Liquid AI's new device-based models create different licensing rules for startups versus enterprises, potentially changing how teams approach AI procurement and data residency decisions.
This approach turns AI from a code generator into a more reliable development partner—one that builds what you actually need.
Consumer industries lead 119% surge in AI agents as retail, travel see 128-133% monthly growth in automation—changing competitive landscape for product teams
Over 90% of travelers trust AI-generated travel information, but almost none want the AI to act on that information independently.
Non-human identities outnumber humans 80:1 in many orgs, but most teams lack visibility into AI agents' permissions, ownership, or lifecycle management—creating major governance gaps.
By demanding useful explanations, installing human failsafes, and requiring clear "nutrition labels" for our AI, we can begin to pry open the black box.
AI agents fail in production not because of bad architecture, but because we test them like traditional software. Complex 30-step workflows can't be tested—they must be reviewed like human work. This shift changes everything for legal and product teams.
The research shows we're moving from AI-as-tool to AI-as-colleague, which means rethinking how we structure accountability and human oversight.
