Applied AI: Systemiq Capital's Investment Thesis
Louis Millon, Investment Principal at Systemiq Capital, shared the reflection below at the firm's annual investor meeting, introducing the Applied AI investment thesis ahead of a fireside chat with Ivan Poupyrev, co-founder and CEO of Archetype AI.
Most people reading this have used an LLM in the past day, whether that's ChatGPT, Claude, or Gemini. That habit marks a shift Marc Andreessen didn't fully anticipate 15 years ago when he wrote that software is eating the world. AI is now eating software engineering itself, with some of the most advanced tech companies reporting that 90% of their code is written by LLMs. The digital economy is being disrupted at a pace with no precedent.
Systemiq Capital views that disruption as the appetizer, not the meal. The bigger opportunity sits in the next phase, when artificial intelligence gets applied to the physical economy, the 85% of global GDP tied to tangible sectors like construction, manufacturing, logistics, energy, and agriculture.
### What "applied" AI means
The meaning of applied AI varies by industry, but the common thread is founders who rewrite the economic equation by removing the bottleneck on human intelligence. Systemiq Capital believes the most valuable companies of tomorrow will build products that do one of three things.
Unlock previously unknowable data. Companies like QFlow and SirenOpt let customers collect and analyze data that was never accessible before, giving operators visibility into processes that used to run on guesswork.
Unify siloed data across physical assets. Companies like Archetype AI and Optiml break down the silos where legacy data lives, building multi-modal, contextual understanding across heterogeneous assets, whether that's a factory floor, a fulfillment center, or a portfolio of buildings.
Replace service-heavy incumbents with expert agents. A third category competes out knowledge-constrained incumbents through hyper-expert agents that never stop learning, bringing 10x to 100x productivity and speed gains to functions that used to depend entirely on tenured human expertise.
Each of these archetypes improves the energy and material efficiency of its customers while sharpening their competitive edge.
### Demand is pulling these companies forward faster than any prior cycle
Customer demand for applied AI products is intense enough to compress the traditional path to scale. Systemiq Capital routinely sees companies contract several million dollars in revenue within their first 12 months, letting them skip entire fundraising stages on the way to billion-dollar outcomes. These are the blitz-scaling companies the firm wants to back.
Picture a world where every employee on a team operates with the knowledge and adaptability of the most tenured expert in the company. That world is emerging now, and it raises questions Systemiq Capital doesn't yet have answers to.
How much faster does hardware innovation move when physics-aware generative AI closes the loop between design and manufacturing, letting engineers get same-day delivery of custom spare parts? How many more homes get built each year when plans and permitting happen instantly instead of taking months? How competitive can SME manufacturers become when a team of AI agents optimizes the supply chain in real time, instead of a human procurement team manually tracking hundreds of suppliers?
We don't have the answers yet. We're backing founders with the ambition to ask these questions and the execution excellence to find out.
Ivan Poupyrev, CEO and co-founder of Archetype AI, picks up that thread next. Archetype AI builds physical intelligence and deploys physical AI agents in high-stakes environments, from the shop floor to the construction site to city traffic control.