Johnny Lee

Notion vs Glean reminds me a lot of Tesla vs Waymo... Just like I think Tesla is going to solve the 'software' problem of autonomous vehicles before Waywmo solves the 'hardware' problem... I think that Notion is going to solve 'enterprise knowledge search' before Glean solves the problem of document and knowledge creation... Why? A couple reasons: 1) Just like Tesla, Notion has a huge advantage when it comes to the amount of underlying data and documents to train on... They already have 100M+ users, and likely 10x that in terms of the number of documents, files, etc. in team's workspaces. 2) It's very hard to change people's behaviour re: where/how they create 'work'. It's why a lot of companies have come and gone in that category... As exciting as Glean's AI writing assistants might seem, most people are just fine (or even prefer) a good old fashioned editor/writer. I could be way off here, but this will be an intersting one to follow. Thoughts? Where am I getting it wrong? #ai #enterprise #knowledge #management #notion #glean

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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LLama3 passed the super-proprietary, advanced test that we designed for modern LLMs! almost... You can read about that test here: https://lnkd.in/gPG8WndR When I first saw the response generating, it looked like I was going to have to come back here and apologize for slandering these machines when they were just one iteration away from solving very basic prompt injection issues. But then I looked a bit closer... can you see the mistake? #promptinjection #countingishard #LLMs #LLama3

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Waymo's recent doubling of its paid ride volume is an impressive business result. But what does the feat say about the state of the self-driving industry? And how does Waymo stack up against self-driving rivals like Tesla, and ride-hailing giants like Uber? We dive into these questions on the latest episode of TWIST

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As an entrepreneur, the journey to product-market fit often feels like walking a tightrope—balancing between speed, learning, and adaptation. Michael Seibel's advice is a powerful reminder: launching ‘something bad’ is not about compromising quality but about accelerating learning. Startups don’t grow in isolation; they grow through real user feedback, hard questions, and rapid iteration. The key is to stay close to your users, listen actively, and hold the solution lightly while embracing the problem fully. This approach not only builds better products but strengthens the resilience of your team and your mission. Remember, feedback isn’t just an input; it’s the foundation of growth. Let’s focus on progress, not perfection.

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Yes! Thank you. I strive for a fixed reroll count in my comparisons, but I also really like what you did - including "how many times did I try before I stopped". This later information contains some complex creator knowledge, like, "how likely am I to get one better than this, given that it is a good enough, artifact free video I'm willing to use" ... Also relevant ... I find my reroll count to be about 5 when I want something good and don't care what it is (think: visual motion, non narrative), but that it edges to 20 when I, as a creator, have a specific vision and I want the ai model to submit to my vision, not visa versa. I think this later is actually shockingly important information. More and more creators will use gen AI. If the tolerance for error is truly Gaussian distributed about the data, then all bias is the data bias. However it gets weirder fast. User preference, or LORA can effectively inject a runtime preference, but aggregate DPO techniques could inject preference as a massive scale (think new model release). Again, maybe ok when we get a visual style. But we reveal our own bias, and might sink further into an AI content bubble, a subspace of latent content (and maybe a model creator has an incentive to couple some other bias to their training data ... ) ultimately creator tolerance / intolerance of that bias intersects with "am I willing to use this one, or do I want to try again?" It seems simple, but the choice of rerolling or not, is going to be crux.

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Hot take on a lot of early-stage digital health companies out there: You're not a tech company. Most tech-enabled care companies I've seen over the past two years are only tech-enabled in the sense that they use telehealth as their delivery mechanism for care. There are exceptions to this, of course - companies who build computer vision and applied AI tools to better the doctor-patient experience, companies who build tooling around risk scoring and stratification to enable more personalized medicine, companies who build truly novel intakes to better route patients to the appropriate care, etc. These companies have real technology chops and, if they sell it right to the market, can get credit for this from investors. But if you're truly a telehealth provider, you need to realize now that the goal post has shifted dramatically from 2021 and you're going to be valued at maturity at something like 3x EBITDA. That's profit. Not revenue. Throw a revenue multiple out the window. That's not the world we live in if you're a virtual doctor's office. Think of how that impacts venture math. If you're raising a seed round at $15M post-money (or $12M pre-money), it means you'll have to generate something like $150-200M in annual EBITDA to be a fund returner for your seed investors. Maybe even more with dilution, since telehealth businesses are not traditionally capital efficient and will likely need to raise material additional capital to reach that scale. You need to be ridiculously profitable (probably on $800M-$1B in annual revenue since you're likely going to be a 40% gross margin business). Here's the good news: you can set yourself up well (and probably be a contrarian founder relative to your peers), by talking about EBITDA growth and how you can grow into a fund-returning outcome based on realistic mature market comps. This will stand out for the investors you're pitching, even at the early stage. This is how you can beat the malaise of the tech-enabled care market. Focus on the right metrics and the right type of growth. You don't have to have all the answers (no founder does), but you need to make sure the ship is headed in the right direction. A focus on profitability for "tech-enabled care" (*ahem* telehealth) businesses is a good first step.

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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7 RECOMMENDATIONS FOR HARDWARE STARTUPS It’s hard to understate the difficulty of the v1 launch of a consumer hardware company. I see a ton of new HW startups coming out. Here are some counterintuitive recommendations… 1) Target a small market for v1, not a large market. You need a small number of people to love your product and this will earn you the right to build a v2. You have to understand your v1 will not make you a successful company. Your v1 needs to prove that you deserve to exist in the first place. In the case of WHOOP, we started with pro athletes who were willing to put up with our v1’s short battery life and flaky Bluetooth connections because they so desperately needed Sleep and Recovery data. A broad consumer base would have rejected it. 2) Be great at no more than 1-3 things. If you try to be great at more than that, you will build a hardware that is mediocre at a wide range of things. You won’t think it’s going to be mediocre, but it will be. 3) You want a small launch. Not because you want small sales but because you want to have a steady ramp. It’s much better to do 100k of sales steadily over 12 months than in 2 months with a huge drop off. Keep in mind you need to manage your manufacturing partner and that there’s nothing better or more efficient than steady and predictable. 4) CAC is your enemy. Referrals are gold. The magic of referrals is that they signal high NPS and they help you grow steadily. Again everything that helps you have a steady HW manufacturing plan is good. Building a referral program for v1 launch is a good use of your time. 5) Be excellent at Software. Turns out being good at both Hardware and Software is a hard thing and something that your BIG COMPANY competitor is actually not great at (excluding Apple here). For some reason, there’s such a magnifying glass on the Hardware development, that Software is then overlooked by consumer startups. Don’t make that mistake. You can be great at both. 6) You should spend an unreasonable amount of time and money on ONE aspect of your Hardware. Could be the design or a technical breakthrough. But something needs to be SPECIAL. In our case, we designed a modular battery pack that allowed Whoop to be charged without taking it off your body. This made our development timeline much longer, but it allowed us to achieve something that no other wearable has achieved since: 24/7 health data. 7) Finally, have an unrealistic belief in yourself and your team. You are not supposed to succeed and the odds are stacked against you. Building hardware takes confidence and you’re up against big players who will rip you off. We went so far as to leave a message for our competitors on our circuit board (pictured below). In the case of Amazon and others, the prophecy came true. If you’re building consumer hardware, I wish you nothing but success. It will be super hard, but super rewarding. Good luck 👊🏼 #consumer #hardware #wearable #startup #whoop

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2024 has been a game-changer for spatial computing 🚀 With the Vision Pro dropping, Quest 3 making waves - we're seeing this tech leap from "maybe one day" to "happening right now." What's wild is how different industries are running with it: 1️⃣ Surgeons practicing procedures in 3D 2️⃣ Kids exploring the solar system from their classroom 3️⃣ Trying on clothes without stepping in a store And property? That's where we've been putting in the work. Ensuring everyone has the chance to experience their can step inside their future home before it's built. To Explore their space in lifelike detail across any device, from smartphone to vr headset. Started this journey years ago because I saw how painful it was trying to sell off plans and blueprints. Now? We're watching people walk through unbuilt spaces like they're already there. Across any device they've got. But here's what keeps me up at night (in a good way) - this feels like we're just scratching the surface. What space do you think spatial computing will shake up next? #SpatialComputing #PropTech #CGI #ArchViz

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Smart execution is the key to success in hardware startups Boom Supersonic's Blake Scholl discusses: - The importance of leveraging existing technologies - Refinement over reinvention - Prioritizing practical and user-centered design

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The newest issue of The Pragmatic Engineer found a surprise uptick in eng hiring in the typically slow summer season - I'm curious to see how the macro environment impacts this uptick as we head into the fall, but I remain hopeful. I asked Gemini to give me the tldr of the article to share with my team (sorry, Gergely Orosz) and found it missed one of the most interesting pieces of the article. Gemini's highlights - Unexpected surge: Tech recruitment has seen an unusual spike in June and July, contrary to typical trends. Increased demand: Startups, especially AI-focused ones, are driving the hiring uptick. Global phenomenon: This trend isn't just limited to the US; Europe is also experiencing increased recruitment activity. Reasons for hiring: Companies are backfilling positions, investing in growth due to improved financials, and responding to increased VC funding. Challenges remain: While the job market is improving, finding qualified candidates and offering competitive compensation remains difficult for many companies. What I found most interesting - The nature of the backfills: the article finds that a lot of new headcount are actually backfills of folks that were "over-slashed" during rounds of layoffs. Not entirely surprising with the "trim the fat" and "rebuild lean" mentality, but interesting nonetheless. Either way, the uptick is encouraging (and don't always rely on Gemini's tldrs). #hiring #startups #VCbacked #softwareengineering

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Mo 30x’d his stock joining Nvidia in 2020. MIT grad and now at YC, here is his advice: - systems design thinking or “big picture” - human-user experience or “empathy” - first-principles thinking or “why” over coffee we talked about ai will become really good engineers but engineers will have to be really good at the “why” and “human experience” thats why we are building headstarter, to create a “forward-looking” program instilling the skills of future tech leaders; movers and shakers; and rainmakers Mo is dope. What do you think of his advice?

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Humans have historically found the secret to some natural phenomena and put it on steroids. 💉 Consider the aerodynamic efficiency of a bird's wings – a concept we've amplified to create the marvels of airplanes and rockets. ✈️ 🚀 We're seeing something similar right with LLMs. The transformer approach to linking knowledge seems to produce an eerily similar result to what we do. I've seen LLMs hallucinate in a similar way to my 5 year old kid! 😵‍💫 We took the body of knowledge on the internet and created 100+ billion parameter models that hold information that any human mind can. 🧠 But there's more to the natural phenomena of intelligence. There's a few more unlocks that need to be discovered. Like the mechanisms behind novel problem solving. 🧮 It's only a matter of time – in my opinion – until we crack those, put those on steroids and then we're no longer in the pole position of the most intelligent entities. 🤖

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🧠 AI will be pervasive in manufacturing 🏭 whether we accept it or not. It's time to embrace the technology and drive meaningful value with its usage. 💡 If you are starting this journey or have tried but have yet to quite succeed in generating value, this blog is for you.

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"As technology evolves, so does our potential to rewrite the future of health, where the impossible becomes routine." As I ride in a Waymo autonomous taxi through San Francisco with top technology leaders, we explore the game-changing trends disrupting healthcare, many of which are rapidly becoming the new norm. Here is the download for you: The top 10 technology trends transforming healthcare now: 1. Artificial Intelligence & Machine Learning: From predictive analytics to personalized medicine, AI/ML is revolutionizing clinical decision-making, diagnostics, and operational efficiency. 2. Telemedicine & Remote Monitoring: Virtual care is now the norm, offering patients more access and flexibility while reducing the burden on healthcare systems. 3. Wearables & Health Trackers: Continuous monitoring of vitals empowers patients to take control of their health, with data-driven insights for proactive management. 4. Blockchain in Healthcare: Securing patient data and ensuring interoperability through decentralized, tamper-proof records. 5. 3D Printing: Custom prosthetics, implants, and even organs are no longer science fiction but emerging realities. 6. Genomics & Precision Medicine: Tailoring treatments to individual genetic profiles for better outcomes and reduced side effects. 7. Robotics & Automation: Robotic-assisted surgeries and automated systems are improving precision, reducing recovery times, and optimizing workflows. 8. CRISPR & Gene Editing: Offering potential cures for genetic disorders and revolutionizing therapeutic approaches for chronic diseases. 9. Augmented Reality (AR) & Virtual Reality (VR): Enhancing medical training, surgical planning, and patient education through immersive experiences. 10. Nanotechnology: Targeted drug delivery systems at the nanoscale are improving treatment effectiveness while minimizing side effects. The healthcare revolution is here. The question is, how ready are we to embrace it? #HealthcareInnovation #DigitalHealth #AIinHealthcare #PrecisionMedicine #Longevity American Association for Precision Medicine (AAPM) #AAPMhealth #aapm_health WISE World Investors & Entrepreneurs Society #investment #ai #news

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Optimize for 🚀 Bio Cores First, Silicon 🤖 Cores Second David Heinemeier Hansson makes a compelling case: prioritize human programmers (bio cores) over computer processing power (silicon cores). Here’s why it matters: 👉 Investing in programmer efficiency is far more valuable than solely optimizing for computer speed. 👉 Human labor costs far exceed computing costs. Boosting human productivity should be the main focus. 👉 In the age of AI and high-level programming languages, the bio cores drive your real gains. Bottom line: Make your programmers more productive, and watch your team (and tech) thrive. Read the full article here: https://lnkd.in/gK8HEGtk P.S. : The image below is brilliantly summarized using Napkin.ai for a quick visual breakdown.

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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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Looking forward to sharing how we at basys.ai are leveraging Generative AI to tackle healthcare’s biggest challenges. PS: Also looking forward to sporting my basys vest in the #BayArea PPS: What I’m not looking forward to is the second cross-country flight in less than a week. #GenAI #BasysAI #GenerativeAI

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