**Will Electricity Digital Twins Mature Before the AI Investment Runway Cuts Short Their Takeoff?**
That’s a question I found myself pondering amidst a growing sentiment that the AI bubble might soon burst — and perhaps it doesn’t have enough power or connections for all the new data centers in the works.
Last week, Gita Gopinath, the IMF’s former Chief Economist, cautioned that a burst AI bubble might torch $35 trillion in wealth. Meanwhile, Andrej Karpathy, who coined the term *vibe coding*, resorted to good old-fashioned programming skills for a new chat app. He subsequently extended his estimates for artificial general intelligence (AGI) to a decade.
### Capacity Constraints: The Fundamental Problem
A fundamental problem underlying all of these concerns is a lack of capacity on two fronts:
1. There isn’t enough **electricity** to go around for all the shiny new data centers, let alone all the electric cars and heat pumps everyone wants to build.
2. The notion of simply scaling Large Language Models (LLMs) to solve every problem is starting to run out of steam.
A moonshot collaboration on electrical and energy **digital twin infrastructure** might help widen both of these bottlenecks.
### The Role of Comprehensive Digital Infrastructure
An essential aspect lies in building more comprehensive knowledge graphs, provenance chains, and interconnected physical models. Connecting the dots between disparate electrical models and control systems would lower the cost of wiring up the grid infrastructure. It would also accelerate the development of millions of interconnected AI, physics, and financial models better suited for the task than LLMs.
This could lend more credibility to initiatives like the UK’s goal of creating 400,000 new jobs in the energy sector, not to mention other ambitious projects globally.
### Current State: Numerous Silos, Limited Integration
Today, there are dozens of electrical modeling, simulation, and management tools, as well as specifications and open source software — but they mostly operate in silos at the level of individual domains and vendors.
NVIDIA has made some progress by plugging a few electrical system vendors together to improve efficiency and capacity within data center walls. However, addressing the broader electrical infrastructure gap powering those data centers will require bridging many more silos.
NVIDIA CEO Jensen Huang suggested that the UK might need more gas plants to power all the new data centers. But these will require wires, gas pipes, and neighbors willing to accept new power plants or wires.
Interestingly, this is in a country that threw away over £1.3 billion in 2023 shutting down wind power and firing up gas plants because it didn’t have enough wires or batteries to absorb all the energy.
Currently, there’s a **10-year queue** for connecting new power plants, representing 400 gigawatts of power. The cost of upgrading all this infrastructure is estimated at **£104 per household per year**. Ratepayers may be willing to cover it — but this isn’t exactly a stellar marketing message for all those promised AI benefits.
### How Electrical Digital Twins Could Help Bridge the Infrastructure Gap
Here are some ways that electrical digital twins might help:
– Demonstrate investor opportunities for innovations in powerlines, control systems, and battery storage.
– Simplify technical and financial modeling for new infrastructure projects.
– Support new market mechanics for decentralized generation and storage.
– Enhance community outreach, buy-in, and incentivization for energy projects.
– Streamline automated code compliance and regulatory approval processes.
– Optimize the development of more efficient infrastructure.
– Improve learning and development of energy-related skills.
– Prototype more resilient and secure control and management systems.
### The Challenge of Digital Silos
There are numerous efforts to build digital twins of various energy infrastructure aspects. The International Electrotechnical Commission (IEC) wrote last year about how digital twins will revolutionize the power sector.
The IEC emphasized that government agencies, standards bodies, utilities, consumers, and researchers must work collaboratively to establish consensus on standards and open data practices, addressing privacy and security constraints.
Currently, utilities operate with multiple models — planning, communications, operations, grid protection, billing — each with its own data formats, detail levels, and domain experts. Crossing these silos requires manual data maintenance, model reconciliation, and creation of new analytics models for new studies.
Streamlining these processes could help utilities and grid operators optimize renewable integration, plan for new data center loads, and devise more efficient market mechanisms and algorithms.
At the same time, communities frequently push back against new solar arrays, battery storage projects, or transmission pylons. Digital twins could make it easier to show how various design approaches and compensation schemes help utilities and data center operators articulate their vision, allowing earlier adjustments to mitigate objections instead of gambling on last-minute approvals.
### Building a Structural Blueprint for Electrical Digital Twins
Creating an electrical digital twin that meaningfully connects existing silos requires unprecedented collaboration across vendors and industries.
This includes:
– A **blueprint for data exchange and simulation** across many models.
– A **semantic layer** for translating context and enabling reasoning.
– A **trust layer** ensuring data and model integrity, privacy, and security.
The IEC has made progress on the **Common Information Model (CIM)**, defining a common vocabulary for components like generators, transformers, wires, and meters. This facilitated the creation of the **Common Grid Model Exchange Standard**, already mandated in Europe.
The next step is improving co-simulation approaches across domains — for example, predicting voltage transients in power networks or analyzing energy market dynamics. Frameworks like the **Hierarchical Engine for Large-scale Infrastructure Co-Simulation** developed in the US show promise.
A more intelligent energy grid supporting automated reasoning also needs a semantic layer to provide context, define relationships, and contextualize data. Microsoft’s work on its **Digital Twins Definition Language (DTDL)** ontology for energy grids is a notable example, creating graphs of interconnected assets.
More work is required to generate knowledge graphs that connect different types of sensors to models.
### Potential Impact on AI Infrastructure
Building large-scale co-simulation capabilities could accelerate embodied AI infrastructure with different scaling properties than LLMs.
For example, the **active inference paradigm** suggests better AI emerges from training many decentralized domain experts that exchange information about their uncertainty. One agent might optimize substation performance, another manage a battery array, and others handle market decisions.
### My Take
Progress in electrical digital twins is likely to slog along for the foreseeable future. Vendors will focus on improving processes within their own tools over the next few years.
That said, some factors could accelerate progress:
– Innovative startups might emerge to connect silos across valuable use cases, pushing legacy vendors to collaborate faster. Just today, the Financial Times reported Tesla is making more money from battery storage than legacy energy vendors, thanks to superior energy trading algorithms — a sign of disruption ahead.
– NVIDIA might strategically expand its **Omniverse** platform to support electrical digital twins across domains and vendors — an extension of its current successes with spatial and mechanical models inside data centers.
Only time will tell if they have the vision to extend this approach beyond data center walls before the AI investment frenzy runs out of oxygen.
I certainly hope so. Because if the AI bubble bursts before then, a lot more than slightly higher power bills will be at stake.
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*Author’s note: This discussion highlights the intersection of energy infrastructure and AI development. Achieving synergy here is crucial for a sustainable and intelligent future.*
https://diginomica.com/could-electrical-digital-twins-solve-ais-capacity-absorption-problems