Gareth Davies

How do you think about measuring success in AI-native businesses? As Poggio co-founder and CEO Matt Slotnick was recently quoted, traditional models don't really apply. Here's why: "People are overly focused on AI margins. Modern SaaS math assumes a certain operating structure that is labor intensive; e.g. you need 80% gross margin to pay for all of the typical costs that allow you to eventually run cash flow positive and 50% gross margin breaks the math...in THAT model. "But AI companies are very unlikely to follow that model. If you're replacing human labor in your own business, your OpEx will be meaningfully lower, and if OpEx is lower, you get a lot more margin to potentially play around with—not to mention the cost of goods sold on intelligence will decrease. "You're injecting intelligence not only into products, but also your operating model, and the cascading effects of that still seem largely underappreciated." Just surpassing 6 months at Poggio as my first time operating in an AI-native enterprise, I'm seeing this play out every day. We run incredibly lean at Poggio thanks to being both an AI-native enterprise and having a bias for leveraging AI tech in our business. My Marketing team is the smallest it's ever been, but is more productive and effective. It's cliche, but it truly is the epitome of operating smarter with AI (and maybe a little bit harder as well, but I chalk that up to being super passionate about our customers and enabling their success). You can catch the latest happenings and conversations in AI, like the above dialogue on measuring success in AI businesses, in Nathaniel Whittemore's The AI Daily Brief wherever you get your podcasts. Links to this episode, The AI Daily Brief newsletter, and starting a trial of Poggio are all in the comments below. ⤵️

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Love it or hate it, being in the SF Bay Area is still one of the biggest hacks for First Builders to discover huge opportunities before anyone else. But what can you do if you’re not here? Cecilia for example moved here in 2001 and instantly joined Yahoo after feeling the palpable energy around early internet. And it’s been nonstop ever since as she later joined Amazon, Cruise, Replit, and her own breakout company GC AI. She credits a lot to the emerging tech density and optimism in the Bay Area. But she also mentions additional ways people can identify these opportunities early on: - Follow intuition around big waves - Follow your envy - Never lose curiosity - Watch the teen hackers - Talk to peers early and often (builds long term networks/insights)

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When the stakes are high, the right tech partner makes all the difference. For LSKD—a global movement rooted in performance, community, and grit—that moment came when they uncovered over $100K in fraudulent affiliate commissions. They didn’t just need data. They needed action. That’s where Moby Agents from Triple Whale stepped in: ✅ $100K+ in fraud detected and prevented ✅ 40% lift in blended ROAS ✅ 3+ hours saved per week on reporting and analysis ✅ $8M AUD in revenue growth—with no increase in ad spend But the real story is how it happened: Agent-powered anomaly detection spotted affiliate abuse during a major sale—something even seasoned analysts could’ve missed. AI-driven reporting replaced siloed, inflated platform data—becoming LSKD’s new source of truth. Smarter budget reallocation fueled efficient growth, driven by real impact—not last-click noise. This is what it looks like when brands use AI strategically, not reactively. And here’s the key: Moby Agents didn’t replace the LSKD team—they enhanced it. By handling heavy, repetitive analysis, agents freed the team to focus on what really moves the needle: strategy, creativity, and innovation. This isn’t about less human input. It’s about higher-value human output. Full story here: https://lnkd.in/eNiVtyrC #eCommerce #ArtificialIntelligence #PerformanceMarketing #RetailTech #DTC #GrowthMarketing #triplewhale #moby

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LLMs aren’t good enough yet for the general public to use it for shopping… but they’re getting dangerously close. When AI cracks commerce, traffic behavior will change faster than most businesses are ready for.👇

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Sierra partnered with Curative to build their AI voice agent that's creating faster, better experiences for members and providers while freeing up their team to focus on what matters most: building deeper relationships

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I’m happy to share that today, Veritone announced its Q1 2025 financial results, which reflect solid revenue performance and demonstrable progress against our growth strategy. Over the quarter, we doubled our qualified near-term pipeline for Veritone Data Refinery (VDR), to over $10 million, demonstrating the insatiable need for high-quality training data for the expanding AI foundational models and infrastructure sector. As the shortage of accessible, clean, unstructured data accelerates, Veritone and our aiWARE platform are strategically positioned to capitalize on this market demand. I’m also proud to report that our Intelligent Digital Evidence Management System (iDEMS) continues to gain significant traction domestically and internationally because of its unique ability to support the evolving needs of our customers in law enforcement, government, and defense, driving operational efficiencies and improving outcomes in mission-critical workflows. Presentation: https://lnkd.in/g7eNqKK8

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We're often asked how Grindr is prioritizing user privacy as we become an AI-first company. It’s a great question and one we spend a lot of time thinking about. Our Chief Product Officer, AJ Balance, outlines our approach in this post 👉https://lnkd.in/gxZ37T_7 #Grindr #privacy #AIinWork

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The question I hear daily from partners, investors, and industry experts: What actually matters most to a hyperscaler? Co-packaged interconnects are deployable now. That’s why everyone’s excited and, frankly, in denial. Deployable is not the same as future-proof. Power density, serviceability, and rigidity don’t go away; they compound. The uncomfortable truth: short-reach interconnects and rack design are about to change. Different environments, different constraints. Fixed assumptions break fast at scale. So what’s the real priority: shipping something that works today, or building infrastructure that survives tomorrow? #wireless #aiinfrastructure #datacenter #hyperscaler #mmwave #6g #interconnects #server #serverrack #aws #google #azure #oracle

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Vertical AI is still the darling of venture capital. This week, Abridge raised a $300M Series E at a $5.3B valuation led by Andreessen Horowitz with Khosla Ventures for its clinical documentation platform. It turns doctor and patient conversations into structured notes, helping clinicians reduce time spent on manual data entry. In it's most reductionist form, Abridge's solution is an AI assistant that organizes and encodes relatively stream of consciousness human inputs. A perfect use case for today's AI. Abridge is a focused product built for a high trust, high regulation environment where inefficient data structure creates significant operational challenges. AI is making huge impact in health tech where systems have clear structure; dense, yet inefficient, documentation; and big value. Success requires dropping into existing complex systems, providing secure input and consistent output, and a deep understanding of data handling and compliance. The combination of predictable processes, strong data loops, and clear value makes healthcare one of the most compelling areas for applied AI right now. And the market recognizes this. Are you building where the technology naturally fits?

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As a Stage 2 LP, I get to observe some fantastic companies scale up close. One of our portfolio companies Letter AI, just announced their $40M Series B. Letter AI modernizes the First-Meeting-to-Closed-Won journey by creating custom buyer agents for pre-meeting practice, in-meeting agents for real-time coaching, and post-meeting automation of CRM updates, forecasts, and next-step actions. It's an intelligence layer that’s tuned to every call prep, embedded in every live interaction, and actively shaping next steps for each deal. Worth the read: https://lnkd.in/ecrAF57p

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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