Marketing

We're about to see an onslaught of consulting and IT services firms going big on working with AI platforms to deploy agents in the enterprise. And if you don’t understand why it’s happening, it’s an opportunity to reset your understanding of how the real world works. The real world will need a ton of help actually getting agents going in the enterprise. Companies deal with significant legacy tech stacks they need to modernize, data in tons of fragmented tools, knowledge that isn’t captured or digitized, and change management needed to actually utilize agents effectively. And they have to do all this while still running their business day-to-day, unlike startups, who can generally just design their organizations from the ground up to deploy agents into new workflows designed for them. This is why there is so much opportunity for companies (software or services) to actually deploy agents in specific domains and workflows. This remains a big opportunity for both existing services providers but also tons of new services startups as well. Every new technology wave produces a new era of consulting firms that can deliver on that technology. We're seeing this a ton at Box, both in partnering with new forms of technology consultancies as well as existing systems integrators that are building out all new agentic practice areas to help enterprises work with their unstructured data and agents. These service providers will have the benefit of being able to work across multiple data platforms, as well as see common practices that work or fail within an industry. This knowledge ends up being incredibly valuable right now, especially given how fast things are changing. A corollary to this is also that the forward deployed engineer (FDE) model is going to be alive and well for a long time because companies will want to have their vendor actually help drive the change management and implementation for their new workflows. There’s no shortcut to getting this work done for the enterprise, and the vendors are going to have to do a lot of this or risk low adoption. All of this type of work is going to be in high demand for quite some time, and it's incidentally another example of jobs that aren’t actually going away.

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.

Disney’s Earnings Didn’t Just Reveal a Quarter — They Revealed a Strategy Shift Most headlines today will flatten Disney’s Q4 into a simple story: streaming up, revenue a little soft, linear TV still fading. But if you actually read between the lines, this quarter tells a much bigger story about where Hollywood is heading. Here are the signals that actually matter: 1️⃣ The center of gravity is shifting from “content” to “experiences.” The Experiences segment once again carried the quarter. Double-digit operating income. International especially strong. This isn’t a “nice win.” It’s a business transformation. It’s The Walt Disney Company quietly saying: theatrical doesn’t end the journey — it begins it. Look at Lilo & Stitch: mid-budget movie → huge streaming wave → $4B in retail → character momentum showing up across parks. That’s the new model. IP that lives across screens and spaces — and monetizes every step. 💡If you care about licensing, franchise health, global momentum… this is the signal to watch. 2️⃣ Streaming is no longer a subscriber race — it’s becoming the operating system. The DTC business posted another profitable quarter. But the bigger move is strategic: Disney is positioning streaming as the connective layer of the entire company. Streaming is now where theatrical, TV, social, and products ladder into each other. It’s where franchise momentum is measured. It’s how stories travel globally. 💡Not “another Netflix.” More like the nerve center of the entire Disney ecosystem. 3️⃣ Linear TV isn’t declining — it’s being deprioritized. Yes, the Networks segment dropped again. Yes, ad revenue took a hit. And yes, the YouTube TV standoff hurts. It’s as if they’ve already accepted where this ends and are now architecting around it. 💡Linear becomes a bonus — not the business. 4️⃣ Experiences are becoming the profit engine everyone underestimated. Parks and cruises aren’t just outperforming. They’re outpacing every other part of the company in a way that’s structurally meaningful. Because experiences create something content can’t: decades-long loyalty. A hit movie gives you a weekend. A hit show gives you a month. A hit attraction gives you repeat visits, lifetime spending, and memories people pass down. That’s why CapEx keeps flowing here. It’s a compounding engine. 💡And it’s why every studio with a recognizable IP library is now studying the Disney playbook. 5️⃣ The interesting part isn’t Q4 — it’s the setup for FY26 and FY27. Disney guided to double-digit EPS growth for the next two years. Not bold optimism — more like quiet confidence. The company is preparing for a world where: ⌙ streaming = core infrastructure ⌙ experiences = highest-margin growth ⌙ theatrical = premium marketing vehicle ⌙ linear = fading but managed ⌙ consumer products = global engine 💡 If you map this forward, Disney looks less like a traditional studio and more like a vertically integrated IP platform. #Media #Disney

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.

Attribution is overrated. Incrementality is what actually matters Every new-age brand wants to know what’s working. Meta ROAS is looking good. CAC is steady. Revenue is growing But here’s the truth: Your Meta ad might get the conversion. But did it cause the conversion? That’s the difference between attribution and incrementality. Most dashboards, attribution tools, and agency reports stop at attribution. But if you’re a brand selling across Amazon, Flipkart, GT, MT, Q-com, and D2C—pure attribution will always lie to you Because the sale might happen on Amazon. But it might have been nudged by a Meta video or a YouTube bumper ad 4 days ago. You don’t need a full-blown Marketing Mix Model to get started. There are simpler, street-smart ways to directionally understand what’s working—and what’s not. Here are 4 that have worked for us at Atomberg: 1. Geo Split Testing Pick two similar markets. Run campaigns in one. Don’t run in the other. Then track: • Branded search volume • Sell-through on marketplaces • Secondary sales from GT counters If the test market moves faster than the control, you’re seeing true lift. That’s incrementality. 2. First-Time Buyer Growth vs Returning Buyer Growth Track whether your growth is coming from first-time buyers or repeats. If your campaigns are just bringing back old customers—you’re not creating net new demand. But if there’s a spike in new buyers across Amazon, Flipkart, D2C—your campaigns are likely working at an incremental level 3. Paid Traffic vs Organic Trend Lines If paid traffic, clicks and spends are going up—but your organic sales or branded search isn’t moving—you’re likely just harvesting demand that already existed. But if organic lifts alongside paid—your ads are creating interest. Not just closing it. Directionally, this is one of the simplest sanity checks most teams ignore. 4. Channel Crossover + Offline Signal Mapping Your Meta ad may not show up in last-click attribution. But it might have nudged the consumer to visit your store or buy on Amazon. You can detect this through: • Post-purchase surveys (Where did you first hear about us?) • Branded search + store footfall spikes in campaign-active cities • And most powerfully—offline signals passed back to Meta At Atomberg, we pass back data from installations and warranty registrations—including pincode and purchase timelines Sometimes, we’re even able to identify this at a unique customer level through their cookies for warranty registration This has helped us understand true incrementality of perf marketing campaigns even for offline sales If you’re only measuring ROAS, you might scale what’s only taking credit for sale about to happen anyway If you chase incrementality, you’ll scale what’s working. For more details, read the full post- link in first comment.

The best personal brand is living a life worth talking about. Not manufacturing one. Most "personal branding" feels like dressing up as someone you think others want to see. But what if you focused on living an interesting life instead? Try something difficult. Make something unusual. Solve interesting problems. Share what you learn along the way. When you're genuinely passionate about your work and experiences, you don't need clever marketing tactics. Your enthusiasm becomes contagious. Your stories become memorable. Your insights become valuable. The strategy isn't complicated: Do things that matter to you. Tell the truth about them. Repeat. No need to manufacture a persona. Your personal brand isn't something you create separately from your life – it's what naturally emerges when you're living authentically. The most compelling stories come from people who aren't trying to be compelling. They're just being themselves. Here's how to tell your story authentically, day in and day out, here on LinkedIn: https://lnkd.in/eh9pVVuf

Is THIS the best ad campaign ever? In 2015, Sport England challenged ad agency FCB Global to close the 2 million strong gender gap by getting women more active. The agency used the insight that women often feel 'fear of judgement' in exercise, to create the campaign 'This Girl Can'. The campaign is a rallying cry to women to get active in THEIR own way by replacing fear with a 'don't give a damn' attitude. This is shown with bold copywriting, relatable casting, REAL moments (the make-up smudged under the eyes, normal jiggling bodies, menopausal sweat, period cramps, tampon string hanging out your pants) and a true sense of female camaraderie. Since it's launch: - 3 million women were inspired to exercise as a direct result of seeing the campaign - 1000+ social media mentions each day - 37m views across social media - 500,000 active members in the This Girl Can community - Cannes Lions award The campaign is evidence that advertising can make great impact and drive change in many little corners of the world. THIS is the result of a clear brief, unifying insight and - in this case - a dedicated female creative team who truly 'understand' their audience. But more than that, it's the result of a LONG-TERM campaign that has been running for almost decade, and continues to re-engage the audience in various different ways, globally. I think there is such a short-term mindset in advertising nowadays. Mainly due to the fast-paced nature of social media, the need to 'go viral' and the economic need for performance marketing tactics to generate cashflow. But without the longer-term brand campaigns, we are missing the ability to build strong narratives and make REAL change in the world. And with that, stronger brand salience, brand love and LEGACY. This is an element of advertising that I fell in love with years ago. And an element that I see really defining which brands stand the test of time, an which fall apart years down the line.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Yesterday, yet another AmLaw100 firm announced a round of layoffs of associates & staff. The layoffs seem to be part of a broader trend driven by slowing demand as a result of rising interest rates. This isn’t the first time that’s happened btw. The legal industry goes through these cycles and usually firms re-hire for those same positions when the economy bounces back. This time might be different, though. Here's why: First, generative AI will enable partners to do more with fewer associates. I’m not sure if the AI is good enough to do that today, but it’ll get there soon. Especially since firms seem to be incorporating it into their workflows right now. When demand eventually returns, these firms will likely have far more tech-enabled processes than they do today—which means they’ll need fewer associates to complete the work. If you want to stay ahead of the curve, pay attention to what legal recruiters are saying about hiring patterns when things bounce back. Second, large clients are more savvy about buying legal services than they’ve ever been. The rise of legal ops over the past decade have helped in-house lawyers make better decisions about where to invest resources. There’s also been an explosion in tech and analytics (e.g. Persuit, SimpleLegal) to help with outside counsel spend. I expect a trend towards greater financial discipline among legal departments, similar to how insurance companies work with their outside lawyers. If you want to stay ahead of the curve, pay attention to rate increase data for various practice areas. Third, there’s an unprecedented amount of high quality talent that now exists outside of traditional Biglaw. The boom-bust nature of the industry means that capable attorneys are constantly being pushed out. It’s not just associates; it’s also partners who are being de-equitized for the sake of PPEP. That, combined with the generational trend towards remote work, rise of boutique / regional firms, and explosion in flex talent providers (e.g. Axiom, Paragon, Latitude). If you want to stay ahead of the curve, pay attention to layoff data & equity partner growth & attrition rates. Now don’t get me wrong. Many AmLaw100 firms and their people will be immune to these trends. Clients will still go to their same go-to firms for high stakes matters, like big time M&A or bet the company litigation. Basically anything that has boardroom visibility. CLOs and GCs are betting their careers on the successful handling of those matters, and they’re not going to be influenced by cost savings or efficiency. As they say, no one ever got fired for hiring Cravath. For everyone else though, you’ve got to stay ahead of the curve. Because it doesn’t matter what school you went to, what firm you worked at, or whether they promised to make you partner. Market dynamics dictate so much of success and failure in our careers—so you always want to be prepared for whatever comes. Good luck my friends. 

Don’t confuse sharing with branding. Last week I had a long convo with a CEO that I manage. It was about how his team suggested raising his personal brand by going hard on social media. He hired a new young hip team for social and PR. Their advice was to start posting all aspects of his life to help brand him and make him more relatable. That might work for them. But when you are a CEO doing over 1 billion in business annually. Raising your profile the right way w def increase opportunity for your company, share holders and family. But if it’s done the wrong way. It can be detrimental. This is not a game! Everything you post is a message. Youre either building equity… or giving it away. Thats why even my posts are very calculated. If Im going to an important event, you might not hear about it until after. If im dealing with a personal problem, you’ll never know. Unless it’s a health matter that we can all learn from. If it involves people I love or my inner circle, it stays protected. If I share a dream or a goal, it’s because I want the right people to see it. the ones who can help build it, not break it. If something is personal to my family or friends, it will never live on social media. Before you post, ask one question: What’s the end goal? • Humor? Show range with intention. • Conflict? Show growth or resolution. • Family moments? Remember the world is watching too. Oversharing without strategy isn’t authenticity. it’s just noise. And noise has no upside. Some things are meant to be protected. Because once everything is public, everyone has an opinion. And not all of them want to see you win. Post with purpose. Remember to Brand yourself on your terms. DJ