Rohan Suri

The big labs are expansive in their product ambition, especially since foundation models have largely improved in lockstep - in order to compete with them you have to do things they won’t which are: - building a very rich software ecosystem around a primitive - orchestration across multiple models - going insanely deep on product and growth for a narrow vertical domain

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Super exciting to see reports this morning that Waymo is in advanced discussions to raise $10–15B at a valuation near or above $100B, with Alphabet expected to anchor the round. That would be a major step up from Waymo’s prior ~$45B valuation in 2024 and a clear step change in investor confidence. Notably, Waymo has not raised this year, despite the broader AI funding frenzy. The size and structure of the round also make this feel like a step toward an eventual spinout or IPO. This matters because capital supercharges growth. Waymo is past the “does this work?” phase and firmly in “how fast can we scale?” mode. The company has already logged ~14M rides this year and expects to reach ~1M rides per week by late 2026 as it expands into markets like Miami, Dallas, and Philadelphia, including expanding onto freeways. At the same time, competition and consumer expectations are accelerating. In markets where Waymo operates, fully driverless rides are becoming normal rather than novel. Once people can reliably hail a car with no one in the driver’s seat, tolerance for “pilot programs” and distant timelines drops quickly. The race is no longer about whether robotaxis work. It is about who can scale them first, safely, and everywhere — a win for consumers who get cheaper, safer, and more reliable rides.

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Just downloaded Tesla Robotaxi in SF, a real threat to Uber, Lyft, and Grab? Most experts measure Robotaxi by today’s metrics: cost of operations, active users, global adaptability. By that logic, scaling looks impossible. But Tesla flips the script. Unlike Waymo’s costly retrofits, a Tesla only needs a software update to become a Robotaxi. With 1.7M+ Hardware 4.0 vehicles already on the road, the scale potential is unmatched. Cars shift from being liabilities to income-generating assets overnight. This isn’t just product innovation, it’s a redefinition of value. At ElasticDash we’re doing the same for QA. Today, QA costs are labor-driven, with humans building and maintaining tests. Our AI agents change that: they build and adapt end-to-end tests automatically as your app evolves. Just as Tesla is rewriting mobility economics, we’re rewriting QA economics. ElasticDash is on the way.

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🚨NEW EPISODE: Recorded live at FUTURE TITANS 2026 - Jeff Perry of Carta sat down with the iconic Seth Levine, co-founder of Foundry. Seth has been in venture for 25 years, built Foundry from scratch as an emerging manager himself, and has backed about 50 emerging manager funds through his fund of funds. He has genuinely seen every side of this table. They went deep on building Foundry, why VCs are in the influence business, not the decision business, and why the concentration problem in venture is not only bad for LPs, but also for the innovation ecosystem overall. And why Seth's new book, Capital Evolution, is so important for the future of America. 🎧 Links to listen... Apple: https://lnkd.in/ehQUQ2EM Spotify: https://lnkd.in/eU4FExpg

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Last week's musings. You'll notice some reoccuring themes... 𝟭. 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗿𝗼𝗯𝗼𝘁𝗶𝗰𝘀/𝗱𝗮𝘁𝗮 𝗳𝗼𝗿 𝗥𝗟 🍇 (𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝘁𝗼 𝗷𝗮𝗺)  -recurring idea i've been mulling -robotics is happening particularly in industrial setting  -still lots of problems, but the revolution is happening and there are  work arounds until the tech is really there -prices in theory are highest they will ever be (impact of tariffs?) -folks looking for pre-training data, but eventually will also need fine tuned data -what data fidelity is good enough? 𝟮. 𝗧𝗿𝗮𝗻𝘀𝗗𝗶𝗴𝗺 𝗮𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 + 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗵𝗮𝗿𝗱 𝘁𝗲𝗰𝗵 🍇  -it's an absolute monster, $75B market cap and up 300% over the last 5 years. - aircraft parts, 90% proprietary but high margin - killer aftermarket business  - how many software biz have lifetime value of 50 years?! -has lots of things software investors look for (sticky revenue, high margin, defensibility in GTM and unique flywheel, focus, etc.)  -makes me think way more about hard tech viability 𝟯. 𝗧𝗿𝘂𝗲 𝗳𝗼𝗰𝘂𝘀 - from Neil Shah on Invest Like the Best - incredible listen - few people have maniacal focus and it's so important for founders 𝟰. 𝗦𝗽𝗲𝗿𝗺 𝗥𝗮𝗰𝗶𝗻𝗴 -WTF  𝟱. 𝗖𝗼𝗿𝗹𝗲𝗼 𝘁𝗵𝗲 𝗥𝗼𝗯𝗼𝘁 𝗛𝗼𝗿𝘀𝗲 -Kawasaki released prototype and it's wild! Substack for full thoughts: https://lnkd.in/gzNtkEHk

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Waymo is moving from a "moonshot" to a mainstream reality. According to recent reports, the autonomous driving leader is finalizing a massive $16 billion funding round, catapulting its valuation to an incredible $110 billion. With over 20 million trips already completed and expansion into new cities like Miami, the future of mobility is arriving faster than many expected. What makes this milestone truly humanistic isn’t just the valuation—it’s the vision. By focusing on "safety-led operational excellence," Waymo is working to solve one of our most fundamental challenges: making our streets safer and more accessible for everyone, from busy commuters to those who can't drive themselves. While the technology continues to navigate the complexities of the real world, this level of investment from Alphabet and top-tier partners signals a profound confidence that self-driving cars will soon be a core pillar of how we move through our lives. The road ahead is long, but the trajectory is clear. https://lnkd.in/gc9zx6BJ #Waymo #FutureOfMobility #AutonomousVehicles #TechNews #Innovation #SafetyFirst #Alphabet #SmartCities

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The best founders don’t just build products; they reframe the problem. Ryan Alshak has done exactly that with Laurel - turning time from a background task into a source of insight, alignment, and leverage. IVP is leading Laurel's Series C to help build the system of record for how work actually happens. Read more from Allie Garfinkle: https://lnkd.in/gweNiwsc

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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Waymo raises $16 billion fundraise is a milestone for autonomous mobility This financing, participated by Sequoia Capital, GV (Google Ventures), Dragoneer Investment Group, and DST Global, brings Waymo’s total funding raised to well over $22 billion. Alphabet Inc. remains the majority owner, preserving strategic continuity and long-term focus while bringing in heavyweight external capital to accelerate commercialization. After more than a decade of intensive R&D, Waymo is no longer in the proof-of-concept phase. The company is running autonomous rides in multiple U.S. cities with real customers, and is generating revenue in markets where driverless service is live. Safety is a core part of the value thesis here. Waymo’s vehicles logged estimates of 80–90 % fewer injury-causing crashes and 82 % fewer airbag-deploying crashes per million miles compared to typical human driver rates. As a consumer, paying for a driverless vehicle that is more predictable and safer feels like a natural evolution in mobility. Technology 🤝 Progress

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Unpopular opinion, but an important one. Autonomous driving can work technically and still fail economically. Waymo is a great example. The tech is impressive. It will likely save lives. It may reshape cities. But the hard question is not autonomy. The hard question is unit economics. Unlike Uber, there is no natural network effect. More demand means: - more very expensive cars - more maintenance - more idle capacity for peak hours To deliver Uber-like wait times, fleets must be sized for peak demand. Most of those cars sit unused off-peak. That is capital heavy by design. Google can afford this. Not because the model works yet, but because Search prints cash. This doesn’t mean autonomy fails. It means autonomy alone is not a business model. The real question: Will it take tens of billions or hundreds of billions before this becomes self-sustaining? And who can survive long enough to find out? If you’re interested in real offline discussions about AI, autonomy, and startup economics, follow the events I organize on Luma: https://lnkd.in/g2Td2N3t For frameworks, breakdowns, and visual thinking, follow my SlideShare: https://lnkd.in/gYrkdj2P #autonomousdriving #waymo #ai #artificialintelligence #startups #founders #venturecapital #mobility #futureofmobility #deeptech #siliconvalley #startupthinking #uniteconomics #scalability #techbusiness #aibusiness #innovation #transportation #robotaxi #platformeconomics

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A lot of VCs use founder age as a proxy for how well they’ll leverage LLMs and the latest AI. In my experience, the stronger signal is how technical they are. Technical founders understand how LLMs work and can exploit them fully; non-technical founders see magic, not mechanisms.

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The Shenzhen X thread below got me thinking about something I’ve been underweighting. We measure manufacturing success in ROIC and margin expansion. But software taught me to look at something else first: learning velocity. How fast does the system improve per dollar spent? In hardware, that means backing companies that sit inside dense supply chains, not isolated labs. It means pushing for AI- and data-first workflows so iteration cycles are captured and compounded instead of lost. And it probably means influencing policy toward smaller, connected manufacturing hubs rather than betting everything on a few giant centralized nodes. If learning velocity is infrastructure, capital shouldn’t just fund assets. It should increase the rate at which knowledge moves. That’s a different optimization target. https://lnkd.in/gwVSKvbm

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spoke with Shimon Rosen this week. he’s a product-led founder. not an engineer. but his speed today is on another level compared to even a year ago. he used to rely on tools like Balsamiq to get ideas out of his head. now he uses Base44 & Alloy . the difference? he can prototype in hours instead of weeks. not mockups. actual clickable products. screens you can click through. workflows that feel real. something you can put in front of users, investors, or engineers and say: this is the thing. then he hands it to developers and says: build this. but better. that shift in product development unlocks a few big advantages: 1. feedback gets real fast people react differently when they can actually use something. vague opinions turn into clear signals. 2. alignment with engineers improves massively everyone sees the same product. less guessing. fewer explanations. the conversation moves from what do you mean to how do we make it great. 3. momentum replaces perfection you stop waiting to be ready. you ship. you learn. you iterate. iv seen this too. i recently used Alloy to give feedback to Menachem Cowen, and it completely changed the conversation. instead of explaining ideas in words, i could show them. what worked. what didn’t. what to improve. curious to hear from product managers. what tools are you using to create mockups or prototypes before engineering gets involved?

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Lessons from YC: Culture Isn’t a Slide Deck YC didn’t just change how we built products. It changed how we worked together. Before YC, “culture” was something we’d figure out later. We thought it meant perks, team dinners, or a list of values on the wall. But inside YC, culture meant one thing: how fast your team can move without breaking. That’s when it clicked. Culture isn’t what you say. It’s what you tolerate. If someone cuts corners and you let it slide, that’s your culture. If someone owns their mistake and you celebrate it, that’s your culture. We started treating culture like product — build, test, iterate. That's when we realized small habits create the real DNA: how you communicate under pressure, how you make decisions, how you handle bad days. YC pushed us to grow up as a team, not just as founders. Because a product might attract customers but culture is what keeps them.

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Robotics is such a fast-moving field right now w/ lots of key strategic questions that need to be answered by founders and investors in that field: - Will hardware be fully commoditized over time and software becomes (again) the differentiator? - Is the opportunity in vertical robotics (focused on a specific use case) or in generalized humanoids (or perhaps both)? - Is Robot-as-a-Service (RaaS) the prevalent business model going forward? Great blog post by my partner Angela Tran investigating these (and other) key questions (link in the comments below)

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We rebuilt Alpaca VC with AI. From the ground up. In production. Not talking about it. Not planning it. Actually running our entire firm on infrastructure we built ourselves. Private MCP server. RAG deployed. Knowledge base living in Supabase. Custom agents covering more surface area than our team ever could alone. Ryan Freedman internally talks about it as "riding on the back of the wave" - implementing tools as they ship, breaking things, rebuilding better. We're former builders. We know how to move fast without waiting for permission. Most VCs use AI to write memos faster. We use it to find founders others miss. Our agents don't just summarize pitch decks - they identify patterns across thousands of data points, surface non-obvious connections, track founder momentum in real-time. They're doing the work that used to take analysts weeks in minutes. But more importantly, they're freeing us to do what only humans can: build trust, make judgment calls, spot that spark in a founder's eye during a tough conversation. The real goal? Not just efficiency... Alpha generation through better sourcing, winning, and supporting. And in our humble opinion, this isn't about being cutting-edge. It's about accepting that the firms still doing venture the old way are already obsolete. They just don't know it yet. Alex Konrad did a deep dive into who is doing this best, and identified the folks alongside us at Union Square Ventures (Rebecca Kaden), Thrive Capital, Topology Ventures (Casey Caruso), DVC (Nick Davidov) + more. Which other VC firms are building with AI? 🙋‍♀️ https://lnkd.in/gDRBvaBz

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Robotics founders: if you know who your buyer is…you should already know them on LinkedIn. The tech’s not the problem. The message is. And the message doesn’t spread if it only lives in a pitch deck or investor memo. Here’s the play that’s working—especially in complex, high-consideration markets where your buyer list isn’t huge… but it is knowable: Step 1: Use Clay to build your exact buyer universe. Real names. Real titles. Real companies. All mapped in under an hour. Step 2: Upload to HeyReach.io. Automate daily connection requests—20, 30, 40 a day—from you, your cofounder, your GTM lead. Not to pitch. To start the relationship. Do the math: 40 connections a day × 30 days = 1,200+ new buyers in your orbit by the end of the month. People who now recognize your name when it hits their inbox or feed. Step 3: Show up with content that actually builds trust. - Insights from the field. Real-world use cases. Founder takes on where the market’s going. - Stories only you can tell—the kind your buyers would screenshot, share, or bring up in their next team meeting. This is founder-led GTM in 2025: No SDRs. No ad spend. No excuses. Just a list, a loop, and a message only you can deliver. Want the exact setup? We’ve helped teams in AI, robotics, and hard tech do this in no time. DM me. I’ll walk you through it.

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