AI in Knowledge Work Productivity

Wikipedia traffic is collapsing — and it’s not just because of AI. Wikipedia just reported an 8% drop in human visits in just a few months. The reason? AI systems — the same ones trained on Wikipedia — are now answering questions instead of sending users there. The free encyclopedia is being replaced by the knowledge it taught. That irony stopped me cold. I’ve always seen Wikipedia as the internet’s moral compass — messy, human, collaborative. When I was learning about anything new, I didn’t go for perfection. I went for context. Now I rarely visit it. AI gives me the answer instantly — but never the understanding that came from scrolling, cross-checking, exploring footnotes. Somewhere along the way, convenience quietly replaced curiosity. Here’s what’s really going on beneath the numbers: → AI is not just summarizing information — it’s absorbing the audience that once sustained the sources. → When answers appear directly on search pages, the human loop of reading, editing, and donating breaks. → And as fewer humans visit, fewer volunteers contribute — shrinking the very ecosystem AI depends on. It’s the classic paradox of automation: AI is killing the teachers it learned from. If knowledge itself is becoming automated, we need to rebuild the habit of participation. Here’s what I believe that looks like: ✅ Credit and link back to the human sources behind AI summaries. ✅ Support open, editable knowledge platforms — not just polished AI outputs. ✅ Remember that understanding comes from reading, not just receiving. Because if we stop feeding the commons of human knowledge, We won’t just lose Wikipedia — We’ll lose the curiosity that made the internet worth exploring in the first place. #AI #Wikipedia #KnowledgeEconomy #AIEthics #Publishing #InformationFuture #DigitalCulture

Everyone's talking about LLMs. I went a different direction 🧠 While everyone's building RAG systems with document chunking and vector search, I got curious about something else after Prof Alsayed Algergawy and his assistant Vishvapalsinhji Parmar's Knowledge Graphs seminar. What if the problem isn't just retrieval - but how we structure knowledge itself? 🤔 Traditional RAG's limitation: Chop documents into chunks, embed them, hope semantic search finds the right pieces. But what happens when you need to connect information across chunks? Or when relationships matter more than text similarity? 📄➡️❓ My approach: Instead of chunking, I built a structured knowledge graph from Yelp data (220K+ entities, 555K+ relationships) and trained Graph Neural Networks to reason through connections. 🕸️ The attached visualization shows exactly why this works - see how information naturally exists as interconnected webs, not isolated chunks. 👇🏻 The difference in action: ⚡ Traditional RAG: "Find similar text about Italian restaurants" 🔍 My system: "Traverse user→review→business→category→location→hours and explain why" 🗺️ Result: 94% AUC-ROC performance with explainable reasoning paths. Ask "Find family-friendly Italian restaurants in Philadelphia open Sunday" and get answers that show exactly how the AI connected reviews mentioning kids, atmosphere ratings, location data, and business hours. 🎯 Why this matters: While others optimize chunking strategies, maybe we should question whether chunking is the right approach at all. Sometimes the breakthrough isn't better embeddings - it's fundamentally rethinking how we represent knowledge. 💡 Check my script here 🔗: https://lnkd.in/dwNcS5uM The journey from that seminar to building this alternative has been incredibly rewarding. Excited to continue exploring how structured knowledge can transform AI systems beyond what traditional approaches achieve. ✨ #AI #MachineLearning #RAG #KnowledgeGraphs #GraphNeuralNetworks #NLP #DataScience 

A lot of AI engineers (even sharp ones) get seduced by the cool factor of vector databases. Cosine similarity, ANN search... it all sounds cutting-edge. But when you're building a Retrieval-Augmented Generation (RAG) pipeline, you're not just doing retrieval. You're orchestrating a semantic symphony between memory, context, and reasoning. And that's where many go off the rails. ❌ The Mistake: Vector First, Think Later Vector DBs are fantastic if: • Your knowledge is flat, unstructured, and mostly text • You want fast nearest-neighbor search over embeddings • You're okay with opaque black-box retrieval But the moment your domain knowledge has structure, hierarchies, relationships, or rules that need to be preserved across hops... vector search starts hallucinating. Hard. Because embedding space flattens knowledge. It smears out the sharp logic. It doesn't understand that "Paris is the capital of France and a city in Europe and has museums related to Impressionism." Vector DB just knows "Paris" is semantically close to "Eiffel Tower." Wow. Groundbreaking. 🧭 What You Should Be Using: Knowledge Graphs If your use case has: • Ontologies (types, classes, hierarchies) • Multi-hop reasoning (A→B→C) • Causality or directionality (X leads to Y, not just related to) • Entity disambiguation (which "Apple" are we talking about?) • Need for traceability and explainability (the why behind the answer) Then a Knowledge Graph (KG) is your divine weapon. Graphs don't just store facts. They encode logic, preserve causality, and let you do symbolic + neural hybrid search. They let you model the world like the world actually works... not just as a soup of cosine-clustered tokens. 🧪 Real-World Case: Ask a medical LLM powered by a vector DB: Can ibuprofen be taken with aspirin? You might get a generic answer scraped from a webpage. Ask the same question in a KG-powered RAG. The graph knows: Ibuprofen is an NSAID. Aspirin is an antiplatelet. There's a potential drug interaction due to increased bleeding risk. This depends on patient profile → age → comorbidities → other meds It can trace a path through nodes and edge types to construct a reasoned answer. This is not just retrieval. This is inference. 🔮 Where This Is Going The future of RAG is hybrid: 🔸️Embeddings for semantic breadth 🔸️Graphs for logical depth You'll embed the leaves of the tree... but you'll walk the branches with graph logic. 🎯 TLDR for the Impatient: Vector DBs are great for fuzzy recall. Knowledge Graphs are necessary for precise reasoning. And most AI engineers forget that precision is not optional in high-stakes domains like medicine, law, or finance. If your system needs to think, not just parrot, start with the graph. #database #vector #embeddings #knowledgegraphs #algorithms #computerscience #software #tech #medicine #law #finance #AI #RAG #LLM

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

Adopting AI tools is easy. Reimagining how we work with them is the real transformation. Across many organizations, teams are being asked to “adopt AI” without the time, training or clarity they need to feel confident. When that happens, progress becomes fragmented—some people race ahead, others hesitate, and morale drops under the weight of confusion. Real AI transformation requires more than deploying technology. It demands deeper shifts that help people work differently and unlock value: → Change management to guide teams through new ways of working → Skilling to empower every employee to thrive in an AI-powered environment → Process understanding to ensure AI augments what matters most → Technology that’s usable, ethical and aligned with business goals As this Forbes article shares, the organizations that succeed will be the ones that treat AI adoption as a human journey, not just a technical one. When teams feel equipped, supported and included in shaping the path forward, that’s when AI truly delivers. What support are you giving your teams to learn and experiment with AI? https://lnkd.in/g2pXBtjm

Your brain on AI: One of the first studies measuring what ChatGPT use does to our brain MIT researchers tracked 54 people writing essays using ChatGPT, web search, or just their brains—while monitoring neural activity with EEG. The findings are striking: 🧠 Brain connectivity weakened with more AI support. ChatGPT users showed the least neural engagement. 🔍 Memory collapsed. 83% of ChatGPT users couldn't quote their own essays minutes later, vs. near-perfect recall without AI. ⚡ "Cognitive debt" accumulated. When ChatGPT users later wrote without AI, their brains showed weakened connectivity compared to those who practiced unassisted writing. 🎨 Creativity declined. AI-assisted essays were statistically more uniform and less original. The twist: Strategic timing matters. Using AI after initial self-driven effort preserved better cognitive engagement than consistent AI use from the start. This isn't anti-AI—it's about understanding the trade-offs. While AI-generated essays scored well initially, participants showed signs of cognitive atrophy: diminished critical thinking, reduced memory encoding, and less ownership of their work. The takeaway: We need to enhance, not replace, human thinking as we integrate these powerful tools. Full study here: https://lnkd.in/e-6urMD8 Note: This is a pre-print study awaiting peer review.

A brilliant idea isn’t a fact—until it is. Many groundbreaking discoveries seem obvious only in hindsight, once they unify a web of seemingly isolated facts into a general principle. Before we connected the dots between evolution, genetics & material science, silk was just a thread, proteins were just biological molecules, & genes were just codes. But once we saw their relationships, we unlocked deep truths about how nature builds materials at every scale. What If AI Could Think in Relationships Instead of Just Memorizing? Most AI today doesn’t work this way. It merely predicts the next token, unaware of whether its own output is meaningful, correct, or groundbreaking. They: ❌ Lack true reasoning—they do not verify if their responses make sense. ❌ Cannot correct themselves—once they generate something, they have no mechanism to reflect and refine their own ideas. ❌ Do not connect ideas deeply—they retrieve, not discover. 💡 SciAgents does something different. Rather than treating knowledge as isolated facts, it builds a massive relational graph, connecting every concept and idea to others. Then, a team of AI agents explores this graph, not just by taking the shortest path between ideas, but by wandering through unexpected links. How SciAgents Reasons over Graphs ▶️Instead of taking the shortest path between two ideas (which can be too direct & limiting), SciAgents samples diverse paths through a powerful algorithm that explores ever-growing sets of diverse waypoints. This allows it to natively explore broader, richer relationships—leading to unexpected discoveries. ▶️For example, to explore the connection between silk and energy efficiency, SciAgents didn’t just look at direct links. It uncovered intermediate concepts like biocompatibility, multifunctionality & structural coloration, revealing new ways to design bioinspired materials that human researchers might have overlooked. Why does this matter for building better AI for science and beyond? 1⃣Generalization is the key to intelligence. Memorization alone won’t get AI to true reasoning—but structuring knowledge in a relational way can. 2⃣SciAgents goes beyond predicting words. It constructs maps of ideas by conceptual blueprints, from genes encoding proteins to evolutionarily refined materials like silk, and extrapolates new designs. 3⃣It refines its own outputs. Rather than passively generating text, SciAgents’ multi-agent system debates, critiques, and improves hypotheses, making its discoveries deeper and more reliable. Graph-based reasoning plus multi-agent collaboration is not just a better way for AI to think—it’s likely on a critical path towards AGI. The ability to form deep, structured insights from sparse information is what separates mere computation from true intelligence. A. Ghafarollahi, M.J. Buehler, SciAgents: Automating Scientific Discovery Through Bioinspired Multi-Agent Intelligent Graph Reasoning, Adv. Materials, DOI: 10.1002/adma.202413523, 2025

AI is not just accelerating research—it is quietly reshaping who holds authority over knowledge.   Artificial intelligence now mediates discovery, reorganizes scholarly labour, and filters access to vast scientific literatures. At the same time, generative models capable of producing text, images, and data at scale introduce new vulnerabilities: • blurred authorship and accountability • mounting pressures on peer review • growing challenges to reproducibility   These risks coincide with a deeper political-economic shift. The centre of gravity in AI research has moved decisively from universities to private laboratories with privileged access to data, compute, and engineering talent. As frontier models become more proprietary and opaque, universities increasingly struggle to interrogate, reproduce, or contest the systems on which scientific inquiry now depends.   In a new draft article (with Hui-chieh Loy), we argue that these developments do more than threaten productivity norms: they challenge research integrity and erode traditional bases of academic authority.   Rather than competing with corporate labs at the technological frontier, universities can sustain legitimacy by strengthening roles that cannot be easily automated or commercialized: - exercising judgment over research quality amid synthetic abundance - curating provenance, transparency, and reproducibility - acting as ethical and epistemic counterweights to concentrated private power   In an era of informational excess, the future authority of universities may lie less in maximizing discovery alone than in sustaining the institutional conditions under which knowledge remains credible, contestable, and publicly valued.   📄 Draft available on SSRN: https://lnkd.in/gGifTmUz We’d love to hear your thoughts: How is AI changing authorship, peer review, or research trust in your field?   Illustration by Margarita Yudina, capturing the tension between automated scale and human judgment. #ArtificialIntelligence #ResearchIntegrity #HigherEducation #AcademicPublishing #SciencePolicy H/T Fakhar Abbas . 1st, Min-Yen Kan, Ben Leong, Hakim Norhashim, Eka Nugraha Putra, Araz Taeihagh, Tsuhan Chen, Melvin Yap, Audrey Yue, and many others for rich discussions on the material presented here. Earlier iterations of this work have benefited from discussions at Lingnan University, Nanyang Technological University Singapore, the National University of Singapore, Peking University, and Shanghai Jiao Tong University. Invaluable research assistance was provided by Yiyang He and Shambhavi Mehra. Errors, omissions, and hallucinations are attributable to the authors alone.

Earlier this summer, Gartner released its Hype Cycle for AI 2025, and it continues to come up in my conversations with leaders. Some placements were expected: • Generative AI is in the Trough of Disillusionment. Enterprises are still investing heavily, but without organizational context, results often disappoint. • AI Agents sit at the Peak of Inflated Expectations: full of promise, but not yet delivering at scale in many organizations. More telling is the placement of Knowledge Graphs on the Slope of Enlightenment, signaling proven value. By mapping relationships between information — like who created a document, what project it supports, or how it ties to a customer — Knowledge Graphs give AI the context to deliver more accurate, useful results. But traditional Knowledge Graphs aren’t enough. They capture how information is connected, but not how employees actually work: their tasks, priorities, who they collaborate with, how they make decisions. At Glean, we call this the Enterprise Graph: the combination of the information your company has and how your people get work done. What excites me most is that by capturing the how of work, AI can shift from reactive — answering questions and following directions — to proactive: flagging risks before they surface, pulling in the right teammates based on their strengths, and recommending the next best action before you even need to ask.

GraphRAG: Teaching LLMs to Connect the Dots 📚 Ever felt like your AI assistant just doesn't get the big picture? Traditional RAG systems are like that friend who remembers random facts but can't quite piece them together. Meet GraphRAG, Microsoft's clever solution to help LLMs see the forest, not just the trees. Imagine trying to solve a puzzle with pieces scattered across different rooms. That's what traditional RAG does - it finds individual pieces but struggles to put them together. GraphRAG creates a map of how all the information fits together. This means LLMs can now understand connections and context in ways they never could before. What all GraphRAG can do?  1. Uncover Hidden Connections GraphRAG is like a detective, finding links between facts even when they're spread out. It helps LLMs tackle complex questions that require understanding how different pieces of info relate to each other. 2. Pinpoint Accuracy GraphRAG uses its knowledge map to find answers that are spot-on and make sense in context. Plus, you can trace each part of an answer back to its source. 3. Unlock Meaningful Insights GraphRAG doesn't just fetch facts, it sees the big picture. It can spot trends, identify themes, and offer insights that would be near impossible to find otherwise. Why This Matters for You? Think about how often you've asked an AI a question and gotten a response that's... close, but not quite right. Or worse, an answer that's just plain wrong. GraphRAG could change all that. It's about making AI assistants that truly understand what you're asking and can give you answers that actually help. What's Next? As GraphRAG like developments mature, we might see: • More intuitive AI assistants that can handle complex, multi-step questions • Better automated research tools that can draw insights from vast databases • AI systems that can explain their reasoning, making them more trustworthy and useful in fields like medicine or law.