Adam Sah

A SAFE and convertible note had a baby and Wilson Sonsini Goodrich & Rosati was the midwife- out popped the D-SAFE! 👶🏽🎉 A new #climatetech funding instrument was announced by Elemental Excelerator last week. One of the company’s main goals is to support smaller, up and coming companies during the awkward “Scale Gap” phase between #VC funding and natural growth. It’s called the #DSAFE (Development Simple Agreement for Future Equity) and it aims to aid the process by giving companies a small amount of cash to overcome initial risks and expenses during the developmental stages of a product launch or company. How does it work? D-SAFE has a redemption clause meaning companies can choose to have their payments offered as a loan or convert to equity at the investor’s discretion. Funders and companies will have clear communication and expectations about where the money is going and how it will be applied to either the company or the projects at hand. Financiers and companies will work together with more #transparency, and that’s a great boon for #startups and VC alike. As of this month, Elemental has already invested using D-SAFE in eight different companies, including Dimensional Energy and Origen — both companies investing in innovative and out-of-the-box solutions to environmental issues! Seems like a win-win on the surface. What do you all think about the D-SAFE? Alfredo Coppola Momoka Ueda Tim Wagner Stephen D. Torres Olya Irzak Natsuho Toyama Noriya Tarutani Shin Ogawa

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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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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💡 Why do we need so many evaluation tools for LLMs? As engineers, we build 'production-ready' LLM products using these metrics. But what happens next? How do we maintain control and ensure reliability? At Feedback Intelligence, we’ve crafted a cookbook to keep your LLMs reliable and aligned with user expectations. 🍲 📖 Give it a read and let’s chat!

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What do you think about AI in senior care? Personally, I think we have to be careful with it. When AI presents itself as human, it can be confusing for seniors who may not have strong digital literacy skills. In fact, if they think they're talking to a person and not an AI chatbot, it can be dangerous if they're having a medical emergency or issue. However, there are some helpful use cases. I think Kristen Fischer's exploration of AI here is really interesting. "Artificial intelligence may be a useful communication tool to help older adults with cancer communicate with their doctors and have more of a say in their treatment," she says. AI can provide more context or explanation about certain treatment plans and translate the patient’s needs to clinicians in situations where the patient doesn't feel understood or heard. AI will never replace professionals, but it could help patients and clinicians better understand each other. Feel free to share your thoughts on this—I'd love to know if you think AI would be an effective tool for these situations. #AI #SeniorCare #FutureOfHealthcare

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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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Very exciting to share that we at LDV Capital are partnering with the DANNCE.ai team by leading their $2.6M pre-seed financing to increase drug efficacy and improve the lives of people with movement disorders!

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Ro consistently uses tech to tackle healthcare problems (often for free). 🧑‍⚕️ In March of 2020, Ro launched (completely free) COVID telehealth assessments. ❤️ In March of 2021, Ro launched (also free) in-home vaccinations for some of NY’s most vulnerable seniors. 💉 Fast forward to today, Ro is tackling the GLP1 shortage. They just launched a GLP-1 Supply Tracker that anyone can use to get real-time updates about GLP1 drug shortages and alerts when supply is available nearby. Oh, and this is completely free too. Huge shoutout to Ro, building what patients need and making it accessible to those who need it. Check out the GLP1 Supply Tracker here: https://776.vc/3X1FovB (the more who use it the better it works, so share with those who could benefit).

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Ro consistently uses tech to tackle healthcare problems (often for free). 🧑‍⚕️ In March of 2020, Ro launched (completely free) COVID telehealth assessments. ❤️ In March of 2021, Ro launched (also free) in-home vaccinations for some of NY’s most vulnerable seniors. 💉 Fast forward to today, Ro is tackling the GLP1 shortage. They just launched a GLP-1 Supply Tracker that anyone can use to get real-time updates about GLP1 drug shortages and alerts when supply is available nearby. Oh, and this is completely free too. Huge shoutout to Ro, building what patients need and making it accessible to those who need it. Check out the GLP1 Supply Tracker here: https://776.vc/3X1FovB (the more who use it the better it works, so share with those who could benefit).

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Check out my recent post on why President Trump is the forcing function for us to solve the “Missing Middle” and catch the industrial Tsunami of onshoring coming to the US!

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Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

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"Falling costs of building new software companies are a mirage, and the reason for that is this thing in economics called the Jevon's paradox... ...the Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost induces increases in demand enough that resource use overall is increased, rather than reduced." Marc Andreessen with a counterintuitive take on what the future could hold for the software industry. #tech #technology #innovation #software #deeptech #startups #entrepreneurs #entrepreneurship #ai #artificialintelligence #vc #venturecapital #money #business

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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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New study suggests that gpt-o1 achieves "superhuman performance" on clinical benchmarks that include differential diagnosis, diagnostic reasoning, and crafting care management plans: * On Management Decisions, o1 outperformed both GPT-4 and clinicians by ~40% in generating accurate and actionable management plans. * On differential diagnosis, o1 included the correct diagnosis in 78.3% of cases, and its first suggestion was correct in 52% of cases. * On 70 diagnostic cases, o1 produced a response with an exact or very close correct response in 88.6% of cases. This was done with relatively simple zero-shot prompts. During the early 20th century, x-ray machines gradually went from "technical novelty" to "it's unethical to diagnose without them" for a range of medical conditions. It would be no surprise if the industry went through a similar transition to require use of medical generative AI over the next 2-3 decades. https://lnkd.in/gd3RhFbr #ai #generativeai #healthcare #healthcareai #healthai #datascience

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Last week, I had the pleasure of moderating a discussion on navigating FOAK in climate tech at the Greentown Labs Summit in Boston (which had >600 people!!). Here’s a quick rundown of what was shared by Katherine St. James of Spring Lane Capital, Paris Smalls, PhD of Eden GeoPower, and Hannah Friedman of Planeteer Capital: 🌱 What is the definition of FOAK? We discussed how the term can be controversial - it's almost too general because it doesn't identify what kind of "First" FOAK refers to - first team project? First geography? First of a technology in a sector? First time for the integration of a system? It's also too specific because it has the word "first" in it, which can be anathema to later stage / project finance investors. 💰 Partnerships for Funding FOAK Projects We discussed how important it is to build a blended capital stack & use off-balance sheet capital for a project so you're not using the most expensive equity dollars to fund an infrastructure project with low-ish IRR. We also talked through being creative about using corporate partners to provide in-kind services or ink some agreement (at least an LOI) to attract this funding. 🏛 Role of Government We talked about how government capital is seen by investors & how it can be catalytic for FOAK. Government capital can provide validation that a startup has passed a rigorous diligence process by a third party - and incentives like grants or credits can help improve returns for the first project- but it's important to be doing this in conjunction with getting customer validation and to be able to present line of sight to competitive economics without concessionary capital. 👥 Advice for FOAK startups The panelists offered some very specific pieces of advice for startups going through FOAK: how to get creative with things like revenue sharing agreements or convertible notes, getting smart with project development early on (like with DeveloperU), being careful about using FOAK too liberally with certain investor groups, getting the right commercialization / FOAK talent in and around you early Huge thanks to the panelists and the team at Greentown Labs (Jill Kirkpatrick, Julia Travaglini, Aisling Carlson, etc you rocked it!!) for hosting this discussion on bridging the FOAK gap. Also immensely enjoyed the rest of the agenda. Check out the full recap & videos here: https://lnkd.in/d9ApYe9Y #ClimateTech #FOAK #ClimateFinance #ProjectDevelopment #GreenTech #CleanEnergy

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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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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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5 hours long Lex and Dario Amodei, the co-founder and CEO of Anthropic, a company behind Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus LLMs, speak about how AI is shaping the future and how it could impact the way we live and work. They discuss how far AI can go. Amodei thinks AI can match human intelligence and even surpass it in areas like biology, where complexity overwhelms humans. But in other areas, like speech recognition or conflict resolution, AI might hit limits close to ours. One of the challenges is the fact of running out of quality data and the cost of scaling compute power. Enjoy the entire talk! https://lnkd.in/dRhpafzU

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Emmett Shear called AI chatbots "highly dissociative agreeable neurotics." Never heard it put quite like that before. But he's right. Current AI tries so hard to please that it loses itself. Like a person with no backbone, agreeing with everything you say. Shear saw this problem and instead of just tweeting about it, he built Stem AI to solve it. That's what separates great founders from commentators. His career path is telling: - Built Twitch - Ran OpenAI during its darkest days - Now building tools for AI alignment Each step gave him deeper insights into the problems he's trying to solve now. Reminds me of why I back founders who've lived through the problems they're solving. They see angles others miss. --- Check out my Newsletter - TheFoundersCorner via the Link on my profile page or the Link at the top of the page ☝️ Jam Packed with Actionable Insights Every Week 💙

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