Balancing AI’s Energy, Security and Scalability Needs

As artificial intelligence (AI) continues to evolve, it is easy for organizations to be drawn to the latest advancements without fully considering their effectiveness and efficiency. Instead, organizations must ensure that their AI goals meet their organization’s needs by developing a strategy that balances novelty with cost-effectiveness, ensuring that deployment aligns with requirements rather than hype.

AI can be broadly categorized into two main types: Predictive AI and generative AI (GenAI).

Predictive AI, also known as discriminative AI, builds algorithms and weighted models to classify data and recognize patterns within datasets. This type of AI has been widely used for decades under the umbrella of big data analytics. It is commonly applied in scenarios that require real-time decision making, such as cybersecurity, where it is used to distinguish malicious software from benign programs. It’s also often used for customer service chatbots, autonomous vehicles and smart-building management.

Generative AI, on the other hand, creates new data that aligns with specified categories within a dataset. Unlike predictive AI, which categorizes existing data, GenAI produces new output in forms, such as text, and increasingly in media, such as images, video and music. Within GenAI, multiple approaches exist, most notably:

• ChatGPT-style models, which gained prominence in 2022 for their conversational query interface and content-generation capabilities.
• Chain-of-thought (CoT) models, such as DeepSeek, which follow structured logic steps that are especially useful in solving complex problems in technical and scientific disciplines.

Each approach has its strengths. Chat-based GenAI tools excel at abstract reasoning, while chain-of-thought models are better for structured problem solving such as computer programming or designing new chemicals. These models differ not only in capability but also in query efficiency, energy consumption and infrastructure requirements.

The Need for Energy-Efficiency

Energy efficiency is critical for the long-term viability of GenAI. Initial versions of chain-of-thought models like DeepSeek appear to be more energy-efficient than chat-based models during large language model (LLM) training. However, some early testing suggests that CoT models may consume more power than the latter when answering queries because their structured approach to problem solving often requires additional processing times.

Although DeepSeek’s structured approach may yield more accurate and useful results for tasks such as pharmaceutical research, it may not result in better output on tasks such as music or video creation. The potential efficiency gains in chain-of logic model training also may be offset by increased usage. Since training is key to model accuracy, as training cost decreases, users may opt to do more training. This situation is an example of the Jevons paradox, where greater efficiency can lead to higher overall consumption rather than net savings.

The ongoing AI revolution underscores the importance of flexibility. Although DeepSeek and similar models hold promise, prematurely standardizing on a single approach could be detrimental given the rapid pace of technological advancements.

The Energy Demands of GenAI

Beyond individual model efficiency, the sheer scale of AI needs presents significant power infrastructure challenges.

Training LLMs consumes up to seven times the energy of conventional cloud computing tasks, and AI workloads often run continuously, placing immense pressure on the energy grids already strained by competing demands, such as electric vehicle (EV) charging networks, advanced manufacturing and climate-related demands for more air conditioning and water.

To address these needs, data center operators are rethinking facility design, moving from traditional city-block-sized centers to sprawling campuses with dedicated on-site power plants. Scaling this infrastructure will require:

• New energy generation and efficiency innovations to meet demand more sustainably
• Streamlined permitting and regulatory approvals to accelerate deployment
• More efficient water cooling solutions at data centers, which can give off as much as 98% of their energy as waste heat

AI as a Critical Infrastructure Target

As GenAI becomes foundational to economic and national security, its dependence on power infrastructure introduces new cybersecurity risks. Nation-state adversaries and cybercriminals already target the U.S. electrical grid, and as AI reliance grows, so does the attractiveness of disrupting it.

The U.S. electrical grid is a heterogeneous system, comprising both large utilities with dedicated cybersecurity teams and smaller rural providers with minimal IT resources. This disparity creates vulnerabilities, as utility providers are breached at twice the rate of other industries. Additionally, their slower response times and higher mitigation costs make them prime targets for cyberthreats.

Given the increasing role of AI in mission-critical applications, strengthening grid security is essential. 

Achieving this will require:

• Increased cybersecurity investment to support both large and small energy providers
• Public-private collaboration to proactively address vulnerabilities
• AI-driven threat detection and response mechanisms to enhance resilience

Addressing the AI Security Gap

Security threats in the AI ecosystem extend beyond infrastructure, encompassing:

• Model integrity: Protecting foundational AI models and their weight structures from manipulation
• Training data security: Preventing adversarial tampering with datasets
• Query privacy: Safeguarding user data from unauthorized access and inference attacks

Unlike cloud computing, which has a well-defined shared security responsibility model, AI security remains fragmented. Effective controls exist, but their implementation is inconsistent, often due to unclear ownership of security responsibilities.

Governments can help drive security maturity by:

• Defining and enforcing explicit security expectations in AI-related procurement contracts
• Tying funding and grants to adherence to security best practices
• Considering regulatory frameworks to establish baseline security standards

Balancing Innovation with Efficiency

The convergence of AI expansion, escalating energy demands and cybersecurity risk presents a complex challenge that requires a multi-faceted response. Organizations must balance adoption with practical needs, while carefully evaluating choices, and policymakers and the industry must collaborate to ensure that the power grid can sustain and support this growing technological frontier.