Global energy demand is consistently rising, yet collective efforts to supply reliable, sustainable, and low-cost energy have reached a period of systemic stagnation.
A breakthrough innovation is needed. However, history suggests breakthroughs of this magnitude are seldom a product of a single sector working in isolation. They emerge from the convergence of multiple domains working in synergy, each solving a critical piece of the puzzle.
The recent disruption in artificial intelligence (AI), stemming from decades of oscillations between stagnation and accelerated developments, offers the energy industry an intriguing parallel.
Understanding what made the AI breakthrough possible may be the catalyst for a transformative energy system that is equitable, secure, and sustainable.
The First Wave of Intelligence
Modern AI can be traced back to Warren Weaver’s 1949 memorandum, Translation, which proposed tackling machine translation using computational and statistical methods.
The following year, Alan Turing introduced the Turing Test, establishing early benchmarks for AI testing and experimentation, while also igniting early ambitions of AI research.
Despite these early efforts, the field quickly faced two primary obstacles that would shape the first period of stagnation.
The first was structural, as statistical methods required data and computational resources that simply did not exist. The second was conceptual, which included the rise of syntactic linguistics, which proposed that language is governed by hierarchical and structural rules rather than statistical patterns.
The field naturally moved toward rule-based approaches, pulling effort away from the methods that would eventually prove most effective.
Two notable examples of rule-based methods illustrated both the potential and the limitation of this approach. The first is the 1954 Georgetown-IBM Experiment, which translated Russian to English using a small data set of 250 words and six grammar rules.
The second is ELIZA, developed in 1966 and widely recognized as the world’s first chatbot, also utilized rule-based approaches such as pattern recognition to mimic human conversation.
Both demonstrated potential, yet scalability was limited due to reliance on manually specified rules.
The publication of the 1966 ALPAC report criticized the lack of progress in machine translation, ultimately leading to a significant withdrawal of research funding.
A similar pattern emerged across the broader AI field, where expectations far exceeded progress. Research across the domain slowed to a near standstill, ushering in the first AI dormancy.
Early Frameworks Formulating
Despite the setbacks, the mathematical building blocks of modern large language models began to take shape in the following decades. This included Back Propagation in 1986, Recurrent Neural Networks in 1988, and Long Short-Term Memory in 1997. Each provided essential frameworks for learning sequential patterns in data.
These frameworks went undeployed, not because they were ineffective, but because the infrastructure needed to realize their potential did not exist. The two structural bottlenecks of scarce data and limited compute remained unresolved.
Post-Winter Thaw: Ideal Conditions Taking Shape
Soon after, two sectors emerged to set up the ideal conditions for an AI breakthrough, resolving the longstanding bottlenecks.
The first was the internet boom in the mid 1990s, which generated data at an unprecedented scale, resolving the longstanding bottleneck of data scarcity. The second was the advances in graphics processing units, originally developed for the gaming industry, which unlocked the parallel computing needed to train the early mathematical frameworks.
Two adjacent industries, with no intentions of advancing AI, provided the two missing ingredients.
While data and compute resolved the essential limitations, the open-source movement, which dismantled boundaries between research institutions and promoted transparency and collaboration, catalyzed the field to unimaginable heights.
The impact was measurable, as can be seen in the rising number of publications in the AI sector, which have more than tripled between 2013–2023, as shown in a report by Stanford’s Institute for Human-Centered Artificial Intelligence. The democratization of knowledge allowed researchers to build upon existing developments, cultivating an ecosystem primed for disruption.
The culmination of these factors was the transformer model, which enabled training on massive data sets and the scale-up to trillions of parameters, making them capable of adapting to a wide range of tasks across various domains and industries.
While disruptive technologies are difficult to predict, they are enabled by the synergy of mature ecosystems shaped by decades of cross-domain research and development.
The First Wave of Climate Efforts
The energy transition is strikingly resembling the cyclical oscillations of the AI evolution.
Early scientific exploration in climate change stretches back to the 19th and 20th centuries. These efforts culminated into global frameworks such as the Kyoto Protocol in 1997, which was the first international treaty to attempt to set global emission-reduction targets.
Structure, however, it established legally binding targets only on developed countries, limiting broader participation and growth in subsequent years.
The Paris Climate Agreement in 2015 represented significant improvement by enabling self-determined emission-reduction targets for all countries. Together, these frameworks fueled climate ambitions. Yet they faced barriers that echoed the early obstacles faced by AI.
Structurally, energy systems were limited by traditional infrastructure including outdated grids and transmission lines and limited storage capacities, incapable of supporting the intermittent output of renewables at scale. Conceptually, competing visions pulled policy and investment in different directions, much like the influence of syntactic linguistics on pivoting the AI field toward rule-based methods.
Climate efforts were concentrated in one direction, leaving critical gaps unaddressed. The result of this was a wave of ambitious, but narrow, frameworks that limited successful deployment.
In 2010, Germany approved Energiewende, a national initiative aimed at expanding renewable energy and reducing dependence on fossil fuels. Following the 2011 Fukushima incident in Japan, global nuclear sentiment declined, accelerating Germany’s decision to phase out nuclear plants.
With baseload power removed, coal and natural gas filled the gap, ultimately raising emissions and dependence on imported gas—an outcome contrary to the original intent.
China pursued significant renewable installations all over the country. As capacity expanded, curtailment rates rose sharply due to grid and transmission limitations.
As a result, fossil fuels are used to balance the grid, increasing emissions and undermining reliability and security goals at scale. Similar trends emerged across the globe, including in the US, Spain, and Vietnam.
As energy systems scale, balancing security, sustainability, and costs become increasingly difficult.
Ongoing Efforts: Policy Diversification and Infrastructural Enhancement
The limitations of these policies shifted the energy and climate domain to focus on infrastructure including grid and transmission modernization, large-scale storage, and diversification of energy solutions.
Germany is readdressing its emission concerns by expanding its grid and increasing storage solutions while slowing down renewable rollout to align with expansion rates. It is also investing in liquefied natural gas terminals to reduce single-source natural gas dependency and to enhance energy security. China invested more than $70 billion in 2024 alone in network construction to ensure renewable electricity is transported throughout the country to reduce high curtailment rates.
Global trends of energy and climate policy are slowly shifting from one-dimensional policies toward coupled frameworks that address the fundamental structural constraints.
Breaking the Silos
The AI revolution was not a product of a single algorithm or research lab working silos; it was a result of decades of cross-domain progress across hardware, mathematics, linguistics, and computer science, each solving an essential piece of the puzzle.
The energy transition is currently at a comparable point. Addressing its structural bottlenecks will require the same cross-disciplinary efforts that preceded the AI breakthrough.
Capital must also be allocated in the right direction. While current investment in emerging energy technologies is double that of fossil fuels, the majority flows toward commercially proven technologies including solar, wind, and electric vehicles, leaving foundational infrastructure like grid modernization and energy storage significantly underfunded.
This imbalance also extends to research, where funding is heavily concentrated on energy efficiency, renewables, and nuclear power with only a small share directed at the critical bottlenecks that have undermined the scaling of existing technologies.
A successful energy transition will depend on systemic alignment of technology deployment, research and development, energy policy, and financial innovation—all working as interdependent pillars, with a focus on solving critical bottlenecks to provide a future energy system that is equitable, secure, and sustainable.
For Further Reading
Translation by W. Weaver. Rockefeller Foundation.
Computing Machinery and Intelligence by A. M. Turing. Mind
Syntactic Structures by N. Chomsky. Mouton
The Georgetown-IBM Experiment Demonstrated in January 1954 by W. J. Hutchins. AMTA
Eliza—A Computer Program for the Study of Natural Language Communication Between Man and Machine by J. Weizenbaum. Computational Linguistics.
ALPAC Report—Language and Machines: Computers in Translation and Linguistics by J. R. Pierce et al. National Academy of Science.
Learning Representations by Back-Propagating Errors by D. E. Rumelhart et al. Nature
Serial Order: A Parallel Distributed Processing Approach by M. I. Jordan. Institute for Cognitive Science
Long Short-Term Memory by S. Hochreiter and J. Schmidhuber. Neural Computation
2025 AI Index Report, Stanford University Institute for Human-Centered Artificial Intelligence.
The Kyoto Protocol, UNFCCC.
The Paris Climate Agreement, UNFCCC.
The German Energiewende
Germany’s Nuclear Shutdown Mistake, ForoNuclear.
China Faces Rising Renewable Energy Curtailment, Power Technology.
Germany Aligns Renewable Rollout With Slower Grid Expansion to Cut Costs by S. Amelang and C. Kyllmann; Clean Energy Wire.
German LNG Terminal
China To Spend $70bn Revamping Grid To Take Renewables by David Rodgers, GCR.
World Energy Investment 2025
Energy Technology R&D Budgets Data Explorer
