Carly Taylor, M.Sc.

Bluenote just dropped a $10M mic on the intersection of generative AI and life sciences, and it’s got the kind of harmony that could change the game—or save lives. Co-founders Fatima Sabar and Katsuya Noguchi (both with Color Health in their DNA) have orchestrated something symphonic here. And with backing from Lux Capital, Elad Gil, Anthropic, Menlo Ventures, and McKesson Ventures, this isn’t just funding; it’s a full-on crescendo for innovation. Here’s the score: Bluenote’s #generativeAI platform is a powerhouse for regulatory and compliance workflows. Think #regulatoryfilings, #SOPs, #riskanalyses, and #manufacturingdocumentation — traditionally a grind, now streamlined with precision that feels borderline magical. Their multi-model approach—tapping Anthropic, OpenAI, and Google tech—turns laborious workflows into fine-tuned efficiency symphonies. That’s 15+ applications and counting, cutting the noise while delivering clarity. And this matters. Bringing a new drug to market takes a decade and burns through $2.8 billion. If Bluenote can shave months—or even weeks—off that timeline, they’re not just helping companies save money; they’re helping save lives. Their fine-tuned models don’t just process; they prefer 90% better than off-the-shelf alternatives. That’s not a stat—it’s a statement. Let’s be real: the #lifesciences world isn’t a place for hype; it’s where results have receipts. Bluenote’s mission aligns perfectly with the rising wave of precision medicine and AI-driven R&D, turning complexity into opportunity. The question isn’t whether generative AI belongs here; it’s how far companies like Bluenote can push the envelope. Congrats to Fatima, Katsuya, and their visionary investors. With this funding, they’re tuning the future of life sciences to a whole new key—and it’s one the entire industry should listen closely to. #Startups #StartupFunding #LifeSciences #HealthCare #PatientCare #HealthTech #PatientTech #Technology #Innovation #Disruption #VentureCapital #GenAI #MultiModel #AIDriven #ResearchAndDevelopmentAI #AI #GodIMissedSayingAI 🙃

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"Our goal is to create operating leverage through AI. ... One of the ways I look at it is, our total people costs will go down, our cost per head will go up, and my GPU per researcher will go up." — Satya Nadella. I finished listening to the BG2 podcast with the Microsoft CEO last night. Extremely interesting conversation with Brad Gerstner and Bill Gurley, and a lot to chew on. I found myself skipping back repeatedly to listen to certain parts again, to make sure I understood exactly what Satya was saying. One example was the quote above, in the context of the long-term implications of AI for the workforce, specifically at Microsoft in this case. I'd be very interested in hearing what everyone thinks of this. Here's that specific portion of the show: https://lnkd.in/gcPjHpeg Here's the full podcast: https://lnkd.in/gHAquZpD

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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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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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Hello from Bio 2024, in San Diego CA! I am excited to be here! Scientists, doctors, clinical researchers, technologists, data analysts, and investors all meet to push the biotech industry forward. And there are about 1500 companies here (and an estimated 20,000 people) so they’re taking over the town. And the tech-side been on a multi-year acceleration phase to map human genomes more cheaply, map protein interactions better, sense and diagnose diseases faster, and treat diseases with novel methods. So the GenAI and deep neural net approaches are tearing through the healthcare industries, just like they are in the rest of life. And to my eyes, it’s where success can be so transformative. But new tech risks are also heightened — it’s the most important stuff we’re talking about here, someone’s life! Lastly and closest to my work at Helpfully, there’s a big track on digital health. We’ve realized that healing requires more than a pill. And if health requires friends, family, doctors, caregivers, coaches, easier ways to track progress, simpler monitoring of data, and a patient’s daily decisions, digital technologies are the glue that ties it all together. I’ll be posting a bunch during the week! The convention hashtag is #bio2024. So stay tuned on this channel! And I’ll be posting a bunch over on Twitter/X @thinky (me) and @helpfullyteam, too.

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AI leaders have a crucial chance to address risks now and strengthen governance practices before playing catch-up to regulations, notes Kjell Carlsson, Ph.D. of Domino Data Lab. In a recent VentureBeat article, he shares his perspective on California's AI bill veto and why proactive governance is key to the future of AI innovation. Learn more about this addressing this opportunity: https://gag.gl/4WCTkP #AI #AIGovernance #AIInnovation #DataScience

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#GenerativeAI in #Lifesciences makes you nervous? That's because you haven't seen the latest use cases. Join our webinar on September 4 at 9am PT | 12pm ET to learn how to accelerate drug development with BioRAG. Overcome data silos and regulatory challenges with a #SOTA architecture created by our partner BIP based on Domino Data Lab, OpenAI on #Azure, and LangChain Sneak peek into the benefits ⚕️ Enhancing Clinical Trials: Improve efficiency and safety with advanced document retrieval and summarization. 📜 Navigating Regulatory Challenges: Maintain compliance with industry regulations. 🛠️ Technical Implementation of BioRAG: Learn the architecture and setup within Domino’s AI Hub. Register now! https://gag.gl/AKxQj8 #LifeSciences #AI #DrugDevelopment #Webinar

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The cost of training top models is increasing 2-3X per year, and at this rate will reach $1 billion per model by 2027. The findings are from Epoch AI and follow their recent analysis of increases in compute for foundation models running at 4-5X per year. They've also provided some interesting data on the relative breakdown of cost components like staff, hardware, and energy. A few thoughts after reading this: - I would assume staff costs won't increase as much as hardware and energy costs, so the relative contribution of human labor to future AI models will decrease, in yet another sign of capital's increasing dominance over labor in the industry (just look at NVIDIA's ever-rising stock price). - If compute is increasing 4-5X per year but cost is increasing 2-3X, is it safe to assume that training runs are getting more efficient? - Some people, like Anthropic CEO Dario Amodei, have said we'll see $1 billion training runs in the next year. Does this mean the 2-3X pace is about to increase? That Epoch is wrong in its estimate? Or that Amodei is overstating the cost, possibly to put fear into competitors who can't keep pace? - Only a few companies are going to be able to train future models. We're likely going to see further consolidation. Perhaps we get to a future with only two large general purpose models, like the Windows/MacOS and Android/iOS duopolies we've seen in the past. Article here: https://lnkd.in/g5SejXen

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Grateful to William Sun and MIT Club of Northern California for organizing an insightful Gene Therapy Conference. Being new to this field, here are some things I learned: 🔬 Delivery of gene therapy treatments is a big challenge – ensuring that they reach the right cells, at the right dose, without causing harm, remains a big hurdle. 💡 The up-front cost of gene therapy can be high (e.g., $3M+), however when weighed against a lifetime of treatments using other expensive drugs it can actually be more economical. ⏳ After receiving a gene therapy treatment, it’s difficult to undergo another. Patients face the tough choice of opting for a moderately effective treatment now or holding off for a potentially better one in the future. 🔍 Advances in DNA testing and treatments could lead to removing and repairing stem cells before a disease even presents itself. 🎯 The sweet spot for gene therapy products lies in targeting niche diseases that also offer broader health benefits to larger populations. 🤝 More transparency around FDA submissions, protocols, and research would streamline progress across the entire ecosystem and be a win for all players + humanity. Special thanks to the panelists for their stories and insights, specifically: Trevor Martin (CEO, Mammoth Biosciences), Amy Pooler (VP and Head of Research, Sangamo Therapeutics, Inc.), and Agnieszka Czechowicz, MD, PhD (Physician Scientist, Stanford University School of Medicine). 🙏🙌

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It is going to be fascinating how #AI will shape the world of #biotechnology. Sherry Comes at PwC shared how Moderna has partnered with OpenAI to revolutionise their operations and innovation with plans for 15 new products in the next 5 years, tackling everything from new drug discovery to compliance and communications. We're currently working on a super interesting consulting project atm with a medical device company, comparing AI models from OpenAI, Google, and Anthropic, to name a few. Will share our experiences in a later post. #Moderna’s use of GPT-4 exemplifies how these technologies can significantly enhance #productivity and #innovation in #healthcare. #HealthTech #PharmaTech #AI #Moderna #OpenAI #GoogleGemini #AnthropicClaude #MarketingAI

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This past week I had the pleasure of attending a Connector Series event Presented by IntePros, where Wil Reynolds, VP of Innovation at Seer Interactive discussed an “AI Survival Guide." A huge thank you to Kristin Zeigler for the invite. It was a smaller, more intimate forum which allowed for really effective networking and deeper conversations.   The conversation centered around #GenAI, most notably #ChatGPT, and what it means for our workforce, how to use it most effectively and the learning & development opportunities & challenges it presents for our youth.   Some of my key takeaways and insights that I wanted to share: ➡ Authenticity will become a competitive advantage - For both companies and individuals, authenticity will become a competitive advantage in an increasingly AI-assisted world. Employers, customers, coworkers, and the alike are quickly becoming very good at distinguishing what messages are authentic vs. AI-generated, or as Wil would call it "Lazy-GPT'd." People and companies who are over-reliant on GPTs for original thought, marketing messaging, texts, social media posts, emails and other forms of communication will quickly lose trust and credibility. However, those that use GPTs to poke holes in their ideas, as a benchmark for framing a message or learning quickly about something new to communicate authentically will develop better relationships overtime, and therefore, standout amongst a crowded market (I am happy to report I did not "GPT" this post 😁) ➡ Current GPTs "spit out" the average, not the exceptional - Current GPTs are trained to predict the "average" response to a prompt based on the data they are trained on. Most of the GPTs that are commonly used by the public (such as ChatGPT) are trained on such a large and broad corpus of text data from the internet. What this means for marketers is that GPTs are not going to be very good at providing differentiated messaging. However, what it can help marketers do is accomplish specific, siloed tasks such as summarizing large amounts of market research, poking holes in a human-generated message, or conducting a keyword analysis. ➡ Leadership honesty will attract talent - Candidly, the way most executives at fortune 500 companies are thinking about AI is "how can I get the same amount of work done (or more) with 10% less of our current workforce?" Basic business principles will tell you cutting costs is often a more streamlined way of boosting profits as opposed to growing revenue. Wil makes the argument that top talent will be attracted to leaders who "keep it real" with their employees. In an environment where employees understand their leadership's vision for AI, they are more effectively able to plan for their career/talent development and carve a path within the company (or outside of it).

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I read this blog a few years back and it resonated quite strongly with me as a founder of a #SaaS company providing AI services to our customers. Thought it was worth highlighting and sharing it again in post-GPT world. AI companies often struggle with lower gross margins of 50-60%, contrasting sharply with traditional SaaS businesses reaching 60-80%+, due to hefty cloud costs and ongoing human support needs. This underscores the reality for #AI startups! Overcoming these hurdles could redefine economic strategies in tech, prompting new approaches that blend software efficiency with service-oriented flexibility to build resilient AI enterprises.

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AI’s potential to reshape scientific discovery is truly extraordinary, going beyond what was once considered impossible. From generative chemistry creating entirely new molecules to AI-driven astrobiology detecting potential life in meteorites, we’re unlocking breakthroughs across disciplines. At RediMinds, we’re inspired by these innovations as they redefine what’s possible and accelerate discoveries in previously unexplored areas. How do you see AI transforming the future of discovery in your field?

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I got a preview of this talk. It's going to be good. If you think we've reached the end of the gen AI opportunity, then you should listen to what Roman Shaposhnik and Tanya Dadasheva have to share about the future of transformer architecture for AI.

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Nearly two years on from ChatGPT's release, we now have enough to understand Gen AI's impact within the enterprise. And research shows that while Gen AI has delivered incremental productivity in specific workflows, it comes with its own risks and challenges In Ema Unlimited's latest blog, seasoned digital ops & intelligent automation expert Ian Barkin analyzes this impact & the challenges, and why enterprises' focus might better be directed towards Agentic AI. • 𝗜𝗻𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝗶𝗲𝘀 𝗮𝗻𝗱 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀: Studies by McKinsey, Deloitte, Asana, and Anthropic all indicate that inaccurate outputs from Gen AI integrations are a major concern for enterprises. There is a need for high-quality training data and for customized models, leading to an increased emphasis on data governance and solutions that can be tailored to the enterprise.  • 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: A majority of orgs report that only 30% (or less) of their Gen AI experiments were taken into production, as these projects were plagued by scalability and deployment issues. Integrating AI into existing workflows without causing disruption to operations is a key problem. • 𝗥𝗼𝗜 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗔𝗜 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗚𝗮𝗽: Out of ~2770 enterprise leaders, 40% struggled to define and measure the impact of their Gen AI initiatives. Most companies do not have a formal AI strategy in place, leading to misalignment between business goals and AI projects. 𝗜𝗳 𝘁𝗵𝗲 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝗴𝗼 𝗮𝗻 𝗼𝘃𝗲𝗿𝗵𝗮𝘂𝗹 𝘁𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜, Ian Barkin 𝘀𝘂𝗴𝗴𝗲𝘀𝘁𝘀 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗹𝗼𝗼𝗸 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻𝘀𝘁𝗲𝗮𝗱. In addition to the above concerns, Gen AI can only deliver incremental productivity when done right. 𝗕𝘂𝘁 𝘄𝗶𝘁𝗵 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜, 𝘁𝗵𝗲 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲: Organizations will no longer need excessive human coordination across data silos with no accountability on workflows actually executed. Instead, collaborative, adaptive, intelligent Agentic AI systems can own and execute workflows end-to-end. By 2028, Gartner predicts 𝟯𝟯% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘄𝗶𝗹𝗹 𝗶𝗻𝗰𝗹𝘂𝗱𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜, autonomously handling 15% of day-to-day work. The enterprise of the future will be agentic. Read why in Ian Barkin's full blog for Ema Unlimited — linked in comments. #AgenticAI #EnterpriseAI #FutureOfWork #AI

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AI Disaster Looming? California Fights Back! A contentious AI-focused bill disturbs the tech scene in California. This isn't just legislative drama; it's a balance between innovation and possible disaster. Imagine an AI-gone-wild scenario. California is ensuring it never occurs. But there's retaliation. The tech industry—California's economic powerhouse—isn't silent. Their rebuttals are sharp and persuasive. Want to know more? Dive in here: https://lnkd.in/efTjPYiU Your insight could be crucial in this innovation vs. regulation battle. Share your thoughts! Follow me for more AI Tips, Tools, News and Fun #ai #aidisaster

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Out of stealth! Reliant AI raised $11.3m in a seed round co-led by Inovia Capital and Tola Capital,  joined by angel Mike Volpi and is featured in TechCrunch. 🚀 🚀🚀 #DataAnalytics #AI #funding #growth #reliantai #tolacapital #inoviacapital

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Recently, Anthropic shared its perspective on California's proposed AI regulation bill, SB 1047, with the California Assembly. While recognizing the importance of safe AI development, Anthropic has raised substantial concerns about the bill's current form, which could potentially hinder innovation and harm the United States' competitive edge in AI. Anthropic's key points include shifting the focus from "pre-harm enforcement" to a more adaptable "outcome-based deterrence" model. They also advocate for regulations that focus more narrowly on frontier AI safety to avoid redundancy with federal requirements. Additionally, they suggest giving regulatory authority to the Government Operations Agency instead of creating a new state agency. Anthropic believes that these amendments will encourage innovation in risk reduction practices and incentivize companies to adopt measures that prevent catastrophic risks. In contrast, the AI Salon Policy & Civic Engagement Guild shares Anthropic's commitment to ethical AI and safe development but has broader concerns about the bills. We believe that overly burdensome regulations may discourage AI development and lead to job losses. Compliance requirements favor large companies, potentially driving smaller startups out of the market, and overregulation might cause California to fall behind other regions in AI development. Furthermore, compliance under penalty of perjury imposes significant personal risk on developers, and strict regulations could disadvantage California-based companies in the global AI market. On behalf of the over 1700+ members of the AI Salon, we urge our larger community to engage in this crucial dialogue about AI regulation. By advocating for well-balanced policies that protect public interests without stifling innovation, we can ensure California remains a leader in ethical and responsible AI development. Please sign the letters of support and join us in shaping a future where AI is developed and deployed with ethical integrity, social responsibility, and inclusive governance. Together, we can bridge the gap between AI technology and policymaking, fostering an environment where innovation thrives alongside robust safety measures. https://lnkd.in/e6MRBmcX #AIRegulation #EthicalAI #Innovation #PolicyEngagement #Anthropic #AISalon #PublicPolicy #TechPolicy Kyle Shannon Leah Fasten Brandon Tidd Stacie L. Matthew Karabinos, MAT Vikki Baptiste Jon Potter Jon Leland Cyndi Coon Daniele Hall

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