Jeffrey Jacobs

I’m genuinely shocked by how many OMs I see with straight up major errors. Not “oops, wrong comma” mistakes. I mean… an OM for a fast food restaurant, but the executive summary is describing a retail center in a totally different city. Are we not reading our own OMs before we send them out? And here’s the thing… when I catch mistakes like this, I usually message the broker. Not to be annoying or to call anyone out, but because I want the right information to share with my clients, and honestly, I think it’s the decent thing to do. But at this point… should I stop proofreading other people’s OMs for free and just call the owner instead? “Hey, if you ever want a broker who actually reads the marketing materials before they go out into the world, I’d love to help.” Details matter. Accuracy matters. And in commercial real estate… credibility matters. Right??

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"All-in-One" Suite 🆚 "Best-of-Breed" Approach Most organizations aren’t 100% on any single platform. Heterogeneity is the norm. That’s why metadata freedom is becoming a critical architectural discussion. 👉 Full read: https://lnkd.in/gHnrPvxd In this article, we break down: • why vendor lock-in matters more at the metadata layer • how multi-engine environments change governance • the tradeoffs between open catalogs and vertically integrated platforms This topic affects every engineering team operating across Spark, Trino, Flink, warehouses, vector stores, and AI systems.

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Operational and analytics capabilities—without the burden of managing servers or pipelines, and with flexibility to choose which operational data becomes analytical.

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Databricks and Perplexity co-founder Andy Konwinski is forming the Laude Institute, a new AI research institute, and backing it with $100 million of his own money. The Laude Institute will function less as a traditional research lab and more like a fund making investments structured as grants. It will focus on "Slingshots" (early-stage research) and "Moonshots" (long-horizon labs tackling species-level challenges). The institute's board includes AI luminaries like UC Berkeley's Dave Patterson, Google's Jeff Dean, and Meta's Joelle Pineau. The institute announced its first flagship grant of $3 million annually for five years to anchor the new AI Systems Lab at UC Berkeley. The new fund aims to catalyze work that doesn't just push the field of AI forward, but guides it toward more beneficial outcomes. Congratulations to Andy Konwinski on this incredible commitment to AI research. TechCrunch: https://lnkd.in/dy7x9zAT #AI #Research #AIforGood #Philanthropy #VenturePhilanthropy #Databricks #Perplexity #UCBerkeley

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As someone operating in both Snowflake and Databricks environments, I’m energized by Snowflake’s vision shared at the summit last week — and just as excited to see what Databricks unveils at their Summit this week. For those of us bridging structured + unstructured, BI + ML, this cross-platform momentum is real and valuable. 🚀 Reflections from #SnowflakeSummit 2025 The big picture laid out by Snowflake’s leadership was bold and forward-looking: a unified platform where governed data, agentic AI, and transactional + analytical apps come together seamlessly. Sridhar Ramaswamy’s keynote along with Sam Altman framed it best: “There’s no AI strategy without a data strategy.” A truism. Key takeaways that stood out to me: 🤖Agentic AI in action Snowflake Intelligence and Cortex Agents bring a new paradigm—AI agents that retrieve, reason, and act—all within Snowflake’s secure data perimeter. 🧠AI for everyone Tools like AISQL and the Data Science Agent lower the barrier to insight. Prompt your data like you prompt ChatGPT—no Python required. ⚙️Smarter infrastructure Adaptive Compute and Gen2 Warehouses optimize cost and performance automatically—bringing efficiency without complexity. 🧾Unified workloads With PostgreSQL stack coming soon, Snowflake is ready to handle OLTP and analytics on the same platform. 🔐Governance at the core Semantic modeling, masking, and secure data sharing are not afterthoughts—they’re foundational to the future of responsible AI. #SnowflakeSummit #Databricks #AIstrategy #DataCloud #AgenticAI #AISQL #ProductLeadership #DataPlatform #Governance #CortexAgents

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Databricks now lets you run OpenAI models (like GPT-5) directly on your enterprise data. Most AI tools or products force you to move your data into their product — adding cost, risk, and endless API hops. This changes that. Now, agents inside Databricks can securely work with large, complex business datasets — accelerating AI-driven processes and giving you faster answers to your most critical questions. At LBMC we remain committed to building AI readiness through data readiness in Databricks. #AI #Databricks #EnterpriseAI #Agents https://lnkd.in/eUYefPhR

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Thank you for your requests for graphics re the 2025 GTM Effectiveness Report we published in December. The deterioration calculation of effectiveness is based on causal models. Here's the first of several that I'll be publishing over the next few weeks. Note: the estimate of total budgetary losses due to GTM ineffectiveness assumes a S&P 500 B2B GTM spend ~$X and effectiveness decline of Y pp. Reality is likely marginally higher, around $560B.

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Snowflake signaling its intent to acquire Observe, Inc. shows how important observability has become to core data infrastructure. Logs, metrics, and traces are now: 👉 Some of the largest datasets teams generate 👉 Increasingly reused across analytics and AI 👉 Too expensive and too valuable to treat as disposable This move suggests the next phase of the data stack will continue to bring high-volume data and metadata closer to the analytics layer. As that happens, storage and data architecture choices become increasingly important. https://lnkd.in/ez73Fhbr

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The organizations that will execute on AI will be the organizations that have the capability to scale quickly as any AI innovation happens. Open AI just announced GPT 5.2, and not only is Databricks the only platform that already supports it, but we used our benchmark to measure it against enterprise tasks.

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