AI Trends and Innovations

What are the trends that will shape fintech in 2026? Here is my top 10 list. 𝟭. 𝗦𝘁𝗮𝗯𝗹𝗲𝗰𝗼𝗶𝗻𝘀 as settlement infrastructure Stablecoins will be used at the settlement layer of financial services to bypass cross-border delays, cut-off times, trapped liquidity, and fee opacity in existing rails. They will sit underneath banks and payment schemes for specific B2B, treasury, and platform payout flows rather than replacing them. 𝟮. Commerce and payments become increasingly 𝗮𝗴𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 An increasing share of commerce and payment activity will be initiated by software agents outside of pilots, as shared protocols, governance models, and accountability frameworks compete for adoption across the value chain. 𝟯. 𝗙𝗶𝗻𝘁𝗲𝗰𝗵𝘀 reclaim the 𝗯𝗮𝗻𝗸𝗶𝗻𝗴 stack More fintechs will pursue banking licences to gain direct control over deposits, settlement, and economics, primarily to reduce dependence on sponsor banks and external balance sheets rather than to operate as full-service banks. 𝟰. The rise of 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 fintechs A new generation of fintechs is being built with AI embedded into core operations by default, allowing them to operate at lower marginal cost and handle higher volumes vs. legacy operating models. 𝟱. The 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗮𝗿𝘀 escalate As AI agents become widespread, incumbents and challengers will increasingly compete for control of the agentic layer. Incumbents will embed agents into existing platforms, while challengers will position agents above multiple services to capture distribution. 𝟲. Fintech play moves 𝗳𝗿𝗼𝗺 𝗯𝗿𝗲𝗮𝗱𝘁𝗵 𝘁𝗼 𝗱𝗲𝗽𝘁𝗵 Fintech competition will shift from broad coverage to execution within specific industries. Advantage will come from handling sector-specific cash flows, risk, and workflows, favouring embedded vertical players over horizontal platforms. 𝟳. Increased fintech 𝗰𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 activity Fintech consolidation will increase as firms acquire capabilities rather than build them internally. Infrastructure providers will add vertical functionality, scaled technology firms will fill capability gaps, and incumbents will consolidate for defense. 𝟴. 𝗙𝗿𝗮𝘂𝗱 shifts to agent manipulation Fraud will increasingly target agent-driven workflows rather than individual accounts or cards. Attackers will influence outcomes through input manipulation, synthetic interactions, and falsified context. 𝟵. Banks 𝘁𝗼𝗸𝗲𝗻𝗶𝘀𝗲𝗱 𝗱𝗲𝗽𝗼𝘀𝗶𝘁𝘀' play Banks will expand focus on tokenised deposits to retain control over settlement and liquidity, particularly in wholesale and treasury contexts. 𝟭𝟬. 𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 moves to the infrastructure layer Tokenization will advance where it improves core market infrastructure, with adoption concentrated in wholesale uses such as settlement, collateral management, and fund administration. What's missing? Opinions: my own 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

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

🚨 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

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.

𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗮𝗻 𝗔𝗜 𝗦𝗧𝗥𝗔𝗧𝗘𝗚𝗬 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆? This is one of the clearest roadmap you’ll ever get to build your own: ⬇️ 1. 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗚𝗼𝗮𝗹 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 (𝗧𝗵𝗲 𝗖𝗼𝗿𝗲): This is your strategic north star — where you define your ambition and guide every downstream decision. • Drivers → Why are you doing this? Clarifies the business/tech forces pushing AI forward.   • Value → What are you aiming to achieve? Links AI directly to measurable outcomes.   • Vision → Where is this going long-term? Provides inspiration and direction across teams.   • Alignment → Is everyone rowing in the same direction? Ensures synergy. • Risks → What could go wrong? Sets the baseline for governance and responsible AI.   • Adoption → Who will actually use it? Anticipates friction and enables change management. 📍 This is the master blueprint — Without this, you’re just building disconnected POCs. No clear target = no impact. 2. 𝗔𝗹𝗶𝗴𝗻𝗲𝗱 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗙𝗶𝘁 𝗬𝗼𝘂𝗿 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀): This is where your AI ambition meets the reality of your broader enterprise. • Business Strategy → AI must serve the core business goals — not exist as a side project.   • IT Strategy → Ensures your infrastructure can support scalable AI.   • R&D Strategy → Aligns innovation with AI capabilities and funding priorities.   • D&A Strategy → Without data strategy, no AI strategy will scale. • (...) Strategy → ... 📍 Connect AI to the real levers of power in your organization — so it doesn’t get siloed or shut down. 3. 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗮𝗸𝗲 𝗜𝘁 𝗥𝗲𝗮𝗹):   Once you know what you want to do, this defines how you’ll deliver it at scale. • Governance → Sets up ethical, legal, and operational oversight from day one.   • Data → Builds the pipelines and quality foundations for smart AI.   • Engineering → Equips you with the technical backbone for deployment.   • Technology → Selects the right tools, platforms, and architecture.   • Organization → Assigns ownership and accountability.   • Literacy → Ensures the workforce can actually work with AI. 📍 This is your AI engine room — without it, strategy stays theoretical. 4. 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 (𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲):   Now it’s time to build — but with structure and intent. • Ideation/Prioritization** → Surfaces the best use cases, aligned with strategy.   • Use Cases → Translates goals into concrete applications and MVPs.   • Buy-Build → Decides how to deliver: in-house, outsourced, or hybrid.   • Change Management → Drives real adoption beyond pilots.   • Value/Cost Management → Measures success and ensures scalability. 📍 This is where value is realized — where strategy finally touches the customer and the business. 𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗼𝗿𝗸 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸: 𝗙𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱, 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲! Graphic source: Gartner

AI holds great potential for the semiconductor industry and will kick-start the next round of innovation for faster, cheaper and more energy-efficient computation – that was my message today at SPIE Advanced Lithography + Patterning. I discussed the potential and the challenges that AI holds for our industry.   The potential is clearly huge. AI is rapidly integrated into applications, and high-performance compute is expected to underpin growth towards $1 trillion of semiconductor sales by 2030. The challenges are around the computing needs of AI models and related energy consumption. The compute workload of training a leading AI model has increased 16x every 2 years in recent years – much faster than the increase in computing power delivered by Moore’s law, which is about 2x every 2 years. The energy needed to train a leading model has not grown so steeply but still rose 10x every 2 years. This computing need has been met by building supercomputers and massive data centers. If you extrapolate these trends, training a leading AI model would need the entire world-wide electricity supply in about 10 years. That’s clearly not realistic, so the trend has to break, by training algorithms becoming more efficient and by chips becoming more efficient. In other words, the needs of AI will stimulate immense innovation in chip design and manufacturing – and the potential value of AI to our society will put urgency and funding behind that drive. As a consequence, chip makers are pulling all levers to accelerate semiconductor scaling. This includes lithographic “2D” scaling: shrinking the dimensions of transistors to pack more into a square millimeter. It will also include “3D” integration, with innovations like backside power delivery, transistor designs like gate-all-around, as well as stacking chips in the package, where holistic lithography will play a critical role to deliver performance requirements. ASML will support these trends through a comprehensive, holistic lithography portfolio. Our 0.33 NA/0.55 NA EUV lithography systems allow chip makers to shrink dimensions at the lowest possible cost on their critical layers, while tightly matched and highly productive DUV systems will continue to reduce cost. More than ever, metrology and inspections tools – whose data is fed into lithography control solutions that keep the patterning process operating within tight specs to deliver the highest possible production yields – will be essential to deliver 2D scaling and 3D integration processes. 3D integration requires wafer-to-wafer bonding, and we have demonstrated the capability to map the stresses and distortions that bonding creates and to compensate for them, reducing overlay errors for post-bonding patterning by 10x or more.   It was a pleasure catching up with the industry’s lithography and patterning experts in San Jose. I’m excited to see our collective innovation power having a go at these challenges. Together, we will push technology forward.

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

I think AI agentic workflows will drive massive AI progress this year — perhaps even more than the next generation of foundation models. This is an important trend, and I urge everyone who works in AI to pay attention to it. Today, we mostly use LLMs in zero-shot mode, prompting a model to generate final output token by token without revising its work. This is akin to asking someone to compose an essay from start to finish, typing straight through with no backspacing allowed, and expecting a high-quality result. Despite the difficulty, LLMs do amazingly well at this task! With an agentic workflow, however, we can ask the LLM to iterate over a document many times. For example, it might take a sequence of steps such as: - Plan an outline. - Decide what, if any, web searches are needed to gather more information. - Write a first draft. - Read over the first draft to spot unjustified arguments or extraneous information. - Revise the draft taking into account any weaknesses spotted. - And so on. This iterative process is critical for most human writers to write good text. With AI, such an iterative workflow yields much better results than writing in a single pass. Devin’s splashy demo recently received a lot of social media buzz. My team has been closely following the evolution of AI that writes code. We analyzed results from a number of research teams, focusing on an algorithm’s ability to do well on the widely used HumanEval coding benchmark. You can see our findings in the diagram below. GPT-3.5 (zero shot) was 48.1% correct. GPT-4 (zero shot) does better at 67.0%. However, the improvement from GPT-3.5 to GPT-4 is dwarfed by incorporating an iterative agent workflow. Indeed, wrapped in an agent loop, GPT-3.5 achieves up to 95.1%. Open source agent tools and the academic literature on agents are proliferating, making this an exciting time but also a confusing one. To help put this work into perspective, I’d like to share a framework for categorizing design patterns for building agents. My team AI Fund is successfully using these patterns in many applications, and I hope you find them useful. - Reflection: The LLM examines its own work to come up with ways to improve it. - Tool use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data. - Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on). - Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would. I’ll elaborate on these design patterns and offer suggested readings for each next week. [Original text: https://lnkd.in/gSFBby4q ]

What do two decades of innovation research reveal about staying power in a shifting world?   This year’s “Most Innovative Companies” report does more than spotlight today’s leaders. It explores what it takes to lead consistently through change, and how innovation excellence has evolved alongside digital disruption, AI acceleration, and growing geopolitical complexity. One of the many findings: Over the past two decades, #VentureCapital has served as an early signal for where technological disruption is heading. In 2005, #IoT led the pack. Today? #GenAI and broader AI applications have taken center stage, commanding the lion’s share of VC interest. Innovation capital is making a clear bet on AI’s disruptive power: ➡️ Agentic AI is rewriting the rules, performing complex tasks like debugging code or generating prototypes autonomously ➡️ Product development cycles are compressing, with some companies seeing up to a 60% faster time-to-concept ➡️ Software engineering is being redefined, as Satya Nadella notes: agents now write 30% of Microsoft’s code This isn’t just a tech trend, it’s a strategic signal for investors, corporates, and founders alike. And it’s redefining the innovation talent model and competitive tempo. What does it take for Europe to be at the forefront of this innovation? 🔗 Read more about it in our full report: https://on.bcg.com/44kEuw7 #Innovation #BCG