Chuck Reynolds

Next week, a federal court is kicking off a trial that could reshape the open web – and the future of digital publishing and advertising. The court has already ruled that Google exercised anticompetitive monopoly power in display advertising. But what happens next has implications far beyond just one company. One proposal on the table is forcing Google to sell off its ad tech. While that may sound decisive, simply swapping one owner for another could create new problems – and doesn’t guarantee a fairer marketplace. What matters most is fixing how the system actually works. We need a digital ads marketplace that is fair, while also keeping the Internet free and open for all. More of my thoughts in The Information: https://lnkd.in/gJs47AvN

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The fact that Anthropic is raising yet another multi-billion round of funding (this time at a reported $170B valuation) has obscured a terrific piece of research they recently published. This summary from The Washington Post tells the story -- Anthropic's research shows that AI is impacting different jobs in different ways. In some cases, automation (and, yes, job replacement). That's the scary side of the equation. And media headlines that are scary tend to attract more clicks. But in other cases, we are seeing augmentation. In fact, augmentation is outweighing automation by a large factor. That's good news for the future of labor. And perhaps a story not being told enough. We are seeing a massive amount of augmentation in the startup ecosystem due to AI. Founder augmentation is leading "10x founders" to have the capacity to execute faster and more efficiently than ever before. So don't let AI automation scare you. Embrace AI augmentation. https://lnkd.in/e2gRDZMz

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I co-founded one of the first streaming app dev companies, Float Left, in 2009, back when Roku had 5 apps in its store. Media companies still drop $100–300k to build a single custom app for a single platform. That’s just the cost of fragmentation. Every OS wants their own build. Their own QA. Their own rules. And that’s before you even get to video delivery, subscription systems, marketing, and content spend. THEN comes the platform tax. Post-launch, the OS forces you onto their billing rails, even if you already have your own subscription system, and skims a cut in perpetuity. There’s no “Netflix Roku app” or “Netflix Fire app.” It’s just Netflix. But everyone in the chain acts like it’s ten different products, each one they deserve a slice of. That’s why we'll continue seeing services (with any inkling of DTC aspirations) leaning more into soft bundles and hard bundles, drive sign-ups outside the app stores, dodge the tax. I unpacked all of this in our latest Basics of Streaming article on The Streaming Wars, breaking down OS fragmentation, dev costs, and the hidden economics shaping the industry. https://lnkd.in/euQkMKty cc: #streamingmedia #tv #media #entertainment #business

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Let's open a can of Woopass on some assumptions about creative automation... Creative automation for CTV isn’t about repurposing social ads. That’s a very 2015 way of thinking. It’s about using AI to create custom ads from scratch — and performance tech to determine what actually delivers against specific advertiser-declared outcomes. For example: a friend of mine made this ad in 20 minutes. No editing, creative professionals, or custom AI tools. Are there gaps? Sure. But it’s a pretty darn good starting point. Now imagine building an ad with a professional team, using specialized AI tools and a full day of work — still a fraction of the time, money, and energy that typically goes into TV creative. Then add performance tech on top of that. With platforms like tvScientific, you get a feedback loop. That enables creatives to use the scientific method to test-iterate-scale based on actual feedback loop data to improve performance. You understand what works. You double down. And your next ad is even better. The big takeaways: Democratizing access is step one. Using data and iteration to drive performance is step two. Understand where the real innovation in CTV is coming from. It’s not repurposed mobile or social ads. It’s true creative automation with performance measurement and outcome optimization built in.

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We just crossed the 100th GitHub issue threshold in our work on the W3C's Web Sustainability Guidelines (WSGs for short, because we love acronyms). Recommendations in the spec are evolving quickly based on an incredibly passionate group of smart volunteer technologists, designers, developers, content strategists, and business leaders. Next version will come out in September for horizontal review as we barrel toward our goal of publishing first a W3C Note next April and, eventually, a W3C Statement. In the meantime, here's the latest: https://lnkd.in/gU5CDRXx

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Which AI teammates do you need for your next project? Most teams use custom GPTs one at a time, briefing each AI teammate separately and losing context in handoffs. But your messaging AI could feed directly into your content AI, which then connects to your social AI... all in one conversation. This is how work should flow when expertise can move freely instead of getting stuck in department silos. Try the GPT Chain Planner (see link in the comments). Choose a workflow and get a suggested blueprint for connecting AI teammates that eliminate handoffs. See which GPTs to build, how they connect, and why each step builds on the last. No more re-explaining context. No more lost insights. Teams using this approach are rethinking how knowledge flows to serve customer outcomes rather than org charts. The gap between "who knows what" and "what needs to be done" shrinks. AI doesn't care about our silos and neither do our customers. My upcoming newsletter shows the frameworks, demos of GPT chains in action, and why connected AI teammates is a stepping stone from org charts to work charts. Subscribe to the newsletter (link in comments) to get it directly in your inbox.

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It's been amazing to see how customers are using AI Custom Fields on the CourtCorrect Platform creatively to transform and evolve their businesses. Naturally, customers adopted AI to review large amounts of evidence via a Q&A style chat interaction quite quickly, especially given the significant operational efficiency gains and quality improvements this enhanced review of evidence delivers. AI Custom Fields on CourtCorrect present a nuanced evolution of this concept, by enabling the consistent and structured extraction of common pieces of information from a multi-modal set of complex evidence. To put it simply, AI Custom Fields can extract the same data from a complex case file every single time with a high degree of accuracy at the click of a button. While many clients have leaped forward to experiment with AI Custom Fields, I am especially proud of our incredible Model Design Team at CourtCorrect, which is collaborating deeply with clients across financial services to design, develop, fine-tune and deploy custom fine-tuned models with unparalleled accuracy, nuance and robustness that allow our most sophisticated clients to tackle even the toughest challenges. AI Custom Fields are an incredible new data format on the CourtCorrect Platform, but when these are connected with custom AI models purpose-built for complex financial services queries, they become an unstoppable force with the potential to permanently transform the way businesses think about their customers. You can learn more about this exciting new technology and how to leverage it to improve the efficiency and quality of your operation on our blog: https://lnkd.in/eWdMKCqS Please don't hesitate to get in touch directly to learn more! #ai #finance #complaints

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