Joy Su

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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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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NEXTGEN INFRA STARTUPS MUST PLAY OFFENSE Historically, software infrastructure / middleware startups have played DEFENSE well. They have built deep, defensible technology moats over a period of time. The founders have been deeply technical, have sold to other developers, and have been content to not have a strong public-facing brand. However, this playbook (and archetype) must change. Infra founders and startups must play OFFENSE and become more dynamic and front footed. Why? First, foundation models are adding more and more infastructural components and nibbling away at what was traditionally considered infrastructure. No company can now comfortably sit and own a part of the stack. It will inevitably erode. Second, end customers are deciding or opining on what infrastructure to use. Guess what? Customers of infra products are no longer just career engineers but ordinary people (including VCs!) Infrastructure now has a PLG motion. Having a customer-facing brand is no longer optional. Finally, it’s possible (or even probable) that your company might need to fundamentally change its business model, and not be an infrastructure company any more. And in this crucible moment, you need to have the muscle and the ability to seize the opportunity and go for it. Consider OpenAI launching ChatGPT or Vercel launching v0. They could never have managed it with a legacy infrastructure mindset. Check out Groq’s homepage - it looks more like a vibecoding startup than a chip company. To summarize, infrastructure startups need to evolve. They must start thinking about the end customers, become much more public-facing with their brand, and develop the muscle to build customer-facing (not just developer facing) products. The archetype of infrastructure founders is changing. They cannot just be technical. That’s tablestakes. They must build a deep understanding of their customers’ customers, be superb at product development, marketing, storytelling and brand building, and be ready to move up the stack at any time. In other words, they must be able to play OFFENSE. That’s what greatness means in infra companies today.

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I’ve come to believe that early experiences carry disproportionate weight - far more than we often acknowledge. For designers and builders, “minimum viable” may suffice for validating core functionality, but it rarely earns trust or sparks genuine delight. While velocity matters, I’ve always struggled with the notion that early versions should be merely viable, especially in a world where attention is scarce and emotional resonance is everything. This extends beyond product. Do you show up as the minimum viable version of yourself in your work, your relationships, your craft? I would encourage you to challenge the conventional MVP framework. Replace it with the Minimum Awesome Product. This doesn’t mean it’s fully-featured or pixel perfect. But it should be worthy of attention. Memorable, even evocative in the way it triggered emotion. The kind of product that prompts someone to say, “I didn’t know I needed this, but now I can’t live without it.” You can refine mechanics over time, but the emotional experience of a first interaction is singular and unrecoverable. Don’t settle for viability. Build something worth remembering… and make it awesome! ❤️‍🔥

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Over the last few years, I have mentored founders across fourteen universities, twelve in Southern California and two in India, including my alma mater. The contexts were different. The ecosystems were different. The constraints were different. Yet the underlying need was the same. Founders everywhere are searching for clarity. Clarity on which problem is truly worth solving. Clarity on who the real customer is. Clarity on what to ignore, even when the opportunity looks tempting. In Southern California, I often meet founders who think global from the first day. They are ambitious and well connected, but many struggle to narrow their focus. In India, I meet founders who are remarkably resourceful and fast-moving, but who sometimes hesitate to expand their ambition beyond immediate markets. Neither approach is wrong. Each becomes stronger when balanced by the other. The founders who endure learn to pair vision with discipline. They resist the urge to chase every option and commit deeply to solving one meaningful problem well. Mentorship, in my experience, is not about prescribing direction. It is about helping founders develop their own internal compass, one that holds steady regardless of geography or environment. Across continents, the truth is consistent. Founders do not lack advice. They lack clarity.

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Yet to see a firm talk seriously about outcome growth from AI. It is all “output, output”. Which is ironic given that for over a decade software has enjoyed compounding output gains through abstractions, while outcomes have stayed flat or even declined. Nobody seemed interested in measuring that. Individual output gains can actually reduce the productivity of the system as a whole. More output does not guarantee better outcomes. My mental model is this: if we are creating software at ten to twenty times the pace we did before, then the upstream and downstream feedback loops would also need to scale by the same factor to make sure we are building the right thing. That is not only unrealistic but undesirable. When everything speeds up, the noise to signal ratio explodes, and it becomes harder to know what actually moved the needle and what is worth iterating on.

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Since recently joining the Scale AI team as SVP of Engineering, I’ve been inspired by how deeply the team understands what it takes to operationalize AI at enterprise scale. Over the past several years, Scale has helped some of the world’s largest enterprises integrate AI across their most complex workflows. Today marks the next chapter in that journey. We’re excited to open-source Agentex, the agentic infrastructure layer in Scale GenAI Platform, built to enable enterprises to manage secure and reliable enterprise AI. We believe Agentex will become the standard layer for hosting and orchestrating AI agents by enabling developers to build freely while giving enterprises the control and reliability they need for mission-critical systems. Starting today, it’s open-sourced and available to everyone. Learn more about how Agentex powers enterprise AI workflows and try it out yourself: https://lnkd.in/gP7kQsMW

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LLMs are powerful, but they are stateless machines. They don’t carry memory or intent, they work with the context you provide at that moment. The same model can feel brilliant in one scenario and completely off in another. The difference is not the model, but the context and use case we design around it. The technology is already here. The real opportunity lies in how we shape it to solve real problems.

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