David Wu

DealWire.TECH Deal of the Week - Feb. 20, 2026. Waymo raises $16B to scale robotaxi infrastructure Deal: $16B late-stage round, Valuation: ~$126B Lead investors: Dragoneer, DST Global, Sequoia, Alphabet Waymo, the autonomous driving unit of Alphabet, has raised a massive $16 billion late-stage round, valuing the company at approximately $126 billion. The financing brings together leading global venture and crossover investors, with Alphabet continuing as the strategic anchor. The raise comes as Waymo transitions from years of R&D and pilots into full-scale commercial deployment. The company already operates fully driverless ride-hailing services in multiple U.S. cities and is targeting rapid geographic expansion, with ambitions to reach 1 million weekly rides by the end of 2026. The new capital will accelerate fleet deployment, expand into new urban markets, and build the infrastructure required to support large-scale autonomous mobility networks. Why this matters: • Autonomous driving becomes infrastructure Waymo’s $16B round reframes robotaxi networks as long-term infrastructure assets rather than experimental tech. • Part of the trillion-dollar AI infrastructure cycle Unlike foundation model raises, this capital is flowing into AI deployed in the physical world—vehicles, sensors, and city-scale operations. • Winner-take-most dynamics emerging Only a few companies globally can fund autonomy at scale, making capital access a key determinant of long-term leadership. • Capital intensity becomes the moat Success will depend not just on algorithms but on financing and operating large fleets and urban logistics networks. • Structural advantage for Waymo More than a decade of development, billions of miles of data, and deep integration with Alphabet’s AI and compute stack give Waymo a meaningful edge as competitors like Tesla and Cruise continue to push forward. • Start of the AI transport wave Robotaxis are likely just the first step toward broader AI-driven autonomy across trucking, logistics, maritime, air cargo, and drone fleets. DealWire Takeaway: Waymo’s $16B financing marks a turning point: autonomous driving is shifting from frontier technology to city-scale AI infrastructure. As AI moves into the physical economy, the winners will be those who combine advanced algorithms with capital, data, and operational scale. Waymo’s latest round positions it among the frontrunners in the emerging autonomous transportation stack. #DealWire #Waymo #AutonomousVehicles #AIInfrastructure #Robotaxi #VentureCapital #FutureOfMobility #ArtificialIntelligence

6

Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

8

Sustained ~252–291 GiB/s of HBM memory throughput on H100s under decode load — essentially hitting the hardware roofline. This matters because HBM throughput, not FLOPs, is the real bottleneck in LLM inference. By keeping memory nearly fully saturated, I’ve unlocked far higher efficiency and throughput than standard engines. The result: 0.9–1.36M tokens/sec with ~0.1 ms first-token latency  #AI #LLM #GPURouter #H100 #Inference #CostEfficiency #Innovation #DeepLearning #AIInfrastructure #HighPerformanceComputing NVIDIA AMD OpenAI Google Shilpa Kolhatkar Keith Strier a16z speedrun

5

Investors (esp deep tech investors) generally think about things/bet on "layers": the application, the infrastructure, the model. I think that's becoming the wrong way to look at it? The real opportunity is in the feedback loop *between* the layers. A new social app creates demand for new hardware, which in turn demands a new kind of intelligence - I'm calling it "The Agency Loop". My latest newsletter explores this thesis through three signals: the "weaponized transgression" of Cluey, the AI-driven materials science in Nature, and the architectural critiques of Kenneth Stanley. Bet the loop, not the layer :) You can read the full analysis here: https://lnkd.in/ggdVGEda #vibecap #vibecapital #vc #deeptech #AI #cluely

4

1 Comment

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

71

3 Comments

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.

37

21 Comments

Valuations Are Vanity, Runway Is Sanity I’ve spent the last few months advising some Bay Area preseed-stage founders on their first fundraise. Seed funding now seems to be split into two camps: profit-first bootstrappers who ship instead of pitch, and “priced-to-perfection” startups with $50–500K ARR snagging $40–60M post-money term sheets from multiple VCs. From my experience, I’ve seen the fallout, higher burn, bigger milestones, and a flat round when growth stalls. My advice is straightforward. Price for runway, not headlines, leaving room for a 2–3× step-up before Series A. Build real revenue momentum and keep churn low before chasing sky-high valuations. I also suggest founders vet investors on how they’ll help you hire, ship, land customers, and reserve follow-on capital, because cash is fuel, not confetti. A true VC partner still answers the call if the market cools or gets competitive, and that’s dilution worth taking. #startups #startupfunding #venturecapital

22

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:

25

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.

75

11 Comments

CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

449

49 Comments

New Substack Post: Demography v. Automation: Births falling, robots rising? Five scenarios! Two big forces are pulling at opposite ends of the same struggle over how to sustain productivity: Birth rates and AI automation. In the West, and increasingly elsewhere, birth rates are falling, which means the supply of new workers is shrinking. At the same time, AI-driven automation is rising, which means the demand for human labour may also shrink or at least change shape. The central question is whether these curves intersect in a way that stabilises living standards and social contracts, or whether one force overwhelms the other, tipping society into imbalance and disruption. Scenario 1: labour shortage ~ AI automation. The two forces cancel each other out. Happy days! Scenario 2: labour shortage > AI automation. Where are the robots when we need them? Scenario 3: labour shortage < AI automation. Fewer humans are still too many. Scenario 4: labour shortage & AI automation. The double whammy. There continues to be a significant shortage of workers in areas that are difficult to automate (like elderly care), while AI is automating large numbers of jobs that humans actually still want to do (like clerk or customer service roles). The result: many people out of work, and many roles left unfilled at the same time. In theory, displaced workers could re-skill and move into areas where humans are desperately needed. In practice, this is not how things look today. Despite rising demand for care workers, technicians, and solar PV installers, these jobs are often filled predominantly by immigrants. Local citizens often do not want work that means night shifts, no home office, and chronic back pain. Higher wages may help, but structural mismatches are likely to persist. The jobs people want ≠ the jobs society most needs ≠ the jobs AI will automate first. What an inconvenience. But not all futures look so grim. Scenario 5: labour shortage ∞ AI automation. The paradox of abundance. AI automation can create more demand for human work. Entirely new markets may open up: personalised education, customised health services, new forms of entertainment, and other industries we can’t yet imagine. In this scenario, AI accelerates demand for human labour in areas where human taste, empathy, and physical presence remain irreplaceable. Whatever projections I try to make today, one thing is certain: they will be wrong. The collision of shrinking populations will unfold differently across sectors, countries and time horizons. What I do know is that demography will steadily erode the supply of human labour, and AI will steadily reshape the demand for it. Whether this brings balance or disruption depends not only on the pace of AI, but on how quickly we adapt to tilt the transition in our favour. Observing the labour market over the coming decade will be nothing short of a thriller. Link to full post in the comments!

50

12 Comments

SF AI-Native Operator Takeaway #2: In AI-native PLG, the hard part isn’t conversion... it’s discovery. Many AI-native teams are still talking about PLG using a classic SaaS mental model, but based on operator conversations in SF, that model is starting to break down in fairly obvious ways. The biggest bottleneck right now isn’t conversion. It’s discovery. In traditional PLG, users generally understood the category before they ever signed up. The problem was obvious, the product’s value was legible from the homepage, and the “aha” moment tended to show up quickly in first use. In that world, PLG meant optimizing onboarding, reducing friction, and improving free-to-paid conversion because user intent already existed. AI changes that assumption. In AI-native products, users are often curious but unclear. They don’t yet know what’s possible, value depends heavily on workflow, context, data, and role, and the product can feel abstract until it’s applied directly to their job. As a result, many users stall not because the product isn’t valuable, but because they haven’t discovered how it fits into their world and how they can't live without it. This is the real distinction people kept coming back to. PLG conversion answers, “Is this worth paying for?” PLG discovery answers, “What problem does this solve for me, right now?” What’s working best in practice is less about funnel polish and more about clarity up front: role- or workflow-specific entry points, guided examples instead of blank states, and opinionated first actions that show users a concrete outcome before asking them to explore. This also explains a broader pattern across AI-native companies. Forward-deployed teams and services-heavy delivery aren’t just implementation tools; they’re discovery mechanisms. They translate abstract AI capability into concrete workflow value, observe real use cases users wouldn’t self-discover, and feed those learnings back into what eventually becomes productized. PLG isn’t going away, but in AI-native companies it’s being redefined. Self-serve no longer means self-explanatory. Education becomes part of the product, and discovery has to come before optimization. The teams making progress aren’t obsessing over conversion rates yet. They’re focused on whether users see themselves in the product, how quickly they reach a meaningful outcome, and whether the product helps users get to a meaningful outcome for themselves quickly, without too much guesswork. Bottom line: in AI, PLG is less about removing conversion friction early and much more about creating understanding first. Once they understand, they may be hooked. Tomorrow is my last SF AI operator takeaway focusing on everyone's favorite topic du jour: 996 work schedules.

15

1 Comment

Stardew Valley meets Enterprise RAG? 🚜 🤖 One of the hardest challenges in moving Generative AI from prototype to production is authorization. How do you ensure the LLM only retrieves data the specific user is allowed to see? Our friends Sohan Maheshwar at AuthZed and Rohit Ghumare at motia just dropped a fantastic reference architecture solving exactly this. Using a Stardew Valley "harvest logbook" as the use case, they demonstrate how to build a complete, multi-tenant RAG pipeline. The Stack: ⚡ Orchestration: motia (Event-driven workflows) 🧠 Memory: Pinecone (Vector search & storage) 🔒 Security: #SpiceDB (Fine-grained authorization) This is a masterclass in building secure, context-aware AI applications. Check out the full tutorial below. 👇 #GenerativeAI #RAG #VectorDatabase #Pinecone #Authorization #Engineering #KnowledgeableAI

17

2 Comments

I often say that most SaaS is there to facilitate people interacting with data to do a job and reach outcomes. Future for a lot of these cases is the agent doing the work instead, it won’t need the interface of SaaS products and will better know how to achieve the job too (it’s an expert) and report its actions to orchestratrators (people). Big shift in the what and why SaaS exists.

6

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

58

3 Comments