Ivan Kuznetsov

What happens when Amazon (Zoox) builds a Robotaxi?  They skip the hype and just ship it. While we continue to wait for Tesla’s much-anticipated Robotaxi, Amazon-backed Zoox quietly launched its autonomous vehicle service to the public in Las Vegas this week. Free and very limited rides for now, but here are 2 reasons why I like Zoox: 1. Designed from scratch Zoox didn’t retrofit an existing car. They reimagined the vehicle altogether. It has no steering wheel. No pedals. No front or back. Just a fully symmetrical electric vehicle, purpose-built for autonomy. 2. Passenger-first interior With face-to-face seating and no driver distractions, the design is built entirely around the rider experience. It’s not trying to replicate driving. It’s trying to make us forget about it. Tesla may have dominated headlines, but Zoox is executing quietly and methodically. And in the world of autonomous vehicles, that might just be the better strategy. It's good to see an innovative company that doesn't shout. It ships. Video Credits: Zoox Channel on Youtube #AutonomousVehicles #AI #Zoox #Amazon #Robotaxi #FutureOfMobility #ProductDesign #Innovation #TechLeadership

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Greylock is leading the seed in SuperMe, the AI-native professional network. Most expertise is not yet legible to LLMs. Professionals demonstrate expertise in private contexts: product docs, meeting notes, and most often - in their own heads. SuperMe is building a way to extract and structure these insights into AI profiles, grounded in an expert's real words and personality. And it is network-first; the more conversations one's AI profile has, the more opportunities for experts to reinforce or create new insights -- expertise compounds. This is one of the strongest examples of founder-market fit we've seen at Greylock. Casey Winters is the most in-demand product & growth advisor that marketplace operators (self included) wish they could hire. And Ludo Antonov built & led engineering teams at some of the most iconic network businesses over the past decade: Whatnot, Lyft, and Pinterest (where he worked closely with Casey). The two started this company out of our offices, and have until now been quietly building a highly curated network with profiles like Elena, Lenny, Brian, etc. We see a unique opportunity to create the professional network graph in a world where likes & follower counts have lost meaning. More on our investment below:

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“𝐈 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐛𝐮𝐢𝐥𝐝 𝐍𝐕𝐈𝐃𝐈𝐀 𝐚𝐠𝐚𝐢𝐧 𝐢𝐟 𝐈 𝐡𝐚𝐝 𝐭𝐨.” That’s Jensen Huang. Completely serious He says building NVIDIA was a million times harder than expected. And that misunderstanding mattered. Because underestimating the difficulty is often the only way founders start at all. The real loop: ▫️ “How hard can it be?” ▫️ You step into something massive ▫️ You adapt, endure, and keep going ▫️ You pay the price over decades That question isn’t optimism. It’s a survival mechanism. I broke down the price Jensen Huang chose to pay, and why it explains how NVIDIA was built to last. Full breakdown here 👇 https://lnkd.in/ePbQi9ig

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Martin Eberhard played a key role early at Tesla and even brought in Elon Musk. Then he was pushed out of the company for pennies. Founders get most of the attention in Silicon Valley, but the ecosystem is built on the people who take a chance and join early stage ventures. Unfortunately, those same people are often the easiest to cut out. Founders can dilute early employees, change vesting terms, and restructure equity with little effort and zero consequences (you can literally do it in Carta in 5 minutes). Daniel Kottke was a close friend of Steve Jobs and helped build the Apple I and II computers. He got no equity (Steve Wozniak eventually gave him a few of his own shares). Brian Acton co-founded WhatsApp and stayed through the Facebook acquisition. When he left Facebook over privacy concerns, he had to walk away from $850 million in unvested stock. There’s more awareness now around how vesting works, what happens during a liquidity event, and how to think about re-ups and dilution. That’s good. But the truth is, if someone really wants to screw you, they can. It still comes down to one thing: trust the people you work with. Or don’t work with them at all.

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GPUs have gotten faster, and models have gotten larger. But storage is still stuck in the past. Modern AI workloads need instant access to huge datasets. Instead, teams waste time on slow retrieval, idle GPUs, and costly pipelines. Archil is changing this. It delivers object-storage scalability at block-storage speeds. The platform allows for seamless, high-performance access to massive datasets. By eliminating cold starts and accelerating throughput 30X, Archil enables teams to fully utilize their infrastructure and move faster on training, analytics, and AI deployment. Archil’s founder Hunter Leath brings rare technical depth and operating experience from building Amazon EFS and optimizing Netflix’s cloud performance. This is a team building the foundational layer that AI workloads desperately need. I’m thrilled to lead Archil’s seed at Felicis with Nancy Wang and support their mission to reinvent cloud storage for the AI era. https://lnkd.in/gWueXhzz

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Here’s a truth that often gets missed: becoming an AI-first company at the enterprise level is still really hard. It requires making big investments in R&D and specialized talent and betting on still-evolving frameworks and tools — and having the commitment to push the field forward yourself where the right tools or integrations don’t yet exist for your specific use case. As deploying AI and agents becomes table stakes in more industries, some companies end up hyping prototypes or easy use cases on earnings rather than building complex, self-improving AI systems to move the needle on productivity and profit. The task of separating these AI talkers from AI doers in public markets  – and leveraging AI to do it – is why my longtime partner John Hughes and I created the Swift AI Index as a fun side project last year. We just updated the index with five new stocks as part of a quarterly refresh; check it out: https://lnkd.in/eNBGjQzK

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Human drivers make mistakes.  Algorithms learn from them. Waymo is quietly rewriting the rules of road safety. Just read Waymo’s latest safety report—50 million self-driven miles across Phoenix and SF. And the numbers are surprisingly solid. → 88% fewer property damage claims compared to human-driven cars. → Only 13 airbag-triggering crashes (vs. 78 expected with humans) → Just 36 crashes with injuries (vs. 190 projected)—that’s an 81% reduction. Waymo also ran a model with Swiss Re. Humans would've triggered 26 insurance claims. Waymo triggered just 2. What's even more impressive is, most of the incidents weren’t even Waymo’s fault. — One got hit during a police chase.  — Another, rear-ended while waiting at a red light. Earlier this year, there was a software recall (it misinterpreted objects like gates and chains), but it was resolved quickly and caused no injuries. While we’re still debating whether we’d ever trust a driverless car, these vehicles are already logging millions of miles, learning as they go— and in many cases, outperforming us. It made me wonder: What if the safest “drivers” of the future don’t drive at all? Not saying we’re fully there yet. But 50 million miles in, it’s worth paying attention. Would you trust it with your commute?

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Building on top of large language models is fun, but getting consistent performance and reliability is extremely challenging. I love this post from Kimberly Patron about how Sierra uses health and performance-based traffic routing and request hedging to get meaningfully better performance and tail latency from high scale LLMs https://lnkd.in/g4qWRCb5

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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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Thrilled to back Dima Shvets and Alexey Moiseenkov on the launch of Mirai On Device AI. If you’re building AI-native products, this one’s for you. Mirai is an on-device AI SDK for iOS and macOS that lets you run high-performance models directly in your app with zero latency, full data privacy, and no inference costs. So instead of sending tokens to the cloud (and paying for it), you keep things local. Even better, you don’t need a dedicated ML team or weeks/days of infra setup. One developer can integrate inference, routing, and optimization in minutes. Key features: — Supports Llama 3.x, Qwen 3, Gemma 3, DeepSeek-R1 — Bring-your-own-model support — Tools for fine-tuning, evals, structured outputs & fallback — Integration in a health tech product with 100M+ MAUs Built by the team behind Reface & Prisma. Backed by scout funds of Sequoia Capital & Index Ventures, and angels from OpenAI, Hugging Face, ElevenLabs, Unity and us, at NP-Hard Ventures 🤩 On-device AI will be a major unlock over the next few years, and help us to shift the way people interact with technology. And Mirai is early, ambitious, and razor-focused on enabling this shift. Go try it out! Link to SDK and GitHub documentations in comments. NP-Hard Ventures // Paul Veugen // Micha Hernandez van Leuffen

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Higgsfield is at ~$200M ARR in <1 year. This incredible trajectory comes from a prosumer tool that can be called "wrapper" "limited moat" "just an orchestrator". They scaled in AI video gen despite there being Sora, Runway, Kling, Veo.   I feel all “thin wrappers” are not the same. Some are exceptionally thicker than what they seem. Few reflections that feel important -   1) Prosumer is going to be huge A large part of what "prof/ business owners" do every day - content creation, quick research, spreadsheets, light analysis, app building - is still weirdly manual. It’s time-taking. It’s expensive. AI collapses a week/ hour long job into a 5 minute output. You’re extracting from an existing value pool (what people already spend time/money on). You’re also unlocking a latent one (people do more only because it becomes effortless). That’s why anything that delivers a true 10x productivity jump gets adoption. Always. And this is why prosumer AI can become bigger than it looks: it’s closer to labor TAM than software TAM.   2) “Usability with least effort” is the real unlock for mass adoption Humans are inherently lazy. Not in a moral way but in a physics way. Every additional step creates drop-off. Every moment you ask the user to “think” creates resistance. Every time you require articulation, you lose people. The secret of mass adoption is: make the user do less. This is what Higgsfield did. Take a simple image and convert it into a usable ad / video / UGC-style asset in under a minute. No complex prompt engineering and precise description. Because most humans are not creative. They can’t articulate what they want. They don’t know what “good” looks like. So if your workflow requires them to specify the output clearly… you’ve already lost. Higgsfield has a 10x experience - templates are pre-set, outputs are usable instantly, iteration is lightweight.   3) Effortless doesn’t happen by accident. It’s a hidden moat When a product feels simple and magical, it usually means an insane amount of hard work happened behind the curtain. In Higgsfield’s case, it's a library of templates built for performance. Someone has already done the research: - what formats go viral - what hooks work - emotion + pacing + timing - camera motion + delivery style - the “shape” of a winning video - which model settings get you the output that’s actually usable   This is packaged taste + encoded workflow. The end user experiences it as “one click”. But the business has embedded hours of experimentation into that click. That’s hard to replicate.   4) Prosumer tools win when they pick a sharply defined persona Ease of usability doesn’t come from adding features. It comes from knowing exactly who the product is for. Sharper the persona, the more predictable the workflow. More predictable the workflow, more you can automate. More you automate, more magical it feels.   Net-net: orchestration can scale massively when it compresses real work into instant outcomes, esp in prosumer. 

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the real reason 73% of european AI engineers are moving to america (it's not the salaries) it's the pace of innovation + the feeling of being where shit happens. tesla just launched robotaxi service in austin while waymo expanded across the entire south bay. waymo's autonomous vehicles are now operating in 250 square miles of california alone. waymo is averaging 250,000 weekly trips across four markets: san francisco, los angeles, phoenix, and austin. they are preparing to launch in miami, washington d.c., and atlanta next. meanwhile in europe, our biggest innovation is... wait, what is it? there is this ongoing joke that we released plastic caps permanently attached to bottles. feels like america builds the future while europe regulates the present.

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Meta’s Llama gets a home — and a strategy shift Meta just tapped Groq and Cerebras Systems (both private!) to host its open-source LLMs — but the real story might be Meta’s first real move into cloud AI. • The news: Meta partners with Groq + Cerebras to host Llama models, bypassing AWS, GCP, and Azure. • The strategy: Start with AI inference, add storage next? Meta might be quietly building an AWS competitor. • The play: Offer Llama hosting at-cost (or less) to win dev mindshare + undercut OpenAI. • Why it matters: Open-source + cheap inference = existential threat to OpenAI’s pricing model. • Trend watch: AI compute costs could race to zero — and Meta just joined the race.

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