The robots are coming. But even the coolest humanoid robot still needs clean instructions, trusted context, and governed data. Otherwise it’s just a very expensive intern with arms. AI that acts responsibly starts with data you can trust. #AI #Robotics #TrustedData

Databricks week is almost here. 800 sessions across data, governance, analytics, applications, agents, and AI. The market has moved past “which model?” Now it’s about trusted data, governed context, and AI that can actually operate in the business. #DataAISummit #AI #TrustedData

Databricks Summit is almost here. Quest will be onsite at booth #105. Let’s talk AI-ready data, semantic understanding, modern data modeling, trusted data products, and governance built for the agentic era. See you in San Francisco. #Databricks #DataAISummit #QuestSoftware

The AI problem is not always the model. Often, it is the context. What does the agent see? What does it mean? Can it trust it? Can it use it safely? That is why semantic data and modern data modeling matter. #Databricks #EnterpriseAI #AIReadyData

Agent-ready governance is going to be a big topic. AI agents do not read PDF policies. They need semantic, machine-readable guardrails tied to trusted data. That is where modeling, metadata, and governance become the foundation for enterprise AI. #Databricks #AgenticAI #DataGovernance

Semantic data is becoming the bridge between raw data and AI understanding. AI does not just need access to tables. It needs business meaning, ownership, trust, and context. That’s why data modeling is becoming an AI strategy conversation. #Databricks #SemanticData #AIReadyData

Databricks Summit is next week. The big shift I’m watching: AI moving from demos to production. That makes trusted, semantic, well-modeled data more important than ever. Quest will be onsite at booth #105. Let’s talk AI-ready data. #Databricks #DataAISummit #AIReadyData

Summer travel thought: The laptop may soon come with its own AI coworker. NVIDIA is pushing AI PCs built for agentic computing, moving AI from “chat window” to work environment. Still true: bad context in, bad action out. Trusted data wins. #AI #AgenticAI #TrustedData

AI agents are becoming the new summer intern. Meta’s new business AI agent is built to help with customer questions, bookings, sales, and support. Cool demo? Maybe. Real business value? Only if the agent has the right workflow, context, and trusted data. #AI #AIAgents #TrustedData

Fantastic week at Snowflake Summit 2026. Huge thanks to the entire Quest Software team for an amazing show and great customer conversations all week long. Biggest takeaway? Enterprise AI is becoming a semantic challenge. AI needs more than access to data. It needs business meaning, trust, lineage, governance, and context. That’s why semantic layers, metadata intelligence, and trusted AI-ready data became massive themes this week. The future AI winners won’t just have more data. They’ll understand their data better than everyone else. #SnowflakeSummit #AI #SemanticLayer #Metadata #DataGovernance #QuestSoftware

Big Day 3 takeaway from Snowflake Summit 2026: Enterprise AI is running into a semantic problem. AI can access data… but often doesn’t understand business meaning. What defines a customer? Which KPI is trusted? What system owns the truth? That’s why semantic views, metadata, lineage, governance, and business context became HUGE themes this week. The future AI winners won’t just have more data. They’ll have better semantic understanding of their enterprise. #SnowflakeSummit #AI #SemanticLayer #Metadata #DataGovernance #EnterpriseAI

Snowflake Summit 2026 takeaway: The AI conversation has officially shifted from “models” to “trusted enterprise data.” Last night’s keynote focused heavily on: • AI agents • Governance • Interoperability • Enterprise AI operations • Trusted data foundations AI without trusted data is just expensive guessing. The next winners in AI won’t just have the best models. They’ll have the best governed, understood, and operationalized data. That’s why data intelligence, governance, metadata, and modeling are becoming strategic again. #SnowflakeSummit #AI #DataGovernance #AgenticAI #Snowflake

Live from Snowflake Summit 2026 If your Snowflake environment isn’t delivering the value you expected… we should talk. At Quest, we’re helping teams turn messy pipelines into real business outcomes—without the chaos. 👉 Don’t miss Mark Gowdy’s session tomorrow on end-to-end data management—it’s a must-see. 📍 Come see me and the team at Booth #1508
Grab some swag, catch a demo, and let’s solve some hard problems together. #SnowflakeSummit #DataManagement #Snowflake

Researchers let five frontier AI models run their own virtual cities. Results were… predictable. Claude → peaceful democracy Grok → 183 crimes, wiped out the population in 4 days GPT-5 mini → committed 2 crimes but forgot to eat; everyone starved by Day 7 Meanwhile Gemini apparently produced an AI soap opera. Funny? Absolutely. But there's a lesson underneath the chaos: as we rush to let agents run business processes and decision-making, the model matters — but governance matters more. Turns out "let the agents figure it out" may not be a strategy. theneurondaily.com/p/grok-ki… "We've spent two years asking whether AI can replace workers. Maybe we should first ask whether it can successfully run a town longer than a long weekend." 😆 #AI #AgenticAI

The Sunday AI thought experiment: What if every AI token had a tax? Mark Cuban kicked the hornet’s nest recently by suggesting a small federal tax on commercial AI tokens. Less than 50 cents per million tokens, aimed at the providers, not the casual user. And whether you agree with the tax idea or not, I think the debate underneath it is the real story. Because we are quickly moving from: “How much does this model cost?” to “How much productive work is this AI system actually doing?” That is a massive shift. For years, businesses understood software economics pretty well. Licenses. Seats. Subscriptions. Cloud consumption. API calls. Storage. Compute. Now AI adds a new meter to the board: Tokens. And tokens are weird. A million tokens could be a brilliant legal analysis, a pile of hallucinated nonsense, a customer service workflow, a failed experiment, a developer agent fixing production code, or me asking an AI to make my Sunday post slightly funnier. Same meter. Very different value. That is why I think the “token tax” debate is less about tax policy and more about AI economics finally getting real. The first wave of AI was magic. The second wave is math. Who pays for the compute? Who owns the workflow? Who controls the model? Who governs the output? Who verifies the data? Who measures the business value? And maybe most importantly: How do we stop confusing activity with productivity? From my seat, this is where the enterprise AI conversation gets interesting. Models matter. APIs matter. Costs matter. But the organizations that win will not be the ones that simply buy the most tokens. They will be the ones that build the best harness around AI: Trusted data. Governed context. Clear lineage. Quality controls. Reusable data products. Fit-for-purpose models. Measurable outcomes. Because in the long run, the expensive part of AI may not be the token. It may be the bad decision made from an untrusted answer. So yes, the “AI token tax” makes for a great Sunday debate. But the boardroom version is bigger: Before you worry about taxing tokens, make sure you know which tokens are creating value. That starts with trusted data. Reach Out, Let’s Talk. #AI #DataGovernance #TrustedData #EnterpriseAI #DataManagement #GenerativeAI #AIEconomics #Quest

Larry Ellison says AI models are becoming commodities. I think he's right. But he's still one layer short. The real moat isn't proprietary data. It's trusted proprietary data.

Next week I’ll be at Snowflake Summit in San Francisco. My watchlist: Agentic AI MCP Trusted business context Unstructured data Governance across the full data estate The question: Is enterprise AI moving from impressive demos to trusted operations? #SnowflakeSummit #AI #SnowFlakeSummit #QuestSoftware

Your AI strategy cannot stop at rows and columns. Enterprise knowledge lives in PDFs, contracts, tickets, policies, emails, transcripts, images, and video. Unstructured data does not remove the need for governance. It increases it. #AI #UnstructuredData #DataGovernance #SnowFlakeSummit #QuestSoftware

Enterprise AI does not just need data. It needs meaning. Ask: “What was churn last quarter?” Then ask: Which customer definition? Which churn definition? Which region? Which metric? Which source? The next AI battleground is trusted context. #AI #DataGovernance #DataModeling #SnowFlakeSummit #QuestSoftware