Phillip Jones

If you are interested in OpenTelemetry and more specifically OTEL fleet management, this interview with Andy Keller (BindPlane) is a must listen. Bindplane has played a instrumental in the creation of OpAMP as well as a number of other OTEL components and Andy has been one of the prime drivers of BindPlane’s contributions.

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Just back from TDK Ventures 100X 2026 Conference where I served on a panel. A few insights from the morning session. THE KEYNOTE: “100X is not an invention. It enables an ecosystem” -- Chuck Mattera Chuck Mattera (former CEO of Coherent, now CEO of Avalanche Thinking) argued that no 100X has ever been a single breakthrough, but a chain: transistor, IC, microprocessor, CMOS scaling, SoC, AI,. The question isn't what you invented — it's what becomes possible to compound. Building an ecosystem is like playing Go: the board is visible to everyone, the opportunities are not. And manufacturing is where compounding starts: making things creates learning, scale compounds learning, learning compounds speed. Bring manufacturing back and you build the ecosystem that learns fastest.  I couldn’t agree more. MY PANEL: FROM MODELS TO MOTION — AI ENTERS THE PHYSICAL WORLD Moderator: Modar Alaoui, Panelists: Les Karpas (NVIDIA Inception), Claire Delaunay (OPALIN), Ben Burchfiel (Walden Robotics) I think my panel agreed on more things than we disagreed on. There seemed to be consensus that there is no single winning embodiment.  The moat is shifting to whoever owns a data engine (which is not the same as owning data) and whoever can learn from the customer feedback cycle. I shared where I'd invest for the next decade: data engines and the application-specific robotic foundation models under them. In hardware, components like actuators, hands and tactile sensing. Models move fast; their advantage decays. Hardware moats are slow and structural: actuators are 25-50% of BOM, and ~90% of permanent magnets are made in China. We need to solve supply chain issues. What we underestimate: simulation, non-visual sensing, and safety as its own software category. The next 100X, in my view: omni-models — robots that take in force, acoustic, tactile and chemical data and tell us in natural language what they sense – replacing judgment, not just hands. AI RUNS ON POWER Panelist Jim Messina's numbers were sobering: opposition to data centers has gone from ~45% to 71% in months, 240 cities have moratoriums, and 10 of the 15 biggest congressional races are running ads on them. Tarun Raisoni's counterweight: someone in a lab at IIT or MIT or Berkeley will figure out how to train the model on 1/100 the power. Both can be true — and the industrial base we build for data centers will serve far more than AI. Thanks 🌱🤝🌍 Nicolas Sauvage, Qianran (Katherine) He, PhD, David Delfassy and Starry Wang for another great 100X. 🌱🤝🌍 #Robotics #PhysicalAI #AI #100X

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Happy Monday :) SF AI-Native Operator Takeaway #1: Forward-deployed engineers and how AI companies are ~actually~ getting built. One of the biggest differences between AI-native companies and traditional SaaS right now isn’t just the technology or the model. It’s how and where the product actually gets built. The strongest AI teams I met aren’t optimizing pitch decks or even demo environments, they’re building inside customer environments. That’s why forward-deployed engineers and implementation strategists keep coming up. In practice this means: engineers sitting directly with customers; shipping integrations, workflows, and edge cases in real time; product managers working with those FDEs to understand what needs to be done; and learning what actually matters to the customer before anything gets productized. This flips the old SaaS playbook. Traditional SaaS assumed engineering was scarce. You build once, sell many times, and customize through Sales and CS. When bespoke engineering was required, it was often rational to walk away. AI-native teams are operating under a different assumption: engineering and GTM are treated as equally flexible. With that, services are often the wedge to becoming a platform, not something to run from. The vision is still to be a platform, but the momentum starts with high tough delivery. That said, there’s an important caveat that came up repeatedly. The forward-deployed model only really works when contract values can support it, and yet right now, it feels like almost everyone is trying to use it. Forward-deployed work drives speed, but it also blurs: ➖ Product versus services ➖ Pricing models, such as software plus implementation or usage plus services ➖ Gross margins once delivery, support, and compute costs normalize The best teams aren’t blind to this, but they still use the approach to rapidly build product. They focus less on feature validation and more on customer willingness to pay for something (before building it!), as well as what multiple customers repeatedly ask for versus what is truly bespoke. With that, they decide what should stay services versus what makes it into the core product. In this rapidly changing time, speed beats elegance, but only if teams are honest about what they’re actually selling and what the economics can support. Forward-deployed teams aren’t a scaling strategy on their own, but they’re an incredibly compelling learning strategy. And right now, learning (and implementing) faster than everyone else is the advantage. Next up: how PLG and GTM are changing in the AI landscape, and why many teams are fixing the wrong problem. #FDE #AI

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