Finance

The latest reporting from the Financial Times highlights a point that energy analysts have been making for years: geopolitical shocks consistently strengthen the case for renewables, electrification and storage. Microsoft’s global vice-president for energy notes that oil and gas price spikes linked to the Middle East conflict reinforce the value of wind, solar and batteries in providing price stability. Once installed, renewables offer predictable cost profiles and reduce exposure to volatile global fuel markets. We saw this dynamic after Russia’s invasion of Ukraine. Europe accelerated solar deployment, heat pump uptake increased in several countries, and governments revisited questions of energy security through the lens of diversification and electrification. The underlying issue remains unchanged. Fossil fuels must continuously flow through complex global supply chains. When those flows are disrupted, prices spike and economies are exposed. Renewables, by contrast, are capital intensive upfront but deliver long term domestic supply and insulation from commodity shocks. There are short term risks. Inflation, higher interest rates and supply chain constraints can slow clean energy investment. Some governments may also respond by doubling down on gas infrastructure. The policy challenge is to avoid locking in further structural vulnerability. Energy security and climate policy are not competing objectives. In a world of recurrent geopolitical instability, they are increasingly aligned.

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.

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 ]

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.

The inventor of the SAFE note Adeo Ressi just eliminated the $150,000 and 6-month tax on starting a VC fund. This is huge, so we need to talk about it. Traditionally: ⏱️ Time: Launching a fund can take 6-12 months from thesis to first investment. 💸 Money: The VC setup cost ranges from $50,000 to $150,000+, with annual operations adding another $50,000+. 😵💫 Complexity: Requires three separate entities (LP, GP, and ManCo), complex legal agreements, and multiple regulatory filings. 🏦 Fund Size: There is a minimum fund size averaging $10M to make the fund economically viable. Each LP typically needs to invest $100K+ minimum because smaller checks are unprofitable due to per-LP administrative costs. 📊 Track Record: In order to raise this type of fund, new managers need larger LPs, and these larger LPs often need to see an existing successful investment track record, which some new managers don't have. These barriers have created a venture ecosystem where only those with established networks, significant resources, and/or institutional backing can participate. In 2025: Adeo came up with the Start Fund, a vehicle addressing all of the above head-on: ⏱️ Time: Set up a fund in ONE DAY vs. 6-12 months. 💸 Money: ZERO setup fees vs. $50K-$150K+. 😵💫 Complexity: ONE Delaware series vehicle vs. three separate entities, with an LPA just 1/3 the size. 🏦 Fund Size: Viable with just $250K+ vs. $10M minimum, and can accept smaller LPs (as low as $25K) because administration is streamlined 📊 Track Record: Fully portable track record that counts as fund one when you move to fund two. The benefits for emerging managers are clear: the barriers to entry are lower, giving a much wider pool of candidates a chance to create impact and shape the future. But here's why this matters for... LPs - The Start Fund allows LPs to participate with smaller check sizes, making it easier to diversify their portfolio - More of their capital actually goes to startups rather than overhead fees Startups: - This means more availability of capital from a wider range of sources - Access to a more diverse pool of venture investors with specialized expertise The Start Fund could fundamentally could change WHO gets to allocate capital to the next generation of startups, and WHO will benefit financially from it. I want to know what you all think. ------------- ✍️ Myrto Lalacos Follow for more content on launching and investing in VC firms

The digital bank is an outdated concept. Fast being replaced by the intelligent bank. The only question is how soon banks can manage the transition. Let’s take a look. I have broken down the main elements that make up the transition to the intelligent bank: 1. From transactional to predictive banking: digital banking enabled 24/7 self-service, but intelligent banking takes it further by predicting customer needs. AI-driven models analyse real-time data to offer personalised financial insights, proactive credit offerings, and automated investment recommendations. 2. AI-powered risk & fraud management: traditional risk assessment relied heavily on historical data. Intelligent banks use AI and machine learning to detect fraud in real time, identify suspicious patterns and prevent threats before they occur. 3. Hyper-personalisation: instead of generic offers, intelligent banks use AI to tailor financial products to individual customers (mass personalisation). 4. Seamless omni-channel experience: customers no longer interact with banks through a single channel. Intelligent banking ensures that a user can start a transaction on a mobile app, continue it via a chatbot, and complete it with a human advisor. All while maintaining a seamless, connected experience. 5. Autonomous banking operations: intelligent banks optimise back-office processes using cloud and AI automation, reducing human errors and significantly improving efficiency. Functions such as loan approvals, compliance checks, and reconciliation are increasingly self-regulated by AI-driven workflows.   Banks are in a time race. They not only need to move from digital to intelligent but also do it fast.   In doing so technology is the biggest dependency. One of the most interesting approaches I have seen on how to best support banks in this transition is Huawei's 4-Zero model, which is based on 4 main pillars:   1. Zero Downtime → Instant Readiness AI-powered predictive maintenance and cloud resilience ensure 24/7 availability, allowing banks to deploy and scale AI solutions without service disruptions. 2. Zero Wait → Faster Customer Experiences AI-driven real-time processing eliminates delays in transactions, approvals, and customer interactions, making banking services ultra-responsive. 3. Zero Touch → Reduced Operational Burden End-to-end automation using AI and machine learning removes manual intervention in processes like KYC, loan approvals, and compliance, freeing up resources for AI innovation. 4. Zero Trust → Seamless AI Integration AI-driven security frameworks continuously validate access, ensuring trust and compliance while enabling banks to integrate AI-powered services without increasing risk. The era of intelligent banking isn’t a distant future - it’s happening now. Banks will not be able to transform in months but getting a head start can make a difference. Opinions and graphics: Panagiotis Kriaris  #HuaweiMWC  #RAAS  #IntelligentFinance

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.

📢 New analysis on the leaked EU Omnibus Proposal – What will be the planetary price of simplification? Can Europe combine sustainability and competitiveness? Big changes are certainly coming to the EU’s sustainability reporting landscape. A leaked draft of the European Commission’s Omnibus Proposal suggests major rollbacks in the Corporate Sustainability Reporting Directive (CSRD), Corporate Sustainability Due Diligence Directive (CSDDD), and the EU Taxonomy Regulation. 💡 To help navigate these changes, our put together a comparison table—let us know if it’s useful! Here are some highlights of what’s being proposed: 🔹 𝗖𝗦𝗥𝗗 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱 𝗿𝗮𝗶𝘀𝗲𝗱 – Only companies with 1,000+ employees and €450M turnover may need to comply (previously 250 employees, €40M). This scopes out 85% of firms previously covered. 🔹 𝗦𝗲𝗰𝘁𝗼𝗿-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝘀𝗰𝗿𝗮𝗽𝗽𝗲𝗱 – Industry-specific ESG reporting rules may be permanently shelved. 🔹 𝗗𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗲𝗮𝗸𝗲𝗻𝗲𝗱 – Companies only need to assess direct suppliers, not the full supply chain. 🔹 𝗖𝗶𝘃𝗶𝗹 𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗿𝗲𝗺𝗼𝘃𝗲𝗱 – Under CSDDD, firms won’t face legal consequences for failing to meet sustainability obligations. 🔹 𝗧𝗮𝘅𝗼𝗻𝗼𝗺𝘆 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗺𝗮𝘆 𝗴𝗼 𝘃𝗼𝗹𝘂𝗻𝘁𝗮𝗿𝘆 (not directly mentioned in the leak) – Instead of mandatory reporting, firms could opt-in, aligning with corporate lobbying efforts. ⚖️ I am wondering about if this is simplification or just plain deregulation. In addition, what will the effects be of a watered-down EU Green Deal for the bloc's sustainability leadership and for firms that have already invested in reporting? How do you see the balance between competitiveness and sustainability? Can we reduce red tape and still protect the planet? Drop your thoughts below! 👇 #CSRD #CSDDD #EU #Sustainability #ESG #SustainabilityReporting #ESGRegulation #Climate #Finance #CorporateResponsibility

How to Do Financial Due Diligence Before Selecting Stocks? Stock picking isn’t just about looking at charts and following trends—it’s about understanding the financial health of a company. Before investing, a structured Financial Due Diligence (FDD) process can help you avoid bad bets and spot strong opportunities. Here’s a framework to follow: 1. Understand the Business Model & Industry - What does the company do? - Who are its competitors? - Is it in a growing or declining industry? 2. Analyze the Financial Statements - Income Statement (Profit & Loss) – Revenue growth, profitability (Gross, Operating, Net Margins), EPS trends - Balance Sheet – Debt levels, cash reserves, working capital position - Cash Flow Statement – Operating cash flow vs. net income, free cash flow trends 3. Check Key Financial Ratios - Profitability: ROE, ROA, Gross & Operating Margins - Liquidity: Current Ratio, Quick Ratio - Leverage: Debt-to-Equity, Interest Coverage - Valuation: P/E Ratio, P/B Ratio, EV/EBITDA 4. Assess Management & Governance - Background & track record of leadership - Insider buying/selling trends - Transparency in disclosures & corporate governance 5. Review Competitive Position & Moat - Does the company have a sustainable competitive advantage (brand, network effect, patents, cost advantage)? 6. Industry Trends & Macroeconomic Factors - Economic cycles, inflation, interest rates - Global supply chain, geopolitical risks - Market trends affecting revenue streams 7. Cross-Check with Analyst Reports & News - Read Equity Research Reports, Investor Presentations, Credit Reports - Stay updated on company news, regulatory changes 8. Look at Historical Performance & Future Guidance - Compare past financials vs. projections - Evaluate management’s growth expectations 9. Risk Assessment & Downside Protection - What’s the worst-case scenario? - How resilient is the business in a downturn? 10. Compare with Peers & Make an Informed Decision No company operates in isolation—compare financials and valuations with competitors before buying. Smart investing is about discipline, not hype. By doing thorough due diligence, you increase your chances of picking winners while avoiding pitfalls. What’s your go-to method for analyzing stocks? Let’s discuss.

The Irish Government has just announced plans to introduce the Regulation of Artificial Intelligence Bill in its Spring 2025 legislative programme, a pivotal piece of legislation aimed at giving full effect to the European Union’s Artificial Intelligence Act (EU Regulation 2024/1689). Even though the AI Act as a regulation has direct effect, this move is set to shape the national regulatory framework for AI governance in Ireland and establish national enforcement mechanisms in line with the EU’s approach. At the heart of the bill is the designation of Ireland’s National Competent Authorities: the entities that will be responsible for enforcing compliance with the AI Act. These authorities will oversee risk classification, conduct market surveillance, and impose penalties for violations. Given Ireland’s role as the EU base for major technology firms including Google, Anthropic, Meta, and TikTok, the effectiveness of its enforcement regime will be closely scrutinised across the EU and beyond. The Irish Government’s approach will be particularly significant due to the country’s track record in regulating the digital sector. Ireland’s Data Protection Commission (DPC) has wielded considerable influence over EU-wide enforcement of the GDPR, given the presence of multinational tech firms within the state. The DPC was designated as one of ireland’s nine fundamental rights authorities under the AI Act in November 2024. The bill will include provisions for penalties, though details remain unspecified. Under the EU AI Act, non-compliance can result in fines of up to €35 million or 7% of a company’s global annual turnover, whichever is higher. For Ireland, the challenge will be ensuring its enforcement framework has sufficient resources and expertise to oversee AI systems deployed within its jurisdiction. Tech industry leaders and legal experts will be closely monitoring how Ireland structures its national framework. The AI Act imposes strict obligations on high-risk AI applications, including those used in healthcare, banking, and recruitment. Companies will be required to maintain transparency, conduct impact assessments, and ensure that their AI systems do not lead to unlawful discrimination or harm. Ireland’s legislative initiative comes at a time of growing regulatory scrutiny over AI’s impact on society, innovation, and human rights. The AI Act represents the world’s most comprehensive attempt to regulate artificial intelligence, at a time other jurisdictions such as the USA are moving in the opposite regulatory direction. The Regulation of Artificial Intelligence Bill is still in its early stages, at the “Heads in Preparation” point. In the Irish legislative process, the Heads of a Bill serve as a blueprint for the eventual legislation. As Ireland moves toward full implementation of the AI Act, the government’s decisions on AI oversight will have significant implications for businesses, consumers, and the broader EU regulatory landscape.