AI-Native
TLDR
Products designed from the ground up with AI as a core component, not bolted on.
Definition
AI-native refers to software where machine learning or large language models are part of the core product architecture, not a feature added to an existing product. The data model, user interface, pricing, and workflow assume the model is doing meaningful work, not just surfacing suggestions.
Why it matters
AI-native products have different economics than AI-augmented products. Their marginal cost per user depends on inference cost, not licensing. Their defensibility depends on proprietary data or domain-specific fine-tuning. Their user expectations are set higher: if the model fails, the product fails.
Examples in the Belgian ecosystem include TechWolf, which built its skills platform around a proprietary Skills Ontology model, and Aikido Security, which uses ML-driven rules for vulnerability detection. Silverfin and Collibra have been retrofitting AI features onto mature products, which is a different pattern than AI-native.
Mechanism
AI-native products usually collect usage data that improves the model over time, creating a flywheel. The key architectural choice is where the model sits: inference at request time, offline batch scoring, or a hybrid. Inference-at-request products carry higher per-user cost but deliver more responsive UX.
Related
- Parent: software architecture
- Sibling: B2B SaaS - business model often used with AI-native products
- Sibling: Skills Ontology - structured data pattern common in AI-native HR products