# Overview of Startups Developing Artificial Intelligence for the Energy Sector
## 6 Top Startups Developing Energy Sector with Artificial Intelligence
It provides a comparative view of financial backing, highlighting leaders in AI and QC applications within the energy industry. Identified companies in this figure are pivotal in driving innovation. By showcasing their ranking based on funding amounts, Figure 6 underscores the scale of investment attracted by these startups. The company names featured represent industry leaders recognized for their contributions to advancing energy technologies. Insights from this figure can reveal correlations between funding levels and factors such as startup origin, technology focus, and industry specialization. Figure 6: Top 25 most funded startups in the energy sector by using AI and QC (data taken from Table 1 and figure developed by the authors). Table 1 and Figure 6 offer a comprehensive view of the dynamic landscape where AI and QC intersect with energy innovation. They provide essential insights for investors, policymakers, and industry stakeholders to understand emerging trends, identify market leaders, and seize opportunities in the rapidly evolving energy technology sector. The bar chart in Figure 7 summarizes the adoption of AI and QC technologies across various energy sectors according to our dataset, which reflects real-world data. This shows that AI adoption is widespread across sectors such as gas, oil, renewable, and smart grids, with some nearing full adoption. This indicates that companies in these sectors leverage AI to enhance their efficiency, predictive maintenance, and data analytics. By contrast, QC adoption is minimal across all sectors. Several factors contribute to this limited usage: QC is an emerging technology with few practical applications in the energy sector; the high costs associated with building and maintaining quantum computers present significant barriers to entry; QC requires specialized skills that may not be readily available within many energy companies; a lack of proven use cases specific to the energy industry deters investment; and the widespread adoption of AI might currently fulfill the technological needs of these companies, reducing the immediate drive to explore QC. The chart also highlights interesting disparities between sectors. For example, the gas sector shows a moderate number of companies, but nearly complete AI adoption, indicating a strong focus on utilizing AI for operational improvements. Similarly, despite having fewer companies, the renewable and smart grid sectors also demonstrate very high AI adoption rates, reflecting a commitment to technological innovation in these forward-looking areas. Overall, this visualization depicts an energy industry that has widely embraced AI while remaining cautious about quantum computing. Low QC adoption across all sectors suggests significant potential for future growth as the technology matures and proves its value for addressing energy-related challenges. As the industry evolves, it will be  
Figure 7: Companies categorized by energy sector with AI and QC usage (data taken from Table 1 and figure developed by the authors). interesting to observe how these adoption patterns shift, particularly with the potential of QC to revolutionize areas such as the optimization of energy distribution, advanced simulations, and solving complex problems that are beyond the capabilities of classical computers.