Marketing

Amazon's $68 billion ad machine now has access to 190 million Netflix viewers. Here's what it means for advertisers. Amazon's ad business makes $68 billion a year. Now advertisers can target right audiences on Netflix through expanded targeting capabilities via Amazon DSP. Starting Q2 2026, brands buying ads on Netflix through Amazon's platform can now use Amazon's shopping data to target their 190+ million viewers. Think about what this means. Amazon knows what a huge chunk of U.S. households buy, browse, and search for. Netflix knows what they watch. That data is now being combined for targeting. So a skincare brand can target someone who searched for serums on Amazon - while they're watching a show on Netflix. Here's why this matters: → Netflix made $1.5 billion from ads in 2025 and is targeting $3 billion this year  → Early tests are already beating previous benchmarks → A large share of new signups now choose the ad-supported plan. Till now, streaming ads were about showing up in front of millions and hoping it works. This changes that. Now brands can connect what people watch to what they actually buy. For anyone running ads, this is worth paying attention to. Shopping and streaming just became one ecosystem. How do you think this will change the way brands plan their ad budgets?

IndiGo (InterGlobe Aviation Ltd) CRISIS WASN’T IN THE SKIES. IT WAS IN THE LEADERSHIP CABIN. Three things stood out. One: Employees were left alone to face furious customers. No leader should ever let that happen. If you don’t stand by your people in a storm, don’t expect them to stand by your customers in the sun. Customer experience collapses the moment employees feel abandoned. Two: In any crisis, honesty is the only strategy that works. This time, the communication wasn’t transparent. When leaders hide the full picture, years of goodwill can disappear overnight. A crisis can earn trust, but only if you tell the truth. Three: The belief that “we are too big to be ignored” has ended more companies than competition ever has. Customers always have a choice. And if they don’t, they will create one. We shouldn’t watch the Indigo crisis like spectators. This is a reminder for every leader to build their own crisis blueprint. Because crises will come, when they do, your response becomes your reputation. There is more to business than profits. There are people, trust, and how you show up when it matters most.

In a MAJOR ruling for European copyright law, the Munich Regional Court has sided with Germany’s music rights society GEMA against OpenAI, finding that the company’s ChatGPT model unlawfully used copyrighted song lyrics in its training and responses. The decision, issued this morning, marks the first major European court judgment holding an AI company liable for using protected works without a licence. I got into AI through being Director of Legal Affairs and Regulatory Compliance in IMRO, the Irish counterpart of GEMA - and I know the people in GEMA - so this is very interesting to me. The case centred on GEMA’s allegation that OpenAI trained ChatGPT on its repertoire of German song lyrics, allowing the chatbot to reproduce works by artists such as Helene Fischer and Herbert Grönemeyer. The court agreed, concluding that the model’s ability to reproduce lyrics word for word demonstrated that the works had been used in training. It ruled that OpenAI is liable for copyright infringement and prohibited ChatGPT from reproducing lyrics from GEMA-represented artists unless a licence is obtained. The court also held that the European Union’s Text and Data Mining exceptions cannot shield generative AI systems that “memorise” and reproduce copyrighted material. This reasoning undermines one of the primary legal defences AI developers have relied upon in Europe. While damages will be determined in a separate proceeding, the court’s finding of liability alone sets a powerful precedent. OpenAI has announced plans to appeal. The 42nd Civil Chamber of the Munich Regional Court had indicated its position in September, when it observed that the model’s outputs could not be explained without training on copyrighted material. The final judgment confirmed that assessment. For the wider AI sector, the ruling suggests that AI companies operating in the European Union may need explicit licences for any copyrighted content used in model training or risk litigation. The decision also has regulatory implications. It aligns with growing momentum within the EU to enforce transparency and rights-holder protections under the AI Act and the Copyright in the Digital Single Market Directive. The GEMA v OpenAI ruling diverges sharply from Bartz v Anthropic in the United States. In Bartz, Judge Alsup found that AI training on copyrighted material could qualify as fair use, meaning no licence is required when the use is deemed transformative and non-substitutive. He viewed training as an analytical process that teaches the model general patterns rather than reproducing expression. The Munich court took the opposite view, holding that using protected works in AI training without permission constitutes reproduction requiring a licence. This illustrates the growing divide between the U.S. model, where fair use can exempt AI developers from licensing duties, and the European approach, which treats copyright as an enforceable economic right demanding prior authorisation.

TSMC posted a $440 million loss at its Arizona factory. American engineers called it "rigid, brutal, prison-like." Taiwanese managers complained about "lack of dedication and obedience." TSMC’s CEO Morris Chang saw this coming. "A very expensive exercise in futility," he called America's chip push. Taiwan doesn't just make chips. It breathes them. Three decades of alignment created something money can't buy. In Arizona, Americans clock out after shifts. In Taiwan, engineers sleep in the fab. In Arizona, decisions need consensus. In Taiwan, orders flow down. In Arizona, it's a job. In Taiwan, it's national service. Chang knew this at 55 when he started TSMC. The playbook worked because a nation aligned behind it: 1. Bet everything on survival Apple wanted impossible chips. Chang bet $9 billion in 2010 - half TSMC's cash. 6,000 people. 11 months. Round the clock. Because missing Apple meant Taiwan missing its future. 2. Never compete with customers Intel Corporation controlled everything. TSMC said: "We will never compete with our customers." When Nvidia shares five-year roadmaps, thousands protect them like state secrets. 3. Make enemies share factories Nvidia and AMD share production lines at TSMC. Works only when factory workers see both companies' success as Taiwan's success. 4. Turn precision into DNA TSMC's latest machines hit tin droplets 50,000 times per second. In Taiwan, this precision extends everywhere - emails, meetings, weekends. Not policy. Culture. 5. Compound for decades Every supplier grew with TSMC. Every university shaped curricula around them. Chang: "You cannot replicate this with subsidies. You cannot legislate dedication." 6. See the future through customers When Qualcomm fled IBM for TSMC in the late '90s, Chang knew IBM was doomed. Intel built walls. TSMC built bridges. TAKEAWAY: 2007: Intel rejected iPhone chip. Too low margin. Cost them mobile. Then AI. Then everything. Intel's real problem wasn't saying no to Apple. It was believing one company could do it all. Meanwhile, a 55-year-old built something stronger: a nation aligned around making everyone else successful. Today: Every ChatGPT query. Every iPhone. Every Nvidia chip. All TSMC. Not because Taiwan has the best engineers. Because Taiwan made engineering excellence a cultural value. And culture, unlike factories, can't be copy-pasted. — Want the full story of how TSMC became Nvidia's $1 trillion secret weapon? I went deep on the untold details: https://lnkd.in/epuWHu8B P.S. All research links, the audio clip, and the full archive are in the first comment below 👇

We analyzed 4 million recruiting emails sent through Gem. Most get opened. But only 22.6% get replies. Half those replies are "thanks, but no thanks." We dug into what actually works. Here are 8 factors that drive REAL responses: 1. Strategic timing beats everything else - 8am gets 68% open rates. 4pm hits 67.3%. 10am lands at 67% - Most recruiters blast at 9am when inboxes are flooded - Avoiding peak times alone can boost your opens by 7-10% 2. Weekend outreach is criminally underused - Saturday/Sunday emails get ≥66% open rates consistently - Why? Empty inboxes. Zero competition. Candidates actually have time - Yet few recruiters send on weekends. Their loss is your gain 3. Keep messages between 101-150 words - Shorter feels spammy. Longer gets skimmed - You need exactly 10 sentences to nail the essentials - Every word beyond 150 drops performance 4. Generic templates kill response rates - Generic templates: 22% reply rate - Personalized outreach: 47% increased response rate - Even adding name + company to subject lines boosts opens by 5% 5. Subject lines need 3-9 words - Include company name + job title for highest opens - "Senior Engineer Role at [Company]" beats clever wordplay - 11+ words can work if genuinely intriguing, but why risk it? 6. The 4-stage sequence is optimal - One-off emails are dead. Send exactly 4 follow-up messages - You'll see 68% higher "interested" rates with proper sequencing - After stage 4, engagement completely flatlines. Stop there 7. Get the hiring manager involved - Having the hiring manager send ONE follow-up boosts reply rates by 50%+ - Yet most recruiters don't use this tactic - Weekend advantage: Minimal competition for attention 8. Leadership involvement is a cheat code - Role-specific timing (tech vs non-tech) matters - Technical roles: 3 of 4 best send times are weekends - Engineers check email differently than salespeople. Adjust accordingly TAKEAWAY: These aren't opinions. This is what 4 million emails tell us. Most recruiting teams are stuck in 2019 playbooks wondering why their reply rates won't budge. Meanwhile, recruiters who implement these 8 factors see dramatically better results. The data is right there. The patterns are clear. The only question is: will you actually change how you operate? Or will you keep sending the same tired emails at 9am on Tuesday? Your call.

The ABCs of Greenwashing 🌍 Greenwashing weakens trust and slows down meaningful progress. When companies present overstated or unverified claims, it creates confusion across markets, misleads stakeholders, and reduces pressure for real change. The cost is not only reputational, it also undermines the credibility of sustainability efforts more broadly. As sustainability becomes a business priority, the risk of misleading communication continues to increase. The pressure to report progress has led to claims that are not always backed by substance. Recognizing the signals of greenwashing is essential to ensure integrity in reporting, communication, and strategy. The ABCs of Greenwashing is a practical reference that outlines common red flags, from vague wording and selective data to unverifiable targets and weak transparency. These signs often appear in sustainability reports, websites, product labels, and corporate campaigns. There is a growing demand for better sustainability communication. However, clarity must come with accuracy. Narratives that focus on ambition without showing results raise concerns. Authentic communication requires alignment between commitments, measurable progress, and public disclosures. Expectations are shifting. Stakeholders, regulators, and investors expect more than general statements. Claims must be supported by credible data, meaningful metrics, and consistent reporting. The absence of independent verification or full scope analysis is no longer seen as acceptable. Regulatory frameworks are evolving to address this. New directives and standards are increasing pressure on companies to validate their statements with clear evidence. This shift will affect how sustainability is communicated, measured, and governed across sectors. Avoiding greenwashing requires clear internal structures, cross functional accountability, and regular review of communication practices. Sustainability performance must be integrated into operations, not added as a marketing layer. This is not a communication issue alone. It is a strategic and operational matter. Claims must reflect business decisions, investment priorities, and outcomes that can be tracked over time. The ABCs of Greenwashing is a reminder of the need for precision, transparency, and consistency. Improving the quality of sustainability communication is essential for building trust, reducing risk, and advancing long term business goals. #sustainability #sustainable #business #esg #greenwashing 

Warner Bros. Discovery is officially splitting into two companies. And the move may reshape the entertainment landscape as we know it. Announced today, WBD will separate into: 🎬 WBD Streaming & Studios – Max, HBO, Warner Bros. Pictures, DC, and content production. 📺 WBD Global Networks – CNN, Discovery, TNT Sports, and other linear TV assets. David Zaslav will lead the Streaming & Studios entity, while CFO Gunnar Wiedenfels takes over Global Networks. This isn’t just operational restructuring—it’s a signal of strategic discipline. In a media world demanding agility and specialization, WBD is choosing focus over entanglement. For years, media conglomerates tried to be everything at once. Today’s move suggests the next era belongs to leaner, purpose-built organizations: one built for growth, another for value extraction. 🔍 Key implications: ⌙ Investor signaling: The market rewarded the move immediately. WBD stock jumped on the clarity and perceived unlock of future deal potential. ⌙ Deal logic accelerant: Each company now has clearer financials and objectives, making it easier to explore mergers, content alliances, or targeted asset sales. ⌙ Creative empowerment: The Streaming & Studios entity can now prioritize storytelling and platform scale without the drag of managing linear economics. Expect more risk-taking, franchise building, and talent-led bets. ⌙ Global strategy divergence: WBD Global Networks, still strong internationally, may double down on licensing and local partnerships, while Streaming leans further into global IP as a differentiator. This also raises bigger questions about how legacy assets are valued. Linear TV isn’t dead—but it’s no longer the center of the media equation. This move implicitly reframes cable and broadcast as supporting players in a world increasingly dominated by platforms, brands, and data-rich direct-to-consumer models. 📈 In short: WBD didn’t just split its balance sheet—it split its future. One side is now primed to scale storytelling in a streaming-first world. The other is free to optimize legacy economics without pretending it’s still the future. This may be WBD’s most forward-looking move since the merger. The media chessboard just changed.

The financial case for brand strategy: Why CFOs should care. Branding isn’t just about looking good.* It drives real financial impact (* if done strategically) Yet, many companies still see it as a cost rather than an asset that increases enterprise value, reduces waste, and boosts profitability. Here’s what most businesses get wrong: - They see branding as expense, not an investment. - They focus on short-term lead generation over long-term equity. - They underestimate how much a strong brand lowers acquisition costs, improves pricing, reduces churn and attracts talent. Here’s how: 01 - Brand Strategy Increases Market Value: Brands are intangible, but they drive real financial value. Today, 80–85% of the S&P 500’s market value comes from intangibles like brand equity. Corporate reputation alone is worth $16 trillion globally. Companies with strong brands deliver 2× higher shareholder returns over 20 years than the MSCI World Index. Why? A strong brand builds trust, reduces risk, and increases pricing, partnerships, and M&A leverage. 02 - A Strong Brand Lowers Marketing Costs: Weak brands must pay to be noticed, they have to keep buying attention…spending millions on ads and lead gen. Strong brands generate attention. Tesla, for example, spends $0 on traditional ads, while competitors spend $495 per vehicle sold. Tesla’s brand, combined with a touch of Elon, drives WOM, earned media, and loyalty...saving hundreds of millions in marketing costs. (And yes, I know it works both ways, for better or worse) 03 - Branding Improves Profit Margins & Pricing Power: A strong brand lets you charge premium prices and avoid price wars. Apple sells iPhones at 40%+ gross margins, while competitors struggle, even with similar hardware. Why? Customers aren’t just buying a product, they’re buying into a brand. Data shows: - Consumers pay 11% more for trusted brands. - Brand-loyal customers pay 38% more, even price-sensitive ones pay 14% more. - Without strong branding, companies must compete on price alone. 04 - Strong Brands Retain Customers Longer: Retention is one of the biggest profitability drivers. It costs 5× more to acquire a new customer than to retain one. A 5% increase in retention boosts profits by 25–95%. Brand loyalty reduces churn, increases lifetime value, and creates repeat buyers without ads spend. 05 - Resilient Brands Outperform in Crises: In downturns, weak brands suffer revenue losses and resort to discounting. Strong brands hold their value & recover faster. During 2020, while most businesses struggled, the top 100 most valuable brands grew by +5.9%. A well-built brand acts as financial insulation, stabilising revenue. The Hard Truth: A strong brand isn’t a luxury, it’s a financial strategy. If your CFO still sees branding as a cost center, send them this. Sources: McKinsey, Interbrand, BrandZ, Bain & Company, Nielsen, Kantar, Invesp, Unilever, Tesla, industry reports on brand valuation, CAC, and shareholder returns.

The Voice Stack is improving rapidly. Systems that interact with users via speaking and listening will drive many new applications. Over the past year, I’ve been working closely with DeepLearning.AI, AI Fund, and several collaborators on voice-based applications, and I will share best practices I’ve learned in this and future posts. Foundation models that are trained to directly input, and often also directly generate, audio have contributed to this growth, but they are only part of the story. OpenAI’s RealTime API makes it easy for developers to write prompts to develop systems that deliver voice-in, voice-out experiences. This is great for building quick-and-dirty prototypes, and it also works well for low-stakes conversations where making an occasional mistake is okay. I encourage you to try it! However, compared to text-based generation, it is still hard to control the output of voice-in voice-out models. In contrast to directly generating audio, when we use an LLM to generate text, we have many tools for building guardrails, and we can double-check the output before showing it to users. We can also use sophisticated agentic reasoning workflows to compute high-quality outputs. Before a customer-service agent shows a user the message, “Sure, I’m happy to issue a refund,” we can make sure that (i) issuing the refund is consistent with our business policy and (ii) we will call the API to issue the refund (and not just promise a refund without issuing it). In contrast, the tools to prevent a voice-in, voice-out model from making such mistakes are much less mature. In my experience, the reasoning capability of voice models also seems inferior to text-based models, and they give less sophisticated answers. (Perhaps this is because voice responses have to be more brief, leaving less room for chain-of-thought reasoning to get to a more thoughtful answer.) When building applications where I need a more control over the output, I use agentic workflows to reason at length about the user’s input. In voice applications, this means I end up using a pipeline that includes speech-to-text (STT) to transcribe the user’s words, then processes the text using one or more LLM calls, and finally returns an audio response to the user via TTS (text-to-speech). This, where the reasoning is done in text, allows for more accurate responses. However, this process introduces latency, and users of voice applications are very sensitive to latency. When DeepLearning.AI worked with RealAvatar (an AI Fund portfolio company led by Jeff Daniel) to build an avatar of me, we found that getting TTS to generate a voice that sounded like me was not very hard, but getting it to respond to questions using words similar to those I would choose was. Even after much tuning, it remains a work in progress. You can play with it at https://lnkd.in/gcZ66yGM [At length limit. Full text, including latency reduction technique: https://lnkd.in/gjzjiVwx ]