Finance

Every business, small or large, private or publicly listed has to choose between borrowed money (debt) and owner funds (equity), and in this sessions I start by looking at the fictional reasons (debt is cheaper than equity, debt increases ROE), the real reasons (the tax benefits of debt vs bankruptcy costs) and the frictional reasons (desire for control, subsidized debt, and protections against bankruptcy). I then look at tax rates (marginal and effective) in 2025 as well as developments on the default front (ratings changes, loan defaults) during the year, before chronicling what companies around the world looked like both on debt comfort ratios (interest coverage and debt to EBITDA) and debt loads (debt to capital). I close by looking at two developments - the immense cap ex in AI and the growth of private credit, and argue that there is a big market delusion embedded here, and when it corrects, it will create a clean up and shrinkage in both.

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

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

Killer graph. Out of the £130 billion online non-food purchases we make in the UK, £27 billion of them get sent back to retailers. Our research with ZigZag Global shines a spotlight on the significant challenge online returns cause in the industry, focusing on those consumers who consistently and intentionally over-order - the "serial returners". Key stats ➡️ Around 11% of online shoppers are serial returners (frequently over-ordering with the intention of returning many items) ➡️They account for 24% of all online returns ➡️Serial returners send back, on average, £1,400 worth of online orders per year, compared with an average of £650. ➡️ This amounts to £6.6 billion of returns. ➡️ Almost three-quarters of serial returners are under the age of 45, and they return more than 42% of all their orders. A 1/4 of serial returners admit to over-ordering just to reach a minimum order value (often to trigger free delivery) only to return goods they had no intention of keeping. The same proportion also said they had returned items after finding them cheaper elsewhere or on promotions. While 18% admitted to returning items having already used them for a short period. There is no silver bullet here that is going to fix this issue for retailers. A nuanced understanding of specific triggers and barriers is essential to effectively target returners through pricing and returns options. 💥 For many boardrooms debating whether they should charge for returns, my thoughts are: 💥 The returns equation transcends simple binary choices between free or paid. Retailers must architect differentiated returns propositions that align commercial realities with customer lifetime value. Smart retailers will segment their returns strategy by customer profitability metrics, leveraging AI to identify purchase patterns that predict long-term value. This enables dynamic returns pricing that protects margins while fostering relationships with truly valuable customers. The goal isn't to punish returns – it's to price them according to their true cost to serve, while rewarding profitable shopping behaviours. There's also a paradox at play where customer acquisition costs are optimised but customer profitability is compromised. Many retailers are essentially subsidising unsustainable shopping behaviours at the expense of margin, unknowingly targeting customers they could do without. The real opportunity lies in leveraging returns data as a predictive indicator of customer profitability. By applying advanced analytics to returns patterns, seasonal purchasing behaviours, and cross-category browsing and mining deep behaviour insights, retailers can enable proactive intervention before profitability erodes. This shifts the conversation from universal policies to personalised solutions that can turn returns from a pure cost centre into a strategic lever for customer engagement and loyalty. Full research is available to download here ⬇️ https://lnkd.in/e5paRNWC

Outcome of SEBI Board Meeting dated 30 September, 2024: Prohibition of Insider Trading Regulations related: - Expansion of the definition of connected persons to include a firm or its partner or employee where a “connected person” is also a partner, as well as individuals sharing a household or residence with a “connected person.” - The provisions related to connected persons will now apply to “relatives” rather than just “immediate relatives” - Insertion of a new definition “relative” to include the spouse, parents (including parents of the spouse), siblings (including siblings of the spouse), and children (including children of the spouse), along with their spouse LODR related - Introduction of single filing system for listed entities to file relevant reports, documents etc. on one exchange which will be automatically disseminated at the other exchange - Filings integrated into two broad categories viz., Integrated Filing (Governance) and Integrated Filing (Financial) - System driven disclosure of shareholding pattern and revision in credit ratings by Stock Exchanges - Detailed advertisement of financial results in newspapers would be optional for listed entities - Additional time of 3 months to fill up vacancies in Board and KMP positions at listed entities coming out of the CIRP - Increased time of 3 hours instead of 30 mins for outcome of board meeting that concludes after trading hours - Additional time (72 hours instead of 24 hours) for disclosure of legal disputes subject to maintaining such information in SDD - Disclosure of tax litigations and tax disputes on the basis of materiality. - Disclosure of fines / penalties imposed on the basis of new materiality threshold Rs. 1 lakh for sector regulators / enforcement agencies and Rs. 10 lakhs for other authorities) as against the present requirement to disclose all fines and penalties ICDR related - Faster Rights Issue: to be completed within 23 working days v/s present average timelines of 317 days. Requirement of filing Draft letter of offer (only issue related incremental information) with SEBI discontinued. Mandatory appointment of merchant banker made optional. - Pre-issue and price band advertisement will be merged into a single advertisement - Issuers can voluntarily disclose proforma financials for acquisitions or divestments already undertaken or proposed from issue proceeds in case of public issue, rights issue and QIPs - Issuers with outstanding SARS granted to employees, which are fully exercised for equity shares before filing the RHP are allowed to file the DRHP #SEBI #Boardmeeting #outcome #insidertrading #ICDR #LODR

People don’t pay for green. Full stop. We see many #climatetech startups marketing their products in this order: 1️⃣ Sustainability - the products are green and have low carbon intensity. 2️⃣ Resilient supply chain - the sourcing of the product is done in a more resilient and reliable way. 3️⃣ Performance - the product is (or nearly is) a drop-in solution. 4️⃣ Price - there is currently a “green premium,” but it will decrease as we scale. Yet, time and again, these companies, especially those selling commodities, experience pushback from an industry unwilling to buy these goods and narratives. The reason is that the industry has the exact opposite set of priorities: 1️⃣ Price - in a high-interest environment where margins are eroded and many businesses face fierce competition (e.g., from China), price parity is the top priority. Even a few cents per kW/h or gallon can make a difference. I recently learned of a battery startup whose raw materials alone cost more than the fully assembled battery of a Chinese competitor. No one will pay that premium. 2️⃣ Performance - many new solutions promise technical performance improvements, but most are not packaged to qualify for all customer requirements and have little evidence to prove long-term benefits. In mega projects, durability is almost always more important than unproven superior performance. Sunfire is flourishing because of their Alkaline cells, not their SoX full cells. 3️⃣ Resilience - following the pandemic and the scarcity of raw materials, this is indeed a growing concern for both industry and governments. 4️⃣ Sustainability - if a product can address all the above topics and also be green, the industry will be happy to adopt it. What does this mean? Startups need to take a market-centric rather than a tech-centric approach. They should develop their go-to-market strategy from day 1 to prioritise customers whose needs align most with their story, and design their entire product and value proposition around those customers requirements. For example, a raw material startup shouldn’t target the battery industry where price and quality are crucial. Instead, they might find success selling to the cement industry, where quality is less critical, and there’s a whole new value proposition around cirularity and sustainability. #venturecapital #fundraising #productmarketfit

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