Fang Cheng

The next frontier of AI isn't just about choosing the right model. It's about building moats through implementation. Across the Emergence Capital portfolio, companies like Bland and Federato are scaling faster by embedding Forward-Deployed Engineers. These engineers bridge the gap between AI and reality, ensuring real results in production. Read more from my colleagues Gordon and Kyle on why FDEs are the key to unlocking AI's full value. https://lnkd.in/gcsRRGX2

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Arintra's CTO Preeti Bhargava, PhD on stage at #ViVEvent discussing the future of AI in healthcare. Autonomous medical coding is a massive opportunity — proud to back this team as they automate and accelerate growth for health systems. #HealthTech #AIinHealthcare #FoundersX #MedicalCoding #DigitalHealth #DeepTech Nitesh Shroff Helen H. Liang, PhD Tom Kosnik Sizi Chen Tristan Staschik Tor Parawell Yicong Li Irene Tsen

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mpathic is becoming THE partner to the AI industry as they work to safeguard users from adverse mental health and safety outcomes As millions of users — including large numbers of young people — increasingly turn to AI chatbots as their first-line “counselors” and confidants, Seattle-based startup mpathic is stepping in to ensure those digital agents don’t provide dangerous advice when it matters most.

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100 founders, builders, researchers, investors and thinkers. One big question: how to solve some of the largest challenges building at the frontiers of AI. From physical compute scaling constraints to applying AI to unlock civilizational progress from material science to drug discovery. Camps, hikes and heated debates on human x AI co-existence: this was Climbing Hard AI Peaks. Thanks to everyone for coming out and making it so special. We learned a lot from all of you. Big shout-out to our founders working on some of the biggest AI unlocks, the many researchers and builders from Center for Digital Technology and Management (CDTM), ETH Zürich, Stanford University and special guests from Anthropic, OpenAI, Google DeepMind and more. David Byrd Michael Wax Jason D. Whitmire Chad Fowler Jonah Anders Kaplan Christine Fuchs Sam Harrison Mehdi Ghissassi Antoine Moyroud Wulfie Bain Eva Spannagl Nathan Gruber Judith Dada Juan Benet Zoe Weinberg Haya Hanna Sophia Kalanovska, PhD Clayton Mellina Ricardo Sequerra Amram Evan Phoenix Hendrik Dietz Katie Hewitt Molly Mackinlay Omer Shlomovits Zied Bahrouni

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Doximity just released a free AI scribe for US clinicians, accelerating commoditization/the race to the bottom. Their product won’t integrate into the EHR like Abridge, but it’ll be interesting to see how much it impacts their ability to acquire customers, drive usage and maintain retention. This is the start of a trend that will continue across every vertical market. Counterpositioning with free products to undercut upstarts and incumbents alike. Buckle up. Your margin is someone else’s opportunity.

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In the latest episode of the OpenAI podcast I got talk to OpenAI CFO Sarah Friar and legend himself Vinod Khosla about the AI industry. Sarah explained the relation between compute and revenue (OpenAI's revenue keeps 3x'ing each year) and Vinod provided an insightful way to tell if we're in a bubble: API calls. Are they going up or down? (Spoiler: Up, up, up!) https://lnkd.in/g2tTQBdp

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For all founders, especially tech founders, here are a few key insights on how to elevate your pitch. Definitely worth a read and some thought... Treeo VC https://lnkd.in/emM8RK_4

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Our latest investment from Blitzscaling Ventures: ManasAI led by Reid Hoffman and Dr Siddhartha Mukherjee: Manas AI seeks to accelerate the development of treatments for aggressive cancers, such as triple-negative breast cancer, prostate cancer, and lymphoma. By combining AI-driven molecular design with laboratory validation, the company aims to reduce the time and cost associated with traditional drug discovery. Read more here: https://lnkd.in/gFGrvDS7

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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Clinicians want to use AI but don’t have the bandwidth to evaluate all the different models. Meet Health Olymp, an AI tool purpose-built for clinicians. Here’s why this matters 👇 1. Health and life science experts gain instant access to frontier models for free. No procurement hoops or IT bottlenecks. Every clinician gets their own sandbox to explore. 2. Clinicians can compare models side by side and pick the one that works best for their use case. That transparency builds trust and sparks ideas for novel AI applications in care delivery, diagnostics, and research. 3. Most AI benchmarks are too generic or disconnected from clinical reality. Health Olymp brings frontline feedback into the loop, helping the industry identify models that perform reliably in real world clinical environments. Congrats to Ruslan Nazarenko and the Lumos team as they put AI model benchmarking directly into the hands of healthcare professionals. ⬇️ (link in comments)

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Jazzed for this keynote by Jeff Erhardt at our 12th annual LDV Capital Vision Summit: The Materials Innovation Gap: Why AI Predictions Aren't Enough — And What It Will Take to Transform a $6 Trillion Industry RSVP: https://lnkd.in/eeUHN_m AI is transforming industry after industry, and generating enormous excitement in the world of materials science. Machine learning models can now predict millions of new material candidates with unprecedented speed and accuracy. At the same time, programmable material classes, including those recognized by the 2025 The Nobel Prize in Chemistry, could unlock breakthroughs in some of the most pressing challenges facing civilization, from environmental remediation to the energy transition. But their impact is constrained by something prediction alone cannot resolve: new materials must be physically produced, scaled, integrated, and qualified within the larger systems into which they are deployed, a journey that remains extraordinarily long, expensive, and unpredictable. Jeff will argue that closing this materials innovation gap requires treating AI and physical experimentation as equal partners, and building a new model of collaborative development that connects materials innovators and industrial partners far earlier in the discovery process.

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HIMSS is almost here, and I’m looking forward to catching up with so many of you. I'm especially looking forward to our session with UC San Diego Health. Karandeep Singh and Jeffrey Pan will showcase how they're using Notable's Platform and AI Agents to act as a critical workforce multiplier, transforming patient access, care operations, and RCM across the enterprise. Learn more about everything we have going on at HIMSS here: https://lnkd.in/gQifSd2A. And definitely come say hello if you're attending!

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🗓️This Week at Anthropic 1️⃣ Releases Claude Opus 4.6 with 1M-token context and stronger agentic enterprise capability 2️⃣ Expands Claude Cowork plug-ins, accelerating AI-driven workflow automation across knowledge work 3️⃣ Nears $20B+ funding round at ~$350B valuation, extending compute and talent runway 🚀🚀🚀

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Scribes, AI or otherwise, are completely useless in healthcare. You CANNOT document patient encounters with "notes" in language. All you've accomplished is putting paper records behind the glass of the monitor. That's stupid and you can tell that bizdiot Judy Faulkner I said so. You need a data driven EMR that is searchable and relatable. Before you say "mine is" or "AI," no it's not. Sentia has provided a data driven EMR that relates symptoms to observations to clinical measurements to lab results to diagnoses to treatments to outcomes. You can literally pick a list of symptoms and get her back a collection of cases with positive outcomes. You can't do that with language. It's also faster, easier and cheaper than typing "notes." Stop. You are doing it wrong. Start over with a real EMR that is built the way it should have been in the 70s if Judy had a brain in her head. Read this: https://lnkd.in/gR3ArMD9

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Wow! Fascinating article Andrew Antos & Nischal Nadhamuni about how the Klarity platform does knowledge capture! A total disruptor indeed for how consultants traditionally did this work! Capturing not just what happens but why it happens has always been the missing link in enterprise AI, and Klarity ’s approach to surfacing human reasoning through its intelligent platform is a real step forward. The #AI Advisor’s ability to traverse vast context graphs, uncover cross-team patterns, and turn knowledge into actionable insights shows how innovation in knowledge capture can truly transform enterprise decision-making.

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Fundamental opportunities worth solving for in AI right now: Predictability of reasoning, planning, execution of AI. Efficiency and ease of use of running AI workloads. Multiple system integrations - from DBs and APIs to infrastructure and biz systems. Trust, security, guardrails, authorization and auth. Business/science domain specific data, workflows, and optimization. AI cost understanding, control, and reduction.

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TheFounderVC has a truly compelling team - entrepreneurs and operators that have scaled from 0-1 and to exits across a variety of markets - some of whom are fellow classmates. They have built strong founder and VC networks over the last 25 years in tech and are now leveraging that access and experience on behalf of their portfolio. It clearly resonates with founders, as their early portfolio is truly compelling.

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What drew us to Arya Health wasn’t just the technology, but the clarity of purpose behind it. Kunal and Arunram are solving one of the hardest and least glamorous problems in healthcare: the broken labor operations that keep post-acute care running. This is a $500B+ market, nearly 10% of U.S. healthcare spend, yet most of it still depends on manual coordination. Every day, agencies are juggling thousands of nurses, last-minute shift cancellations, credential expirations, and payroll calculations across dozens of systems. The administrative burden is crushing, and it’s only getting worse as the population ages and demand for home-based care accelerates. Arya has built AI agents that act as a true digital workforce for healthcare operations - autonomously handling scheduling, onboarding, compliance tracking, and payroll. Their scheduling agent alone manages complex variables like patient needs, clinical fit, distance, and bonus pay, continuously rebalancing assignments in real time. The early traction has been remarkable. Customers whom we talked to consistently describe Arya as transformational - “what they’ve always dreamed of” and a product that will “forever change the industry.” Agencies are already seeing utilization rise from roughly 60% to over 80%, driving significant improvements in coverage, patient outcomes, and operating margins. What stands out most about Kunal and Arun is how deeply they care about the people behind these operations. They spend time in the field with schedulers, caregivers, and agency leaders - the unsung heroes who hold the healthcare system together - and they build with empathy and urgency. That care shows up in the product and in every customer relationship. At ACME Capital, we believe the most impactful AI companies will rebuild critical industries from the inside out. Nurses, home health aides, and post-acute caregivers are the backbone of our healthcare system, and often the backbone of their communities. Arya Health is giving them the modern infrastructure they deserve. Congratulations to Kunal, Arun, and the entire Arya team on your oversubscribed $18.2M Series A. We’re proud to lead this round and partner with you as you reimagine how care gets delivered across homes and communities.

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