Subscribe • Previous Issues The World Model Minefield: A Guide for AI Teams The term “world model” has quickly migrated from research papers to the center of the artificial intelligence conversation, frequently cited in media coverage of next-gen AI. At a high level, the concept promises AI that does not merely predict the next word in aContinue reading ““World Model” is a mess. Here’s how to make sense of it.”
Author Archives: Ben Lorica
2025 AI Governance Survey
As artificial intelligence systems become increasingly integral to business operations and societal functions, establishing effective AI governance is paramount for managing risks, ensuring ethical deployment, and fostering responsible innovation. The 2025 AI Governance Survey was conducted online for 105 days, spanning from February 14 to May 29, 2025. This initiative gathered responses from 351 participants,Continue reading “2025 AI Governance Survey”
Beyond Black Boxes: A Guide to Observability for Agentic AI
The core mindset shift for agentic systems is simple: observability isn’t an add-on, it’s a production prerequisite. Enterprises are unwilling to trust black-box agents; they expect to understand behavior, decision-making, and reasoning. This means architecting for visibility from the very first design doc and building a culture where “how will we measure this?” is asContinue reading “Beyond Black Boxes: A Guide to Observability for Agentic AI”
Are Your AI Agents Flying Blind in Production?
Subscribe • Previous Issues Beyond Black Boxes: A Guide to Observability for Agentic AI The core mindset shift for agentic systems is simple: observability isn’t an add-on, it’s a production prerequisite. Enterprises are unwilling to trust black-box agents; they expect to understand behavior, decision-making, and reasoning. This means architecting for visibility from the very first design docContinue reading “Are Your AI Agents Flying Blind in Production?”
Multimodal lakehouses: The architecture AI teams are migrating to
When the “lakehouse” was first introduced in 2020, the goal was to reconcile data warehouses and data lakes in a single architecture: open formats on cheap object storage, with ACID transactions, schema enforcement, governance, BI support, and streaming built in. The promise was simple: one system for SQL analytics, data science, and machine learning, insteadContinue reading “Multimodal lakehouses: The architecture AI teams are migrating to”
The Rise of the Multimodal Lakehouse
Subscribe • Previous Issues Multimodal lakehouses: The architecture AI teams are migrating to When the “lakehouse” was first introduced in 2020, the goal was to reconcile data warehouses and data lakes in a single architecture: open formats on cheap object storage, with ACID transactions, schema enforcement, governance, BI support, and streaming built in. The promise was simple:Continue reading “The Rise of the Multimodal Lakehouse”
80% of AI Startups Are Built on Chinese Models: The New Reality for 2026
Chinese developers have overtaken American creators in open-model downloads, with partners at Andreessen Horowitz estimating that nearly 80% of new AI startups are now building on Chinese open-source infrastructure. This shift in developer mindshare stems from a vacuum in the US market: while both nations possess a comparable number of active labs, American releases haveContinue reading “80% of AI Startups Are Built on Chinese Models: The New Reality for 2026”
Legal AI Unpacked: What Works, What Fails, What’s Next
Law firms were early adopters of tools for searching and classifying large document collections, so their current enthusiasm for generative AI follows a familiar pattern. Business press coverage reinforces this interest: articles forecasting the disruption of knowledge work routinely single out legal services as particularly vulnerable to automation, given the profession’s reliance on text-intensive research,Continue reading “Legal AI Unpacked: What Works, What Fails, What’s Next”
AI’s biggest enterprise test case is here
Subscribe • Previous Issues Legal AI Unpacked: What Works, What Fails, What’s Next Law firms were early adopters of tools for searching and classifying large document collections, so their current enthusiasm for generative AI follows a familiar pattern. Business press coverage reinforces this interest: articles forecasting the disruption of knowledge work routinely single out legal services asContinue reading “AI’s biggest enterprise test case is here”
Gemini 3: Google’s Pitch vs. Users’ Reality
With each new foundation-model launch, the provider arrives with a familiar script: emphasize the novel capabilities, publish benchmark charts, and explain why this version is different from both its predecessors and its rivals. Users, for their part, increasingly assume that any new flagship model will proclaim state-of-the-art scores on an array of benchmarks. I’ve evenContinue reading “Gemini 3: Google’s Pitch vs. Users’ Reality”