WIPO’s global report on IP filings is out and records are being broken. 2024 saw the highest ever patent filings – 3.7 million worldwide. Design filings also peaked at a record 1.6 mln, while trademark filings stabilized after two years of decline. But within this rich trove of data from nearly 150 IP offices, a few deeper insights stand out. First, emerging and developing countries continue to embrace IP-driven growth and transformation, whether driven by the need to diversify engines of growth, support increasing aspirations of local innovators and entrepreneurs, create more attractive investment environments, or simply seek new sources of growth. For the sixth consecutive year, India posts double-digit growth in patent filings, with Türkiye also up some 15%. Among the top 20 countries of origin, 12 saw increases in trademark filings, led by Argentina, Brazil and Indonesia, and with strong growth in upper middle-income economies like Colombia, South Africa, Thailand and Viet Nam. Design filings tell a similar story, with the fastest growth in India, Morocco and Indonesia. What this means is that many emerging economies are following the path of the world’s established innovation powerhouses in using IP as a strategic lever for economic growth, diversification, development and resilience. The next challenge is commercializing more of these filings, so they become real-world products and services. Second, we’re seeing more domestic, or “resident” filings. In areas like trademarks and designs, resident filings have traditionally made up the vast majority (+70%) as local businesses often register IP to protect brands and designs serving domestic markets. Now, we’re seeing the same dynamics in patents. Resident patent filings grew almost 7% last year, the fastest rise since 2016, to 72% of the total. This growth in domestic filings suggests that innovation ecosystems are maturing (even for high-tech discoveries, inventors typically file at home first before expanding abroad). It may also reflect shifts in global trade flows, with some industries becoming more localized. Third, many of the major trends in recent years continue to accelerate. Just as AI and digital innovation dominate the headlines, computer technology remains the top field for patent activity, with its growth outpacing all others. The gender balance in innovation is also improving. The proportion of women inventors in international patent applications has increased from 11.6% in 2010 to 18% last year. Beyond the individual data points, the value of this report lies in what it reveals about the global state of innovation and the direction it’s heading. This year’s WIPI shows that people everywhere continue to believe in the power of IP to protect ideas and incentivize innovation, and it gives WIPO the energy to continue strengthening IP ecosystems everywhere to give these innovators and creators the tools to protect and commercialize their ideas. 🔗 https://ow.ly/gub150XqnE7
AI Trends and Innovations
Explore top LinkedIn content from expert professionals.
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🚨 Introducing the AI Apps 50: Startup Edition Ever wondered how startups are spending their money when it comes to AI? Our team at Andreessen Horowitz worked with Mercury to crunch the numbers and rank the top applications by spend. The list + what we learned from it ⬇️ - Horizontal apps have a slight lead over vertical (60% of the list). This includes general assistants (ex. Perplexity) and SIX different meeting support tools (ex. Fyxer AI). But, it also encompasses creative tools and vibe coding tools that are used in roles across orgs. - Vertical apps can augment human labor...or replace it. We're mostly seeing the former - but five companies on the list allow customers to "hire AI" (ex. Crosby Legal, Cognition, 11x). Labor augmenters mostly assist with customer service, sales, and recruiting. - Vibe coding has landed in enterprises. It's not just a prosumer trend! Number three on the list, below OpenAI and Anthropic? Replit. Other listmakers in the category include Lovable and Emergent, while Cursor made the ranks for more technical users. - Products are making the consumer -> enterprise jump. 12 cos also appeared in our most recent Consumer AI Top 100 - almost all of which started out B2C and have migrated B2B over time. In fact, 70% of listmakers are available for individual use (no enterprise license needed)! Check out the full report: https://lnkd.in/gmMvfvSv
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AI is rapidly moving from passive text generators to active decision-makers. To understand where things are headed, it’s important to trace the stages of this evolution. 1. 𝗟𝗟𝗠𝘀: 𝗧𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗙𝗹𝘂𝗲𝗻𝗰𝘆 Large Language Models (LLMs) like GPT-3 and GPT-4 excel at generating human-like text by predicting the next word in a sequence. They can produce coherent and contextually appropriate responses—but their capabilities end there. They don’t retain memory, they don’t take actions, and they don’t understand goals. They are reactive, not proactive. 2. 𝗥𝗔𝗚: 𝗧𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗔𝘄𝗮𝗿𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Retrieval-Augmented Generation (RAG) brought a major upgrade by integrating LLMs with external knowledge sources like vector databases or document stores. Now the model could retrieve relevant context and generate more accurate and personalized responses based on that information. This stage introduced the idea of 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗮𝗰𝗰𝗲𝘀𝘀, but still required orchestration. The system didn’t plan or act—it responded with more relevance. 3. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 Agentic AI is a fundamentally different paradigm. Here, systems are built to perceive, reason, and act toward goals—often without constant human prompting. An Agentic system includes: • 𝗠𝗲𝗺𝗼𝗿𝘆: to retain and recall information over time. • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: to decide what actions to take and in what order. • 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: to interact with APIs, databases, code, or software systems. • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆: to loop through perception, decision, and action—iteratively improving performance. Instead of a single model generating content, we now orchestrate 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀, each responsible for specific tasks, coordinated by a central controller or planner. This is the architecture behind emerging use cases like autonomous coding assistants, intelligent workflow bots, and AI co-pilots that can operate entire systems. 𝗧𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 We’re no longer designing prompts. We’re designing 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 capable of interacting with the real world. This evolution—LLM → RAG → Agentic AI—marks the transition from 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 to 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.
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AI is becoming a make-or-break factor for banks. But success will not depend on their ability to offer #AI, but on their competence in integrating it. Let’s take a look. Banking is forecasted to feel the biggest impact from generative AI among sectors and industries as a percentage of their revenues with the additional value calculated between $200 bn and $340 bn annually (source: McKinsey). But why is the impact so powerful? One of the main reasons is because the abrupt surge of gen AI is exponentially increasing the speed with which #banking is being transformed. That is not to say that the transformation has started with or due to AI. On the contrary: during the past 10 to 15 years banking was already in the middle of transforming from a human-based, relationship-first industry to a more automated and technology-driven business following the #fintech revolution and the ascend of nimbler and more innovative competitors. But AI now does 2 things: — It brings the transition to a new level, across 3 dimensions: speed, outcome and impact. — It turbo-charges one of the biggest challenges in modern FS: the combination of AI and data that brings under the same roof two inherently opposing forces: mass and customization. In other words, AI seems to find a credible answer to achieving hyper-personalization. In a recent report Deloitte has provided realistic examples on how this is done across both cost efficiency and income growth: Cost efficiency: — Workforce acceleration efficiencies across the board: 0–15% of total staff cost — IT development and maintenance acceleration: 10–20% of IT staff cost — Improved credit-risk assessment leading to 10-15% savings in impairment charges — Improved FinCrime/fraud detection reducing litigation/redress charges and fraud losses Income growth: — Next generation market analysis / predictive trading algorithms: 5–7% uplift on trading income — Improved customer retention: 1–2% uplift on fees & commissions — Improved customer acquisition through hyper-personalised marketing: 5-10% uplift from interest income and fees & commissions — Tailored loan pricing based on credit risk assessment: 2–3% increase on net interest income Despite all the excitement around these estimated benefits, success will not be a walk in the park. It will depend on the banks’ ability to integrate AI in a seamless way into their day-to-day operations. Going forward AI will be re-writing much of the scenarios and use cases of the banking value chain. That doesn’t necessarily mean that they will all be different, but most will certainly be enhanced with impact spanning both across the back-end and the front-end. Given that resources are limited, one of the main challenges will be how to identify the ones to focus on. Factors such as #strategy, potential impact and a match with the existing skillset should be guiding the selection process. Opinions: my own, Graphic source and use cases: Deloitte
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Smart materials in this futuristic design shift color and texture based on temperature, motion, or light — turning fashion into adaptive tech. Would you wear it? 🧬 This isn’t sci-fi. + Smart textiles are forecast to grow into a $17.6 billion industry by 2030, driven by innovations in nanomaterials, thermal sensors, and electrochromic coatings. + AeroSkin’s concept shows what happens when AI, material science, and design collide — and it raises the question: What happens when your clothes start thinking for you... 🎯 Imagine soldiers with adaptive camouflage. ⚡ Athletes wearing gear that adjusts cooling zones dynamically. 🌆 Or professionals using color-shifting jackets as expressive, data-driven fashion statements. We’ve made phones smart, homes smart, even cars autonomous… yet most of us still wear “dumb fabric.” Maybe the next frontier of computing isn’t a screen — it’s the skin you wear. #WearableTech #SmartMaterials #Innovation #FutureOfFashion #AI #ChameleonJacket #AeroSkin #TechDesign #MaterialScience #AdaptiveClothing
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OpenAI, calm down. I can only type so fast. Let’s cover the new releases and why they matter: Since last Thursday, OpenAI has launched ChatGPT Pulse, Commerce in chat, parental controls, and Sora 2 + Sora app. Here’s the full rundown: → ChatGPT Pulse (preview): Pro users on mobile can now get a daily, proactive research cards based on your chats, feedback, and optional calendar. You can thumbs-up/down and curate what shows next. Why does this matter? AI is getting more proactive! You won’t have to prompt all the dang time! → Commerce in chat: OpenAI rolled out Instant Checkout using the open Agentic Commerce Protocol (built with Stripe). You can already buy from U.S. Etsy sellers inside ChatGPT, with Shopify merchants “coming soon.” Why does this matter? ChatGPT was already being used to shop and browse, now it’s going fully vertical. More “complete” experiences will happen inside ChatGPT. → Parental controls: New teen safety features now let parents link accounts, set limits, and dial down sensitive content. It’s imperfect (and will evolve), but this seems to be the most concrete teen-focused guardrail set we’ve seen from them to date. Why does this matter? AI is becoming more integrated into our personal lives and full families are signing up for it as an app they use together, this was the obvious next step. → Sora 2 + Sora app: A major step-up in physical realism and control, now with synced dialogue/SFX, plus a new (currently) invite-only iOS app that looks and feels like TikTok, but for AI-generated video only. Cameos let friends appear with consent and revocable control. Available in US/Canada first. Why does this matter? Meta and TikTok own a huge part of the commerce chain because of a social-first strategy, OpenAI wants a piece of the pie. Overall: more proactive and personalized systems, more vertical integration, more connections with family and friends, and more weirdness still to come. What say you on these releases? Stay tuned for more. Dev Day is in less than a week 👀
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The real AI war is being fought in the deployment layer. While everyone obsesses over GPT and Claude, these platforms, which process billions of inferences daily, are quietly determining who actually wins in AI. In just 12 months, the AI deployment landscape transformed more dramatically than cloud infrastructure did in 5 years. Big one-year changes in Mosaic scores (company health and trajectory metric) across the model deployment & serving market signal a fundamental reshaping of the AI infrastructure landscape. Market leaders redefining AI deployment: → Databricks dominates with the highest Mosaic score and $100B valuation, reaching $2.6B revenue with 60%+ growth → Baseten’s rapid rise just attracted a fresh $150M in funding, driven by their serverless GPU infrastructure → Together AI capitalized on generative AI demand, raising $533.5M at a $3.3B valuation with in-house LLMs using reinforcement learning → VESSL AI and Modal are winning with pay-per-use GPU compute models Current market leaders are split into distinct camps that will likely converge or consolidate sooner than we all expect. → Infrastructure specialists like Together AI and Fireworks AI focus on serverless inference for production environments. → Platform plays like Databricks leverage their existing enterprise relationships and massive resources to both build and buy innovation. → Developer-centric players like Modal attract startups with zero fixed costs. The winners share proven technical foundations driving their success: ↳Scale: Hugging Face hosts 500,000+ models for 5 million developers ↳Architecture: Serverless infra eliminates DevOps complexity (Baseten, Modal, Fireworks) ↳Business Model: Pay-per-use pricing removes barriers for growing startups ↳AI-Native: 80% of Databricks’ new databases are now AI-created vs. 30% last year ↳Generative AI Focus: Together AI and Fireworks built specifically for LLM inference demands Critically, these platforms combine efficient compute, intelligent orchestration, and developer-friendly abstractions – creating defensible moats against hyperscaler competition. In turn, this makes these companies prime acquisition targets for the established cloud leaders. With 96% of enterprises deploying AI models (up from 25% in 2023), infrastructure choice has become strategic. Massive YoY revenue growth numbers across both the hyperscalers and emerging players demonstrate the market's trajectory. While the world debates which LLM is smartest, the companies controlling how those models actually reach users are building the real moats. Incredible recent funding rounds and major acquisitions (Nvidia acquiring OctoAI) will define which platforms become the become dominant AI infrastructure players. P.S. Want more insights on the companies powering AI deployments? Drop "deploying" in the comments for *free* access to CB Insights' data and insights on the model deployment & serving market.
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2026 is the year AI stopped being a feature. It became part of the operating system of the enterprise. Across the latest 2026 flagship reports, one message is clear: this is the inflection point for enterprise AI. Despite different perspectives, they converge on 3 strategic signals: 1️⃣ Agentic AI is shifting from experimentation to enterprise infrastructure. 2️⃣ Value comes from redesigning work, not layering automation onto legacy processes. 3️⃣ Strong data foundations and governance enable speed rather than slow it down. This is less about adopting technology and more about redesigning how the enterprise actually runs. For any executive planning the next 12–24 months, these are the anchor reports: 1/ BCG: AI Radar 2026 🔗 https://lnkd.in/gckyZdsV AI transformation is shifting from a CIO-led initiative to a CEO-led mandate. 2/ IBM Institute for Business Value: The Enterprise in 2030 🔗 https://lnkd.in/g_u9d8t4 Five bold predictions for the "smarter enterprise" of 2030. 3/ McKinsey: Global Tech Agenda 2026 🔗 https://lnkd.in/gJGsqpkT Why top CIOs are evolving from cost managers to strategy architects who rewire enterprises for growth. 4/ Deloitte: State of AI in the Enterprise 2026 🔗 https://lnkd.in/gE3-e9XT Exploring the edge of AI adoption, including the rise of sovereign, agentic, and physical AI. 5/ World Economic Forum: Proof over Promise 🔗 https://lnkd.in/gQtVQN8t A practical anchor on how organisations are scaling AI and turning it into outcomes. 6/ Microsoft WorkLab: Agents are here: Is your company prepared? 🔗 https://lnkd.in/g5TXhQyW A readiness & adoption focused on people, process, culture, and governance for agentic AI rollout. 7/ Google Cloud: AI Agent Trends 2026 🔗 https://lnkd.in/gU7ucWye A trends report mapping how AI agents are being used across industries. 8/ Accenture: The New Rules of Platform Strategy in the Age of Agentic AI 🔗 https://lnkd.in/gpreVptN A playbook for reinventing platform strategy to align people, systems, and agentic AI for growth. Now it’s time to consider how deeply you’re willing to redesign the enterprise around AI. These decisions compound. The workflows you hand to agents, the data foundations you build, and the controls you put in place will shape your performance for years. ♻️ Repost to help someone get their AI strategy together. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI.
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PwC is Training Junior Accountants to Work Like Managers AI is changing the job before it even starts. According to PwC’s AI assurance leader Jenn Kosar, automation is taking over much of the repetitive audit work traditionally assigned to entry-level staff. As a result, new hires are being trained to supervise AI and take on responsibilities that used to be handled by accountants with 3–4 years of experience. Key changes in PwC’s approach: • Shift in skills focus: Critical thinking, negotiation, and professional skepticism are now core from day one. • AI integration: Routine tasks are automated, freeing staff to focus on higher-value work. • Revamped training: Career development now includes “assurance for AI” and preparing accountants to guide tech-driven workflows. PwC says these changes are designed to prepare staff for the future of accounting where technology handles the volume, and humans handle the judgment. This is where our industry is going. The sooner we adapt, the better we come out on the other side. #AI #Accounting #PwC #FutureOfWork #Careers