Amit Kumar

Don't Build a Better Wheat Farm" - Why Defensibility Stakes Are Higher in Deep Tech Just published a new piece on my "Ignore the Confusion" blog, building on thoughtful insights from Eric Ver Ploeg at Tunitas Ventures about startup defensibility. Eric's core thesis: Too many startups pitch like wheat farmers - "huge TAM, slow incumbents, growing market, domain expertise" - but fail to think through long-term defensibility until it's too late. From a deep tech perspective, the stakes are even higher: ** Unlike software, deep tech founders must commit to defensibility strategies from day one - their funding depends on it ** Patent vs. trade secret decisions are often difficult to reverse and shape your entire competitive strategy ** Even "picks and shovels" providers (the tools that make industries more efficient) become commodities without proper moats The key insight that resonates: Defensibility can't be retrofitted. Whether you're building software or deep tech, your moat must be architected into the business model from the start. Thanks to Eric Ver Ploeg for sharing these insights on startup strategy and letting me build on his framework from a deep tech lens. Read the full post: https://lnkd.in/dEj_iF-Q #DeepTech #StartupStrategy #Defensibility #VentureCapital #Innovation

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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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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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Hot take: lot's of #LP's are growing underwhelmed with traditional venture investing. 💰 Fees are high given the level of engagement with the company operators. ❓ Access is opaque - LP's are unclear on the portco operations and outcomes. Outcomes are scattered - power law investing can certainly work, but many feel that venture capital investing is just a numbers game. There's another way. I'm biased, but I think it's better: #VentureStudios as an asset class. They blend: 🤜 🤛 Control and ownership 💡 Thematic focus 👬 Partnership with operators Studios offer a fund-like structure — but with more alignment, higher value conversion and less noise. If you're interested in understanding more about the studio model, we have resources to share. Please reach out and let's connect!

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"By the time rank and file employees find out, it's too late." Alessandro Chesser told Rachel and I how exercising his stock options early at Carta allowed him to take advantage of Qualified Small Business Stock (QSBS) tax law in the US and saved him a life changing amount of money when Carta later had an exit. But if he would have waited for the company to become less risky (aka well funded) like many others did, it would have been too late. This ultimately led to his decision to found Dynasty and help everyday founders, investors, and families generate affordable Nevada trusts – something that was previously only common amongst billionaires. Today they also announced they've raised $6M to continue building toward this mission. Congrats Alessandro!!

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SF VC Takeaway #2: Pricing expectations, performance bars, and what’s actually getting funded. One theme that came up repeatedly in SF was how far pricing expectations and performance bars have shifted, even compared to just a few years ago. A few things investors kept anchoring to: 1️⃣ Median Series A valuations are higher than their 2021 peaks, but fewer of them are getting done. 2️⃣ Capital is being concentrated into fewer and fewer companies (and lots of capital!) 3️⃣ “Good” progress is no longer enough and the bar for standout performance has moved in an AI-native world So what does “top performance” mean right now? One investor told me that top quartile seed companies in their portfolio are going from $0 to $2M in ARR in <12 months. Outside of pure traction numbers, a few other themes that came up to describe "top performance": 📈 Explosive early revenue ramps (or a very credible path to them) 📊 Strong velocity and momentum for 2 quarters in a row, even if the baseline is small. 🚀 Clear signals of category leadership, not just product-market fit. Sometimes shown by either domain expertise, speed of product optimization, or by lack of competition in the category. This creates a counterintuitive dynamic where it can be easier to fund a company with strong pedigrees in a hot space and no traction yet than a company that went from 0 to $1M ARR at what used to be considered a rapid pace. Pricing today is driven by trajectories, not moments in time. We used to say investors invest in lines not points; I think that's more true than ever now because crossing certain milestones doesn't carry as much influence as it once did. Finally, investors still say that valuation matters, but many of them are acting differently. Pace and belief in category-defining companies really sets the price; while slower growth gets scrutinized rather than discounted. One silver lining in the camp of durable growth: Series A rounds are happening so fast that many companies don’t yet have meaningful history of retention data. Large bets are being made on velocity before the durability is proven. Several investors told me the same thing: we may soon swing back to a market where retention, not growth, becomes the defining metric. Let's hope so. I'll share my third SF VC takeaway tomorrow!

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Every hire, product feature, and GTM experiment is a capital allocation decision, most teams just don’t call it that. I’m kicking off a 3-part series on capital allocation for founders, operators, and finance leaders with a simple question: ➡️ Will the next $1 you deploy become worth more than $1? Series overview Part 1: defines the core concept + the math Part 2: where capital goes + how to measure whether it’s working Part 3: AIMS framework for communicating allocation decisions to management teams and your board Part 1 (attached here) lays the practical foundation: 🔹 The $1 invested test and ROIIC vs. hurdle rates (scoreboard vs. decision lens) 🔹 A lightweight “investment brief” to evaluate bets (GROW / BUILD / BUY) 🔹 The small metric toolkit that translates finance into operating decisions (NPV / payback / ROIIC / incremental margin) 🔹 Early-stage proxies when DCF inputs are unknowable (burn multiple + revenue quality) 🔹 Operator “vital signs” to spot drift early (profit engine, leverage, cash conversion, optics) Series Roadmap ✔️ This series (Parts 1–3): breadth-first. shared, lightweight framework to define value creation, choose decision-grade metrics, and communicate tradeoffs clearly ✔️ Next series: depth-first. momentum drivers + operational decisions that need near real-time measurement to spot drift early and course-correct fast Read Part 1 here and Part 2 and 3 on substack. Next Series will be out next week. 💰 What’s one bet you’re funding right now, and how will you prove it’s working?

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I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.

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