Tech Ecosystem Collaboration

Generative AI is evolving at metro speed. But the ecosystem is no longer a single track—it’s a complex network of interconnected domains. To innovate responsibly and at scale, we need to understand not just what’s on each line, but also how the lines connect. Here’s a breakdown of the map: 🔴 M1 – Foundation Models  The core engines of Generative AI: Transformers, GPT families, Diffusion models, GANs, Multimodal systems, and Retrieval-Augmented LMs. These are the locomotives powering everything else. 🟢 M2 – Training & Optimization  Efficiency and alignment methods like RLHF, LoRA, QLoRA, pretraining, and fine-tuning. These techniques ensure models are adaptable, scalable, and grounded in human feedback. 🟤 M3 – Techniques & Architectures  Advanced reasoning strategies: Emergent reasoning patterns, MoE (Mixture-of-Experts), FlashAttention, and memory-augmented networks. This is where raw power meets intelligent structure. 🔵 M4 – Applications  From text and code generation to avatars, robotics, and multimodal agents. These are the real-world stations where generative AI leaves the lab and delivers business and societal value. 🟣 M5 – Ecosystem & Tools  Frameworks and orchestration platforms like LangChain, LangGraph, CrewAI, AutoGen, and Hugging Face. These tools serve as the rail infrastructure—making AI accessible, composable, and production-ready. 🟠 M6 – Deployment & Scaling  The backbone of operational AI: cloud providers, APIs, vector DBs, model compression, and CI/CD pipelines. These are the systems that determine whether your AI stays a pilot—or scales globally. 🟡 M7 – Ethics, Safety & Governance  Guardrails like compliance (GDPR, HIPAA, AI Act), interpretability, and AI red-teaming. Without this line, the entire metro risks derailment. ⚫ M8 – Future Horizons  Exploratory pathways like Neuro-Symbolic AI, Agentic AI, and Self-Evolving models. These are the next stations under construction—the areas that could redefine AI as we know it. Why this matters: Each line is powerful in isolation, but the intersections are where breakthroughs happen—e.g., foundation models (M1) + optimization techniques (M2) + orchestration tools (M5) = the rise of Agentic AI. For practitioners, this map is not just a diagram—it’s a strategic blueprint for where to invest time, resources, and skills. For leaders, it’s a reminder that AI isn’t a single product—it’s an ecosystem that requires governance, deployment pipelines, and vision for future horizons. I designed this Generative AI Metro Map to give engineers, architects, and leaders a clear, navigable view of a landscape that often feels chaotic. 👉 Which line are you most focused on right now—and which “intersections” do you think will drive the next wave of AI innovation?

After spending three decades in the aerospace industry, I’ve seen firsthand how crucial it is for different sectors to learn from each other. We no longer can afford to stay stuck in our own bubbles. Take the aerospace industry, for example. They’ve been looking at how car manufacturers automate their factories to improve their own processes. And those racing teams? Their ability to prototype quickly and develop at a breakneck pace is something we can all learn from to speed up our product development. It’s all about breaking down those silos and embracing new ideas from wherever we can find them. When I was leading the Scorpion Jet program, our rapid development – less than two years to develop a new aircraft – caught the attention of a company known for razors and electric shavers. They reached out to us, intrigued by our ability to iterate so quickly, telling me "you developed a new jet faster than we can develop new razors..." They wanted to learn how we managed to streamline our processes. It was quite an unexpected and fascinating experience that underscored the value of looking beyond one’s own industry can lead to significant improvements and efficiencies, even in fields as seemingly unrelated as aerospace and consumer electronics. In today’s fast-paced world, it’s more important than ever for industries to break out of their silos and look to other sectors for fresh ideas and processes. This kind of cross-industry learning not only fosters innovation but also helps stay competitive in a rapidly changing market. For instance, the aerospace industry has been taking cues from car manufacturers to improve factory automation. And the automotive companies are adopting aerospace processes for systems engineering. Meanwhile, both sectors are picking up tips from tech giants like Apple and Google to boost their electronics and software development. And at Siemens, we partner with racing teams. Why? Because their knack for rapid prototyping and fast-paced development is something we can all learn from to speed up our product development cycles. This cross-pollination of ideas is crucial as industries evolve and integrate more advanced technologies. By exploring best practices from other industries, companies can find innovative new ways to improve their processes and products. After all, how can someone think outside the box, if they are only looking in the box? If you are interested in learning more, I suggest checking out this article by my colleagues Todd Tuthill and Nand Kochhar where they take a closer look at how cross-industry learning are key to developing advanced air mobility solutions. https://lnkd.in/dK3U6pJf

During the ascent of #fintech as a disruption driver in #finance, digital banks have been the first and most impactful use case. Let’s take a look at their playbook. The term itself – alternatives include challenger banks or neobanks – characterizes players (usually new entrants) challenging the traditional banking model with a #technology-first approach that involves flexible, branchless, digital-native (mobile) banking, often focusing on or starting from niche segments and customers. An increasingly digital arena, a shift in consumer behaviour and a gap in product and customer focus by incumbents have enabled these new players to challenge the status quo. Their success and proliferation around the globe is a clear sign of agile, digital-first, product-niche strategies prevailing over traditional, monolithic, vertical banking #business models. Whereas different patterns can be identified in their evolutionary path, the successful models can be aggregated to two broad categories: — Greenfield players starting completely from scratch by means of identifying a niche market or segment, often neglected by incumbents, and focusing on seamless customer experience, attractive design, competitive pricing and a digital or mobile only set-up. In terms of strategy two elements clearly stand-out: 1) hyper-growth and scale as the core - sometimes only - metrics (which explains why so many have been unprofitable) 2) an ecosystem play, driven by horizontal partnerships (vs the vertical traditional model). N26, Revolut and Nubank are typical examples of this model. — Large, closed-loop ecosystem players with a non-finance business geared on technology and an anchor in #ecommerce launching (digital) #banking spin-offs as a means of converting (and monetizing) their existing client-base. Most (or almost all) of the examples here come from Asia (i.e. Webank, Kakaobank), mainly due to the set-up of the #economy (lacking a robust, finance architecture and, in effect, benefiting private, BigTech players covering the gap). Webank, for example, is owned by Tencent, China’s largest social-media BigTech company (owner of WeChat, China’s equivalent of Facebook). It has managed to reach a value of $33 billion and a base of more than 320 million active users by focusing on building a modern IT stack (as a competitive edge to traditional banks) and leveraging on the data generated by the Tencent ecosystem (i.e. retail lending credit scoring built on Tencent data, resulted in a non-performing loan ratio of just 1.2%, about half (or less) of the industry average for such non-secured loans). Irrespective of their origins, both models have been (fast) converging to what has become the new holy grail of modern finance: platform #economics and ecosystem plays. These are the concepts that will be defining the boundaries in an increasingly network and technology driven field. Opinions: my own, Graphic source: Momentum Works, Decoding digital banks

The technologies of the future are created and commercialized in innovation hubs that combine scientific excellence with entrepreneurial ambition. There are thousands of such hubs around the world, and our Global Innovation Index (GII) 2025 seeks to shine a light on those doing well through the GII Ranking of World’s Top 100 Innovation Clusters. For the first time, we have included VC data alongside international patent filings and scientific publications. Adding the VC lens has shifted the top of the table slightly, helping to push China’s Greater Bay Area into number one spot, nudging the Tokyo-Yokohama cluster into second, and lifting Silicon Valley from sixth to third spot this year. Beijing was ranked fourth. Each of those clusters led in a different way. Tokyo-Yokohama was the single biggest source of international patent filings, while the Silicon Valley cluster (around San Jose and San Francisco) attracted more venture capital than anywhere else. Beijing led the world in terms of the number of scientific publications. The Greater Bay Area, which encompasses Shenzhen, Hong Kong and Guangzhou, did not lead in any of the three categories, but its strong showings across the board gave it a balanced profile and put it in first place overall. This cluster ranking, as well as our flagship Global Innovation Index (out on 16 September), is designed to help policymakers, business leaders and researchers better understand the local and global innovation landscape, and to design policies that make innovation ecosystems more vibrant. 33 economies are covered by our list of the top 100 clusters, including Germany (which has seven clusters), India and the United Kingdom (four each) and Canada and the Republic of Korea (which has three, like Japan). Propelled by the new methodology and strong performance in VC deals, Indian clusters have made remarkable advancements, with Bengaluru jumping from 56th to 21st position, Delhi to 26th (compared to 63rd) and Mumbai to 46th (compared to 88th). In addition to the dynamic hubs in China and India, six vibrant innovation hubs from middle-income countries also feature in the top 100: Brazil (São Paulo), Egypt (Cairo – the top-100 cluster in Africa), Iran (Tehran), Malaysia (Kuala Lumpur), Türkiye (Istanbul) and Mexico (Mexico City) – which enters the top 100 this year for the first time and makes up the second innovation cluster within Latin America. Outside the top 100, some of the leading middle-income economy innovation clusters are Ankara (Türkiye), Bangkok (Thailand), Buenos Aires (Argentina), Islamabad and Lahore (Pakistan), and Rio De Janeiro and Porto Alegre (Brazil). These clusters show how the combination of strategic investments coupled with supportive policy frameworks can build thriving ecosystems. More: https://lnkd.in/e882jzRp #WIPO #GlobalInnovationIndex #GII2025

FY2026 Signals Joint Defense Tech The Pentagon isn’t looking for more tech. It’s looking for tech that fits the fight. What wins? interoperable, multi-domain, coalition-ready tech that aligns with how the U.S. and its allies will fight. Hear me out… 1. Integration Is the Mission PE 0604826J is the COG for CJADC2. It funds interoperability pilots with NATO, secure data sharing across services, and cross-domain C2 experiments like Bold Quest. Your tech needs to plug into this joint ecosystem. 2. Multi-Domain C2 Is Non-Negotiable The budget holds firm on digital datalinks, secure comms, and allied data exchange. Your tech must talk across domains and allies, don’t expect traction. 3. Rapid Prototyping Isn’t Dead—It’s Evolving RDER may be gone, but its intent lives on. The budget still backs prototypes that can shape joint force design. Demo utility in a joint context and watch your TRL skyrocket. 4. Congress ‘All In on Joint Tech’ is a buying signal. • $400M → Joint Fires Network • $400M → Joint battle management tools • $1B → Accelerated tech fielding • $2B → DIU scaling commercial tech 5. AI/ML, Autonomy, C5ISR—Joint prioritization isn’t just lip service. Budget lines explicitly call out: • Multi-service unmanned systems • Maritime robotics • Coalition-ready EW and ISR

I worked with 70+ companies on circular business models. Here are the top 5 opportunities to make money in the circular economy⭕. 1. Create circular supplies and inputs:   🔄 Develop and provide bio-based and 100% recyclable materials; 2. Develop sharing platforms for the new economy: ♾️ Use the rising market demand for collaborative models for usage, access, or ownership - in huge industries such as fashion, construction or mobility; 3. Create product-as-a-service models: 💡 Innovate and disrupt the traditional market by offering customers the use of products through a lease or pay-for-use arrangement versus the conventional buy-to-own approach. 4. Retain value through product life & use extensions: 📲 Develop opportunities to extend the lifespan of a product; through remanufacturing, repairing, upgrading or re-marketing. Companies can therefore retain value and discover new sources of revenue with its products; 5. Turn waste into a new valued resource: ♻️Leverage new technology and biz models innovations to recover and reuse resource outputs; this eliminates material leakage and maximizes economic value (examples: closed loop recycling, industrial symbiosis or cradle-to-cradle designs). We all together can accelerate the transition if we get the economics right - and demonstrate the business potential. This is your chance to bring together purpose💟, impact 🎯 and financial success. --- What's your favourite circular business model and which ones should be added? --- 🙏🏽 Please spread the word or leave a comment! Credits for the visual: Circular Innovation Council #circulareconomy #businessmodels #innovation #enterpreneurship

𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝘀𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 — 𝘀𝗲𝗰𝘂𝗿𝗲𝗹𝘆, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝘆 𝗮𝗻𝗱 𝘃𝗲𝗻𝗱𝗼𝗿-𝗻𝗲𝘂𝘁𝗿𝗮𝗹? Multi-agent networks and agent-to-agent communication are set to become some of the most important topics in AI over the next few years. A2A - the Agent-to-Agent Protocol from Google, launched yesterday, could be an important building block for the future. It could be the missing layer that finally makes multi-agent AI work at scale. It’s open-source by default and already backed by 50+ players — including Salesforce, LangChain, and SAP. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔2𝗔? A2A is an open standard that lets AI agents: - communicate   - coordinate   - and complete tasks together — across orgs, tech stacks and frameworks. So it works basically like: → One agent sends a task   → Another agent completes it   → No brittle integrations. No vendor lock-in. No proprietary walls. 𝗛𝗼𝘄𝗲𝘃𝗲𝗿, 𝘁𝗵𝗲𝗿𝗲’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — 𝗔2𝗔 ≠ 𝗠𝗖𝗣. A2A is not a replacement for MCP. It complements it. MCP (Model Context Protocol) connects agents to tools, APIs and enterprise systems. A2A connects agents to each other across organizational and technical boundaries Think of it like this: → A2A = agents talking to agents → MCP = agents accessing tools and resources Both are designed to work together as part of a broader, interoperable agent architecture. (see diagram below.) --- Multi-agent networks and agent-to-agent communication will become increasingly important topics in AI over the next few years. And the Google release is a strong signal that we now have an open standard designed specifically for this. Most agent-based systems today are constrained by brittle integrations and closed ecosystems. A2A introduces a shared language for agents to collaborate — in a way that can actually scale. But for couse, it’s early and only time will tell if it becomes the standard. But for now, this is a meaningful step forward. Here’s the full announcement and more info: https://lnkd.in/dkCxu-kb

"The report outlines four key regulatory approaches to AI governance—industry self-governance, soft law, regulatory sandboxes, and hard law—each offering distinct advantages and challenges: 1. Industry Self-Governance • Strengths: Can directly impact AI practices if integrated into business models and company cultures. • Limitations: Non-binding; not appropriate for sectoral use-cases with particularly high risks – e.g. financial sector or healthcare; risk of ‘ethics-washing’. 2. Soft Law • Strengths: Soft law includes nonbinding international agreements, national AI principles, and technical standards, providing adaptable frameworks that promote responsible innovation. Early governance efforts by intergovernmental bodies have set important precedents. • Limitations: While soft law encourages innovation, it focuses on high-level principles rather than binding rights and responsibilities. 3. Hard Law • Strengths: Binding legal frameworks provide clear, enforceable guidelines that ensure AI stakeholders comply with established standards and regulations. • Limitations: Given the rapid pace of AI development, hard laws risk becoming outdated and can be extremely resource-intensive to implement. 4. Regulatory Sandboxes • Strengths: These controlled environments allow for real-world experimentation with AI technologies, supporting innovation and providing valuable insights without exposing the public to unchecked risks. • Limitations: Sandboxes can be resource-intensive and have limited scalability, making them less feasible for wide-scale governance across diverse sectors." Read/download: https://lnkd.in/etwyUaUK

How to work with industry as an academic: Old way - Waiting for industry to approach you - Publishing research without industry relevance - Limited networking within academic circles - Minimal collaboration opportunities New way - Proactively reaching out to industry partners - Designing research with practical applications - Building strategic cross-sector relationships - Creating mutually beneficial collaboration frameworks Industry collaboration > Traditional academic isolation Throughout my academic journey, I've discovered that bridging the gap between research and real-world application isn't just possible. It’s essential. Academic research becomes truly impactful when it solves tangible industry challenges. The key is transforming your academic expertise into a valuable industry asset. This means understanding industry challenges, communicating practical implications of your research, and demonstrating how your work can drive innovation and solve complex problems. Have you successfully bridged academia and industry in your professional journey? What strategies worked best for you? #Science #ResearchImpact #Scientist #PhD #Postgraduate #Professor #Research #Collaboration

Circular Value Chain 🔄 Circular business models are instrumental in driving positive change for nature action. They focus on maximizing the value and utility of materials across the entire product lifecycle, thereby minimizing environmental impacts and promoting active participation in the economy. The role of circular strategies becomes increasingly critical when addressing biodiversity impacts. The challenge lies in pinpointing which business models effectively alleviate environmental pressures. ▪ Circular Inputs and Valorization model advocates for the use of renewable, reusable, recyclable, and land-efficient materials. It stresses the importance of valorizing underutilized products and side streams. ▪ Product-Life Extension encourages the use of durable products and supports their longevity through maintenance, repair, refurbishment, and remanufacturing. ▪ Product as a Service shifts the focus from product ownership to utility and service, fostering the use of durable products across multiple users. ▪ Sharing Platforms use digital technology to facilitate the sharing, renting, selling, and reuse of goods, optimizing the use of resources. ▪ Resource Recovery captures and repurposes products and raw materials at the end of their lifecycle. ▪ Regenerative Solutions are focused on enhancing the vitality of the bioeconomy through practices that improve soil health, carbon sequestration, and biodiversity. As organizations adopt these models, they move towards a harmonized approach where economic activities support and sustain natural habitats and biodiversity. This alignment is essential for the long-term viability of both the environment and the industries that rely on its resources. Source: Sitra #sustainability #sustainable #circular #circulareconomy #circularity #esg #climatechange #climateaction #climatecrisis