Peter Hamilton

NVIDIA just hired WSJ's tech editor as their new managing editor. RH's Editor in Chief is the former EIC at Architectural Digest Robinhood's Editor in Chief is the former EIC at The Outline and BDG The Trade Desk's Editor in Chief is the former EIC at AdWeek Jerry's Lead Data Journalist is former Bloomberg/Mother Jones Data Reporter Mastercard's VP of Editorial Content is former cnet/wsj reporter CARFAX's EIC is the former editor at Consumer Reports IBM's Editorial Lead is a former AFP reporter BlackRock's VP Content is former editor @ Fortune SoFi's Managing Editor is former cnet managing editor Resy/Amex's Managing Editor is former SF Chronicle Wine Editor Redfin's Chief Economist came over from Boston Fed ... and so many more These companies are not replacing journalists with AI. They are hiring great talent and putting them to work on great coverage. A real reason to be excited about the reporting coming out of these orgs you would not traditionally think of as media companies. And a reason to be excited about job prospects for talented editorial folks. Slowly curating the best of the best of the output from this effort - if your team is following suit, or you're looking for roles in this space... say hi//follow along. Also - a follow up conversation here with examples of the work these orgs are doing (and tradeoffs they face) - https://lnkd.in/egkmumhM

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We are seeing the cliff-edge moment for the car industry. It feels like we're watching a slow-motion game of chicken, and a lot of the big car companies are about to lose. A great read from McKinsey, featuring Waymo, really drives the point home: the self-driving car future isn't a far-off concept anymore. It's here. Here's my take: As self-driving tech becomes normal, way fewer people are going to want to own a car. The current predictions from most European and American carmakers are going to be proven very, very wrong. Why are the traditional guys in trouble? They've been slow to change. For decades, they've competed on who has the best cup holders or the shiniest grill. That doesn't work when the real competition is about software and AI. They're looking at the wrong numbers. Their entire business is built on selling cars to people. But as the article points out, services like Waymo are already replacing the need for a family's second car. Habits are changing. It's too expensive to do both. They're spending a fortune just to switch to electric vehicles (EVs), and now they need to also fund self-driving tech? That's a brutal financial squeeze. So, what's going to happen? I think we'll see a lot of mergers and acquisitions. Companies that waited too long will become bargains for someone else to snap up. I'm not a gambler, but I'd bet against some of these legacy automakers. I don't say that happily—because it's the factory workers and engineers whose jobs are on the line, not the executives'. It's a classic case of leadership being too comfortable and not innovating, which risks an entire industry and its workforce. I also want to give a shout-out to folks who actually talk about this stuff, like Philipp Raasch and Lukas Timm. A lot of people just talk about last month's sales figures, but it's thinkers like them who are asking "what's next?" and forcing these tough conversations. Check out the article here. It's worth your time: The inflection point has arrived: Waymo’s vision for the future of mobility. #SelfDrivingCars #FutureOfMobility #AutoIndustry #Innovation #BusinessStrategy #Leadership #ElectricVehicles #Tech

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I'm thrilled to share that Clay is one of 6 companies to join OpenAI’s new Frontier program. Tomorrow's best companies will run on AI across every function. Clay is already the creative tool powering growth teams at Cursor, Ramp, Figma, and OpenAI itself. Now we're making that same power available to enterprises through OpenAI's new platform. This partnership will allow us to help more companies use OpenAI's models for growth, sales, marketing, and RevOps workflows to find and expand their best customers. Here are some ways Clay and OpenAI help GTM teams use AI to grow together: - RevOps and GTM engineering teams can turn their CRM into a living entity: one that automatically refreshes, dedupes, and surfaces accounts that are ready to buy - Sales teams can score companies showing buying signals, pull verified contacts, generate personalized messaging, and sync everything to their CRM. - Marketing teams can build targeted audiences and ship relevant creative campaigns, from ads to landing pages to email I'm grateful to be building the future of GTM alongside OpenAI and this incredible group of companies -- Harvey, Sierra, Abridge, Ambience, and Decagon Learn more about OpenAI's Frontier Partner Program here: clay.link/kp6KIJy

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"Being 3 years ahead makes you look smart. Being 6 months ahead gets you paid." - Derek Gibbs in a meeting, speaking on our positioning in the AI market At Casper Studios, we nerd out on memory layers, MCPs, agent-to-agent workflows, multi-modal APIs from Google, all of it. It’s fun, and we’re building with it internally or working on small POCs with several founders. But that’s not what most clients are asking for. They’re asking us to take a workflow someone used to do manually—6 steps, 3 tabs, 2 email messages, and automate it with AI. Not fancy agents reasoning across knowledge graphs, speaking with each other in different languages, then making your breakfast. It's more like: 1.) Internal Workflows: Can we take a process that used to require multiple people… and make it faster and smarter with AI? 2.) External Products: Can we rethink the core product experience using new AI capabilities, without losing the familiar features users already rely on? And I think that's where most of the value is right now. We’re still pushing into the future, but we test everything on ourselves first. Take MCP. We haven’t shipped it in any client builds. We're not sure it's stable enough yet. But we use it internally to link Figma, Linear, and GitHub. We'll keep testing here, then roll out when we think it's ready. That’s the balance we try to strike: Experiment like a lab. Ship like a product team.

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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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Lucas and Thomas founded Deepnight (YC W24) to replace analog night vision systems with software. Their models are able to render images like the one on the right at 90fps on commodity hardware.

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Your CEO says AI is a priority. So, ask them to demo one workflow they personally built. Watch what happens.   At Zapier's last exec offsite, Wade and Cortney introduced something that changed how we think about AI leadership. Wade asked every exec to stand up and demo one AI workflow they 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗹𝘆 𝗯𝘂𝗶𝗹𝘁 𝗮𝗻𝗱 𝘂𝘀𝗲. Importantly, Wade went first. We plan to do this exec “AI demo day” quarterly. It’s become one of the most honest moments we have as a leadership team. It’s another example of how show > tell when it comes to AI adoption, learning, and getting actual results. You don’t have to be a CEO to lead the way—try this at your next team meeting or offsite. Invite every person to demo one AI workflow they personally use. No slides. Just show the work. And if you make the ask, go first. You'll know within five minutes who's actually building and who's just talking… Our teams are watching what we do with AI, not what we say about it 👀

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Kicked BBG Ventures Breakfast Series for 2025 with Flybridge & OMERS with a discussion on Vertical AI with Nick Ornitz (CEO Topline Pro) and Julisa Salas (GTM, Toast) talking about customer discovery, GTM, hiring your first head of sales and building a moat. Full discussion at the link below, but here are the 6 top takeways on scaling Vertical AI for SMBs: 1. Customer feedback is the holy grail - don't solve the problem YOU think exists; solve the hair on fire problem for your customers. Nick Ornitz's company went from Dwelling (home maintenance) to ProPhone (business phone lines) to Topline Pro (a better way to grow your home service business) before it nailed their unique value proposition. And keep that customer closeness even as you grow - even today as they grow everyone at the company has a workflow touchpoint for customer feedback and pain points. 2. GTM for SMBs is notoriously hard - your playbook has to understand them as consumers AND businesses. Nick's strategy at Topline mirrored that with a founder-led sales strategy that incorporated creative tactics like targeted Facebook DMs, a database of potential customer profiles and a "sales lab" to test new sales tactics. Use data and iterate quickly to optimize for the right acquisition channels. 3. When building out your sales team hire at least two sales reps to compare their performance and tactics. Then, even as your team grows make sure you've built out a version of a repeatable sales motion/playbook that is working before you hire a Head of Sales; that hire can then work to improve your playbook and help coach/lead your sales team. 4. Consider the appropriate way to grow your ACV - in some industries moving upmarket might work, but there is often an undertapped opportunity to increase prices as you deliver increasing value - you just never know whether you're under-pricing. Topline actually saw their growth increase when they increased their price point: customers can associate a higher price with better quality. 5. Make sure you have a moat: 1) own the workflow - automate an end to end process, not a feature so you can differentiated from the pack; and 2) think early about what your proprietary data can be - that's the clearest moat. A word of caution? Don't forget that customer experience and brand differentiation can supercharge all of that in the game of defensibility - it's easy to leave it by the wayside 6. Leverage AI in company building - AI isn’t just powering the product — it’s transforming how AI companies sell and support customers. It can unlock a more efficient GTM strategy from live coaching during sales calls to AI for customer success. https://lnkd.in/gSZPDn-q Claire Biernacki Carol Magalhães Isaacs, CFA Julia Maltby

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Funny but unsurprising that Roku's CEO changed his tune on Home Screen ads in order to increase revenue: "He repeatedly rebuffed employee suggestions for Roku to sell home screen ads to a wide array of marketers, three former employees said. That was despite the fact that the home screen is the highest-margin ad inventory Roku sells, one former employee said. When Roku hired salespeople, its staffers told recruits the company prided itself on the fact that its viewers didn’t see random ads from car dealerships or insurance companies when they turned on the TV, said a person who interviewed with the company." Reminds me of ad execs who always wanted Facebook to sell home page takeovers, decrying their apparent "short-sightedness" at turning down all those brand dollars. I think it's likely the opposite - eventually overloading users with ads will bite you in the ***.

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"For all the talk of corporate disappointment with AI, the most important story may be happening at the individual level. Millions of consumers are already spending their own time and money on tools that entertain, organize and even console them. That shift points to an industry where monetization will come increasingly from subscriptions and advertising—just as it did with gaming companies and social media businesses—and where the firms best positioned to thrive are those willing to build for feelings as much as for efficiency." An insightful Bloomberg piece from Parmy Olson on what’s driving profits in AI. Spoiler alert: it's consumers, not enterprise! Millions of consumers are already spending their own time and money on AI platforms that will entertain and organize them. With 20M monthly active users, Character.AI is defining what AI entertainment can be 🚀 🚀 🚀 Link 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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We just launched the biggest update to Ascend.io yet. And over the next 2 weeks, I’m spotlighting 10 delightful new features you have to see. Let’s start with my favorite one: Otto. Otto is our built-in AI agent—and one of my favorite coworkers. Last week, he migrated an entire pipeline from Ibis to Snowpark in a single prompt. Just told him: "Convert all Ibis components in this flow to Snowpark." He analyzed the whole pipeline, rewrote the logic, and committed the changes to git—like magic. But migration is just the beginning. And that’s because Otto is deeply context aware. Otto can see your metadata, schemas, and pipeline logic, even data itself (with permission). That means you get… ✅ Contextual assistance: Ask him why a job failed or what’s slowing down a component—he knows ✅ Smart suggestions: Need data quality checks? Schema validations? He’s on it ✅ Everywhere you work: in chat, your code editor, in the background like a secret agent—Otto shows up where you need him And—we’ll soon let you build your own agents to handle custom tasks and logic. I’m really excited to see what teams do with this in their hands! If you're leading a data team, think about what this means: Every engineer with a co-pilot that actually understands the data stack. That’s a productivity multiplier.

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