Zach Tratar

A Founders Guide to The Long Build™ - Physical AI Thesis Part III This one is for the builders. In this final post, my partner Will Porteous and I along with insights from builders in the trenches and investors who play the long game share our guide for founders building intelligence in the real world. A few core takeaways: • Sim before steel: Runway is precious, model the system behavior • Capital intensity is not the enemy — misaligned capital is • Build complete solutions before you talk platform • Hardware opens the door, software wins the room • Trust is earned through resilience, not hype

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Health Hack for High-Performance Founders As a startup founder juggling NetZeroAI's roadmap and family time in SF, I've learned health isn't a luxury—it's your ultimate leverage for wealth and impact. Tactical routine that's transformed my energy: the "4-7-8" breathwork (inhale 4s, hold 7s, exhale 8s) before every pitch call to crush anxiety, paired with 20-min walk-and-podcast sessions at dawn (today's was on EU green tech). Ditch the all-nighters; science shows micro-habits like this boost decision-making by 23% per Stanford studies. Aspirational nudge: Elon swears by fasting—I've adapted it to 16:8 windows, dropping 10lbs while sharpening focus. Founders, test this tomorrow and track your output. Health first, or burnout wins. How do you stay sharp? #FounderHealth #Wellness #Startups

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In 2026, everything I do will be in service of founders. Specifically: helping founders cross the line from 'builder' to 'operator.' It’s the first Sunday, and I felt inspired to share a mini-manifesto for the year ahead. This transition (from builder to operator) is where I see many high-potential founders struggle most. Building is the easy part (for most of you). - You can ship. - You can hustle. - You can close early deals. Where companies actually die? Business systems and operations. I’ve helped build three different businesses past $20M ARR. Never alone. Always with teams. Always with systems. At a certain point, founder-led everything becomes the bottleneck. Sales is usually the first place this shows up. When sales lives in the founder’s head + inbox, growth caps. Not because the founder can’t sell, but because the business can’t operate without them. From ‘Founder Chapel’ to ‘David Beats Goliath,’ and through the projects and partnerships I take on this year, everything will serve one goal: Helping founders transform into EXCEPTIONAL operators. The kind people want to work with, follow, invest in, and trust with real responsibility. Builders are common these days. Operators are much rarer. Companies scale when systems carry the weight founders were never meant to carry alone.

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💥 Metropolis Technologies Raises $𝟭.𝟲𝗕 💥 Funding comes in 2 rounds: $500M equity round led by LionTree and $1.1B from J.P. Morgan. Metropolis is now 𝘃𝗮𝗹𝘂𝗲𝗱 𝗮𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 $𝟱𝗕. With the fresh funding, the plan is to take their "recognition and payment-automation" tech beyond parking assets. This would include retail stores, drive-throughs, gas stations, hotels and partnerships with real estate/asset owner. Congrats to Alex Israel, Peter Fisher, Courtney Fukuda, Travis Kell, Ryan Hunt,Chris Sherman, Robert Bland, Paul Rooney, Lookman Olusanya and the rest of the Metropolis family. #fundraise #parkinginvestment #parking #parkingindustry #metropolois Parking Today Media Parking Network B.V. National Parking Association International Parking & Mobility Institute (IPMI) Tim Maloney Advisory Tim Maloney Reservations Lab

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🔥 𝗙𝗶𝗿𝗲𝘀𝗶𝗱𝗲 𝗖𝗵𝗮𝘁 𝗼𝗻 “𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀” Had an incredible evening hosting a fireside chat with Matt Kraning (𝗙𝗼𝘂𝗻𝗱𝗲𝗿 & 𝗖𝗧𝗢 𝗼𝗳 𝗘𝘅𝗽𝗮𝗻𝘀𝗲, 𝗲𝘅 𝗖𝗧𝗢 𝗣𝗮𝗹𝗼 𝗔𝗹𝘁𝗼 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀) and a group of technology and product leaders from some of the world’s biggest 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗯𝗿𝗮𝗻𝗱𝘀, 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 in the Bay Area. We discussed one of the most urgent questions facing every product and engineering organization today: building 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘂𝘀𝗲𝗿𝘀, and 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘁𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗵𝗼𝘄 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗲, 𝘀𝗵𝗶𝗽, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲. 𝗔𝘁 𝘁𝗵𝗶𝘀 𝗽𝗼𝗶𝗻𝘁, 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. The companies that win won’t be the ones waiting for others to define it - they’ll be the ones learning, experimenting, evolving, and writing it themselves. Because the reality is: Your competition is already re-architecting their business around AI agents - driving higher efficiency, faster iteration, and reinvesting those gains to build better products. The next era of product leadership isn’t about adopting AI. It’s about adopting an 𝗔𝗜-𝗮𝗴𝗲𝗻𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁. Grateful to everyone who joined and contributed such sharp perspectives and open conversations. Deepak Kumar P Vache Moroyan DB Banerjee

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What if launching a custom lending automations and products took days, not months? We're building that. timveroAI just passed its first real test - 2 features implemented, tested, and documented in <10 min. And this is the first overview:

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Ride the fractious horse 🐎 Had a blast talking with Harrison Chase from LangChain the other day to a great group of founders, builders and investors about building agentic systems. One learning I shared: our team builds effective agentic systems by spending way more time doing than speculating. 🏇 In other words, we "ride the fractious horse": New models drop daily. New frameworks emerge constantly. Best practices get declared and discarded overnight. By the time you survey the landscape and theorized about how today's reasoning models can serve your system, both the landscape and capabilities have shifted. So we write playbooks instead of adopting them. The only way to develop those playbooks? Build and experiment at the fastest possible rate. ✈️ Wilbur Wright laid a great analogy back in 1901: He'd just realized that existing "standard" measurements of lift and drag were wrong, and that solving flight meant spending serious time actually trying to fly (previous "pilots" had logged minutes, at most, of cumulative airtime across all experiments). From his Chicago speech (this guy was such a clear writer btw): "There are two ways of learning how to ride a fractious horse: one is to get on him and learn by actual practice how each motion and trick may be best met; the other is to sit on a fence and watch the beast awhile, and then retire to the house and at leisure figure out the best way of overcoming his jumps and kicks." "The latter system is the safest; but the former, on the whole, turns out the larger proportion of good riders." As for planes: "It is very much the same in learning to ride a flying machine; if you are looking for perfect safety you will do well to sit on a fence and watch the birds; but if you really wish to learn you must mount a machine and become acquainted with its tricks by actual trial." We're not inventing flight, but constantly evolving LLMs and probabilistic workflows won't behave as you hope on first contact. If you want to figure out how to build effective products around those fractious beasts, get on the horse (keyboard) and start riding.

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This Abram Brown piece captures something I'm seeing everywhere: Tools like Claude Code are giving founders way more agency. For many, it's a reminder of the old days when they were ICs or running tiny companies. To be clear, this is not every founder. But those that were already hands on, in the weeds, in the codebase have just been handed Thor's Hammer.

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Aways fun talking AI and Bio with Andrew Dunn from Endpoints News! We cover a lot of ground, but there's one persistent theme: 𝗔𝘀 𝗔𝗜 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲, 𝘁𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬 𝗮𝗻𝗱 𝗽𝗮𝘁𝗶𝗲𝗻𝘁 𝗜𝗠𝗣𝗔𝗖𝗧 𝘀𝗲𝗲𝗺𝘀 𝗼𝗻𝗹𝘆 𝘀𝗲𝗲𝗺𝘀 𝘁𝗼 𝗴𝗿𝗼𝘄. Some thoughts on what that means and what we can do about it…   𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 - There’s a mismatch between where AI is working well (early discovery, lead optimization) + the big drivers of risk/cost in drug discovery (target selection, toxicity, clinical, commercial) [1]. But that's changing with AI progress in target discovery [2], toxicology [3] and new AI tools for clinical development [4] and commercial pharma [5] 𝗦𝗵𝗶𝗳𝘁 𝗟𝗲𝗳𝘁 - As AI capabilities shift right, there's an opportunity to incorporate them earlier in the process as a filter for early discovery. We're already seeing protein models bake developability into sequence generation [1] and synthesizability-guided chemical design [2] - but the real unlock will come from stacking properties and integrating them more deeply into the models [3] 𝗧𝗵𝗲𝗿𝗮𝗽𝗲𝘂𝘁𝗶𝗰 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 - AI models trained on public literature over-index on academic targets and data from non-human model systems, hurting their performance on real-world discovery tasks. Aligning AI models to human biology and therapeutically relevant problems is a major challenge, but we're seeing progress across the stack -- from few-shot learning [1], in situ human data generation [2], therapeutic proxies [3] and even whole organ screening [4] 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 - Most promising drugs never see the clinic and many of those that do drop out for external/ macro reasons, creating a buyers market for in licensing. As AI gets better at predicting clinical risk [1], sizing commercial opportunities [2] and accelerating trials [3] we should see an rise of AI-native biotechs [4] rescuing, repositioning and purposing promising late-stage assets where they have differentiated insight and strategy. 𝗠𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 - Lastly, we need to reposition AI as an enabling technology, not just a productivity tool. The original sin of TechBio was the promise of cheaper and faster drugs it would never be able to deliver. Instead we should focus on what new capabilities AI is enabling — expanding druggable targets [1], discovery novel biology [2], enabling new therapeutic strategies [3]. Even if this does nothing to improve the cost or speed of a drug approval (which I think it will) it will give us better drugs for more diseases and more patients, which in my book is even ore important! Curious what others are seeing + doing to close the translational gap? https://lnkd.in/daYXihS6

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Harpoon Ventures + Black Flag is teaming up with Microsoft for Startups to provide founders with access to up to $250,000 in Azure credits, empowering builders to scale faster, train models more efficiently, and access world-class AI infrastructure. What’s included: ⚡ Up to $250K in Azure credits (usable across Azure OpenAI, Grok 3, Llama 4, and more) 💻 On-demand GPU VMs for model training and fine-tuning 🧠 Access to GitHub Enterprise + Microsoft 365 Business Premium 🤝 Dedicated Microsoft technical + GTM support This partnership is designed to give deep-tech and dual-use founders the tools and resources they need to win. Because when they win, we all win. 🏴‍☠️

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If you know... A 10x engineer Who loves the speed and intellectual challenge of early stage but also Wants job stability and Equity diversification then This is the one job that checks all of their boxes

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Founders, feel like AI headlines are moving faster than your roadmap Here is how I am navigating the noise this week and what it means for build decisions Regulation is getting real. On Oct 13 2025, California signed SB 243, the first companion AI law that forces clear AI disclosure, adds break reminders for minors, and requires annual safety reporting. If you are building anything with companion style chat, design the disclosures and crisis protocols now so you do not refactor under pressure later.

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The Headline family is experiencing incredible momentum, and today our portfolio company DeepIP is announcing a $25M Series B, bringing total funding to $40M.  This is the kind of company that doesn’t just add AI to a workflow, but it sets the standard for an entire category. The momentum speaks for itself: 1️⃣ 10x ARR growth in < 18 months 2️⃣ Trusted by 400+ law firms and leading corporate IP teams globally 3️⃣ 2M+ chats on the DeepIP platform We’ve had the chance to back FX (Francois-Xavier) L. and Edouard D., leading their Seed round with Headline. Watching them take on a problem as complex and high-stakes as patent work has been genuinely impressive. And working with them has been an absolute pleasure: these 2 are coupling very high ambition and humility. Tech built in Europe, but opening the US office very early and FX starting a life in NYC with his family with 0 hesitation in order to build a global category leader in the IP industry. What convinced me early is still what stands out today: patent teams don’t need another standalone AI tool. They need something that fits the way the work actually happens: documents, long-running matters, multiple stakeholders, and zero room for sloppy context handoffs. Big congrats to the whole DeepIP team + Korelya Capital, Balderton & Serena Proud that we’re doubling down at Headline and continuing the support for what’s next!

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While there has never been a more exciting time to be a founder building dev tooling or next-gen infra, it has also never been less investable at seed/pre-seed. I'm either really missing something or a lot of my peers are lost. As someone who has not just written, but also SHIPPED, about 75k lines of code in the past 6 months I can tell you that the evolution of how to build products has changed as much in the past year as it did in the entirety of 2007-2017. The complete rise and fail of frameworks, platforms, methodologies, etc... paved over and forgotten... that is of course except for the 1 company that gets a 1000x return from a wildly overvalued hyper-scaler or drunken growth stage investor obsessed with compounding at scale. Imagine a world where any seed investor in trends like Openstack, Hadoop, PaaS, etc all took a full loss on their investment. That's what we're looking at right now. I personally know of over a dozen well-funded seed-stage companies building in these spaces, with years of runway, scrambling to get acquired for a return of capital + several million personally while they're still relevant. If you're not seeing this unfold in front of you, you either aren't paying attention or you're satisfied playing the lottery instead of investing.

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Friends in #healthcare, need your help! What are your favorite communities for #builders or #AIengineers in healthcare? Here’s the ones we’ve mapped at Klira AI: Health Tech Nerds (HTN) - 6,000+ Slack community of healthtech builders & founders. PM’s in Healthcare - 1,000+ product & engineering minds across health tech startups. Great for tech stack, HIPAA & build vs. buy conversations. Digital Medicine Society (DiMe) - Focused on digital therapeutics, AI governance & regulatory topics. Essential for teams building in regulated environments. Mental Health Startup Community - 3,000+ founders, engineers & investors in mental health & telehealth. Niche but highly relevant if you’re building in behavioral health. AI Product Hive -1,000+ tech professionals building AI products. Good for real-world GenAI implementation strategies & use cases. AI Tinkerers - A global community of 89,000 AI engineers and researchers building real systems, sharing unfinished work, and pushing the practical limits of modern models. Any missing from my list?

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Dispersion Capital led the pre-seed round for ARO Network with participation from Maelstrom, Escape Velocity (EV3) We spent real time with the team, tested their assumptions, and dug into the architecture. What stood out wasn’t just vision. It was execution. ARO isn’t starting from scratch. Their prior deployments reached over 1.5 million edge nodes and generated more than $140 million in revenue. That’s rare proof in a space full of whitepapers and stalled testnets. This aligns with our core thesis. At Dispersion, we’ve been building conviction in decentralized infrastructure since 2014. We backed Filament when DePIN wasn’t even a word. We believed in Theta Network’s decentralized video CDN. We helped build Packet (acquired by Equinix) and Volterra (acquired by F5), both acquired. We supported Helium and FreedomFi as they helped Nova Labs decentralize wireless. We’ve seen what works and what doesn’t. The majority of decentralized compute and CDN plays fail. ARO didn’t. Their architecture has real throughput, real proofs, and a clear path to scale. This is the programmable edge, purpose-built for content and compute where latency matters. Read Why We Invested in Aro Network: https://lnkd.in/ew8xge9T Join PreviewNet: https://lnkd.in/euC-4JrZ Preorder ARO Pods: https://shop.aro.network/

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We were recording a podcast episode last week and the question came up: why do studios actually fail? I expected the conversation to land on bad venture selection. That's what most people point to. But three quieter patterns kept surfacing — and they're the ones that actually kill studios. Sequencing failure. Doing the right things in the wrong order. Raising before you have a track record. Hiring a full team before validating a single venture. Everything looks productive — you're shipping, hiring, raising. But the order is wrong, and the compounding works against you instead of for you. Incentive misalignment. The studio operator wants equity upside. The institutional sponsor wants de-risked innovation. The founder wants autonomy. None of those are wrong. They're just different objective functions in the same room. The misalignment doesn't surface in year one. It surfaces the first time someone has to make a hard call — and the people in the room realize they want different outcomes. The governance gap. Studios start lean, which is smart. Then they stay lean on governance, which is fatal. No kill criteria. No stage gates. No framework for when to double down or shut down. Death by accumulated decisions that nobody had the structure to stop. What stuck with me after that conversation: all three of these are invisible from the inside. Bad company selection is loud. These are silent. And by the time you feel them, they've been compounding for months.

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Today I'm presenting to The Council Angels' latest cohort, focused on product leaders. They're on week 6 of our angel investing 101 curriculum. By now, it's becoming more clear to most of them whether angel investing is likely to be a hobby or an accelerator into to a future career in VC. My biggest advice to those who want a career in VC someday is to build their own proprietary flywheel for deal flow. It may start with angel communities and syndicates, but eventually putting in the work to build their own sourcing mechanism will make them very attractive candidates! I love to highlight these 4 who have masterfully executed this pole vault into VC: ⭐ Ruffin Mitchener: (TikTok → MALIAM) ⭐ Sri Varre (Lazy Girl Capital → Pivotal VC) ⭐ Zehra Naqvi (The Z List → Headline VC → founder of Lore) ⭐ Michelle F. (SF IRL on X → Headline VC) #angelinvesting #venturecapital #risingstars

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