Guy Levy-Yurista

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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The (Healthcare) Ground Beneath Our Feet… This year it is likely that over $10 billion will be invested in the digital health sector – a robust amount, no doubt, but inconsequential when compared to the amount of capital tied up in healthcare real estate assets, which is estimated to be $1.2 trillion by Jones Lang Lasalle. Of that amount, approximately $790 billion is in the hospital sector while the remaining $490 billion is in medical office assets. It is estimated that there are 48.6k facilities with 3.4 billion square feet of healthcare real estate, 42% of which are owned by REITs and another 33% held by other private investment vehicles. Much of the digital health investment is meant to make the physical healthcare infrastructure more productive: greater operating efficiencies, greater patient throughput, more relevant personalized care models with better outcomes. As the healthcare system is being pushed to be more distributed to lower cost sites of care, accelerated now by the rapid proliferation of automation and AI capabilities, the potential impact on healthcare infrastructure assets will be profound. Thoughts on what that might look like... https://lnkd.in/eTfAdUbS #digitalhealth Flare Capital Partners

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This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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Excited for DCVC portfolio co Alta Resource Technologies to emerge from stealth! The design space of biology is many orders of magnitude larger than inorganic chemistry, and Alta uses it to massively reduce the cost of extracting rare earth metals. https://lnkd.in/g_q5UwRF

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We see this very often in deals, the request of a staple, but I personally think there are ways to navigate this, with everything being above board , both for the selling LP, buyer and the GP. We have had situations where the GP preferred buyer who could also do a staple, actually price the fund better... because they want to strengthen that relationship, and ultimately of course have had situations where the GP preferred buyer is the bad actor in the equation, leveraging their preferred position to down play price for the seller... it's all part of the negotiation process. #secondaries #setterstrong

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Excited to announce Frist Cressey Ventures' investment in Qualified Health and privileged to partner w/ SignalFire, Healthier Capital, Town Hall Ventures and others!   Healthcare costs are increasing at a faster rate than ever before, with the US spending nearly twice as other wealthy countries and still ranking bottom in outcomes. It's a story you've all heard - - US healthcare is known for astronomical inefficiencies, waste, workforce supply constraints, fragmented access, appalling patient experiences, and driving patients and businesses into debt.   Yes these problems present opportunities. But opportunities are limited to the system's appetite for change, desire and incentive to try something new and the innovation available. At FCV we believe we've hit the trifecta with Generative AI. Generative AI has given us truly transformational tools in the toolkit and the healthcare ecosystem is demanding to absorb its benefits. This perfect collision of supply and demand means healthcare is poised for big change.   Generative AI is everywhere, it's buzzy, it dangles hope and opportunity. But many things need to be true for the Gen AI transformation in healthcare to take hold, such as but not limited to:   💡 Patient lives and data need to remain safe: We believe in a highly regulated and human life-touching sector such as healthcare we must ensure Gen AI can drive to unrivalled savings and improvements in the quality of care WHILE not putting patient lives or data at risk.   💡 Systems need to own their utilization of Gen AI: We believe systems will use some hybrid of external partners and homegrown solutions but importantly will want full control of data provisioning and utilization as well as the ability to solve an unlimited number of nuanced issues and not cookie cutter algorithms.   💡 Costs need to be looked at on an enterprise level: We believe the speed of innovation in Gen AI means costs will continue decreasing dramatically but at an enterprise level this will still not be a small detail. Systems will want the ability to understand costs and delegate as needed for the outcomes desired at the organizational level.   Enter Qualified.   We knew when we met Justin Norden, MD, MBA, MPhil and team we were standing in front of changemakers. These individuals have lived and breathed the true application and implementation of Generative AI far before we were asking ChatGPT to write our emails. In fact, if you’ve ever driven in a Waymo you’ve benefited from past products this team has built.   Qualified Health is on a mission to enforce governance in Gen AI, allow systems to rapidly build for their needs, monitor and make decisions for all GenAI (homegrown and external) and accelerate the value the sector can glean from these new technologies.   Huge shout out to William T., Tommaso Auerbach and Jamie Kuntz for their hard work!

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Anne Wojcicki, CEO of 23andMe, has announced plans to take the genetic testing company private. This strategic move aims to provide more flexibility in navigating regulatory challenges and to accelerate growth opportunities. The decision follows a trend of health tech companies seeking private ownership to focus on long-term objectives. Wojcicki highlighted the importance of this shift to maintain the company's commitment to innovation and customer-centric services. Taking 23andMe private reflects a bold step towards enhancing its position in the rapidly evolving genomics industry. Read more about this exciting development here: https://lnkd.in/gdrC3a64 #genomics #IPO #privatisation #23andMe

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If you are in the VC space, you should know one of the early drivers of "Moneyball for VC". That's Gopinath Sundaramurthy, Ph.D Gopi partnered with the Kauffman Fellows Fund on implementing these concepts in their investment process. The results were staggering. 50% of the investments the fund made became unicorns. In Gopi's words 👇🏻 "We knew something was working, so we thought: Let's build this a little bigger." What did that look like in practice? 1️⃣ Gopi's firm uses a single ‘source of truth’ platform to centralize information, improving decision-making and consistency. 2️⃣ They maintain an even 50/50 split between data engineers and investors to ensure they leverage data to gain insights into teams, markets, and products. Want to learn more about what Gopi and the team at Ensemble VC has implemented at their firm? Check out the case study that our sponsor Harmonic published about the firm -->

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I’m optimistic about the potential of generative AI to reduce provider burnout and improve provider-patient interactions. However, that optimism is tempered by the data that indicates the technology is not quite ready yet. Bright Ventures is interested in digital health founders who understand the realities of provider-patient interactions, and the role technology can play in strengthening clinical outcomes. Which founders and teams are getting this right? Tag them in the comments below.

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ex Google CEO Eric Schmidt spoke at Stanford this week. Lots of nuggets here, but most interesting for the near term, Schmidt believes in the next year AI is set to make a big leap forward. He predicts AI will combine three important features: 1. Vast knowledge retention (1 million token models) 2. Text-to-action capability 3. Personal AI agent fleets While the full impact is unknown, everyone may soon command their own AI teams. Here's an example: Imagine you're developing new quantum computing algorithms. Your AI assistants could: - Continuously scan and synthesize global quantum research papers - Generate novel algorithm ideas based on this knowledge - Simulate and test these algorithms across various quantum architectures - Automatically document findings and prepare draft papers - Identify potential collaborators and draft outreach emails All initiated by a command like "Advance our quantum algorithm research." This could dramatically accelerate R&D cycles and scientific breakthroughs.. https://lnkd.in/eJbg6cnD #frontiertech #AI #futurefrontier

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Key takeaways from VC Goat - Vinod Khosla podcast. - The firm cares more about working on interesting problems with large technical solutions rather than just maximizing IRR. The team is there because they believe in the mission. - Khosla assumes they've lost the money the day they invest, and then maximizes for the upside opportunity. He calls it "option value investing" rather than IRR investing. - Khosla has contrarian bets in AI (neuro-symbolic computing, probabilistic approaches), biotech, robotics, crypto, and more. - He believes aviation fuels, fusion energy, new transit systems, and other contrarian areas ignored by most investors will be huge opportunities. - Most large innovations come from outsiders, not industry insiders. Khosla looks for founders who can learn a business rapidly rather than have deep domain expertise. The Future Impact of AI - AI will be deflationary and increase productivity growth to 4% annually vs the typical 2% forecast. This will cause great abundance but also increasing income inequality. - Bipedal robots will take over most manufacturing and manual labor jobs within 20-25 years. This will free humans to be more creative and pursue their passions. - Education will shift from job training to creativity. Uniquely human elements like taste and curation will be most valued in an AI-enabled world. https://lnkd.in/gqBsmbks

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The Cynicism of It All: The Domino Effect of Impunity If such behavior is allowed to persist unchecked, what message does it send to other aspiring corporate puppeteers? Will Singapore become a haven for those seeking to exploit loopholes and manipulate the system for their own gain? Will the pursuit of justice become a Sisyphean task, reserved only for those with the deepest pockets and the strongest resolve? Let's not forget the grand HC spectacle, a legal drama worthy of Shakespeare himself. It's a tale of convoluted claims and counterclaims, a labyrinth of procedural maneuvers designed to confuse and confound.  The irony is palpable. The very individuals who claim to be champions of corporate governance and minority shareholder rights are the ones actively undermining those principles. They're playing a game of legal chess, where the pieces are not pawns and knights, but the livelihoods and reputations of those who dare to question their authority. https://lnkd.in/eJ9hGQmw

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Interesting piece from Marc Andreessen on how startups are not getting the regulatory support they need to sustain America's technological leadership. "Regulatory agencies have been green lit to use brute force investigations, prosecutions, intimidation, and threats to hobble new industries, such as Blockchain. Regulatory agencies are being green lit in real time to do the same to Artificial Intelligence." While many investors themselves (both in the US and Europe) are worried how AI will change our lives, our jobs and social interactions, the top VC opinion leader thinks regulation is already too restrictive. #techstartups #techleadership #strategicgrowth

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Lexington Books managed to release my book Disagreeing despite the Data before the election. It argues the belief segregation underway in the US may be irreversible. Today's battles over climate change, disease mitigation, and election results run deep -- they're cognitive, not just strategic. Here's the gist of it. 20th-century revolutions in the philosophy of mind, meaning, and matter uncover three surprising requirements for widespread factual agreement. Critical thinking and community building have the potential to reverse two of them -- the growing refusal to test assumptions and individual isolation. Factual agreement between groups is impossible without the third one, though -- shared projects or other meaningful interaction. And a large part of American society has insulated itself from the rest. Without shared projects, communities gradually lose the ability to tell whether they agree or not regardless of the words they use. Belief segregation with similar roots has led several developing countries to the brink of failure. We'd better watch out. The attached flyer gives a discount code that makes the book -- priced in this initial run for academic libraries -- a little more affordable. Please feel free to forward it and, if you're connected to any libraries or book stores, pass it on!

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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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ISP Strategic Leadership, Engineering, and Operations As an organization scales, there is often a need to separate strategic technology leadership under the CTO from internal execution under engineering and operations management. This allows the CTO to focus on outward-facing duties while enabling streamlined operational efficiency. However, it's crucial to recognize the differing motivations and needs of these distinct technical roles. On one hand, engineers thrive on tackling interesting, challenging projects that push their abilities and knowledge. They value working alongside capable peers, genuine recognition of technical accomplishments, and contributing to products/services that reach the market. In contrast, operations teams should strive for repeatable processes, continual improvement, and driving efficiency at scale. Their priorities revolve around standardizing workflows, minimizing disruptions, and incrementally optimizing existing systems and procedures to maintain consistent service delivery as the company grows. An effective organizational structure acknowledges this dichotomy, fostering an environment that fuels engineering innovation while providing operations with the stability and process-orientation they require. Striking this balance enables the company to stay agile and innovative through its engineering efforts, while simultaneously scaling operational execution in a reliable and repeatable manner. Competitive compensation remains an important motivator across both domains. By properly aligning structural elements, motivational factors, and leadership priorities, a rapidly growing ISP can position itself to attract and retain top talent across engineering and operations, driving sustainable growth and success in the dynamic technology landscape.

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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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One conversation we should be having more, especially in healthcare: is this an AI problem or a policy problem? A big challenge in American healthcare stems from the reality that our healthcare strategy changes with every administration. Throwing AI at problems that are well-addressed by policy innovation (e.g., AI for payer-provider RCM war) feels like a gross misallocation of resources. Solution sets are rarely zero sum – in truth, most problems will require policy, tech, and human interventions, but it's worth asking ourselves whether AI is the best solve when we have a multitude of options.

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In our latest publication in Health, Management, Policy, and Innovation – "Why AI Is Good for Our Health but May Hurt Our Wallets," we examine how current regulatory frameworks, reimbursement structures, and business models for AI in healthcare are decoupled, creating an environment in which AI may significantly increase costs without necessarily improving outcomes. We recommend three interconnected reforms to unlock the full potential of healthcare AI: (1) Rigorous pre- and post-deployment evaluation to verify proposed clinical value, (2) Development of assessment standards through shared guidelines, and (3) Strategic alignment of AI deployment modalities with sustainable business models – "modality-business model-market" fit 👉 https://lnkd.in/d8NiczyM Sneha Shah Jain, MD, MBA, Clinical Professor and Kevin Schulman Professor of Medicine at Stanford University, Nigam Shah, Chief Data Scientist, Michael Pfeffer, Chief Information and Digital Officer, and Linda Hoff Chief Financial Officer at Stanford Health Care

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