Finance

The European Central Bank is now making the economic case for decarbonisation. Not as climate policy. As monetary policy. Frank Elderson, ECB board member, argues in the Financial Times that Europe's dependence on imported fossil fuels is a structural threat to price stability (👉 https://lnkd.in/eKWWjKbh). The data is damning: energy price shocks pushed euro area inflation to 10.6% in October 2022. Every geopolitical tremor in the Middle East shows up in European energy bills. And the ECB is caught in an impossible bind: tighten to fight inflation and deepen the slowdown, ease to support growth and entrench inflation. The solution is not better forecasting models or finetuned monetary policy. It is cheaper energy. Spain shows what is possible. Wholesale electricity prices in early 2024 were approximately 40% lower than they would have been had wind and solar generation remained at 2019 levels ( 👉 https://lnkd.in/edXgxh9q). Once the infrastructure is built, the energy itself is virtually free. Volatile global commodity markets simply become less relevant. Elderson is explicit: €660 billion per year in clean energy investment sounds large. But Europe already spends nearly €400 billion annually on fossil fuel imports, money that leaves the continent and buys geopolitical vulnerability. Analysis in the UK shows that for every pound invested in sustainable energy, benefits outweigh costs by a factor of 2.2 to 4.1 ( 👉 https://lnkd.in/emEXVfiw). This is precisely what I argued in my piece for Triodos a few weeks ago: Europe's crisis response has been backwards. We keep treating energy dependence as a shock to manage rather than a structural problem to fix. (👉https://lnkd.in/ehFqA6iY) The ECB cannot decarbonise Europe. What it can do is name the conditions: keep the ETS, mobilise capital toward renewable capacity, strip out fossil fuel subsidies, and stop confusing cheap fossil fuels with affordable energy. If people need help with energy costs, target it: don't suppress the price signal that drives the transition. The cheapest energy is the energy we no longer have to import.

Energy is once again dominating headlines all over the world. Gas and oil prices are volatile, key shipping routes face geopolitical pressure, and policymakers are concerned about supply risks. The renewed uncertainty is a reminder of an uncomfortable reality: the next energy crisis isn’t an if – it’s a when, and a question of how prepared we are. A defining challenge of this decade, and one that now feels more urgent than ever, is how to build a resilient energy system. One that minimises structural dependencies and is designed for rising electricity demand. The imperative of our time: The more we electrify, the less we import fossil fuels. The less we import, the more resilient we become. The course of action is clear: ▪️ Relentlessly scale renewables: Slowing the buildout will not reduce costs. Quite the opposite – delay compounds system costs for the entire economy. ▪️ Fix the grids: As fast as possible, as efficiently as possible, and at the lowest possible cost. Before they become even more of a bottleneck. ▪️ Secure 24/7 electricity supply: When the wind isn’t blowing and the sun isn’t shining, renewables need reliable backup in the form of battery storage and hydrogen-ready gas fired power plants. But gas should serve only as a backup, with renewables and batteries reducing its utilisation. ▪️ Reduce gas supply dependence with infrastructure and diversification: We must not replace old dependencies with new ones. Diversification of gas supplies is key. And the physical prerequisite is an import infrastructure with buffers. We need the planned LNG terminals, complemented by a nationally held gas reserve to help ensure secure supply in winter. ▪️ Electrify everything that makes sense: The more we can power with mostly homegrown electrons, the less dependent we become on fossil imports. Other energy import-dependent countries like Japan and China have electrification rates that are around 10 percentage points higher than Germany’s. This shows where the path forward lies. Electrification reduces reliance on imported fossil fuels, which in turn strengthens overall resilience. The time to act is now.

NEW RECORD: For the first time, fully electric vehicles outsold petrol-only cars in the EU in December. This is a genuinely historic moment for Europe’s transport transition and a powerful signal that the market, consumers, and policy frameworks are aligning towards cleaner transport. This milestone reflects a shift in consumer confidence and commitment to decarbonisation across the transport sector. It highlights the importance of continued support mechanisms, smart regulation, and investment in charging networks that make EVs a feasible choice for more people and businesses. It shows that policy works. Clear CO₂ standards, investment in charging infrastructure, and long-term signals to industry and consumers are translating into real market change. The direction of travel is unmistakable. That’s precisely why backtracking now would be a serious mistake. Rolling back targets, delaying standards, or creating regulatory uncertainty would not “help industry” — it would undermine investment decisions, slow down innovation, and risk Europe falling behind in one of the most important global growth markets of this decade. The transition is not finished. There are still challenges around affordability, infrastructure rollout, grid integration and skills. But these are reasons to stay the course and improve implementation, not to weaken ambition.

Should regulators certify agents like pilots or doctors? Doctors and pilots can’t take a single step without a license. Yet AI agents, increasingly making medical judgments or piloting decisions in simulations, face zero checks. That contrast keeps me up at night. I’ll be honest: I use AI every single day. It makes me faster, smarter, and more productive. But here’s the thought that gnaws at me: if my AI agent makes a mistake, do I own it? Or does no one? That gap—between power and accountability—is what worries me most. Licensing is more than bureaucracy. It’s a social contract. → A pilot’s license means: “You can trust me to carry 200 lives safely.” → A doctor’s license means: “You can trust me to act in your best interest.” → But when an AI agent makes a decision, who signs that contract? Here’s the deeper challenge people overlook: AI doesn’t stand still. A doctor retrains every few years. A pilot re-certifies on new aircraft types. An AI agent changes with every update, every dataset, every fine-tune. That means a license can’t be a one-time stamp. It has to be continuous, dynamic, evolving. Otherwise, yesterday’s “safe” agent could be tomorrow’s liability. In my opinion, here’s the only way forward: ✅ Extend human licenses in high-stakes domains. A doctor can vouch for their medical AI. A pilot can vouch for their cockpit assistant. Accountability flows through them. ✅ Require continuous certification of agents—not every decade, but every update. ✅ Guarantee human override. People must always have the right to say: “I want a human.” For me, this isn’t about slowing progress. It’s about protecting trust—the one currency we can’t afford to lose in the agentic era. Do we copy old licensing systems, or invent a new, living framework for AI accountability? #AI #Leadership #AIagents #FutureOfWork #Regulation #Ethics

The European Parliament has officially passed Extended Producer Responsibility (EPR) legislation that fundamentally shifts the responsibility for textile waste management to fashion brands and retailers – with far-reaching global implications. This new law requires all producers, including e-commerce platforms, to cover the full cost of collecting, sorting, and recycling textiles, regardless of whether they are based within or outside the EU. The financial burden of Europe's textile waste now falls squarely on the brands that create it. What are the critical business implications? UNIVERSAL SCOPE: The legislation applies to all producers selling in the EU market, including those of clothing, accessories, footwear, home textiles, and curtains. No company is exempt based on location. FAST FASHION PENALTY: Member states must specifically address ultra-fast and fast fashion practices when determining EPR financial contributions, creating cost penalties for unsustainable business models. GLOBAL SUPPLY CHAIN DISRUPTION: As the world's largest textile importer, the EU's new rules will ripple across global supply chains, particularly impacting exporters from Bangladesh, Vietnam, China, and India who supply much of Europe's fast fashion. TIMELINE PRESSURE: Officially adopted September 2025, this creates immediate operational and financial planning requirements. COMPETITIVE RESHAPING: Brands and retailers will inevitably pass increased costs down their supply chains, fundamentally altering supplier relationships and pricing structures globally. What are the implications for various stakeholders? For CEOs and board members: This represents more than regulatory compliance – it's a complete business model transformation. Companies must now integrate end-of-life costs into product pricing, rethink supplier partnerships, and accelerate circular design strategies. For sustainability and decarbonisation executives: This creates unprecedented opportunities for circular economy solutions, sustainable material innovation, and traceability system development across global supply chains. Link: https://lnkd.in/dTyHtHuD #sustainablefashion #circulareconomy #textilwaste #epr #fashionindustry #sustainability #supplychainmanagement #fastfashion #environmentalregulation #businessstrategy #decarbonisation #textilerecycling #fashionceos #boardgovernance #climateaction #wastemanagement #producerresponsibility #fashionsustainability #textileindustry #greenbusiness

My team and I get pitched 5–10 new businesses every week. Mostly from entrepreneurs trying to raise money. If you want your message or pitch to stand out to investors, do this: 1. Start with the problem, not the product. If I don’t feel the pain, I won’t value the solution. 2. Be brutally clear. My team should understand your business in 10 seconds or less. 3. Show traction, not just vision. Even if it's small, show me that the market wants it and you know how to deliver. 4. Tell me why you’re the one. I’m investing in you as much as the idea. Show conviction, not just ambition. 5. Make it a conversation, not a monologue. Curiosity builds trust. Ask good questions and make it collaborative. Keep it simple.

Everyone is talking about agentic AI and yet the next frontier is already in the making: Multi-Agent Systems (MAS). AI didn’t arrive all at once – although in many cases it might seem it did. It evolved in distinct phases, each unlocking new capabilities and changing how work gets done: 𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜): - Systems powering rule-based models and statistical inference to detect fraud, recommend investments, and process documents - all in response to human prompts. - Financial Services (FS) example: Credit scoring models and fraud detection engines improved efficiency, but remained passive tools waiting on human input. 𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜): - LLMs and foundation models that brought language fluency and contextual understanding. These systems can create, explain, and summarize - moving from data crunching to content generation. - FS example: Chatbots that summarize regulatory filings, generate client reports, or support advisors with contextual investment narratives. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: - Systems that can interpret goals, plan actions, and operate independently within constraints. These agents shift the human role from executing tasks to defining intent. - FS example: AI agents that autonomously rebalance portfolios based on client preferences and market movements - no human intervention required. 𝟰. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗠𝗔𝗦): - MAS represent the next leap. Multiple agents - each specialized - work together, negotiate, and adapt in real time to achieve shared outcomes across environments. - FS: Agents handling client onboarding, AML checks, credit assessment, and regulatory filings collaborate seamlessly to approve new clients in minutes. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: MAS enable distributed, intelligent systems that can self-organize, learn continuously, and respond dynamically to change. They reduce operational bottlenecks and shift digital architectures from static pipelines to living systems. 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: - Efficiency: MAS collapse multi-day processes into seconds - from KYC to loan origination. - Mass hyper-personalization: Real-time tailoring of product decisions across customer journeys and risk contexts. - Resilience: Distributed agents can recover from local failures, reroute tasks, and maintain service continuity without manual intervention. - Compliance: Agents track regulatory changes and trigger operational updates autonomously. MAS aren’t just the next step in AI - they’re how AI starts to really work like a system. The real transformation won’t be about bigger models anymore, but about smarter collaboration between them. Opinions: my own, Graphic source: Capgemini 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration.

Founders are turning down millions in venture capital. Their reason? "I don't need the money. We're already profitable." 10 years ago, unthinkable. Today, common. The Information wrote an insightful piece on "Seed-strapping"—raise once, focus on profitability: → $3.7M revenue per employee (10X industry standard) → 80% lower development costs → 90% less capital to reach profitability The uncomfortable truth for VCs: → Companies need just one funding round → SAFEs never convert → Founders keep 70-80% ownership → The traditional model breaks For investors, survival requires reinvention. New Fund Economics: → Smaller funds with more concentrated bets → Lower management fees, higher carry → Faster distribution timelines → Many smaller wins vs. few unicorn exits New Deal Structures: → Revenue-based financing with capped returns → Dividend rights if companies don't raise again → Profit-sharing without requiring additional rounds New Value Proposition: → Capital efficiency expertise over growth-at-all-costs → Customer connections & distribution support → Operational support over financial engineering → Alternative liquidity paths beyond traditional exits The era of "We'll figure out profitability later" is over. What comes next? Imagine a VC landscape dominated by smaller, specialized firms helping founders build profitable businesses from day one. In this new world, the winners won't have the biggest funds—they'll understand AI has fundamentally changed capital efficiency. For founders: Why dilute when you can profit after one round? For investors: How do you add value when capital isn't the constraint? The answer determines who thrives—and who vanishes in 24 months.

Pick a company Read last 3 annual reports Read last 12 earnings call transcripts Find relevant information on the company Calculate key ratios for it Repeat for another company in the same sector See your understanding of the sector soar in a few weeks. Not sure how or where to start? 4 resources to help you 1) What to read in an earnings transcript  (using Eicher Motors as example) https://lnkd.in/gqaYwkNM 2) What to read in an annual report  (using Titan as example) https://lnkd.in/dtt674gu 3) Quick Financial Analysis using Screener  (using Ultratech Cement as example) https://lnkd.in/dFM9ypEa 4) Ratio Analysis: A Step by Step Guide in Excel  (Using SAIL as an example) https://lnkd.in/dd9HwiqC Subscribe to our channel for more such videos. https://lnkd.in/dR4nvGxd ------- Peeyush Chitlangia, CFA I help you build a career in Valuation and Investment Banking