Jason Mitura

Everyone on here talks about moats, so here's ours. Recruiting is one of the most overlooked spaces when it comes to defensibility. The standard playbook is scrape a list, do outreach, fill the role. It works, but anyone can do it, which means no one actually has an edge doing it. What we've been building at Nodes and DevSignal for the past two years is a network of engineers who genuinely want to be part of something like this and bring their people along with them. That compounding trust is what lets us move faster than anyone else in the space. You can't scrape your way into that.

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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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Columbus, Ohio has 13 percent growth in company formation YoY. We also just passed Chicago as the 5th largest concentration of data centers worldwide. Construction is accelerating. #ohiotech -

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AI won’t replace game developers. But devs who use AI will change the industry! My teammates, Bruce Lam 🔜 GDC and Michael Dennedy, had a great time diving into this with Ben Anchor... Unpacking how AI is transforming the game development process. From faster iteration to unlocking new forms of creativity, the potential is massive! But what really resonated? The human-in-the-loop approach. It’s not about replacing developers. It’s about amplifying their creativity with AI as a tool, not a substitute. Big thanks to Ben for the thoughtful conversation and for spotlighting this important shift in the industry! 🙌 If you’re curious about where AI and creativity meet in gaming, this one’s worth a listen! #AIingaming #gamedevelopment #humanintheloop #futureofgaming #gamingindustry

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The Shift from Specialization to Generalized Autonomy Industry patterns are reversing. After five years of domain-specific AI tools, the most defensible systems are now those that generalize execution logic across domains. Amazon’s #OmniFlow pilot shows a single orchestration kernel powering 14 functions, from supply chain to HR, reducing duplication by 72%. By 2028, #multi-domain autonomy frameworks will represent >60% of enterprise AI spend. #Source: Amazon AI Summit 2025 Keynote; McKinsey AI Trends 2025.

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Waymo collected ~20M miles over 15 years before launching L4 in SF. That’s years of data collection, billions in capital, and a scale most companies simply can’t afford. And in many industries — especially defense — you don’t even have the luxury of commercial-scale deployment in the first place. The reality? Autonomy doesn’t fail because of models. It fails because of missing edge cases, weak coverage, and slow data iteration cycles. That’s exactly the problem we’re solving at DiffuseDrive. In this Forbes piece, Sabbir Rangwala breaks this topic down. Synthetic data isn’t about replacing reality. It’s about completing it. https://lnkd.in/dqNSA4t7 Roland PinterJordan KretchmerOutlander VCPresto Tech Horizonse2vc #PhysicalAI #Autonomy #DefenseTech #SyntheticData #Robotics #GenerativeAI

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NVIDIA's DGX Spark started shipping this week. $3,999 for a petaflop on your desk. Research labs currently skip experiments because justifying cloud spend takes three approvals. Startups burn runway on compute before knowing if ideas work. Teams prototype in the cloud, then find their models behave differently in production. Run 200B parameter models locally. Fine-tune 70B models without cloud dependency. Link two units for 405B parameters. The development cycle tightens considerably. The GB10 Grace Blackwell chip is purpose-built for this—MediaTek collaborated on the CPU side, 128GB of memory sits between the CPU and GPU without the usual bottleneck. Different from retrofitting gaming hardware for AI workloads. Training frontier models still requires massive infrastructure. Production inference at scale needs proper compute. But prototyping, testing, fine-tuning—that friction just dropped significantly. Useful ideas die when the barrier to experiment is too high. The path from concept to test matters. $4,000 isn't nothing, but it's orders of magnitude less than building a cluster. NVIDIA's framing this as "democratizing AI"—partially marketing, partially true. The number of teams who can now iterate at this level just expanded considerably.

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