Hollie Wegman

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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The biggest operational complaint in the 2026 Art Basel & UBS Art Market Report isn't about sales or market sentiment. It's tariffs. The real problem is unpredictability. Buyers don't know what they'll owe at customs until a carrier calls them weeks after purchase. That surprise — sometimes 20% VAT on a $30,000 item — is what creates friction, delays, insurance risk and the kind of bad experiences that make international buyers think twice next time. We built something to fix the predictability problem at the API level. It's called the Import Cost Estimates API. You pass us an object value, destination country, HS code, and country of origin. We return a real-time estimate of the VAT, GST, and applicable duties before the shipment books — so you can show a buyer in London or Tokyo their true landed cost at checkout, not as a surprise three weeks later. A few numbers that make this concrete: — The blended effective VAT rate across our 2025 international transaction mix was ~11%. On a $25,000 work, that's $2,750 your buyer didn't know was coming. — The highest-volume international corridors on our platform — UK (20% VAT), France, Germany, Italy (21%), Japan (10%), Australia (10%) — are exactly the markets where the Art Basel report shows cross-border complexity is driving sellers to pull back from international sales. — HS code accuracy drives both the duty calculation and customs clearance speed. We built a companion HS Code Recommendation API alongside this to take that burden off clients. The Art Basel data describes a market that wants to sell internationally but is being quietly pushed toward domestic-only sales by cost and complexity. This API is one part of how we're pushing back on that. Next up: we're taking this a step further — so that calculated number doesn't just inform the buyer, it clears customs for them automatically. API docs: https://lnkd.in/eBkws9p7

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SaaS pricing is getting harder to predict, with seat-based pricing giving way to usage-based models. For Velocity by Ramp, I spoke with Sara Yamase of Simon-Kucher Partners, procurement advisor James Meads, and Chris Sawchuk of The Hackett Group Inc. about how finance teams can manage that volatility. https://lnkd.in/eAy38ZxB

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"Everyone has AI. Not Everyone Has Credible Data." Amen to that! Read today's blog from Seerist CEO Alexander (Alex) Berry on how to make sure your AI driven decisions are grounded in truth!

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This Martechvibe report put words to a lot of conversations I’m having with CMOs right now. Most teams are not trying to chase the next shiny tool. They’re trying to make what they already have actually work together. Too many platforms, too many handoffs, and not enough shared data. When marketing shifts from experimentation to execution, those gaps show up fast. Progress depends less on adding technology and more on simplifying how it’s run and measured. I highly recommend reading! https://lnkd.in/eZTES875

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Talking to users and thoughtfully integrating their needs and wants was woven through everything we did at IDEO—threaded through every prototype and product decision. Reading my colleagues Joanne and Leo's latest work brought that to mind. They describe a future where research runs continuously, with AI agents recruiting participants and instantly surfacing insights. Synthetic personas, carefully trained on consumer signals, mirror human behavior closely enough to extend live studies and compress timelines from weeks to hours. Today user research mostly runs in fits and starts, prohibitively expensive and locked away in dedicated project cycles when it could be a constant companion, seamlessly integrated into the product development process. Joanne and Leo lay out the full vision here: https://lnkd.in/gEUBSG_J

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Game-changing. Leverage. Seamless. Cutting-edge. Have you read a post, webinar description, or internal memo lately that feels like a word salad? With LLMs at our fingertips, everyone seems to be using the same language, either assuming whatever the model spits out is inherently superior or simply moving too fast to rework it. Tools like ChatGPT and Gemini make it incredibly easy to produce content without truly vetting it or asking whether the words actually hold value anymore. I recently went down a rabbit hole on linguistics and the homogenization of language in the age of generative AI. Historically, language innovations (new slang, new structures, even grammar shifts) came from humans breaking the rules. But when we increasingly use generative AI to produce content and then reuse that content to train future models, we reinforce patterns instead of challenging them. The feedback loop tightens, the language flattens, and in my opinion, everything starts to sound meaningless. A recent MIT study examining large language models found that while LLMs generate coherent and plausible text, they fall short in reproducing the full richness of human language. Specifically, the study measures three dimensions of diversity: lexical (word choice), syntactic (sentence structure), and semantic (meaning variation). The researchers found a significant gap between human-created language and LLM outputs in creative tasks. In other words, the text sounds right, but it lacks depth, variation, and nuance. As a marketer in the technology space, my motto has always been: “Understand deeply to explain simply.” You have to truly understand the nuances of the technology so that when you synthesize it into short, pithy marketing language, you know exactly which words capture the core meaning and essence of the thing. That depth doesn’t automatically happen with generative AI, and nuance can get lost. To be clear, I am a huge proponent of using LLM-powered tools to work smarter and more efficiently. I use them every day. But we have to be more intentional about the words we publish. At minimum, reread your generated output through this lens: If someone on the street read this, would they actually understand what I’m trying to say? If the answer is no, then go a little old-school and rewrite it yourself. Let’s drive alignment in optimizing our lexical synergies to unlock semantic scalability across the conversational value stack 😜

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I’ve been talking with founders about how painful enterprise customization still is — even in 2025. David Dworsky and Gordon Ritter just put words to what we’ve been seeing: the age of point-and-click configuration is ending, and the next category winners will make customization conversational. For early-stage founders, this is the wedge: - Own one painful workflow - Capture the data - Build toward becoming a system of record If you’re building in this space, I’d love to hear how you’re approaching it. Link to the full article on this in the comments. 

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