill" wasn't on my 2024 bingo card, but I'm feeling energized by the bipartisan discussions on healthcare AI. The numbers tell a compelling story: while nearly 2,000 healthcare AI startups have received funding over the last two decades, only 1 in 20 provider-facing companies reach late-stage maturity. Beyond natural market dynamics, two critical barriers are holding back AI's full potential in healthcare: unsustainable payment models and inadequate evaluation infrastructure – only 5% of research studies on LLMs in medicine utilize real patient care data, and studies advancing our understanding of performance beyond medical licensing exam benchmarks remain anemic at best. At CHAI, we're advocating for essential foundations: • Rigorous technical model performance evaluations • Comprehensive bias assessments across diverse patient populations • Thorough pre-deployment simulations and prospective studies • Robust ongoing local monitoring The path forward demands strong reporting infrastructure, advanced healthcare AI benchmarks, and meaningful public-private partnerships to drive real-world AI deployment in healthcare. Stay tuned for CHAI's upcoming initiatives in 2025 as we expand our engagements with the startup and investment ecosystem. https://lnkd.in/enw-GQcA

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Hot off the pre-print server – an important publication authored by the Atropos Health team exploring the performance of LLMs in answering real-world clinical questions. LLMs excel at summarizing and contextualizing existing literature, but cannot generate de novo medical evidence today. The results of the study highlight performance gains attributable to injecting novel, relevant, and up-to-date medical evidence into LLM-powered clinical decision support systems. Brigham Hyde Nigam Shah Saurabh Gombar Read the study here: https://lnkd.in/ecuU25Pq

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Worth reading: A Set of Benchmarks for Long-Context Code Models It is interesting to see the evolution of long-context code models for repairing build breaks, etc. See the blog post below and the paper

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Movac KiwiNet Deep Tech is difficult because you do not comprehend 95% of the universe using the Standard Model of Particle physics. Either learn that measurement is flawed due to human bias or experience and unbiased state through transcendence experiences corresponding to Christ consciousness. The physics of life is based on LENR and not the Standard Model of Particle physics. Unfortunately, scientific research has been captured by the materialism perspective. This is mutually exclusive to the light observed in consciousness. Physics of life. Deep Tech https://lnkd.in/gKqC3PAR Gateway into the aromatic ring Faraday cage system. Time to see the light 😁.

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It's been 100 years since Hans Berger made the first EEG recording of the brain, but we still don't fully understand what those brainwaves are telling us. That's why I was so excited to talk with Dimitris Fotis Sakellariou and Kris Pahuja, the co-founders of Piramidal (YC W24), a Y Combinator company building an AI model to unlock the secrets of brainwaves. Dimitris, a neuroscientist with a PhD, and Kris, a tech executive, have a unique story. They've combined their expertise to turn their groundbreaking idea into a thriving startup. On this episode of Lobster Talks - Y Combinator Secrets, we talked about Piramidal's journey, including: - The challenges of building a groundbreaking AI model for brainwaves. - The importance of data access and the ethical considerations surrounding this sensitive technology. - How Y Combinator has accelerated their growth. - Their ambitious vision for the future of brainwave AI, including expanding beyond EEG to create personalized AI solutions. Listen to the full episode to learn about Piramidal's mission and their vision for a future where AI can unlock the secrets of the human brain: Listen to the full episode now: https://lnkd.in/eaJuUZ-Q #ycombinator #venturecapital #investing #startup #growth #neuroscience #ai #artificialintelligence #brainwaves #lobstercapital #piramidal

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