Introduction
When discussing the potential industries of the future, few attract as much attention as Artificial Intelligence (AI) and Green Technology. The success of green technologies is imperative in combatting climate change, while AI is expected to catalyse efficiency and automation on an unprecedented scale. Integrating AI systems into renewable technologies could accelerate environmental progress, offering large financial rewards for whoever realises this potential. This has numerous geopolitical implications, which this article seeks to explore by assessing the current progress.
Exciting Prospects
AI-Green Tech synergies centre around boosting efficiency. AI’s predictive capabilities could help optimise energy demand and supply. This addresses a critical issue in solar and wind sectors: variable weather conditions. AI can match variable supply with fluctuating demand, maximising the financial value of renewable energy. This could quicken the adoption of renewables. For example, after DeepMind (Google’s AI subsidiary) integrated AI into their wind power fleets, the financial value increased by 20%. This was due to AI predicting future output up to 36 hours before, allowing for optimal hourly delivery commitments to the power grid a full day in advance. AI can also bring lesser-known renewable technologies into the spotlight. Researchers at Heriot-Watt University have managed to ‘slash the time required for modelling carbon capture storage methods from 100 days down to just 24 hours using advanced AI simulators.’ This will help high-pollution industries meet their sustainability targets.
The energy industry is primed for this transition. With the recent rollout of ‘smart meters,’ which produce and send thousands of times more data points to utilities than their analogue predecessors, AI’s ability to process massive quantities of data will be invaluable. Estimates suggest that AI already serves more than 50 different uses in the energy system, with a market potential of up to $13 billion. This implies that Green AI could become a critical area for international competition. The United States is currently the world leader in AI-related developments, with China in second. However, the Chinese are the industry leaders in renewable energy. This sets the stage for an innovation standoff between the two superpowers. However, what obstacles must be overcome to dominate this industry?
Potential Challenges
Energy Usage
AI uses a tremendous amount of energy on a consistent basis. AI data centres must run continuously year-round, rather than alter energy usage over time. According to Barclays Research, the AI revolution is expected to more than double data centre electricity needs in the US by 2030. Nuclear and LNG energy sources may be necessary to sustain AI’s consistent need for energy flows, as renewable generation still fluctuates significantly with weather conditions. Despite current concerns, this is unlikely to be an issue in the long run, with improved green energy abundance and more reliable renewable energy flows. According to the Tony Blair Institute for Global Change, this could unlock a positive feedback loop, where energy abundance drives AI innovation, causing further green AI breakthroughs. Which nation will unlock this potential first remains uncertain.
Availability of Workers
AI is poised to drive productivity and efficiency in all markets, not just renewables. As a result, demand for skilled professionals is enormous, with LinkedIn data showing a 74 percent rise in demand for AI specialist roles over the past four years. However, business leaders are increasingly worried they will not be able to fulfil their demand. According to a survey conducted by Microsoft and LinkedIn, 55 percent of business leaders expressed worry about their ability to find talent to fill positions in 2025. Renewable companies will have to compete with other industries for their AI expertise, which seems increasingly challenging in a market with supply-side bottlenecks.
Ethical Concerns
One major ethical concern with AI integration is the protection of private data. AI develops through the process of ‘machine learning’ which enables it to learn and adapt without specific coding. This involves tracking relevant data and observing patterns. However, AI could inadvertently track private individuals, which raises major privacy concerns. This is particularly significant in the energy sector, which deals with substantial amounts of sensitive data, such as grid information, customer data, and operational details. AI integrated systems can also be exposed to cyber threats. With Russia’s war in Ukraine demonstrating how destabilising energy insecurity can be, it is essential that AI systems are integrated with caution.
Conclusion
The integration of AI into renewable energy systems is clearly in its early stages, and much of the research remains nascent. Progress may remain slow due to the global shortage of skilled AI professionals. However, advancements made by the likes of Google demonstrate the promise of this sector. Unlocking the positive feedback loop between green technology and AI will incentivise nations to invest heavily in these technologies. Western democracies may pay closer attention to ethical and privacy concerns, imposing stringent regulations. This could pave the way for more authoritarian regimes, such as China, to break through as the global leader. This uncertainty, combined with the ethical and geopolitical challenges, makes this a fascinating area to watch in 2025.