AI agents make it dramatically easier to extract and migrate data from platforms, reducing vendor lock-in. In response, platforms like Snowflake are embracing open file formats (e.g., Iceberg), shifting the competitive basis from data gravity to superior performance, cost, and features.

While AI inference can be decentralized, training the most powerful models demands extreme centralization of compute. The necessity for high-bandwidth, low-latency communication between GPUs means the best models are trained by concentrating hardware in the smallest possible physical space, a direct contradiction to decentralized ideals.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

The current focus on building massive, centralized AI training clusters represents the 'mainframe' era of AI. The next three years will see a shift toward a distributed model, similar to computing's move from mainframes to PCs. This involves pushing smaller, efficient inference models out to a wide array of devices.

Simply adding an AI layer on top of a traditional SaaS stack will fail. A true AI-native architecture requires an "AI data layer" sitting next to the "AI application layer," both controlled by ML engineers who need to constantly tune data ingestion and processing without dependencies on the core tech team.

Dell's CTO identifies a new architectural component: the "knowledge layer" (vector DBs, knowledge graphs). Unlike traditional data architectures, this layer should be placed near the dynamic AI compute (e.g., on an edge device) rather than the static primary data, as it's perpetually hot and used in real-time.

While AI training requires massive, centralized data centers, the growth of inference workloads is creating a need for a new architecture. This involves smaller (e.g., 5 megawatt), decentralized clusters located closer to users to reduce latency. This shift impacts everything from data center design to the software required to manage these distributed fleets.

Legacy companies are siloed, creating IT "spaghetti" that blocks AI progress. In contrast, AI-native organizations structure themselves around a central "AI factory" or unified data platform. Business units function like apps on an iPhone, accessing shared, controlled data to rapidly innovate and deploy new services.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.