Finance

It might not feel like it, but Britain is not unique in facing difficult budget choices. France and Germany have also seen governments destabilised or fall over attempts to pass budgets. Low growth, ageing populations and rising demands on the welfare state are putting pressure on public finances right across the continent. What is striking, however, is how some of the countries that were once held up as cautionary tales during the eurozone crisis (Portugal, Italy, Ireland, Greece and Spain) have responded. They undertook painful reforms: raising retirement ages, restructuring their welfare systems, making labour markets more flexible and, in some cases, linking pensions to life expectancy. As a result, they are now seeing stronger growth and lower borrowing costs than many of their northern neighbours. By contrast, there has been less urgency in the UK this past year. We are already on course to spend far more on benefits and debt interest in the next decade, even before additional pressures on the health and welfare systems are factored in. Simply opting for higher spending without confronting the underlying structure of the state is not a sustainable strategy. The lesson from Europe is not that reform is easy or popular. It rarely is. But it is better to confront these choices on your own terms than to wait for markets or external shocks to force them upon you. That is the debate we need to have in Britain: how to protect the most vulnerable while reshaping the welfare state and public spending so that our economy can grow and our finances remain credible. Read more in my column for today's Sunday Times here: https://lnkd.in/eTuWcNBK

Media is hiding red flags in boAt’s IPO papers, even stuff which its own auditors have highlighted 🚨🚨 See, when you go IPO, you clean the house. You make sure everything is PERFECT. You're asking for public money, after all. But digging into boAt's "Risk Factors" section reveals a …lot of disturbing things. Details below. .. Red Flag 1: The Books Don't Match. For THREE straight fiscal yrs (FY23, FY24, FY25), the auditors found a major problem. The "quarterly returns or statements filed with banks or financial institutions [were] not in agreement with the books of account of our Company." Let me translate that from accountant-speak. What they told their lenders... did not match what was in their own internal records. FUNDAMENTAL failure. .. Red Flag 2: Classic Asset-Liability Mismatch. For FY23 and FY24, the auditors flagged that the company used "funds raised on a short-term basis... for long-term purposes." Why is this bad? It's simple. You're using money you have to pay back soon (short-term loans) to fund things that only pay off later (long-term assets). This is exactly how a liquidity crisis starts. If your short-term lenders come calling, you don't have the cash to pay them. It shows a DEEP misunderstanding of basic financial management. Or worse, a reckless disregard for it. .. Red Flag 3: Paying Directors Too Much. As if that wasn't enough. For FY23, the auditors found the "remuneration paid to the directors... in excess of the limit laid down under Section 197 of the Companies Act, 2013." They literally broke the law on how much they could pay their own leadership (incl founders). And, we aren't looking at one mistake. We are looking at a clear, documented pattern of weak internal controls. A culture that seems to play fast and loose with financial rules. Plus, if the internal controls are this weak... If the auditors are repeatedly finding these kinds of "unfavourable remarks"... How can anyone be confident in the reliability of the very financials being presented to IPO investors? .. The entire IPO is built on a foundation that the company's own auditors have called out as shaky. How can you trust the numbers in an IPO if the company can't even keep its bank statements aligned with its own books? A total lack of discipline from a company wanting to join the big leagues. As a stakeholder, as a potential investor, you have to ask... Is this the kind of governance you want to bet on? Or so is my personal analysis - which is no recommendation or advisory. .. PS: I share several biz/economy deepdives daily, with 36k+ people on WhatsApp. Do check out here: https://t.ly/h2jq1 Best, Jayant

INDIA GOES OFFLINE, DIGITALLY! The Reserve Bank of India has launched the Offline Digital Rupee, a Central Bank Digital Currency that can move from one wallet to another even without internet or mobile network. Imagine paying for a cup of tea in the Himalayas or for groceries in a rural market where connectivity is zero and still completing the transaction in seconds. ✅ Digital trust has reached a new level. Money that works without the internet is not a product of convenience. It is the evolution of trust. When the value can move offline yet remain verified and authentic, we are witnessing the future of financial inclusion, not just technology. ✅ It solves the last-mile problem. For years, digital payments depended on networks, servers, and gateways. Rural India, remote areas, and even disaster zones were often left behind. The Offline Digital Rupee removes that dependency and gives digital money a physical character. This changes how we think of accessibility forever. ✅ It is faster, cheaper, and smarter. No third-party switches. No failed connections. No dependency on payment gateways. The value moves directly from one device to another, just like cash, but secured by blockchain-based architecture and backed by the central bank. The power of digital efficiency now exists without digital dependence. ✅ Programmable money means purposeful money. The RBI’s Programmable Central Bank Digital Currency model means money can be coded for a reason. Subsidies can be released only for their intended use. Corporate payouts can have specific validity. Social benefits can be tracked transparently. It adds responsibility to the currency itself. ✅ It redefines how economies will interact. Offline CBDC is not just a domestic innovation. It opens the door for new models of cross-border settlements, disaster-resilient financial systems, and new layers of fintech innovation. The world will look at this model as a live example of how technology can merge with human need, not just convenience. ✅ It reminds us what innovation truly means. The right innovation is not when a feature gets smarter, but when it becomes more inclusive. When a person in a no-network zone can transact as easily as someone in a metro city, that is when digital transformation turns into social transformation.

Now it's getting serious. #China introduces new #exportrestrictions on batteries, battery materials and production technologies. China introduces new export restrictions on battery materials and production technologies. Exporters must now apply for a licence at the State Council's trade department. 💡 Affected are: #batteries: Lithium-ion batteries (including cells and battery packs) with an energy density greater than or equal to 300 Wh/kg #cathodematerials: LFP cathode materials with a density greater than or equal to 2.5 g/cm³ and a capacity greater than or equal to 156 mAh/g as well as goods related to NMC and NCA cathode material precursors #anodematerials: Anode materials consisting of a mixture of artificial and natural graphite #manufacturingequipment for lithium-ion batteries, anode and cathode materials 🚢 The introduction of these export restrictions will lead to supply chain constraints in the short term. This will give the Chinese government a powerful political tool and allow it to learn in detail how it works in practice through the temporary shortages. European automotive #OEMs have three options for risk mitigation:  🚂 Escape: Focus on the combustion engine. This provides short-term leeway at the expense of global competitiveness in the future automotive market. 🌍 Friend-shoring: Make efforts to build a resilient battery industry with European players and trusted partners. 😵 Surrender: Relocate EV production to China and countries that are unlikely to be affected by the utilization of export restrictions.   ⏰ It's time to wake up. Once again. 👉 Further information: * Chinese Ministry of commerce of the PRC: https://lnkd.in/ex2uvNjF * Global Times: https://lnkd.in/ejWi6eKb

Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable. After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design to orchestrate how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips. Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for Taiwan manufacturing. If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence. Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more. But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways. [Original text: https://lnkd.in/gxR48TK8 ]

Every time a card payment is processed, 𝘁𝗵𝗿𝗲𝗲 main types of fees are involved. Here’s a simple breakdown of the Three Core Fees: 1️⃣ Interchange Fee This is paid by your acquiring bank (or payment processor) to the cardholder’s bank (the issuer). It’s set by the card networks (like Visa and Mastercard; sometimes regulated), and is designed to cover things like fraud, credit losses, and infrastructure costs. 2️⃣ Scheme Fee Charged by the card networks themselves, this fee covers the operation of the payment system (“rails” that process the transaction). 3️⃣ Acquirer Markup This is the fee your acquirer or payment service provider (PSP) charges you, the merchant. It includes their costs, risk management, and profit margin for processing and settling the payment. The total cost a merchant pays is called the Merchant Service Charge, which is the sum of these three components. The Main Pricing Models: ► Bundled Pricing All fees are grouped into one flat rate. This is very common with small businesses. It’s easy to understand but doesn’t provide insight into what you’re actually paying for. ► Interchange+ The interchange fee and the acquirer’s fee are shown separately, but the scheme fee is typically bundled with the markup. This model offers some transparency. ► Interchange++ Each fee—the interchange, scheme, and acquirer markup—is itemized separately. This is the most transparent model and is favored by larger or multi-country merchants who want to track costs precisely. Who Chooses the Pricing Model? Most acquirers and PSPs decide what pricing model you’re offered. Unless you negotiate or have significant transaction volume, you’re likely to get bundled pricing by default. Larger or more experienced merchants who understand payments often push for Interchange++ for its clarity and fairness. Smaller merchants often aren’t aware that alternatives exist or find it difficult to compare offers. How Interchange Fees Vary Globally: Some regions (like the EU, UK, China, and Brazil) cap interchange fees to lower costs for merchants and stimulate competition. The US regulates only part of the system—such as capping debit card fees for large banks (the Durbin Amendment)—while credit card interchange remains uncapped and usually higher. Other countries, like India and Brazil, regulate interchange as part of broader financial inclusion goals. In markets with stricter regulation, merchants often benefit from lower, more predictable fees, making it easier to accept cards. Where fees are higher and less regulated, issuers can offer consumers more rewards (like cashback), but those costs are passed back to merchants—and sometimes their customers. Every model shifts the balance of costs and benefits between banks, merchants, and consumers in different ways. More info below👇, and I highly recommend reading my complete deep dive article about Interchange Fee and what factors impact the rate: https://bit.ly/44T4VJA

In the last five years, MicroStrategy has effectively converted itself from a software company to Bitcoin SPAC, and its success at driving its market cap upwards has led some to argue that other companies would be well served following that model and redirecting their cash holdings into bitcoin. I disagree, and not because I have a point of view on bitcoin (I do.. but it is not relevant). It is not a good substitute for cash (which is held as a shock absorber), it steps on and obscures your business narrative, managers are terrible traders (of bitcoin or any other investment) and it opens the door to self-dealing and worse. Put simply, if you are a shareholder in a company with a large cash balance, and you think bitcoin is the place to be for the future, you are better served asking for the cash to be returned to you (in dividends and buybacks) and doing It yourself. There are four exceptions to this general rule - a company with a bitcoin savant in change (MicroStrategy), companies with bitcoin businesses (PayPal and Coinbase), companies in countries with failed currencies and companies with failed businesses that have become meme stocks (AMC, Gamestop). Even in these companies, you need governance, disclosure and accounting guardrails in place, to prevent abuse.

9 out of 10 CEOs are tracking the wrong metrics. (I learned this the hard way.) So many are flying blind. Making gut decisions. Wondering why growth feels so hard. But these 18 KPIs change everything. Here's what every CEO should be watching: REVENUE & PROFITABILITY ↳ Revenue Growth Rate shows if you're gaining momentum ↳ Gross Margin reveals your pricing power ↳ Net Profit Margin tells the real health story CASH & RUNWAY ↳ Operating Cash Flow confirms you're funding yourself ↳ Cash Runway warns when to raise or cut spend ↳ Burn Multiple shows capital efficiency to investors CUSTOMER METRICS ↳ Customer Acquisition Cost guides marketing budgets ↳ Customer Lifetime Value validates if CAC is justified ↳ LTV-to-CAC Ratio predicts long-term profitability RETENTION & GROWTH ↳ Net Revenue Retention measures product stickiness ↳ Churn Rate gives early alerts on product issues ↳ Net Promoter Score predicts retention and referrals OPERATIONAL EFFICIENCY ↳ Sales Cycle Length impacts cash flow forecasts ↳ Days Sales Outstanding signals collection efficiency ↳ Employee Turnover Rate reflects culture and hiring FINANCIAL HEALTH ↳ EBITDA strips out accounting noise ↳ Growth Efficiency Ratio reveals expansion quality ↳ Average Revenue Per Account tracks upsell impact The magic isn't in tracking everything. It's in tracking the RIGHT things consistently. Most CEOs drown in vanity metrics while missing the signals that actually predict success. These 18 KPIs cut through the noise. They give you the clarity to make confident decisions. And the confidence to sleep better at night. 🔖 Save this cheat sheet. Review it monthly. ♻️ Share it. Help a CEO in your network. P.S. Which KPI do you watch most closely? Share in the comments below. Want a PDF of the 18 KPIs for CEOs? Get it free: https://lnkd.in/dhh5irfH And follow Eric Partaker for more CEO insights. ————— 📢 Ready to become a world-class CEO? I'm hosting a FREE TRAINING: "7 Steps to Become a Super Productive CEO" Thur, June 12th, 12 noon Eastern / 5pm UK time https://lnkd.in/d9BuZcrd 📌 20+ Founders & CEOs have already enrolled in our  next CEO Accelerator cohort, starting July 23rd. Earlybird offer ENDS SOON. Learn more and apply: https://lnkd.in/dwjGUkEN

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.