US Power Markets

 

US Power Markets

PJM Service Area (inc. DC)

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The Future of AI: Deployment, Disruption, and Dystopia?

The world of Artificial Intelligence (AI) is evolving at an unprecedented pace, even surprising industry insiders. This rapid acceleration is particularly evident in breakthroughs in next-generation AI and embodied AI, such as humanoid robotics, as well as the advanced development of AI agents.

The Future of AI: Deployment, Disruption, and Dystopia?

We can anticipate a broad deployment of AI applications in 2026 and 2027. We're already witnessing the initial phases of this integration, with some companies experiencing layoffs and the incorporation of AI into their business models. A recent example is Business Insider, which reportedly laid off 20% of its workforce. Interestingly, some early adopters are experiencing a sense of "regret," realizing that human involvement is still crucial for AI's success.

The 2020s are poised to be a period of massive innovation in AI. However, projections suggest the 2030s could lean towards a more dystopian future, potentially necessitating a fundamental shift in the social contract. Looking further ahead, the 2040s might bring about a period of abundance, positively impacting both work and leisure.

The Lag in AI Regulation

The current pace of AI regulation is significantly lagging behind technological advancements, and it appears this trend will continue. A sovereign arms race in AI is likely to take precedence over ethical considerations. Many major risks are currently being "auto-regulated" by individual companies. It's crucial to acknowledge that AI can exhibit behaviors as detrimental as humans, highlighting the urgent need to instill morality within AI systems.

Current Leaders in the AI Landscape

Google is viewed very positively, having successfully delivered usable AI to consumers. Companies like Apple and Intel are perceived as being behind in the AI race. Nvidia is considered to be performing well. The rumored merger between OpenAI and io is believed to be focused on acquiring an ambient AI-related device.

DeepSeek's Impact and the Future of AI Scaling

DeepSeek did not prove to be the disruption many anticipated. Its emergence demonstrated that simply scaling AI through more training and data is not the future of the technology. There are natural limits to the availability of information for AI training, and in the future, the marginal value of information and knowledge is expected to fall to zero. The availability of lower-cost AI will inevitably lead to increased demand and more diverse use cases. Nvidia, for instance, believes the market will absorb all the chips it can produce. The question of whether we have enough data centers remains an open one, reflecting the speculative nature of AI's future importance.

The Sovereign AI Race

The sovereign AI race is a tangible reality and will likely overshadow ethical concerns. China possesses approximately half of the world's AI researchers and can leverage its state control to its advantage. China is also reportedly leading in terms of power and compute capabilities. However, the United States still holds an edge with superior AI models and excellent access to capital for funding AI initiatives.

How AI Will Reshape Our World

AI will fundamentally change the world by driving the cost of knowledge to zero. AI businesses will likely adopt a tiered model to maintain profitability. Applications as we know them may fade into the background, with users relying on an AI assistant to "search" for them. Business costs, including labor and capital, are expected to decrease significantly.

Ultimately, AI is an amplifier of human talent. The key differentiator between businesses will remain human talent, as technology becomes increasingly accessible to everyone. While AI promises immense benefits, it will also introduce dystopian and ethical risks.

Bottom Line

• AI chip and energy demand are projected to remain high, as lower-cost solutions will only fuel increased demand.
• Labor disruption is likely to be very significant and occur in the near future.
• Productivity and profitability are expected to increase very soon.

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OPINION: Supply Chain Woes

Supply chain risk was a child of COVID-19. With mass lockdown, industrial closures, limitations on transportation and shipping, supply chains were stretched. This was then exacerbated post COVID-19 as the world ramped back up to normal and supply chains unable to cope. This manifested in delivering delays and higher transportation costs along the entire value chain.

Now geopolitics are entering the mix of reasons for supply chains disruption. Protectionism and tariffs are increasing costs - project developers have been bulk ordering to try and get ahead of the curve - and reshoring are pushing up costs further. Restrictions on exports are adding to the woes. Think US CHIPS Act and Chinese restrictions on rare earths exports.

All this at a time when the world needs an affordable and functioning supply chain to deliver the energy transition, growth in power demand and AI compute.

This is what happens when supply chains were built for just-in-time to slim down costs and working capital. What the world needs now to rebuild is a just-in-case supply chain.

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Real-time power consumption from two US hyperscale data centres

 

Real-time power consumption from two US hyperscale data centres (MW)

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US Large Load Tariffs

 Large load datacenters are driving a large proportion of investment by utilities required for load augmentation. This introduces asymmetry with significant long-term investment to serve one specific group of customer over the current broad retail/commercial customer base.

This one customer group - hyperscalers - are also prone to rapid technological change. This poses the dilemma of how to ensure this group pays for the cost of serving them and shielding existing customers from the risk of future datacenter power demand being lower than expected.

Utilities are adopting several mechanisms to balance attracting new large customers while protecting existing customers (ratepayers) with "large load tariffs":

• Rates based on the marginal cost of serving the new customer
• Long-term contracts obliging payment of service regardless of whether power is required, to provide revenue certainty; an option to exit the contract for a fee could be a feature
• Minimum monthly demand and energy charges - i.e. take-or-pay, so that large customers contribute to grid costs even during low usage periods. Foe example, AEP Indiana uses a charge based on 80% of contracted or historical peak demand
• Collateral requirements

Examples of large load tariff protectionist features include:

State	Utility	Tariff Features	Purpose / Notes
New Mexico	Multiple Utilities	Special rates allowed by law if they recover incremental service costs	Supports economic development while protecting existing ratepayers
Indiana	AEP Indiana	Minimum demand charge (80% of contracted/historical peak)	Ensures cost recovery even during low usage periods
Kansas	Evergy	15-year contracts, collateral requirements, early termination fees	Provides revenue certainty and risk mitigation
Georgia	Georgia Power	Load forecast shows high growth; cautious tariff commitments due to project risk	Focus on data centers; many projects not yet committed
Texas	Oncor, CenterPoint	Tariffs include peak demand incentives and grid contribution requirements	Designed to manage grid stress and incentivize off-peak usage
Virginia	Dominion Energy	Tariffs include clean energy integration options and flexible load management	Aligns with state clean energy goals and large customer flexibility
Arizona	APS, SRP	Tariffs include time-of-use rates and infrastructure cost-sharing	Encourages load shifting and shared investment in grid upgrades
North Carolina	Duke Energy	Tariffs include customer commitment thresholds and performance guarantees	Ensures reliability and investment justification

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Waldorf Production North Sea Operator Enters into Administration

 

Sources:

• https://www.insider.co.uk/news/waldorf-production-intends-appoint-administrators-32962536
• Waldorf Production files notice of intention to appoint administrators
• Finance Top executives depart distressed UK oil and gas producer
  

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Renewables vs. Datacenter Financings

With power hungry AI hyperscaler datacenters now being large buyers of power, the debt financing markets for the two sectors are beginning to converge.

Both sectors are characterised by upfront capex, followed by cashflows generated under long-term contracts - PPAs in the case of power, and datacenter leases in the case of hyperscaler datacenters. The counterparties in each case are typically large, creditworthy institutions.

Given the above, hyperscaler datacenters provide similar risk-return charateristics to some renewables projects.

However, hyperscaler datacenter debt financings can be superior in a number of ways:

1. Datacenter lease revenue is highly stable and not reliant on variable weather conditions that drive revenue in renewables
2. Broader financing sources with datacenter debt sitting in real estate, infrastructure or project finance lending books
3. Datacenters as a sector have a much broader range of refinancing options including the ABS and CMBS securitisation markets, private placement and institutional debt investor markets, reducing refinancing risk. This is not the case for even utility scale renewable portfolios
4. Datacenter supply is in a short market with no alternative sources of supply - apart from building more datacenters. This increases the value of the asset from a lien or residual value perspective. This contrasts with renewable assets - where more can be built quickly, and power supply can be backstopped by conventional generation sources
5. Tax equity structures do not exist for datacenter financings, meaning debt is senior as opposed to second lien behind such structures which feature heavily in the renewables world

Lenders to both renewables and datacenter projects will have seen the two sectors converge and credit exposures doubling up, when the underlying credit of a renewable project is a hyperscaler datacenter. When comparing the relative value of renewable vs. datacenter financings, the latter is certainly attractive with a current pricing premium of 100-150bps for essentially taking on the same counterparty risk and superior credit quality characteristics noted above.

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