The oil and gas industry is widely recognized for the poor quality of its accounting data and information. This deficiency is not attributable to the competence of accounting professionals or the limitations of ERP systems, both of which operate under challenging circumstances. Rather, it appears to stem from a desire among officers and directors to maintain opaque accountability, achieved by deliberately underfunding these areas. Consequently, accounting capacities are underdeveloped and existing capabilities degrade, leading operations departments to often disregard accounting information in favor of generic reserves estimates for decision-making.
A concerning trend, framed as addressing 'speed and complexity,' is emerging within oil and gas accounting and potentially other sectors. This could pose significant challenges for unprepared companies. In contrast, agile producers could potentially leverage innovative business models like our Preliminary Specification. And achieve superior results with fewer resources, faster timelines, and lower costs. Technologies such as the Internet of Things (IoT) enable field data capture, but their true value lies in data utilization. Our Material Balance Report, for instance, captures production-related data and seeks to automate derivative processes.
However, it is unlikely that officers and directors within producing firms will endorse the Preliminary Specification. Its adoption would implicitly acknowledge their responsibility for current industry inefficiencies. We estimate ongoing monthly natural gas revenue losses across North America due to overproduction at $16.8 billion per month, presenting both strong motivation for corrective action and significant reasons for their continued inaction.
If it’s the Fourth Industrial Revolution…
What “lessons learned” can we gain from a review of the prior three Industrial Revolutions? In terms of time and major economic findings, what, why and how can we project economic value will arise during Artificial Intelligences, Industrial Revolution? What are the impacts we can anticipate in the areas of productivity, markets and labor?
Time Dynamics of Past Industrial Revolutions
As we navigate the Fourth Industrial Revolution, spearheaded by Artificial Intelligence (AI), examining the preceding three revolutions offers valuable lessons. Key questions arise regarding the projected timeline for AI's economic impact and its anticipated effects on productivity, markets, and labor.
Historically, the First Industrial Revolution (1760-1840), driven by steam and mechanization, unfolded over approximately 50-80 years, its pace dictated by infrastructure development and capital investment. The Second (1870-1914), characterized by electricity and assembly lines, took roughly 40-50 years, benefiting from existing industrial foundations. The Third Industrial Revolution (1969-2000s), centered on computing and the internet, matured over 30-40 years, accelerated by hardware standardization and global communications.
This historical context prompts debate about AI's maturation timeline. One perspective suggests that Information Technology, a precursor, took approximately 65 years (from 1950 to 2015) to fully deliver its material gains. By this measure, AI, despite its current rapid advancements and unprecedented infrastructure investment, is only now beginning to generate widespread, tangible value.
However, venture capitalist David Sacks (President Trump’s AI and Crypto Czar) projects a dramatically faster trajectory: a potential million-fold increase in AI capability within just four years. His forecast rests on exponential advancements in three key domains:- Algorithms/Models: Sacks anticipates a 3-4x annual improvement, potentially leading to a 100x enhancement in four years.
- Chip Technology: New GPU generations are expected to offer 3-4x performance increases per cycle, also potentially reaching a 100x improvement in four years.
- Data Center Compute: The rapid scaling of GPU deployment (e.g., xAI's growth from 100,000 to 300,000 GPUs, with millions projected) could yield another 100x increase in computational capacity.
Sacks argues that the multiplicative effect of these concurrent exponential growths (100x × 100x × 100x) could result in a million-fold capability boost by 2029. He notes that exponential growth is frequently underestimated; for instance, a 10x improvement every two years compounds to 100x in four years, not a linear 20x.
While Sacks' optimistic claim is rooted in current technological trends, its realization hinges on sustained innovation and continued infrastructure expansion. Such advancement promises breakthroughs across numerous fields, including engineering and geoscience, but also brings significant concerns regarding safety, ethics, and energy consumption. Given the transformative potential, minimal costs and associated risks, underestimating this exponential trajectory could prove to be a significant miscalculation.
Major Economic Findings of Past Industrial Revolutions
Learning from past industrial revolutions, we understand that despite predictions of rapid AI-driven change, full societal and economic integration will likely span decades. This extended timeline involves not just technological breakthroughs but also crucial infrastructure development, widespread business model adaptation, new regulatory frameworks, and societal adjustments. Consequently, initiatives like our People, Ideas & Objects framework, designed for continuous, iterative development with our user community and developers, are essential for this transition. A key lesson from history is the rising demand for new skills, highlighting the critical need for massive reskilling and upskilling to meet these evolving workforce requirements.
This need for profound adaptation is especially urgent in the oil and gas industry. The sector faces a severe crisis of trust in its leadership, exacerbated by significant financial damage across its broader economy. Amidst the unfolding Fourth Industrial Revolution, oil and gas producers must urgently move beyond a 'muddle-through' culture to one centered on resource preservation, performance, and profitability.
The future will be defined by the ability to effectively harness data, often in ways currently unanticipated—just as no one in 1760 could have envisioned today's world. This raises a critical choice: continue with strategies that have demonstrably failed, or implement a solution like the Preliminary Specification? This approach aims to establish the vital data, information, and infrastructure needed for a dynamic future, securing long-term capacities and capabilities within our user community and ERP software developers. To merely 'muddle through' with current approaches, given the stakes, is familiar but a deeply concerning prospect.
