Insights on K-12 communications, AI in education, and district operations.
AI tools often fail not because models are weak but because they lack business context. Package your company's reality so AI can act like a dependable teammate.
AI is forcing a licensing reset: seats are mismatched to agentic usage, tokens are a transitional proxy, and outcomes are the endgame. Here’s what it changes for procurement, budgeting, and the rise of fractional AI-native work.
Full-time, single-employer knowledge work is shifting toward a fractional model. This piece explains why that change is accelerating and what leaders and workers can do to adapt.
Enterprise-grade AI is often slower and more constrained by design; frontier AI explores faster but carries higher risk. Solo bridges the gap with orchestrated agents under a meta-agent—trading raw speed for reliable action.
A practical, no-nonsense comparison of Solo and other approaches—from DIY email stacks to OpenClaw—so you can choose the operating model that actually runs your business.
Solo is an agentic operating system: a coordinated team of specialists with orchestration, redundant queues, audit logging, and a shared context contract — designed to reliably converge to outcomes.
In the AI era, quality becomes table stakes. The real moat is switching costs: workflow, data gravity, and habit.
Efficiency can lower costs and expand use. When that additional demand outweighs per-unit savings, total consumption can rise—this is the Jevons Paradox.