The article argues that AI buyers give away proprietary knowledge when they feed data into models, creating an asymmetry where providers learn from user inputs while customers gain little insight in return. It calls for enterprises to build private learning environments, control their evaluation metrics and data traces, and decouple orchestration from specific models to protect and compound their unique intelligence.

• Feeding proprietary data into AI models lets vendors learn from your patterns while you get no equivalent insight back into theirs
• Vendors lock down rights to ingest usage logs, corrections, and evaluations, compounding their advantage over time at the enterprise's expense
• The fix is a private AI environment inside company infrastructure that owns evals, memory traces, feedback loops, and adapted weights
• Four pillars—control, capability, choice, and cost efficiency—let firms improve their own systems instead of fueling the vendor's global model

Databricks is launching a Software-Defined Storage ecosystem that uses the open-source OpenSharing protocol to link on-premises, edge, and private-cloud systems directly into its Data Intelligence Platform. This zero-copy approach lets teams run serverless compute and train models on local datasets under Unity Catalog governance without migrating any data.

• Databricks now lets you query on-prem/edge/private-cloud data (via MinIO, Everpure, Qumulo, VAST Data) directly through Unity Catalog with zero-copy access and no egress fees, using the open-source OpenSharing protocol
• MinIO's AIStor integration is already GA, letting users run live queries on on-prem Iceberg and Delta tables; Everpure and Qumulo are in private preview, VAST Data joins in August
• Targeted at regulated/data-sovereignty-heavy industries (banks, healthcare, semiconductors, trading firms) facing GDPR/HIPAA/NIS2 rules and untenable cloud egress costs at exabyte scale
• Connections must pass Databricks's Partner Well-Architected Framework for security/certification before going live, preserving full lineage, access controls, and audit logs across hybrid environments

This article discusses the rising demand for private AI solutions in Europe, particularly among small to medium enterprises. It highlights LokalGrid, an open-source platform that enables organizations to easily deploy and manage AI models on their own infrastructure without needing specialized teams.

• Mistral's €775M funding round underscores how urgent European data sovereignty has become, but small/medium orgs can't afford similar MLOps resources
• LokalGrid targets the gap between "too complex enterprise tools" and "no private AI at all" by letting general IT staff self-host models like Llama and Mistral
• Revenue model is freemium (free core, paid Team/Enterprise tiers), with growth driven by GitHub developer adoption and SEO tools like a "Sovereignty Cost Calculator"
• Competitive moat comes from API lock-in—once integrated, switching away from LokalGrid becomes costly, not from technical superiority alone