Craig Zeldin

Love this - AI is the Oppenheimer moment for marketing. It fundamentally collapses the time and cost of production. A $2.5 million campaign that used to take four months can now be delivered for $500,000 in four weeks. Legacy firms that charge based on hours with the 'time-driven' model are existentially threatened by this.

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Excited to share that Aperiam has invested in Bedrock Platform. Obvious statement of the century: programmatic advertising has a complexity problem. The infrastructure that was built to give media buyers control has, over time, done the opposite. Burying teams under layers of operational overhead, legacy tooling, and opaque supply chains. Bedrock is built on a simple but powerful thesis: what if you stripped away the bloat and rebuilt the stack around what actually matters... precision, transparency, and speed to execution. Their platform combines AI-driven campaign automation with curated, premium inventory across video, CTV, DOOH, and audio, and aims to be flexible and get smarter over time. Plus, the team (Shane, Austin, Ryan, James) has deep roots in the infrastructure layer of ad tech and understands the plumbing better than almost anyone. We believe the next generation of programmatic will be leaner, faster and AI-first. Bedrock is building that.

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Leading product at Haast, I've seen skepticism that surrounds AI firsthand, especially when it comes to legal and compliance. In this space, speed isn't enough. Enterprises demand accuracy, explainability, and reliability—without compromise. Building our product is more than great design and code. It requires training AI on real-world compliance scenarios, partnering closely with legal experts, and embedding human-in-the-loop validation at every step. That behind-the-scenes rigor is what makes the difference. Our AI not only flags risks, but also explains *why* they are risks, referencing the exact policy or regulation. We’ve baked transparency and data security into the product from day one, because in compliance, trust isn’t a feature, it’s the foundation. Curious to hear from others: What’s your biggest trust barrier when evaluating new AI-powered tools? #ProductLeadership #AIEthics #EnterpriseSoftware #TrustInTech

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We’ve been shifting toward an experimentation culture at Fairing. Our scale gives us feedback fast, so we can validate ideas and reach statistical significance quickly. Here’s a simple example: small design tweak, sizable measurable impact. These experiments are already shaping the UI updates we’ll roll out next quarter.

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U.S. energy demand is surging — don’t get left behind. New gas projects and large industrial loads are reshaping power markets at breakneck speed. In this high-stakes landscape, timely and accurate data is essential. Halcyon’s Data Products, powered by a regularly updated authoritative data catalog of 4.5+ million documents from all 50 public utility commissions, ensure you always have the latest energy market intelligence. Download this guide to see how AI can help power sector stakeholders find new growth opportunities first.

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See how agencies are shrinking time-to-insight from hours to minutes and delivering trusted outputs with full traceability. 👉 Watch this quick demo of Domino Data Lab Document Intelligence's power to transform mission-critical records, policies, reports and multi-modal data into auditable, operational insights at speed. 🎥 https://gag.gl/MfxQ0b

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Most teams don't struggle with speed anymore. They struggle with building the right things. That's exactly where my brother thrives. He's a Principal Product Designer who brings real product thinking into design. The kind of person who helps teams cut through complexity and focus on what matters. If you're building serious products and need that level of thinking, take a look at his post below.

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Checked out Forerunner/Kirsten Green's "The Architects of Experience" piece (linked in comments). Sharing some thoughts below. Every 10-year platform shift resets consumer expectations (Web → Mobile → AI). Kirsten says the AI era belongs to products that feel effortless: proactive, predictive, always learning. It’s not about adding an LLM garnish; it’s about architecting the entire business around a compounding data loop. So, why does it matter? Moat ≠ code → The defensible layer is a proprietary data flywheel that learns faster than users can churn. Talent remix → Winning teams look less like pure engineers, more like “orchestrators” who weave off the shelf AI, data strategy, and distribution. Trust is the gate → Human in loop design and transparent governance are table-stakes for adoption, and eventual regulation. Questions to consider for consumer AI founders... 1. Moat depth: What makes your data flywheel unique, and how quickly does it compound vs. the big LLM platforms? 2. Distribution edge: When AI tooling is commoditized, what non-technical channel (community, brand, regulated access) gets you in front of users first? 3. Human-machine balance: Where does your product deliberately stop automating to preserve agency and delight? 4. Regulatory resilience: How will privacy portability rules affect CAC, margins, or data lock in? My counterview: Total automation can flatten delight. Some categories thrive on intentional friction (e.g. luxury, craft, serendipity). Winners will know when to keep a human in the loop.

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S4 Capital is betting big on an “agents over agencies” future - framing its downturn while positioning Agentic as a loss leader. The goal: shift from human-led services to AI-led platforms, compress production cycles and repackage creative delivery as managed services. It’s a bold strategy, but also a tough execution challenge: quality, governance and enterprise integration remain critical. The future may not be “agents instead of agencies” but “agents and agencies” working together - using AI to enhance creativity, not hollow it out. Scale, trust and deep client partnerships will ultimately decide who wins. https://lnkd.in/e3zffAyy

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My First AI Post @2025 (and thank you to the Aperiam AI Fellows and to Dawn West and Alex Netelkos for making that happen!): If viewability was the training wheels of digital measurement, attention is the carbon-fiber racing bike we’ve just learned to ride. Erica kicked us off by pointing out the obvious: “viewable” doesn’t mean “seen,” and certainly doesn’t mean “engaged.” McKinsey’s neat formula — time × focus × intent — resonated as a starting point, but the real magic is in using that across channels and across the funnel. Why does this matter now? Because omni-channel is no longer a buzzword — it’s the messy, lived reality. CTV, digital out-of-home, mobile gaming, social feeds — each has wildly different engagement norms. An attention metric gives marketers and publishers a lingua franca, a Rosetta Stone to compare value and outcomes without pretending a click on a banner is the same as leaning into a big-screen spot. Publishers want credit for premium environments. Marketers want to normalize buys across hundreds of brands, countries, and media types. Attention-weighted CPMs (ACPMs) start to solve both. Even a global CPG can look across Meta, YouTube, stadium signage, and print — and make sense of it. Is it perfect? No. Is it better than a stack of inconsistent KPIs and gut feel? Absolutely. And yes, AI is in the wings — not as the protagonist, but as the stagehand making the hard stuff easier. It can normalize messy data, connect attention signals to outcomes, and embolden teams to try bolder experiments. Which brings us to the closing provocation: What if a few brave brands and publishers agreed to really transact on attention — rough edges, no gaming, shared responsibility for outcomes? The moment feels right to test whether attention can become not just a metric, but a currency. Marc Guldimann Adelaide David Zapata AB InBev Michael Shields Cory Treffiletti Rembrand McKinsey & Company

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Ignoring and “coping” with the change isn’t an option. USAGE DATA = REVENUE DATA Don’t make do with ETL and homegrown integration into production platforms, it puts pressure on your billing system and contributes to revenue leakage. SAP + DigitalRoute + Acuiti Labs Market leading solutions for hybrid and usage-based business. #usagedata #enterprise

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Bruce Shields from Carhartt joined us on the most recent episode of The Frictionless Experience podcast, and he gave Nick Paladino and me a masterclass in how data and design actually come together. Here are some of the biggest takeaways: - UX maturity is a spectrum. Carhartt started with “UX = design.” Today, it’s about testing, personalization, and optimization at scale. - Data before design. Wireframes now get modeled against revenue goals before a single creative decision is made. - Confirmation bias is real. Early testing was used to prove assumptions. The shift came when Carhartt reframed “failed” tests as wins that brought them closer to the truth. - Benchmarks change the game. Two years of homepage interaction data turned into predictive models that can forecast component performance. - The metric to watch. Conversion rate between category pages and product detail pages is one of the most reliable predictors of revenue. - Clicks aren’t the enemy. An extra click that gives users clarity is better than a shorter, confusing journey. - Segmentation matters. New customers and loyal repeat buyers need completely different experiences. - Culture counts. A low-ego, high-curiosity environment made it possible to embed testing and data into everyday decisions. Every one of these points could have been its own episode. Big thanks to Bruce for bringing so much depth (and for making me jealous of Carhartt’s framed jackets).

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Today we're changing Clay's pricing. Pricing changes are scary (for you and me!), but we are trying to do this in the most authentically Clay way possible - thoughtful, transparent and community-first. We've been thinking about this for almost a year (maybe too long Karan!), and have talked to hundreds of customers and partners. We want to align our business model with how our product is being used today, and set us up to grow sustainably together. When we launched, our customers used us for data enrichment. And now our community is using Clay to power their GTM engineering motions. But the pricing model hasn't evolved, so we're making some changes to reflect that. Here’s what’s important to note: First: nobody is getting forced off their plan. Every existing customer can stay on their existing plan. We have to earn the right for you to move to a modern plan. Second: most customers will see better value for their money with this change. If we think you'll save money by switching to the new pricing, we're going to reach out and tell you proactively. Third: we're separating the cost of data from the cost of the platform. We want to keep bringing down the cost of data in Clay, and make the value tied to what you orchestrate and build with it. Fourth: our best features are now more accessible. The new Growth plan includes things that used to live exclusively on Pro at a 38% lower price. Many of our most advanced users will save thousands a year. Our plans now come with a new metric called Actions, which measures the orchestration work Clay does for you: the workflows, AI research, and logic that turns raw data into a GTM motion. 90% of customers will never hit their Actions limit. We want most people to never think about it. That way, we’re only winning if you are. Data Credits now cover just the data - and they're cheaper. We’re reducing the cost of the data in our marketplace by 50–90%, making prices comparable to what you’d pay externally. Karan and I recorded a video walking through the full reasoning and who gets affected - positively and negatively (including us – we’re taking a 10% revenue hit). If you're a Clay customer and have questions, we want to hear them! Full pricing announcement and the internal memo we sent our team below. Announcement: clay.link/nF1eKMH Internal memo: clay.link/wiw2NHK

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Product marketing doesn’t have a slide problem. It has a positioning problem… for the function itself. Over years of mentoring PMMs, the same challenge keeps coming up: how do you make product marketing matter inside a company? I started a weekly column called Ask a Product Marketer to work through these questions in public. Real questions, real answers, no fluff. This week’s question is about escaping the slide factory. https://lnkd.in/gwfks48J If you have a question about product marketing, send it my way. I answer one each week. #productmarketing #gtm #advice

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If you work in advertising, photography, design or production, what follows probably sounds familiar: High upfront costs, lumpy cashflow, and banks that don’t have a lot of time for how your business works. Valiant’s put together a solid guide on funding options that actually suit creative businesses. If you’ve ever been knocked back by a lender (or just want more flexibility), get in touch! 👇 Link in the original post.

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