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How Software Engineers and Students Use AI to Move Faster than Ever (without breaking things)
The “AI revolution” is really augmented intelligence: humans + tools. super{set} backs teams that are AI-native—putting AI at the core, not as an add-on. For students, the risk is either over-relying on AI (and skipping fundamentals) or under-using it (and falling behind). Universities should teach both rigorous reasoning and modern AI workflows. For developers, agentic tools (e.g., Cursor, Copilot) turn one engineer into a mini dev team—automating boilerplate, tests, docs, and legacy audits—so humans can focus on architecture, edge cases, and creative problem-solving. AI won’t erase jobs; it shifts them, rewarding those who adapt. The near future belongs to builders who pair deep software craft with AI fluency.
This VMblog Q&A with super{set} founding GP Vivek Vaidya explains how the studio builds AI-native enterprise companies by pairing capital with hands-on company building (engineering, GTM playbooks) and focusing on proprietary data as the core moat. It spotlights the $200M acquisition of Habu by LiveRamp (2024) as proof of the model, and details what they seek in technical co-founders, how they hard-wire governance/privacy from day one, and which enterprise workflows are ripe for AI over 2025–2030.
As a co-founder at Lucenn, an innovative AI Dev shop, Adrien Le Gouvello, a partner at super{set} gets to see how AI is automating and transforming all kinds of business processes daily. Not a software engineer, not an expert, but understands the requirements and functionality needed to create an effective coordination tool for Burning Man.
ARaaS is the next evolution beyond SaaS—businesses pay for outcomes, not just software features.
A Healthy Debate at super{set}’s Evening Roundtable on AI Possibilities for Marketing
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In an age when algorithms can generate information on demand, our capacity to fact-check and trust that information is diminishing. This paradox raises profound questions for society and business: What does it mean when AI can “know” almost anything, but we can’t agree on what’s true?
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