Pardha Ponugoti

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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Boston doesn't just breed startups. It breeds operators who've seen the inside of billion-dollar exits and still come back hungry. That's the DNA behind Jellyfish. Founded in 2017 by Andrew Lau, David Gourley, and Philip Braden, the company set up at 225 Franklin Street with one mission: bridge the gap between engineers building and executives betting. The founders lived that disconnect at Endeca, the Cambridge outfit Oracle scooped up for $1B+. They knew the gap wasn’t abstract. It was personal. Jellyfish was their answer. Eight years later, Jellyfish is back with a launch that pushes its platform closer to an operating system. On Sept 9, 2025, the company released its Model Context Protocol server as a Claude Desktop extension. That may sound technical, but the shift is anything but academic. What used to require API integration is now a desktop download. Ask Claude “Where are we investing engineering effort this quarter?” and the answer arrives conversationally, in real time, safeguarded by Meta's #Llama #PromptGuard2. This isn’t a gimmick. It’s a structural change in how engineering intelligence gets delivered. Jellyfish already powers 500+ organizations, Mastercard, PagerDuty, Priceline, ZoomInfo, Hootsuite among them, but this extension lowers the barrier for every stakeholder. Finance can query allocation. Product can explore delivery. Leadership can ask about throughput. No dashboards, no gatekeeping. Just data in plain language. The MCP push carries Nicholas Arcolano, Ph.D.’s fingerprints. Jellyfish’s Head of Research, a Harvard University-trained mathematician, has published lessons from MCP experimentation since Anthropic unveiled the protocol in 2024. Others are validating too: Jamie Newcomb at Ably has shared how MCP integration evolved from basic retrieval to analysis that shaped decisions. The official blog post was signed by Sophie, a Jellyfish research intern. Light on detail, heavy on intent: take the intelligence already humming inside Jellyfish and make it available in workflows people adopt daily. Momentum backs the move. Jellyfish has raised $114.5M through Series C, tripled revenue and headcount in 2021, launched an AI Impact Dashboard in 2024, and landed on Forbes’ Next Billion-Dollar Startups in 2023. Competitors like LinearB, Swarmia, and Waydev can’t match the same focus. Jellyfish has always been less about vanity metrics, more about business clarity. Now that clarity speaks back. The extension is more than a technical win. Once your engineering intelligence plugs seamlessly into AI workflows, it stops being optional, it becomes indispensable. The only real question is how many teams will be waiting when Jellyfish expands to more platforms. #AI #EngineeringIntelligence #EngineeringLeadership #SoftwareDevelopment #SoftwareEngineering #Integration #Data #Infrastructure #Innovation #TechEcosystem #StartupEcosystem #TechNews If software engineering peace of mind is what you crave, Vention is your zen.

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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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Palantir Technologies is moving its headquarters to Miami. Its legal address is a coworking space with convenient access to the Cheesecake Factory and Lenscrafters.

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We published our 2025 report. ~45.7M tonnes CO2e avoided. ~300M people protected. Yeah we know impact is out of fashion, but we weren't in it for the fashion anyway. AI seems like a major unlock to go deeper on methodologies and associated math. We'd love feedback so we can revise and improve before updating for the rest of the portfolio. Featuring Singularity Energy Kelvin Wasted* PBC Thalo Labs Mill Earth Force Technologies Revivn Grey Rhino Mark43 HAAS Alert Gisual Near Space Labs Resonant Link Medical Arbor Robot.com Shellworks Nevoya Circuit CYCLE Applied Carbon Flair Future Motion Rachio cove https://lnkd.in/gXAsfp7Y

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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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The most expensive YAML file I ever touched took down production for a full week. One bad indent. One missing key. No rollback. No safety net. Just… poof. We traced it back to a config that had quietly rotted over time. Nobody owned it. Nobody tested it. Everybody assumed it "just worked." Here’s what made it a perfect storm:  ❌ No schema validation    ❌ No linting    ❌ Copy-pasted from an ancient PR    ❌ Zero test coverage for infra changes We were treating infra as code (without treating it like software). And that’s where it all broke. So we made a hard shift:    ✅ Linted every YAML + HCL    ✅ Validated schemas on every commit (kubeconform, tflint, etc.)    ✅ Moved to GitOps: every change tracked, reviewed, and reversible    ✅ Added tests for Helm charts, Terraform modules, and pipelines    ✅ Introduced CI gates just for infra config So, I put together a YAML best practices CHECKLIST that you’all can use right away. COMMENT “YAML” and I’ll share it with you.

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I had Sam D'Amico (CEO & Founder of Impulse) come by the studio to talk about the future of induction cooktops. Wild stats: - A battery-backed induction cooktop that delivers up to 10 kW per burner, nearly 5× the effective power of gas and ~3× standard induction. - 3 kWh onboard battery. No panel upgrade needed. Standard outlet. - Single-degree temperature control inside the pan. - $25M+ raised from Lux Capital, Fifth Wall, Construct Capital, and others. Bonus: It boils water in under 40 seconds. Rules: 10 questions. 30 seconds each. Zero prep.

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Seth Levine nailed it! The concentration problem isn't just a GP pain point... it's choking innovation. When capital pools around the same 20 funds, emerging managers and founders get shut out. Love that he's using his platform to call this out. Foundry has backed 50+ emerging managers. That's the diversification the ecosystem needs. This is exactly why Carta exists — making capital allocation visible and accessible. Capital Evolution hits at exactly the right moment. Thanks for having us Daniel Dart. Team Carta loves the community of FUTURE TITANS you have built 🚀

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🤔 LLMs will continue to make people a lot of money. They will also continue to pay for massive infrastructure investments. However… …they will not produce an AGI. While I believe humans will eventually create an AGI, it will likely be a very different style of mode (pipeline) than what we have today and probably created or at least enabled by people and companies who don’t yet exist.

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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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This post is related to hallucinations but it delves into a couple of interesting points 1) Should we expect that LLM's will flag non-sense prompts, I would vote yes 2) Users need to stress test models by asking them non-sense questions

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It took 67 years to go from steam engine to factory. Then another 144 years to optimize and organize those processes into productive assembly lines. Implementing isolated pieces of technology is often pretty straightforward. Finding the most efficient way for humans to work alongside it usually takes a little longer. Slide courtesy of Konstantine Buhler at Sequoia Capital (video linked in comments)

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Big fan of what Luke Winston-Almanzar and his team at Reservoir are building right here in Boston. Love seeing companies at the intersection of AI @ hardware. ~12 months ago we held one of our first WHOOP Tech Pulse events focused on this intersection. Flash forward 12 months later, and events like those, celebrating Founders like Luke, is what gave rise to the Massachusetts AI Coalition. We're pumped to have them on Team Massachusetts. They are a great example of a company building the future of smart home infrastructure right here in Massachusetts.

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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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Developing a new thinking: Memory > Pipes AI isn’t destroying software moats. It’s relocating them. The 3 Phases of Software Moats: 1) Vertical SaaS (Past) Moat = Data lock-in. Proprietary schemas. High switching costs. Backward-looking defensibility. 2) Embedded AI (Present) AI inside silos is context-starved. LLMs aren’t weak — system architecture is. Fragmented context = fragile outputs. 3) Horizontal, Agentic AI (Emerging) Tools like Claude Code and open agent frameworks point to a new layer: AI reasoning across systems. Interface collapses. AI becomes the orchestration layer. Apps become tools. Where the Real Moats Will Form: Not in integrations. Not in wrappers. Durable defensibility will come from: * Compounding Memory → persistent, longitudinal understanding of users and teams * Domain-Specific Orchestration → reliability in high-stakes workflows (finance, legal, healthcare) * Workflow Network Effects → shared institutional intelligence Trust Infrastructure → auditability + compliance + governance Hence: Memory > Pipes APIs are commodities. Context is compounding. The next $10B companies won’t just connect data — they’ll accumulate intelligence. Stateless systems are replaceable. Stateful, memory-rich AI systems are not. That’s where long-term enterprise value will accrue, I think :) Entrepreneurs Roundtable Accelerator Remarkable Ventures

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We made a huge bet that engineers will not want to be forced into using python just to use LLMs. The most underrated thing about BAML isn't that we improve tool calling on every model (including gpt-5), or that we have the fastest iteration loop for testing prompts. Its the fact that as your team scales and inevitebly uses different languages, they can ALL still use BAML to write their AI pipelines. Python devs love BAML. Typescript devs love BAML, and now we're starting to see Go devs on it too!

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I really love what's going on in the human physical augmentation space. There are new exoskeleton companies popping up here and there, new exos start to cost below $1,000 per unit. I see huge potential in improving lives, especially in the aging population. Check out this open-source exo project from the Northern Arizona University: https://lnkd.in/dWEKS3g9

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