March 13, 2026
Right now, there are three foundational requirements for artificial intelligence to work at scale: 1) Computational infrastructure: chips, GPUs, accelerators, and the massive compute clusters required to run them, 2) Physical infrastructure: electricity, water, cooling systems, and the broader consumables required to power these systems. Trillions of dollars are now being invested globally across these two layers. Governments are reorganizing semiconductor supply chains, hyperscalers are building enormous compute campuses, and energy markets are beginning to reprice themselves around the demands of AI, and 3) Data infrastructure. This layer receives far less attention, yet it will ultimately determine whether the other two actually produce meaningful results.
AI does not simply run on compute. AI runs on context. Without the correct data architecture feeding it, more compute and more power simply allow the system to generate incorrect answers faster.
A great deal of confusion in the industry comes from a misunderstanding between model training and model inference. During training, more high-quality data improves the model's capability. But at runtime, when the model is operating inside its context window, the dynamics are completely different. The context window functions as the working memory of the system. When too much data is poured into that window—especially duplicated, poorly structured, or conflicting data—the model’s ability to reason degrades. This phenomenon is increasingly referred to as context rot.
Most enterprises have unintentionally engineered this problem into their own environments. Over the past decade, organizations centralized everything into massive data lakes under the assumption that consolidation equals efficiency. Every document, spreadsheet, report, and record was ingested into a single repository. During that process, the contextual signals that differentiate information were frequently stripped away. Source attribution, version lineage, authorship, state changes, and process metadata were flattened in order to force everything into standardized schemas.
The result is that most enterprise environments now contain numerous copies of the same documents, multiple versions of those documents, and layers of homogenized metadata that no longer reflect the true origin of the data. When an AI system queries this environment, it retrieves fragments that appear similar but are not equivalent. The model receives overlapping signals without clear provenance and must attempt to determine which information is authoritative. Accuracy drops, responses become generic, and hallucinations increase. The enterprise concludes the models are not yet intelligent enough when the reality is that the architecture feeding them is fundamentally flawed.
At the same time, the market celebrates ever larger context windows as if they represent progress. One million tokens. Two million tokens. Entire document repositories dropped into a single prompt. The belief seems to be that if the model can see everything, it will understand everything. In practice, the opposite often occurs. As the context grows larger, the signal-to-noise ratio declines, and the model must distribute attention across an expanding universe of tokens. The system spends more effort sorting through irrelevant or duplicated information than reasoning about the problem itself. More context does not automatically produce better intelligence.
This is why the real constraint on enterprise AI is not compute. It is context.
Several years ago, we made a decision at Inveniam that was controversial at the time. We rejected the assumption that all data should be centralized. Our belief was that data ownership and provenance mattered. The entity that creates data should maintain control and/or ownership of it, and the contextual metadata that describes its origin, state, and process should never be sacrificed when shoehorning information into a common repository.
Instead, we built a decentralized data architecture designed to transform unstructured information into trusted, machine-readable intelligence while preserving provenance. Each participant maintains control and ownership of their own data, and the architecture connects those sources without flattening their contextual relationships. What emerges is not a data lake but an intelligence layer capable of reasoning across verified signals.
At the time, this decision was largely driven by our view of how capital markets would evolve. We believed that the systematic trading of private market assets would require a trusted data infrastructure capable of converting unstructured information into verifiable machine-readable inputs. AI is now accelerating that timeline dramatically.
Enterprises that have centralized their data environments are discovering that their AI systems struggle to reason effectively about their own information. Too many duplicates. Too many conflicting versions. Too little provenance. The architecture itself creates context rot before the model even begins reasoning.
Decentralized architectures that preserve provenance, on the other hand, dramatically improve retrieval quality because the contextual signals remain intact. AI systems do not need access to everything. They need access to the correct context with clear attribution.
This becomes even more important in collaborative data environments where multiple institutions must interact without surrendering control of their information. Private market transactions are a perfect example. General partners, limited partners, lenders, servicers, and regulators each hold pieces of the same dataset. Historically, the industry attempted to centralize this information, which created version conflicts, reconciliation costs, and operational friction. A decentralized architecture allows each participant to maintain ownership of their data while exposing verified signals into a shared intelligence layer that AI systems can reason across.
The AI stack that is emerging globally has three layers: compute, energy, and data. The market is pouring enormous capital into the first two while largely ignoring the third. Yet the third layer is the one that determines whether the system actually works.
At Inveniam, we have spent nearly a decade building the data infrastructure layer for the next generation of capital markets. Our core business is transforming unstructured data into trusted, machine-readable intelligence and creating the infrastructure necessary for the systematic trading of private market assets in an agentic world. What began as a thesis about the future of markets is now becoming essential to the future of AI itself.
The shortcuts of the past decade—centralized data lakes, duplicated documents, flattened metadata—are beginning to collide with the reality of how intelligent systems operate. Context matters. Provenance matters. Architecture matters.
The future is now.
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