Productivity

In the inbound marketing era, content was queen. In the AI era of marketing, yes, content is still queen. Last week, I spoke to a CMO whose content marketing strategy revolves entirely around SEO. After years of solid growth, her company’s traffic is declining. It’s a familiar story in 2025. She asked: “Now that SEO is less effective, is content marketing less important?” I told her the opposite is true. Content is more important than ever but a few things are different. 1. Content needs to be specific. Answer Engine Optimization (AEO), or how your brand shows up in AI-driven responses is becoming important. The difference between being cited or invisible often comes down to one thing: specific, high-quality content. Marketers leading in AEO are seeing 3–4x higher conversion rates, but only when their content goes deeper than surface-level keywords. Authority, clarity, and originality are the new ranking signals. 2. Content needs to be multi-modal and multi-channel. Buyers no longer follow a linear journey. They read, watch, listen, chat, and scroll, often in the same hour. AI makes it easier to meet them in those moments, but you still need powerful assets to show up well: Video and interactive demos for discovery, Long-form explainers for education, Bite-sized insights for social. The format changes, but the foundation doesn’t: clear, helpful, human content. 3. Content needs to be dynamic and personal. AI gives us the signal—who’s interested, what they need, when they’re ready. But only great content makes the connection. Dynamic, intent-based content can turn data into meaningful engagement. That’s how you create moments that feel personal instead of programmatic. The tools have changed. The algorithms have changed. The constant is content. It’s still queen – because it’s still how trust, engagement, and growth begin. Ps: Huge shoutout to our HubSpot partner Mole Street, who’s all-in on helping customers grow through great content. Whenever I speak with Brendan Walsh or Brian LaPann, “Content Hub” comes up within the first two minutes. Last week they released a fantastic whitepaper on it, link in the comments if you’d like to take a look.

STUDY FINDS COST PER WEAR INFORMATION SHIFTS SHOPPERS TO QUALITY: A new study published in Psychology & Marketing offers a fascinating look at what fashion drives fashion purchasing decisions. Researchers from the University of Bath and Cambridge University found that simply showing consumers the cost per wear (CPW) of garments (price divided by the number of times an item can be worn) can shift preferences away from cheap, low-quality clothing toward higher-priced, longer-lasting options. The findings draw on behavioural psychology to reveal that people respond more to perceived 'economic value' than to abstract sustainability messages. When shoppers could compare CPW between garments, and especially when figures were backed by trusted certification, they were far more likely to choose quality over quantity. The authors suggest CPW could be a powerful tool for brands and policymakers seeking to reframe sustainability as smart spending. Full story in comments.

AI Product Management AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed. Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)! In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production. Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice. Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility. [Reached length limit. Full text: https://lnkd.in/gYY-hvHh ]

RAND ‘s report on wartime #disinformation : Applying lessons learned from #Ukraine to other contexts. 92 pages, 3 chapters, 12 lessons learned : 🪖Before the war: shaping operations. 🔹 2014-22: building government &civil society institutions countering adversary disinformation 🔹 steps to stop the flow of Russian propaganda targeting the country 🔹 intelligence-driven “prebunk” informing international audiences about planned Russian operations 🪖During the war: countering false narratives across the 3 theaters of the information war. 🧰12 lessons learned : 🔸Prepare and plan for 3 theaters of information war, look for innovative ways to reach & communicate with populations in totalitarian countries; rally international institutions to effectively prebunk adversary campaigns targeting the rest of the world; support a broader array of institutions residing in host nations. 🔸Build critical host nation institutions in advance of and during conflict 🔸Build and maintain capacity to counter disinformation: assess own doctrine, training, and wargaming efforts to ensure it is able to counter disinformation during conflict. Ensure that institutions & psychological operations forces retain their capability. 🔸Invest in and work with civil society 🔸Build and maintain trust to effectively dispel adversary narratives. 🔸Work with and empower local and military influencers: promote online voices to help support national security objectives. 🔸Build #resilience of troops: to avoid frontline soldiers being a target of adversary campaigns, undermining their will to fight. Develop a mandatory media literacy education campaign to help deployed and garrison personnel recognize malign influence attempts and foster safer online behavior. 🔸Do not allow coordination to sacrifice speed in responding: the Ukrainian experience highlights the value of a loosely coordinated and redundant network response that involves multiple actors both monitoring media and communicating key narratives. 🔸Be prepared to take risks: accept that government communicators outsource their efforts to creative and agile civil society institutions. Allow communicators to quickly create unique, humorous, and engaging content. 🔸Plan on resourcing and executing 3 critical counterdisinformation tools: Debunking (fact checking), prebunking, and the promulgation of proactive information narratives. Ensure the 3 are integrated in military theaters of operation. 🔸Be prepared to build the capacity of key institutions: In future contingency operations, consider adversary targets for propaganda and disinformation and evaluate the ability of local institutions to effectively respond. 🔸Recognize the risk of waning support over time, as the time engaged in conflict increases and influence of messaging decreases & adversary disinformation narratives may become more influential. Wargame these risks and consider incorporating them in war plans. 👏🏼 Todd Helmus Khrystyna Holynska

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

My biggest takeaways from Ethan Smith on how to win at AEO (i.e. get ChatGPT to recommend your product): 1. Being mentioned most often beats ranking first. In Google, the #1 blue link wins. In ChatGPT, the answer summarizes multiple sources—so appearing in five citations beats ranking #1 in one. Ethan’s strategy: get mentioned on Reddit, YouTube, blogs, and affiliates. Volume of mentions matters more than any single placement. 2. LLM traffic converts 6x better than Google search traffic. Webflow saw this dramatic difference because users who come through AI assistants have built up much more intent through conversation and follow-up questions, making them highly qualified leads. 3. Early-stage startups can win at AEO immediately, unlike with SEO. Traditional SEO requires years of domain authority. But a brand-new Y Combinator company mentioned in a Reddit thread today can show up in ChatGPT tomorrow. The playing field is finally level. 4. The long tail of AEO is 4x bigger than SEO. People ask ChatGPT questions with 25 or more words (vs. 6 in Google). Ethan found gold in queries like “Which meeting transcription tool integrates with Looker via Zapier to BigQuery?”—questions that never existed in search but are perfect for AI. Own these micro-niches. 5. Reddit is proving to be the kingmaker for AI visibility. ChatGPT trusts Reddit because the community polices spam better than any algorithm. Ethan’s exact playbook: make one real account, say who you are and where you work, give genuinely helpful answers. Five good comments can transform your visibility. No automation, no fake accounts—just be helpful. 6. YouTube videos for “boring” B2B terms are a gold mine for AEO. Nobody makes videos about “AI-powered payment processing APIs”—which is exactly why you should. While everyone fights over “best CRM software,” the high-value, zero-competition long tail is wide open in video. 7. Your help center is now a growth channel. All those “Does your product do X?” questions flooding ChatGPT can be answered by help-center pages. Move them from subdomain to subdirectory, cross-link aggressively, and cover every feature question. Ethan calls this the most underutilized opportunity in AEO. 8. January 2025 was the inflection point in AEO growth. That’s when ChatGPT made answers more clickable (maps, shopping cards, citations) and adoption exploded. Webflow went from near zero to 8% of signups from AI. This channel is accelerating faster than any Ethan’s seen in 18 years. 9. The AEO playbook: (1) Find questions from competitor paid search data, (2) set up answer tracking, (3) see who’s showing up as citations, (4) create landing pages answering all follow-up questions, (5) get mentioned offsite via Reddit/YouTube/affiliates, (6) run controlled experiments, (7) build a dedicated team. This exact process is driving real results at scale.

The most expensive mistake in business is assuming your customers will never change. Last year, something shifted in Indian retail. Gen Z (377 million) overtook millennials (356 million) to become our largest consumer group, influencing $40-45 billion worth of apparel and footwear purchases. But they're not shopping at the stores we built for them. [Et Retail] Brands watched their growth collapse in just 12 months. → ZARA fell from 40% to 8% growth, [Et Retail] → Levi Strauss & Co. crashed from 54% to 4% growth [Et Retail] → H&M dropped from 40% to 11% growth [Et Retail] Here's why the growth has slowed down: 📌 Gen Z discovered new brands like Freakins and Bonkers Corner, offering trendy clothes at ₹500-800 📌 They chose self-expression over brand loyalty 📌 70% of their shopping moved online, heavily influenced by Instagram 📌 They demanded inclusive sizing (XS to XXL) and unisex options that legacy brands ignored Take FREAKINS, which clocked ₹25 crore in FY2023, or Bonkers.corner, clocked ₹100 crore. [The Economic Times] [Et Retail] These brands understood what Gen Z wanted: crop tops, baggy clothes, Korean pants, and oversized tees at prices that let them experiment with three different outfits daily. Body positivity isn't a marketing campaign for this generation. It's how they think. When they couldn't find the sizes or styles they wanted at premium stores priced at ₹1,200-1,500, they simply went elsewhere. Myntra saw the shift and launched FWD with ₹500 price points. The result was explosive: 100% year-on-year growth and 16 million Gen Z users, who now represent one in three e-lifestyle shoppers. [Et Retail] Legacy brands bet that Gen Z would "grow up" and pay premium prices. Instead, 377 million young Indians chose values over logos. The most expensive mistake in business? Assuming your customers will never change. What changes in your customer base have surprised you recently?

I built a Top 100 Global video podcast on Spotify. Here’s exactly how I did it. Six months ago, I launched Hot Smart Rich, a video-first podcast for anyone obsessed with the future of culture, creators, startups, and self-growth, on Spotify. We hit Spotify’s Top Business Podcasts in week one. Since then, we’ve charted 7 times, peaked at #5 in the U.S., and landed in Spotify’s Top 50 US podcasts overall. What surprised me most? How quickly video unlocked growth. On Spotify, my audience could seamlessly switch between watching and listening—just like the 300M+ listeners on the platform doing the same thing. That flexibility helped us attract not just more fans, but the right fans. The kind who binge episodes, send me DMs, share clips with their group chats, and now proudly call themselves HSR Angels. And yes, I turned it into a business. Through the Spotify Partner Program with Spotify for Creators, creators can monetize video content without giving up creative control. It’s real revenue, real reach, and a real community. (And let’s be honest: most platforms can’t say that.) If you’re thinking about launching, here’s what I’d tell you: - It is not too saturated. But you do need a plan. Get clear on your tone, flow, format, and point of view. Your audience doesn’t want a copy—they want something new. - Don’t waste money on aesthetic fluff. No one cares about your new photoshoot. Spend that cash on solid audio, decent lighting, and a camera that works. We started with iPhones. - Cut up your clips like your life depends on it. Post. Everywhere (Including Spotify). All the time. - Be consistent. Experiment early. When no one’s watching, try things. Switch formats. Get weird. Then double down on what hits. - Make it your personality. If you’re not hyping your own show, no one else will. You don’t need millions to start. You just need a camera, a mic, a message, and Spotify. Check out how to grow your video on spotify below. https://lnkd.in/gnB5ejaS #podcast #business #spotify #spotifypartner #videopodcast #growth

For my first 16 years in tech sales, I averaged 240K/year W2 income. In my last 4 years, I averaged 720K/year. In order to triple my income, I had to change my sales approach entirely. Here's what I changed: I started using a new approach that I now call Yo-yo selling: 🪀 Yo-yo selling emphasizes starting at the executive level, conducting thorough discovery within the organization, and then returning to the executive with a tailored business case. Like holding a yo-yo, you are constantly in communication with the Executive Sponsor and updating them as you collect information and conduct deep discovery lower down in their organization. You are literally going up and down the organization, but always taking everything back to the Executive Sponsor to surface your findings along the way. Here's a breakdown of the framework: 🎯 𝐈𝐚𝐧 𝐊𝐨𝐧𝐢𝐚𝐤’𝐬 “𝐘𝐨-𝐘𝐨 𝐒𝐞𝐥𝐥𝐢𝐧𝐠” 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 This strategy involves a three-step process: 1. Start at the Top (Executive Engagement) Initiate contact with a senior executive to understand their most pressing challenges, the reasons behind the need for change, and the consequences of inaction. If your solution aligns with their needs, secure their sponsorship for further discovery within their organization. To secure the Executive Meetings, it's essential to create a tailored POV (point of view) on where you think you may be able to help them based on your initial research of their highest level goals and priorities. Chat GPT has made this research a LOT faster now. 2. Conduct In-Depth Discovery (Middle Management) Engage with department heads and key stakeholders to uncover the day-to-day challenges they face. Focus on understanding their processes, pain points, and the implications of current inefficiencies. Gather direct quotes and insights to build a comprehensive view of the organization's needs. 3. Return to the Executive (Present Findings) Compile the insights gathered into an executive summary and business case. Present this to the executive sponsor, highlighting how your solution addresses the identified challenges. Tailor your demonstration to focus solely on relevant aspects that solve their specific problems. 🚀 Why It Works 1. Accelerates Sales Cycles: Engaging executives early ensures alignment and expedites decision-making. 2. Builds Credibility: Demonstrates a deep understanding of the organization's challenges and showcases a tailored solution. 3. Facilitates Internal Buy-In: By involving various stakeholders, you ensure that the solution meets the needs of all parties, increasing the likelihood of adoption. I'm pleased to share that that Yo-yo selling was recently awarded as a Top 15 Sales Tactic of All Time by 30 Minutes to President's Club, and I received a cool plaque for entering the 30MPC Hall of Fame. Since I have no chance of entering the Hall of Fame for my baseball or golf game, this is a nice consolation prize 😁

Law school taught me the law. But building a career? That’s a different story. Many years ago, I walked into my first day as a lawyer, armed with my 2nd Upper Degree, thinking I was ready. I WAS NOT. Here are 12 lessons I learnt the hard way: (I wish someone had shared with me before I started) 1️⃣ It’s Okay to Ask for Help Pretending to know everything? Rookie mistake. Ask questions. Get clarity. Even top-tier lawyers do. 2️⃣ Networking > Billable Hours Winning cases builds a reputation, but relationships build careers. That partner you avoid at events? Talk to them. 3️⃣ Reputation Is Currency Every email. Every call. They all shape how people see you. Guard your reputation like it’s your most valuable client. 4️⃣ Billing ≠ Just Hours Worked It’s not about grinding for numbers—it’s about delivering value. (And yes, padding your billables will get you noticed—for all the wrong reasons.) 5️⃣ Clients Crave More Than Advice They want trust, empathy, and someone who listens. Legal skills matter, but human connection wins clients for life. 6️⃣ The Best Lawyers Never Stop Evolving The law changes, and so should you. Stay curious. Stay sharp. Stay ahead. 7️⃣ Mentors = Secret Weapons Find someone who’s been where you want to go. The right mentor will save you years of trial and error. 8️⃣ Burnout Is the Silent Killer The late nights will come, but don’t make them your norm. Protect your energy—because no case is worth your health. 9️⃣ Pick Your Battles Not every fight is worth the courtroom. Strategic restraint is a superpower. 🔟 Mistakes Are Inevitable Here’s the secret: It’s not about never failing—it’s about how you bounce back. Own it, learn from it, and keep moving. 1️⃣1️⃣ It’s a Marathon, Not a Sprint You don’t need to win every deal or impress every partner. Pacing yourself is how you last in this game. 1️⃣2️⃣ Never Lose Sight of Your WHY When the grind feels endless (and it will), your WHY will keep you grounded. Don’t let go of it—it’s your anchor. Law school taught you the law. But no one taught you how to build a career in it. Lawyers reading this, did I miss anything? What else would you add to my list? --- Repost this♻️ to help the juniors out there! ➕ Follow Shulin Lee for more. P.S. To the trainees starting out: It’s okay to feel scared. P.P.S. The partners you’re intimidated by? They were once where you are. Everyone starts somewhere. You've got this!