Inside LinkedIn’s AI ‘cookbook’: How engineers scaled GenAI to 1.3B users by distilling models to 220M parameters for a 10x throughput gain.

Everyone's racing to build AI agents. Few are thinking about data permissions. Box spent two decades building a boring moat- a detailed map of who can touch what document, when, why, and with what proof. This invisible metadata layer is now their key moat against irrelevance. Q2 FY26: → Revenue: $294M (+9% YoY) → Gross margin: 81.4% → Operating margin: 28.6% → Net retention: 103% → Enterprise Advanced: 10% of revenue (up from 5%) Slow-growth, high-margin business at a crossroads. The Permission Graph Every document in Box has a shadow: its permission metadata. Who created it, modified it, can access it. What compliance rules govern it. Which systems can call it. When an AI agent requests a contract, it needs more than the PDF. It needs proof it's allowed to see it, verification it's the right version, an audit trail. Twenty years of accumulated governance that can't be easily replicated. Why This Matters Now The CEO Aaron Levie recently told CNBC: "If you don't maintain access controls well, AI agents will find the wrong information - leading to wrong answers or security incidents." Every enterprise faces the same AI crisis: scattered data with inconsistent permissions, no unified governance, one breach risking progress. The permission graph solves this. The Context Control Problem Box recently launched Enterprise Advanced: AI agents, workflow automation, document generation. They are adding contextual layers because they see a future where AI agents calling their API while users never see Box. Microsoft owns the experience. Box becomes plumbing. This push is their attempt to stay visible. But it's still Product Rails, not Operating Rails. They're adding features to documents, not deepening their permission moat. The Bull vs Bear Case Bull: Enterprises will pay for bulletproof governance even if transformation happens elsewhere. The permission graph remains valuable. Bear: Microsoft acquires or partners with Varonis + Cloudfuze to recreate the graph. The moat may not be deep enough. Every SaaS Company's Dilemma Box isn't alone. Every legacy SaaS faces the same question: how do you avoid becoming invisible infrastructure? They're all trying the same failing playbook. Add AI features, claim "AI-native," hope the moat holds. Box's advantage: the permission graph is genuinely hard to replicate.  Box's disadvantage: they still think like a document storage company. Market's View Box has 81% gross margins on commodity storage because of the permission graph. Yet the market values them at 24x forward P/E, not pricing the graph premium. The other factor is that Box is led by Aaron Levie. He's a founder who's spent two decades obsessing over one problem: enterprise content governance. ​ That obsession matters now more than ever. ​ The question isn't whether the permission graph has value. It's whether Box can deepen the moat before others make it irrelevant. (Full version sent to subscribers) | 25 comments on LinkedIn

palantir hit $175/share because they understand what 99% of AI companies don't: ontologies. in 2021, the word "ontology" appeared 0 times in their earnings calls. by Q3 2024? 9 times. their US commercial revenue is growing 153% YoY. why? because LLMs are becoming the commodity, while ontologies are becoming the moat. let me explain why most enterprise AI initiatives are failing without one: every enterprise has the same problem: 47 different systems ❗️ 19 definitions of "customer" ❗️ 34 versions of "product"❗️ business logic scattered across 100+ applications ❗️ you throw AI at something like this? it hallucinates. but if you build an ontology first? it gains the context and data depth to be able to reason. palantir figured this out years ago. but here's what palantir doesn't do: verticalize at scale. they're brilliant at defense, government, contracting. but specialized industries need specialized ontologies. take telecommunications. a telco's "customer" isn't just a record - it's: ➕ a subscriber with multiple services ➕ a hierarchy of accounts and sub-accounts ➕ real-time network states ➕ billing cycles across geographies ➕ regulatory compliance per jurisdiction Orgs have tried to standardize this before. but standards aren't ontologies. they're just vocabularies. this is why Totogi has spent so much time and effort building their telco-specific ontology layer while palantir was perfecting horizontal enterprise ontologies, we went deep on telecom's unique semantic complexity. now telcos can deploy AI that takes one action - 'activate new customer' - and correctly translates it across systems that call it 'create subscriber' (BSS), 'provision user' (network), 'establish account' (billing), and 'initialize profile' (CRM). No more manual steps, no more dropped handoffs between systems. palantir proved the model. but they can't be everywhere. the future belongs to industry-specific semantic platforms like Totogi's BSS Magic 🚀 | 18 comments on LinkedIn

How organizations must shift from data-driven to knowledge-driven approaches with knowledge graph powered semantic layers

This is it. This is the conversation every leadership team needs to be having right now. "The Orchestration Graph" by WRITER product leader Matan-Paul Shetrit linked in comments is a must-read. The primary constraint on business is no longer execution. It's supervision. For a century, we built companies to overcome the high cost of getting things done. We built hierarchies, departments, and complex processes — all to manage labor-intensive execution. That era is over. With AI agents, execution is becoming abundant, on-demand, and programmatic. The new bottleneck is our ability to direct, govern, and orchestrate this immense new capacity. The firm is evolving from a factory into an "operating system." Your ORG CHART is no longer the map. The real map is the Orchestration Graph: the dynamic, software-defined network of humans, models, and agents that actually does the work. This isn't just a new tool or a productivity hack. It's a fundamental rewiring of the enterprise. It demands we rethink everything: Structure: How do we manage systems, not just people? Strategy: What work do we insource to our agentic "OS" versus outsource to models-as-a-service? Metrics: Are we still measuring human activity, or are we measuring system throughput and intelligence? This is the WRITER call to arms: The companies that win won't just adopt AI; they will restructure themselves around it. They will build their own Orchestration Graph, with governance and institutional memory at the core. They will treat AI not as a feature, but as the new foundation. At WRITER, this is the future we are building every single day — giving companies the platform to create their own secure, governed, and intelligent orchestration layer. The time to act is now. Read the article. Start the conversation with your leaders. And begin rewiring your firm. | 37 comments on LinkedIn

🚀 When I shared our 2025 goals for the Enterprise Data Organization, one of the things I alluded to was machine-readable column-level metadata. Let’s unpack what that means—and why it matters. 🔍 What: For datasets we deliver via modern cloud distribution, we now provide human - and machine - readable metadata at the column level. Each column has an immutable URL (no auth, no CAPTCHA) that hosts name/value metadata - synonyms, units of measure, descriptions, and more - in multiple human languages. It’s semantic context that goes far beyond what a traditional data dictionary can convey. We can't embed it, so we link to it. 💡 Why: Metadata is foundational to agentic, precise consumption of structured data. Our customers are investing in semantic layers, data catalogs, and knowledge graphs - and they shouldn’t have to copy-paste from a PDF to get there. Use curl, Python, Bash - whatever works - to automate ingestion. (We support content negotiation and conditional GETs.) 🧠 Under the hood? It’s RDF. Love it or hate it, you don’t need to engage with the plumbing unless you want to. ✨ To our knowledge, this hasn’t been done before. This is our MVP. We’re putting it out there to learn what works - and what doesn’t. It’s vendor-neutral, web-based, and designed to scale across: 📊 Breadth of datasets across S&P 🧬 Depth of metadata 🔗 Choice of linking venue 🙏 It took a village to make this happen. I can’t name everyone without writing a book, but I want to thank our executive leadership for the trust and support to go build this. Let us know what you think! 🔗 https://lnkd.in/gbe3NApH Martina Cheung, Saugata Saha, Swamy Kocherlakota, Dave Ernsberger, Mark Eramo, Frank Tarsillo, Warren Breakstone, Hamish B., Erica Robeen, Laura Miller, Justine S Iverson, | 17 comments on LinkedIn

Gartner 2025 AI Hype Cycle: The focus is shifting from hype to foundational innovations Knowledge Graphs are a key part of the shift, positioned on the slope of enlightenment By Haritha Khandabattu and Birgi Tamersoy: Al investment remains strong, but focus is shifting from GenAl hype to foundational innovations like Al-ready data, Al agents, Al engineering and ModelOps. This research helps leaders prioritize high-impact, emerging Al techniques while navigating regulatory complexity and operational scaling. As Gartner notes, Generative AI capabilities are advancing at a rapid pace and the tools that will become available over the next 2-5 years will be transformative. The rapid evolution of these technologies and techniques continues unabated, as does the corresponding hype, making this tumultuous landscape difficult to navigate. These conditions mean GenAI continues to be a top priority for the C-suite. Weaving in another foundational concept, Systems of Intelligence as coined by Geoffrey Moore and reference by David Vellante and George Gilbert: Systems of Intelligence are the linchpin of modern enterprise architecture because [AI] agents are only as smart as the state of the business represented in the knowledge graph. If a platform controls that graph, it becomes the default policymaker for “why is this happening, what comes next, and what should we do?” For enterprises, there is only one feasible answer to the "who controls the graph" question: you should. To do that, start working on your enterprise knowledge graph today, if you haven't already. And if you are looking for the place to learn, network, and share experience and knowledge, look no further 👇 Connected Data London 2025 has been announced! 20-21 November, Leonardo Royal Hotel London Tower Bridge Join us for all things #KnowledgeGraph #Graph #analytics #datascience #AI #graphDB #SemTech 🎟️ Ticket sales are open. Benefit from early bird prices with discounts up to 30%. 2025.connected-data.london 📋 Call for submissions is open. Check topics of interest, submission process and evaluation criteria https://lnkd.in/dhbAeYtq 📺 Sponsorship opportunities are available. Maximize your exposure with early onboarding. Contact us at info@connected-data.london for more.

In this Breaking Analysis we examine how Snowflake moves Beyond Walled Gardens and is entering a world where it faces new competitive dynamics from SaaS vendors like Salesforce, ServiceNow, Palantir and of course Databricks.

The new AI powered Anayltics stack is here…says Gartner’s Afraz Jaffri ! A key element of that stack is an ontology powered Semantic Layer that serves as the brain for AI agents to act on knowledge of your internal data and deliver timely, accurate and hallucination-free insights! #semanticlayer #knowledgegraphs #genai #decisionintelligence

ServiceNow will acquire AI data quality vendor Data.world in a quest for better management of corporate data for AI.

To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren't a good fit to train large language models.

This study, sponsored by Graphwise, explores LLMs and RAG in enterprises and provides statistics from executives across various industries.

🚀 Knowledge graphs are shaping the future of data and AI, and I’m excited to see them featured in the Data Gang’s predictions for 2025! 🚀 Every year I enjoy… | 10 comments on LinkedIn

Ashley Faith hosted Amy Hodler and me on a recent episode about “2025 Predictions” — here are notes I prepared, to review in 2026…

I want to emphasize some things that most people don't seem to understand, specially managers in the AI space. 1. Knowledge Graph/Ontologies without a way to… | 14 comments on LinkedIn

It’s decision time for Galaxy users as Samsung responds to new iPhone threat.

Following its successful Series A funding, DevRev drives forward its mission to revolutionize customer support and product development.

Learn how the 2023 Gartner Hype Cycle™ identifies exciting innovations in artificial intelligence that will be beneficial to business.

Writer CEO May Habib says its semantic graphing approach is an alternative to the chunking process of RAG using vector databases.

Enhancing AI Accuracy: Telco Network Knowledge Graph's Role in Overcoming LLM Hallucinations LLMs are known to create responses that, while appearing valid…

Before gearing up for yet another AI project, ask if it's really the best approach.

Working on a LangChain template that adds a custom graph conversational memory to the Neo4j Cypher chain, which uses LLMs to generate Cypher statements. This…