Jason G. Cooper

Snowflake buy the dips and wait Conclusion and Recommendations The Snowflake story is not "a year out"—it's unfolding now. The company delivered 29% growth in Q3 FY26, raised full-year guidance to 28% growth, crossed $100M in AI revenue, and accelerated RPO growth to 37%. These are not the metrics of a story that's delayed. However, the full potential may still be 6-12 months from materializing. Snowflake Intelligence, Adaptive Compute, and the channel strategy are all in early innings. If you believe in the long-term thesis (data platforms powered by AI are critical infrastructure), starting a position now makes sense rather than waiting for more confirmation—by which point the stock may have already priced in much of the upside. My recommendation: Invest now with a 12-24 month time horizon. The combination of re-accelerating growth, emerging AI monetization, stable net retention, and improving margins suggests the risk/reward is favorable. The "year out" framing was relevant 6-9 months ago; today, the company is executing and the evidence is mounting that the strategy is working. For more cautious investors, a phased approach makes sense: establish a partial position now to capture the current momentum, then add on evidence of sustained AI adoption, NRR stability above 120%, and successful channel scale. But don't wait for all the evidence—by then, the opportunity may have passed. "We are executing with urgency and focus and maintaining deep partnerships with our customers that enable us to capture the opportunity in front of us and sustain durable momentum." — Sridhar Ramaswamy, SNOW Q3 2026 The data supports this statement. The question isn't whether to invest—it's whether you're comfortable with the consumption model's inherent variability and believe the AI-driven data platform thesis will play out over the next 2-3 years. If yes, the time to act is now, not later.

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Global powers are no longer just “debating” AI. They’re rewriting the rules. The new US AI Action Plan is one of the most aggressive national strategies we’ve seen. – Deregulation in the name of speed – Framing AI as national infrastructure, like energy or defense – A clear bid for global standard-setting power For Big AI, it’s a win: faster deployment, lighter compliance, stronger procurement pipelines. But for society, it raises questions we can’t ignore: – Who pays the cost if safety checks come after deployment? – What happens when diversity, misinformation, and workforce impact are pushed aside? – Can we treat AI as an arms race without destabilizing global cooperation? The US is betting that permissionless innovation will deliver dominance. The risk is hollowing out equity, resilience, and trust in the process. For those of us working at the intersection of health and AI, this isn’t abstract policy. It shapes the frameworks we’ll operate under, the funding available, and the global norms that decide how safely or recklessly AI enters care systems. 👉 Worth reading (and reflecting on) how these choices ripple beyond borders. https://lnkd.in/eeDaCgT9

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AI dashboards do not create clarity without governance. Many organizations implement analytics tools expecting insight, but insight without accountability leads to inaction. When no one owns the data, patterns repeat. A governance-based assessment asks different questions. Who reviews the data Who validates anomalies Who decides when action is required Who is accountable for outcomes ✅ Data must have an owner ✅ AI insights must be explainable ✅ Escalation paths must be defined ✅ Human judgment remains essential ✅ Leadership must act on signals Technology surfaces information. Governance turns information into decisions. #MedicalPracticeConsultant #GoHealthcarePracticeSolutions #AIGovernance #AIinHealthcare #PracticeAssessment #HealthcareCompliance #PracticeManagement #Leadership

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I did a double take when I saw a Forbes headline claiming that 80% of databases are now being created by AI agents. Whether it’s one platform today or many tomorrow, this trajectory feels inevitable—and likely already happening in more places than we realize. One line from the article stopped me: “An AI agent working through a complex coding problem may spin up dozens of isolated database environments in parallel, test multiple hypotheses at once, evaluate results, and then tear everything down—all within seconds.” Let that sink in. This isn’t just automation at scale. It fundamentally redefines the data layer itself—governance, cost control, security, lineage, resilience, and ownership are no longer static design decisions. The upside is enormous: ⚡ experimentation at machine speed ⚡ innovation humans simply can’t match The risk is just as real: ⚠ unmanaged sprawl ⚠ opaque decision-making ⚠ architectures we no longer fully understand The risks and rewards are too large to ignore. If AI agents are becoming first-class actors in creating and managing data infrastructure, leaders need to start asking harder questions: • Who governs data environments that exist only for seconds? • How do we secure what’s constantly created and destroyed? • What does “data architecture” even mean in an agent-driven world? And critically: Is this only happening at hyperscalers? How do we support highly regulated environments like finance and healthcare—where auditability, change management, and lineage aren’t optional? Curious how others are thinking about this shift—especially those responsible for data, platform, and risk. https://lnkd.in/gdfQEr-Y #AI #AIAgents #DataArchitecture #AnalyticsLeadership #DataGovernance #CIO #CDO #RiskManagement

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In the latest All-In Podcast, Gavin Baker highlighted something important: #Grok has meaningfully improved on the quality-cost Pareto frontier. He showed graphs from Cursor and Databricks illustrating Grok offering comparable or higher intelligence at lower cost. A real step forward for frontier models. Continued advances from Claude, ChatGPT, Grok, and strong open-source models matter. Better models at better prices expand what’s possible. But there’s another, larger step-function in cost efficiency available today. When organizations deploy their #AI models on the Anna platform through our patented deterministic approach. And lower token utilization is just one of many benefits: ✅ Lower and far more predictable costs ✅ Dramatically reduced hallucination and output variability ✅ Traceable, governed results that enterprises and regulated industries can actually rely on Predictability is the point. When variability and hallucination drop, token spend stabilizes and total cost of ownership improves in ways pure model-price competition cannot deliver. Model progress is beneficial, but deterministic orchestration on top of those models is what turns that progress into reliable, governable, cost-efficient production systems. That’s the layer we’re building at Geo Orchestration AI. If you’re exploring how to make frontier models more predictable and cost-efficient in production, I’d welcome a conversation. Feel free to connect or message me. #Anna #AllInPod

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💊 "Data List FINAL FINAL (use this one).xlsx" Every month, our external partner sends updated data files. Seems simple enough, right? Except that each of our internal teams renames the same files differently. So the “official” file somehow ends up being labeled as: 📂 Data_Update_Oct.xlsx 📂 Marketing_Team2025.xlsx 📂 Data List FINAL.xlsx 📂 Data List FINAL FINAL (use this one).xlsx You can imagine the chaos that follows. It’s amusing until someone has to confirm which version is correct and maps to which data. 😅 What appears to be a minor issue, like naming conventions, can actually disrupt data workflows. Consistency isn’t just important for coding; it’s crucial for communication, too. 👉 What’s the most “creative” file name you’ve encountered at work?

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