Business Strategy

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

The Sustainability Framework 🌍 This framework by Preferred by Nature provides a comprehensive snapshot of the key themes and operational areas that organizations must address to implement robust sustainability practices across sectors. It structures sustainability into four interconnected principles: responsible business conduct, human rights and well-being, environmental protection, and climate impact mitigation. The first principle emphasizes sound governance and responsible management practices. It includes secure land tenure, compliance with legal obligations, anti-corruption measures, responsible procurement, and infrastructure development that minimizes harm. These elements form the foundation for credibility and resilience in sustainability efforts. The second principle focuses on human rights, labor conditions, and community engagement. It outlines clear criteria to prevent forced labor, child labor, and discrimination, while promoting fair wages, occupational safety, and gender equality. It also recognizes the rights of Indigenous Peoples and calls for respect for cultural heritage and community wellbeing. Environmental protection is at the core of the third principle, with a strong focus on avoiding deforestation, preventing ecosystem degradation, and conserving biodiversity. The framework mandates the responsible use of chemicals, improved waste and pollution management, and efficient water and soil stewardship, aligning with international conservation standards. Animal welfare is also addressed within the environmental domain, establishing safeguards for animal health, nutrition, and natural behaviors. This criterion reinforces the framework’s integrated view of environmental and ethical performance in land-based production systems. Climate action is treated as a distinct principle, recognizing the urgency of reducing greenhouse gas emissions. The framework calls for best practices in land use, material sourcing, and energy efficiency, while encouraging companies to align with sectoral emission targets and national climate policies. Adaptation to climate risks is also prioritized. Organizations are expected to assess climate vulnerabilities and implement proportional adaptation measures, particularly in high-risk contexts where social, economic, and environmental impacts are significant. The final climate-related criterion encourages ecosystem restoration and carbon removal where feasible. These actions are framed not only as mitigation strategies but as opportunities to enhance long-term ecological function and community resilience. Overall, the Preferred by Nature Sustainability Framework offers a technically sound and adaptable structure that integrates legal compliance, ethical standards, and environmental integrity. It serves as a practical reference for certification, due diligence, and investment alignment in sustainability-driven supply chains. #sustainability #sustainable #business #climatechange

In the U.S., you can grab coffee with a CEO in two weeks. In Europe, it might take two years to get that meeting. I ’ve spent years building relationships across both U.S. and European markets, and if there’s one thing I’ve learned, it’s this: networking looks completely different depending on where you are. The way people connect, build trust, and create opportunities is shaped by culture-and if you don’t adapt your approach, you’ll hit walls fast. So, if you're an executive expanding globally, a leader hiring across regions, or a professional trying to break into a new market-this post is for you. The U.S.: Fast, Open, and High-Volume Americans love to network. Connections are made quickly, introductions flow freely, and saying "let's grab coffee" isn’t just polite—it’s expected. - Cold outreach is normal—you can message a top executive on LinkedIn, and they just might say yes. - Speed matters. Business moves fast, so meetings, interviews, and hiring decisions happen quickly. But here’s the catch: Just because you had a great chat doesn’t mean you’ve built a deep relationship. Trust takes follow-ups, consistency, and results. I’ve seen European executives struggle with this—mistaking initial enthusiasm for long-term commitment. In the U.S., networking is about momentum—you have to keep showing up, adding value, and staying top of mind. In Europe, networking is a long game. If you don’t have an introduction, it’s much harder to get in the door. - Warm introductions matter. Cold outreach? Much tougher. Senior leaders prefer to meet through trusted referrals—someone who can vouch for you. - Fewer, deeper relationships. Once trust is built, it’s strong and lasting—but it takes time to get there. - Decisions take longer. Whether it’s hiring, partnerships, or leadership moves, things don’t happen overnight—expect a longer courtship period. I’ve seen U.S. executives enter the European market and get frustrated fast—wondering why it’s taking months (or years!) to break into leadership circles. But that’s how the market works. The key to winning in Europe? Patience, credibility, and long-term thinking. So, What Does This Mean for Global Leaders? If you’re an American executive expanding into Europe… 📌 Be patient. One meeting won’t seal the deal—you have to earn trust over time. 📌 Get introductions. A warm referral is worth more than 100 cold emails. 📌 Don’t push too hard. European business culture favors depth over speed—respect the process. If you’re a European leader entering the U.S. market… 📌 Don’t wait for permission—reach out. People expect direct outreach and initiative. 📌 Follow up fast. If you’re slow to respond, the opportunity moves on without you. 📌 Be ready to show value quickly. Americans won’t wait months to see if you’re a fit. Networking isn’t just about who you know—it’s about how you build relationships. #Networking #Leadership #ExecutiveSearch #CareerGrowth #GlobalBusiness #US #Europe

Let's build a Real Time ML System to fraud. Step by step 🧵↓ 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 💼 Every time your credit card is used online by someone (hopefully you), your card issuer (for example Visa, Mastercard or PayPal) has to verify if it is you the person trying to pay with the card. Otherwise, the transaction is blocked. Now the question is: ““𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗩𝗶𝘀𝗮 𝗱𝗼 𝘁𝗵𝗮𝘁?”” And the answer is… a real time ML system! 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 📐 As any ML system that has existed, exists and will exist, this one can be broken down into 3 types pipelines 1️⃣ Feature pipelines 2️⃣ Training pipeline 3️⃣ Inference pipeline Let's go one by one 1️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 💾  The feature pipelines are the Python services that produce the inputs (aka features) our ML model needs to generate its predictions. In our case, we have (and I bet Visa has) at least 3 feature pipelines: ▣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 feature pipeline from recent transactional data. - runs 24/7 - consumes incoming data from an internal message bus (like Kafka, Redpanda) - transforms this data on-the-fly using a real-time data processing engine - saves the the final features in a feature store, like Hopsworks. ▣ 𝗕𝗮𝘁𝗰𝗵 pipeline from historical features in the data warehouse. - runs daily - reads data from the data warehouse/lake, and - saves it into another feature group in our feature store, so it can be consumed by our ML model really fast. ▣ 𝗟𝗮𝗯𝗲𝗹𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, so the ML model can be trained with supervised ML. Each completed transaction that is not claimed by the card owner within 6 months can be safely called non-fraudulent (class=0). We call it fraudulent (class=1) otherwise. Once we have these 3 feature pipelines up and running, we will start collecting valuable data, that we can use to train ML models. 2️⃣ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🏋🏽 We can use a supervised ML model (a boosting tree model like XGBoost does the job in most cases) to uncover any patterns between > the features available in your Feature Store, and > the transaction class: 0 = non-fraudulent, 1 = fraudulent. The final model is pushed to the model registry (like MLflow, Comet or Weights & Biases), so it can be loaded and used by our deployed model. And this is precisely what the last pipeline in our design does. 3️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🔮 The inference pipeline is a Python streaming application, that at start up loads the model from the registry into memory and for every incoming transaction > loads the freshest features from the store for that card_id, > feeds them to the model, and > outputs the predictions to another Kafka topic. These fraud scores can be then consumed by downstream services, to > Block the card, and > Send an SMS alert to the card owner, for example. BOOM! No dark magic. Just Real World ML. Follow Pau Labarta Bajo for more Real World ML

The shift from seats to agents pressures SaaS margins. At the same time, the longstanding practice of getting enterprise customers to pre-commit and also prepay for functionality they may never deploy will get harder as CIOs look to free budget for their own LLM costs. To weather the storm, some SaaS companies have increased prices. This boosts revenue and margins in the short-term but can't be done repeatedly and creates even greater scrutiny over shelfware as procurement teams right-size and shift contracts to "pay as you go." To achieve sustainable growth, SaaS companies need to become hyperefficient at sales and marketing. Here are common ways to do so and who's doing it well: 1. PLG. Shopify and Atlassian exemplify efficient go-to-market based on product-led growth with free trials, low-friction upgrades and upsells. Their sales teams only need to get involved in the biggest opportunities at the largest accounts; every other step in acquisition, commercial transaction, activation, onboarding, and growth is self-service and automated. 2. Vertical SaaS. Guidewire Software and Veeva Systems are laser-focused on insurance and life sciences, respectively. Rather than casting a wide net, they spear-fish with deep domain knowledge and purpose-built solutions for that industry's specific workflows and regulatory requirements. Guidewire doesn't need to buy Super Bowl ads– their annual customer conference is the Super Bowl for property & casualty insurance executives. Nearly zero GTM effort is wasted– unsurprisingly they're the two most efficient on the list. We modeled Hearsay Systems after both these companies, and this focus allowed us to win incredible market share among Fortune 500 banks & insurers despite only raising $60M in totality. 3. Relocate operations to lower-cost regions and AI. This is private equity's favorite playbook to take costs out of companies they buy. Field sales continues to shift more to Zoom, which means you can hire AEs anywhere. Inside sales contributes a greater % of revenue as PLG motions are established. AI handles top-of-funnel leads qualification and generating marketing content and campaigns. 4. Focus on gross revenue retention. Because of high customer acquisition costs in #SaaS, leaky buckets are margin killers. Use LLMs to help customer success teams analyze product usage, segment cohorts, and identify opportunities to increase value realization. Put in guardrails to prevent sales reps from overselling an account, as doing so only creates churn in the next renewal cycle. 5. Introduce another product line. This only works if your new product has the same buyer as your existing products. Many SaaS acquisition pro formas fail to actualize for this reason, as it's not actually feasible to have the same AE sell both old and new products. Every SaaS company right now needs to double down on one or more of these levers in the AI era.

In Case You Missed It: The World's Largest Asset Manager, BlackRock — Private Markets Outlook 2025 🏆 With industry estimates projecting the industry to reach >$20T by 2030, private markets has quickly become one of the hottest areas in finance. From private credit, private equity, infrastructure and real estate, here are the key takeaways from BlackRock's 2025 outlook titled "A new era of growth": • Outlook: The brightest days for private markets are still ahead, driven by higher investment activity, elevated-but-lower financing costs and greater demand for long-term capital. • Industry estimates: project private markets could grow from $13T today to more than $20T by 2030 - they believe private debt and infrastructure will grow the fastest. • Private debt: continues to expand globally, and into new avenues of finance, with wide performance dispersion depending on borrower size and sector. • Opportunity in AI: Investors can access the transformative possibility offered by artificial intelligence through infrastructure, as well as debt, private equity and real estate. • Key drivers: A series of profound changes in the world’s demographics, energy demand, digital technology and supply chains continue to propel investment across private markets. • Deal activity: is rising in both the M&A and IPO markets, which should drive more exits and distributions across private equity. • Real estate: valuations are nearing their bottoms, creating opportunities, though price recovery will take time, with wide dispersion among sectors and regions. What is driving the rapid and continued expansion of private markets? Companies staying private for longer: • The median time a “unicorn” stays private is 10.7 years, up from 6.9 years in 2014 New fund structures, broader access: • Retail-focused (ELTIFS, LTAFs) and fully-funded solutions • Evergreen fund structures Market development: • Asset-backed finance market growing in private debt • Regional market growth, in places like India Structural forces: • New technologies require early-stage investment • Real estate is evolving to meet the needs of a changing world Private Markets.

Load balancing is crucial for scaling applications and ensuring high availability. Let's examine key algorithms: 1. Random    • Distributes requests randomly across servers    • Pros: Simple implementation, works well for homogeneous server pools    • Cons: Can lead to uneven distribution in short time frames 2. Round Robin    • Cycles through server list sequentially    • Pros: Fair distribution, easy to implement and understand    • Cons: Doesn't account for server load or capacity differences 3. IP Hash    • Maps client IP addresses to specific servers using a hash function    • Pros: Ensures session persistence, useful for stateful applications    • Cons: Potential for uneven distribution if IP range is narrow 4. Least Connections    • Directs traffic to the server with the fewest active connections    • Pros: Adapts to varying request loads, prevents server overload    • Cons: May not be optimal if connection times vary significantly 5. Least Response Time    • Routes requests to the server with the quickest response time    • Pros: Optimizes for performance, adapts to real-time conditions    • Cons: Requires continuous monitoring, can be resource-intensive 6. Weighted Round Robin    • Assigns different weights to servers based on their capacity    • Pros: Accommodates heterogeneous server environments    • Cons: Requires manual configuration and adjustment Choosing the right algorithm depends on your application architecture, traffic patterns, and infrastructure. What challenges have you faced implementing these in production environments? Any performance insights to share?

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas

I used to think charging less would get me more clients. After my trip to the US I realised it just made them trust me less. when i was cheap, clients questioned everything. "why this approach?" "can we try something else?" "i'm not sure about this." so when i raised my rates, they trusted my decisions completely. same work. different psychology. so here's what i've basically realized about pricing: when someone sees a low price, their brain doesn't think "great deal." it thinks "what's the catch?" they start looking for problems. inexperience. desperation. corners being cut. low prices trigger fear of loss, not excitement about savings. but when they see premium pricing, something else happens. "if they can charge this much, they must deliver results." "other people are paying this, so the value must be there." "the risk of not solving this problem costs way more than the investment." premium pricing signals confidence in your work. think about it. rolex doesn't make better watches from a functionality standpoint. but the price tells you everything about what owning one means. same thing with services. a premium project isn't necessarily 10x better in execution. but the price signals experience, systems, proven results. and here's the shift that changed everything for me: i stopped anchoring clients to the price and started anchoring them to the outcome. not "this costs X" but "this will generate Y for your business, and the investment is X." when they're thinking about ROI, the price becomes secondary. your pricing isn't just a number. it's a signal to the market about who you are and what you deliver.

Ever heard of the Lippitt-Knoster Model for Managing Complex Change? It's a classic in the change management world, laying out the essential pieces needed to navigate big transformations. Taking a cue from that, I've adapted it to fit the world of digital transformation. There are seven key elements you can't afford to miss: Vision, Strategy, Objectives, Capabilities, Architecture, Roadmap, and Projects & Programs. Skip any one of these, and you're asking for trouble. Here’s why each one matters: • 𝐕𝐢𝐬𝐢𝐨𝐧: This is the 'what' of your transformation. A clear vision gives everyone a target to aim for, aligning all efforts and keeping the team focused. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Think of this as the 'why' and 'how.' A solid strategy explains the logic behind your vision, showing how you plan to get there and why it's the best route. It’s designed to guide everyone in the company on how to make decisions that support the vision, aligning all efforts and keeping the team focused. • 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: These are your milestones. Clear, specific objectives make it easy to measure success and ensure everyone knows what's important. Without them, you can easily veer off course and waste resources. • 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: These are what your company will now be able to do that it wasn't able to before in order to achieve the objectives. These can be organizational capabilities (like improved decision-making), technical capabilities (such as real-time operational visibility), or other types like enhanced customer engagement or streamlined processes. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A robust architecture ensures all your tech works together smoothly, preventing inefficiencies and costly headaches. This includes various types of architecture such as data architecture, IT infrastructure architecture, enterprise architecture, and functional architecture. Effective architecture is central to reducing technical debt and aligning software with broader business transformation goals. • 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Your roadmap is the game plan. It lays out the sequence of actions, helping you avoid uncertainty and missteps. It's your guide to getting things done right. • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬: These are where the rubber meets the road. Actionable projects and programs turn your strategy into reality, making sure your plans lead to real, tangible outcomes. From my experience, I think '𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬' and '𝐑𝐨𝐚𝐝𝐦𝐚𝐩' are the two most overlooked. What do you think? ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!