21st Century Marketplace Service Providers - Part XXIX
Rotation
A Service Provider Organization is owned, operated and led by a single member of our user community. That member has an Intellectual Property license for the process they will architect, design, develop, and implement within Synallagi. They are licensed to work exclusively with People, Ideas & Objects’ software developers and serve as the industry’s first point of contact for issue resolution, enhancements, and continuing process improvement within that user community members licensed domain.
It is through those organizations that our user community members are responsible for the day-to-day operation, maintenance, support, and improvement of oil & gas accounting and administration processes assigned to their domain. This structure secures each member of our user community as the permanent authority for that process. However, our user community members remain independent business principles. They may assign, swap or trade their interest where doing so is permitted under their license and consistent with People, Ideas & Objects’ Intellectual Property requirements.
The permanence of process authority within each of our user community members does not imply that individual service providers must remain permanent, full-time participants of one of our user community members. The roles performed by service providers will be technically broad, commercially important, and yet applied to narrow fields of oil & gas accounting and administration processes. Over time, excessive permanence in one position will become counterproductive. Hyper-specialization may begin to degrade the broader skills, judgment, and motivation of the individuals employed as service providers if they were dedicated permanently to one user community member. More practically, it may produce boredom, stagnation, and declining contribution. Continuing to administer the same process beyond a reasonable period would eventually limit the value of the specialized skills they bring to our user community.
This creates two issues that must be resolved. First, the industry benefits when service providers gain a broader understanding of Synallagi, oil & gas administration, accounting, operations, producers, Joint Operating Committees, and marketplaces. Second, stagnant resources can create openings for unethical conduct, informal accommodations, or procedural complacency. Synallagi should not create the conditions for those risks. After a service provider has worked in one process for a reasonable period, the innovation, learning, and new thinking they contribute will begin to taper off. Once their skills have been translated into process improvements, and once the producer and Service Provider have captured the available yield from that contribution, movement to another process becomes beneficial for all parties.
Rotation addresses these issues. It reduces stagnation. It broadens industry knowledge. It supports professional development. Expands service providers revenue opportunities. Exposing greater industry value. It limits the risk of entrenched conduct. It also renews the opportunity for innovation across multiple process domains.
Compensation earned by service providers from developments that generate greater profitability and value for the industry would travel with them across the process areas in which they’ve participated. (Through our Targeting Framework, first discussed in 21st Century Marketplace Vision - Our User Community paper and a forthcoming paper dedicated exclusively to the topic of Targeting.) Their innovative thinking, and the value it generated for industry and themselves, will be visible to the service providers who follow them. Successive service providers will then understand where value was created, how it was created, and where comparable opportunities may exist. This creates a competitive and cumulative process of improvement. The more prior innovations are circulated, understood, and extended, the greater the profitability and value generated for producers. That outcome also benefits service providers and members of our user community financially. Standing on the shoulders of giants will benefit the participant service providers, their user community organizations and producer firms.
The timing of rotation should be structured but not rigid. One possible standard would require each service provider to complete a minimum of two rotations during each fiscal year, with timing determined by the service provider’s individual schedule, the needs of the Service Provider Organization, and the requirement to maintain continuity (ie. minimum 3 month tour.) This assumes that each of our user community members will employ a meaningful number of service providers, potentially more than ten, within a given process domain. Such a structure would preserve continuity and capability while also creating a broader pool of knowledge. It would allow some service providers to identify areas of rich compensation opportunity and producer need, while allowing others to gravitate toward more stable process environments where their skills and preferences are better aligned.
There is also an inherent cost discipline built into the structure of both our user community and their Service Provider Organizations. Neither group is naturally positioned to build bureaucratic empires. Their economic interest is to maximize revenue, minimize cost, and increase measurable value for producers. This is materially different from producer-owned overhead structures, where fixed administrative costs can become permanent, opaque, and self-protecting.
The Intellectual Property dimension reinforces this discipline. One of the most important sources of Intellectual Property is tacit knowledge: the knowledge that cannot be fully captured in documentation, software, procedures, or any other medium. This is what defines the value of a service provider and our user community members. Their trade secrets, judgment, methods, pattern recognition, and applied experience are proprietary economic assets. They will want to protect those assets carefully, deploy them selectively, and monetize them over time. Rotation therefore must balance knowledge transfer with the preservation of individual proprietary capability.
In our next paper, we describe how members of earlier generations often worked approximately 3,000 hours per year, compared with the 1,700 to 2,000 hours typically worked today. People, Ideas & Objects believes Artificial Intelligence will reduce this commitment further, to approximately 900 to 1,000 hours annually.
This reduction will not necessarily diminish compensation. On the contrary, we expect the total compensation earned by highly specialized individuals to substantially exceed current levels because their work will generate considerably greater value. This value-based compensation will be earned in addition to their regular hourly wages or salaries and will reward innovation, profitability, performance, accountability, and other measurable contributions.
Concerns regarding job losses are legitimate. Positions that no longer generate sufficient value, or that become redundant through automation and Artificial Intelligence, will be eliminated. However, this transition will also create an abundance of new occupations, businesses, and specialized roles. The service provider organizations being developed through Synallagi are one example of the new employment structures that will emerge.
The future of work will therefore not be defined simply by fewer jobs or fewer hours. It will be defined by less low-value work, greater specialization, substantially higher productivity, and compensation increasingly aligned with the value an individual creates.
The objective is not to make every service provider interchangeable. The objective is to create a disciplined market for specialized capability within Synallagi, where knowledge moves, innovation compounds, stagnation is reduced, costs are minimized and producer value is increased. Rotation is one mechanism by which Service Provider Organizations can maintain high performance, protect ethical standards, and continually renew the productive capacity of our user community.
Data
There is a clear consensus that oil & gas financial data desperately needs a disruptive overhaul. The substandard quality of data, aggregated, multiple copies, not normalized, unstructured, unsecure, unusable, inaccessible and a myriad of other difficulties captured by producers stems from the extensive use of spreadsheets, the loss of necessary detail due to data volumes being too large to manage, and a predominant focus on corporate needs rather than a dual focus on both corporate (financial accounting) and Joint Operating Committee (management accounting) requirements. These are rooted in legacy issues common to all organizations such as technological developments and maturation over decades leading to technological disparities across the organization, or growth achieved through internal or external means, differing needs of the same data managed in other departments in their own systems.
People, Ideas & Objects assert, with the objective evidence of investors abandoning the industry a decade ago, that accountability was deliberately sidestepped to perpetuate substandard accounting and systems capacity and capability, through what we term "second-hand shoestring budget allocations." The questions we must ask ourselves at the end of this paper: can we continue operating this way within the environment forming in today's corporate world? What data demands would this new environment place on oil & gas?
Those in the know understand the necessity for much larger datasets to effectively manage the granularity of currently available, usable and expanding data. Accommodations must be made for data set growth, and the data model used to manage this data must be held to the highest standard of quality. People, Ideas & Objects Synallagi' data model will meet this quality standard and will be maintained by our user community, their service providers, and our developers within a dedicated software development capacity and capability. This capability will be change-enabled and change-oriented, addressing the North American producers' needs for at least the next 25 years.
This data will be standardized across the industry, objective, actual and factual in nature and of a high level of granularity. Captured by Internet of Things (IoT) when appropriate and managed through Oracle Autonomous Database. Tools such as DataBricks and Palantir address this issue directly. Businesses know their data is scattered and disparate. With some being structured and unstructured. Having AI have access to all of the firm's records, these applications are able to compile a model of the firm's data and information, both structured and unstructured, in a reasonably accurate manner for analysis. This is not data that can be used for any form of regulatory reporting that I am aware of. It is not a system that’s capable of processing transactions and the types of global strategy implementations such as our Synallagi price maker strategy. A strategy dependent upon reorganization not technology.
An Alternative Data Vision
A core concept of this paper, building on our September 18, 2025 paper "President Donald Trump’s Vision & Economic Developments in Oil & Gas," is the capacity and commitment to tokenize oil & gas producer firms, their assets, and reserves. For producers to enter the crypto market, they must satisfy specific compliance requirements: the financial operations for each tokenized asset must be SEC compliant, GAAP, industry standardized, objective, accountable, and profitable, potentially also meeting Commodities Futures Trading Commission (CFTC) standards. Therefore, People, Ideas & Objects establishes the Joint Operating Committee financial statements as the foundational reporting requirement for a tokenized crypto asset. Investors today are aware of the accountability issues within the current oil & gas sector. To gain investor confidence, producers must address this lack of accountability by ensuring their crypto assets adhere to new, standardized, objective and stringent requirements, free from today's existing financial cultural influences. People, Ideas & Objects believes this can be achieved through our proprietary “rip and replace” rebuilding and implementation process of Synallagi.
Our “rip and replace” rebuilding process places additional requirements on North American oil & gas producers. We have established, through our user community and service providers (who represent a reallocation of the accounting and administrative resources of the producer firms), that their work processes and output must be sourced from independently evaluated, standardized, objective, actual, and factual data and information. Our goal is twofold: to realize the cost-sharing benefits of building and maintaining an industry-wide ERP system for oil & gas—which producers can access for a simple fee, similar to a Cloud Computing model—and to achieve the benefits of the shared infrastructure and resources expanded use of hyper-specialization and division of labor that are otherwise unattainable, even for the largest of producers. Furthermore, People, Ideas & Objects convert all of a producer's costs, including overhead, to variable costs, variable based on profitable production. This ensures that all production is genuinely profitable, or alternatively, not incurred if the property is shut-in. Incurring a null operation, or no profit or loss.
Synallagi will provide producers with the necessary data foundation to rely on unimpeachable facts and information. This integrity is critical. As we increasingly rely on Artificial Intelligence, the reliability and integrity of the data and information it generates diminishes rapidly if the source data is flawed. Manually checking and repairing data integrity will be orders of magnitude more costly, time-consuming, and damaging to a producer's reputation than getting it right from the outset. Implementing the proper procedures and allowing for necessary changes to accommodate industry growth and development avoids the risk of AI irrelevance. Losing control of data may render producers and the industry, much like today, reputationally uninvestable. People, Ideas & Objects offers the long-term solution to resolve this critical industry challenge.
Oracle Autonomous AI Lakehouse
The Oracle Autonomous AI Lakehouse resolves a long-standing accounting trade-off between timeliness and accuracy. With contemporary information technology and artificial intelligence, timeliness is now measured in milliseconds rather than reporting cycles. This allows accuracy—rather than speed—to become the dominant objective. In practical terms, periodic reporting timelines can be materially compressed, for example reducing a six-day year end close to three days, while simultaneously improving data completeness, consistency, and reliability.
When enterprise resource planning data is processed by artificial intelligence, the resulting outputs must remain fully auditable to the originating source transactions. This is a non-negotiable requirement for regulatory compliance, fiduciary accountability, and executive confidence. The issue is amplified as fiduciary responsibility increasingly extends beyond traditional equity holders to investors holding tokenized interests in a producer firm or a Joint Operating Committee. Artificial intelligence outputs that cannot be traced, reconciled, and defended undermine trust rather than enhance it. Public auditability therefore becomes foundational, not optional.
A central governance and security challenge arises from the access privileges granted to artificial intelligence. Individual users operate within tightly defined read and write permissions under Synallagi. Artificial intelligence, however, may derive insights across a broader data domain than any single human is authorized to view. This creates a structural tension: while the output may be permissible, the lineage of that output must be provably linked to authorized data sources. Without explicit lineage and access controls, Artificial Intelligence introduces unacceptable governance and security risk.
This leads directly to the economic question of artificial intelligence deployment in enterprise resource planning systems. Should artificial intelligence be constrained to the same access boundaries as individual users, potentially limiting its analytical value? Or should broader access be permitted, and if so, under what governance, oversight, and accountability framework? These questions expose the limitations of general-purpose data platforms. Tools that aggregate dispersed enterprise data—often commingling structured records with unstructured sources such as spreadsheets, email, and documents—tend to produce outputs that are statistically defensible but operationally approximate. Over time, approximation erodes confidence. For producers making capital-intensive, long-lived decisions, this approach is unlikely to be sufficient.
The Oracle Autonomous AI Lakehouse, built on Apache Iceberg, represents a partial solution by extending data warehousing into an artificial intelligence-native domain. Within Synallagi, outputs are transformed into a distinct database representation that consolidates producer interests at the corporate level rather than the Joint Operating Committee level. The result is a single, aggregated repository optimized for analysis. Access privileges are deliberately decoupled from transactional enterprise resource planning systems, and interaction increasingly occurs through natural-language queries that return governed, aggregated results rather than raw transactional detail.
In the intelligent corporation, Oracle functions as the control spine rather than merely a transaction processor. A rigorous governance framework is therefore essential to ensure artificial intelligence strengthens—rather than degrades—financial discipline, operational clarity, and executive control. Without this discipline, artificial intelligence simply accelerates ambiguity at scale.
The starting point is data purity. By managing data defined in Synallagi through an industry-specific, artificial intelligence-ready data model, the structural causes of “garbage in, garbage out” are eliminated. Early investment in data quality, governance, and disciplined system design materially reduces long-term costs while delivering higher-quality information, greater confidence in results, and a deeper, more reliable understanding of the business.
NVIDIA Steps In and Defines a New Storage Paradigm
January 2026 marked a pivotal moment for enterprise computing when Jensen Huang, CEO of NVIDIA, unveiled a radical shift in his keynote address at the Consumer Electronic Show. He asserted that just as Artificial Intelligence has fundamentally "re-invented the whole computing stack," its deployment within enterprises is destined to "re-invent the way that storage is done."
Huang articulated that traditional SQL-based data management is insufficient for the demands of modern AI workloads, which instead rely on semantic information. He introduced the concept of KV Cache (Key-Value Cache), which he described as the AI's "temporary knowledge, temporary memory," or "working memory." Crucially, this working memory is stored in the GPU's high-bandwidth memory (HBM).
He detailed the intensive process during model inference: "Every single token the GPU reads in the model, it reads in the entire working memory and stores it in one token and it stores that one token back into the KV Cache. And then the next time it does that, it reads in the entire memory, reads it and streams it through our GPU and then generates another token." This constant, high-speed read/write requirement demands a new architecture.
To address this performance bottleneck, NVIDIA created Bluefield 4. This next-generation Data Processing Unit (DPU) is specifically designed to function as a "very fast KV cache context memory store right in the rack." By offloading and managing this critical working memory on a dedicated, high-speed DPU within the data center rack, NVIDIA is effectively carving out an entirely new tier of enterprise storage.
The implications for data producers are transformative. They now have an additional, dedicated level of storage that captures the real-time, high-context results and queries generated by their Artificial Intelligence models. This is not merely storing raw input or final output; it is preserving the reasoning and contextual state of the AI. Over time, this cumulative AI-generated context creates a novel and deep layer of data, providing the firm with a dynamic new perspective on its operations, customers, and market—a crucial asset in the 21st-century knowledge economy. (Actual data, unlike DataBricks or Palantir.)

