Christopher Paquette

For anyone interested in healthcare data or analyzing large-scale consumer conversations — Today we’re launching a free demo dataset of GLP-1 social conversations on Snowflake Marketplace. This sample dataset is: • Fully anonymized • AI-ready • CoCo-ready • Designed for sentiment analysis, marketing effectiveness, and competitive benchmarking GLP-1 is one of the most important healthcare categories today, generating massive consumer conversation online. Yet historically, analyzing this data at scale has been difficult due to privacy, anonymization, and processing constraints. With data now able to live natively inside Snowflake, it can be analyzed securely and compliantly — and is immediately ready for AI workflows. If you’re exploring use cases in: • Healthcare • Pharma • Brand monitoring • Consumer intelligence this dataset is a great place to start. Access the demo here: https://lnkd.in/g7wyEVsA If you're interested in GLP-1 insights or other AI-ready datasets, feel free to reach out. --- Kok i#GLP1 #Snowflake #HealthData #SocialListening #Pharma #DataMarketplace #ConsumerInsights #LifeSciences

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LLMs are getting commoditized. The pace of core model breakthroughs has clearly slowed. We are no longer seeing step-function jumps from “just a bigger model”. For most real systems today: • architecture matters more than the base LLM, • planning, orchestration, memory, and verification dominate quality, • the model itself is increasingly a replaceable component. In my own code factory, I can swap GPT, Claude, or Gemini with minimal impact. There are differences in latency and edge cases, but they no longer define the system. The next real breakthroughs will not come from GPT-6. They will come from new architectures built around models, not inside them. LLMs are becoming infrastructure. Systems are becoming the product.

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The things we do well in the West: disruptive technology Things we do really badly: deal with institutional inefficiency Good things are on the other side of improving institutional inefficiency

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Agentic AI Isn’t Coming — It’s Already a Pressure Point in Healthcare M&A Healthcare consolidation is accelerating in 2026 — providers, payers, and marketplace platforms are combining to gain scale, margin control, and operational leverage. Take for instance Spring Health announcing their acquisition of Alma. What’s changing quietly in these deals is how AI is being evaluated. Agentic AI is no longer a roadmap item or a demo feature. It’s becoming a diligence issue. Here’s why. 🔹 Consolidation turns AI from a feature into an operating risk. In a standalone company, an AI agent can live inside a narrow workflow. But post-merger, that same agent spans: - multiple care models - different policies - incompatible data standards - conflicting incentives At scale, agentic AI doesn’t just automate work; it arbitrates between systems. That’s power. 🔹 Buyers aren’t asking “does it work?” anymore. They’re asking: - Who controls the agent when workflows collide? - How are overrides handled across merged organizations? - What happens when an agent optimizes locally but harms the system globally? - How do we shut it down without breaking operations? If those answers aren’t clear, the AI isn’t an asset; it’s integration debt. 🔹 Agentic AI exposes governance gaps faster than any other technology. In traditional systems, unclear decision rights slow down execution. In agentic systems, they decide outcomes automatically. Once agents act across scheduling, billing, care navigation, or utilization workflows, ambiguity stops being theoretical. It becomes encoded behavior. 🔹 This is why consolidation favors platforms with restraint, not just intelligence. The winning healthcare marketplaces won’t be the ones with the most autonomous agents. They’ll be the ones that can: - scope authority precisely - enforce policy consistently - surface decisions transparently - and absorb accountability when automation moves faster than certainty That’s not an AI capability problem. It’s an operating model problem. In the next wave of healthcare consolidation, agentic AI won’t differentiate products. It will differentiate platforms that can be trusted to operate at scale. And that trust won’t come from model performance. It will come from governance, ownership, and control — designed before automation hardens them. #HealthTech #AIinHealthcare #ProductLeadership #ChiefProductOfficer #HealthcareInnovation #OperatingModel #BoardroomReality #DigitalHealth

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The most spooky thing about AI is you can flip a coin for accuracy ☠️ The benchmark crisis is real with current metrics not telling you if a model is actually safe to deploy. MedPI is different. We built a comprehensive evaluation framework with 54-63 clinical competencies (varies by specialty) that measures: safety discipline, diagnostic reasoning, urgency recognition, interaction efficiency, empathy, adaptive dialogue. This means measuring a big umbrella metric like "safety" without breaking it down into specifics (e.g., medication errors) only tells half the story. To truly improve, you need to dig deeper and figure out what part of the clinical process went wrong: Was it the medication decision itself or was it the model’s failure to capture all clinical details in prior conversation? Understanding these nuances is key to developing safer, more effective medical AI. Here's how we validated it: We tested 9 major language models: GPT-5, Claude Opus 4, Claude Sonnet 4, Gemini 2.5 Pro, MedGemma, LLaMA 3.3, and others across 7,010 multi-turn patient conversations. Real clinical workflows such as general practice, dermatology, oncology, internal medicine, neurology, psychiatry, OB-GYN. And the results were surprising. GPT-5 topped the leaderboard at 53% accuracy - the best among them all and still being terribly inaccurate. Tf you mean, that’s crazy? Just flip a coin, and you’ll get the same level of accuracy. We're making this available because the industry needs a better standard, and MedPI Eval sets a new benchmark for understanding where medical AI stands today and how far it still has to go. Full whitepaper in the comments.

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AI results with quantified and compounding outcomes. Healthcare is one if the most inefficient and arcane systems perfectly positioned to harness the promise of AI. Congratulations Autonomize AI. Thrilled for ATX Venture Partners as debut and ongoing investor in this phenomenal team and market opportunity. Stay tuned for more raving customer results and case studies…

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If you’re running ClinOps at a Series A biotech, there is one trap that will kill your pipeline before you ever reach Series B. The manual execution death trap. I see this constantly: A small biotech secures funding. They have one or two early-stage trials that are their absolute lifeblood. Because the stakes are so high, the team gets complete tunnel vision on execution. With runway on the line and investors watching every milestone, scalable platform evaluation feels like an unaffordable risk. Teams have no option but to brute-force. They manage everything manually and rely entirely on a CRO to "just handle it for now." They prioritize the comfort of familiar processes over the efficiency of scalable technology and infrastructure. This works, just barely, for a small Phase I safety study. But here is what happens when the clinical data is positive and it’s time to scale: You hit an operational wall. You have limited internal infrastructure, you never were able to optimize your trial design with RWD, and you're suddenly trying to run a complex, multi-center Phase II trial using the exact same manual spreadsheets and ad-hoc processes that barely survived your first study. Tufts CSDD's 2024 research puts the direct daily cost of a clinical trial delay at roughly $40,000 - with every lost day compounding against your runway. The operational debt crushes your timeline. The best ClinOps leaders don't just build for the trial right in front of them. They build for the trial they need to run two years from now. When you are in the early stages, you cannot afford to just outsource execution and look away. You have to build a foundation. Invest in a sponsor-controlled operational platform; one that keeps your clinical intelligence in-house, regardless of which CRO is executing on the ground. Select vendors that easily allow you to transition from Phase I to Phase II without ripping out your core systems. Implement technology that fits seamlessly into your workflows, so it can immediately benefit you with reduced CRO outsourcing costs today and scale with your headcount tomorrow. Your molecule might be what secures your Series B funding, but your operational infrastructure is what actually gets your drug to market.

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