2025 Thesis

The AI Application Layer & the Future of Software

What:

  • AI is shifting software from deterministic, rule-based execution to probabilistic, adaptive intelligence.

  • Traditional SaaS moats (integrations, UI, feature sets) are being eroded by AI-native software that dynamically restructures itself based on user behavior.

  • The future winners will be AI-native applications that continuously refine intelligence through usage-driven data network effects, much like how TikTok personalizes entertainment.

Why Now:

  • AI foundational models (LLMs, multimodal models) are rapidly improving and commoditizing.

  • The competitive advantage moves from owning the model to owning the interface and workflow adoption.

  • AI software will function more like a feed, restructuring itself dynamically to optimize for specific Jobs-To-Be-Done (JTBD).

Key Investment Areas:

  • AI-Powered Vertical SaaS: AI-native workflow automation for highly regulated industries (healthcare, finance, legal, logistics).

  • Autonomous Agents for Business Operations: AI agents that can execute tasks across multiple enterprise systems (procurement, HR, FP&A).

  • Usage-Driven AI Learning Models: Products that continuously learn from human interaction, increasing efficiency over time.

  • AI-Native Developer & Productivity Tools: AI-powered IDEs, documentation automation, and real-time code copilots.

The Rise of Autonomous Systems & Frontier Tech

What:

  • AI is moving beyond knowledge work automation into physical autonomy, transforming manufacturing, defense, logistics, and energy infrastructure.

  • Intelligent systems will augment human labor, managing industrial-scale decision-making in real-world environments.

Why Now:

  • Robotics & autonomous systems are at an inflection point with advancements in AI perception, edge computing, and reinforcement learning.

  • AI’s demand for energy is growing exponentially, requiring fundamental rethinking of power generation, grid intelligence, and energy storage.

  • Space and bioengineering will be AI-native domains, leveraging automation for manufacturing, material science, and resource extraction.

Key Investment Areas:

  • AI-Powered Industrial Robotics: Advanced robotics for precision manufacturing, warehouse automation, and construction.

  • AI-Energy Infrastructure: AI-driven grid optimization, modular nuclear energy, and next-gen storage solutions.

  • Autonomous Defense & Aerospace Systems: AI-native autonomous drones, unmanned aerial & naval systems, and space-based logistics.

  • Self-Driving Labs & AI-Powered R&D: AI-driven experimentation in biotech, materials science, and synthetic biology.

  • AI-Enhanced Space Infrastructure: Autonomous satellite servicing, in-orbit manufacturing, and AI-driven mission planning.

The Great Redeployment & the Future of Work

What:

  • AI will augment rather than replace most human labor, fundamentally shifting intelligence allocation across industries.

  • The next decade will see a massive reallocation of human capital, as AI automates repetitive work and humans move up the value chain.

  • The winners will be those who build tools that make workers 10-100x more productive, rather than replacing them.

Why Now:

  • Every technological revolution (Industrial, Computing, Internet) has led to job displacement followed by massive new job creation—AI will be no different.

  • The limiting factor isn’t AI capability—it’s human adaptability. Reskilling, workflow re-engineering, and better tools will be necessary to fully realize AI’s potential.

Key Investment Areas:

  • AI-Augmented Workforce Platforms: AI copilots that enhance decision-making across professional services (law, medicine, finance).

  • AI-Powered Blue-Collar Automation: AI copilots for manufacturing, logistics, and skilled trades (e.g., AI-enhanced construction planning).

  • Reskilling & Adaptive Learning Platforms: AI-driven career transition tools that dynamically match workers with emerging job opportunities.

  • AI-Human Collaboration Interfaces: Natural language and multimodal AI systems that seamlessly integrate into existing workflows.

  • AI-Powered Creator & Knowledge Work Tools: AI copilots for content creation, research, and digital media production.