# Nick Woolley

Nick Woolley founded ev.energy and works on software for utilities to coordinate flexible energy resources such as electric vehicles and batteries. Woolley also introduces Eve, ev.energy’s platform for managing flexibility programs, and writes about applying AI to energy systems and company operations.

- Profile: https://b2binfluencer.marketing/creators/nick-woolley/
- LinkedIn: https://www.linkedin.com/in/nickwoolley
- Topics: artificial-intelligence
- Based on 11 dated posts, latest 2026-09-17

## Summary

Useful for utility AI and flexibility program discussions; not a consumer creator sponsorship offer.

- Best for: Utility or energy company campaign needing expert thinking on AI and grid flexibility.
- Not for: Consumer-facing EV or home energy brands seeking sponsored social posts for everyday drivers.
- Check first: Confirm whether Nick Woolley offers commercial creator arrangements, terms, format, and audience reach.

### Takeaways

- Eve does not make autonomous grid-control decisions; utility teams review outputs.
- Every action is logged and every output cites its sources.
- AI can model program performance before utilities commit to lengthy pilots.

## Main ideas

- **AI should assist grid operators, not run the grid**: Nick Woolley's central argument is that AI-native software in a regulated industry must be capable without being autonomous. They describe intelligence built into forecasting, dispatch optimization, and program insight rather than bolted on top, while control decisions stay with utilities and their grid management systems. The reasoning is about what survives scrutiny: regulated buyers need tools that move faster than a consulting engagement and still hold up in a rate case, so auditability and compliance are part of the architecture, not add-ons.
- **The bottleneck is deployment speed, not the technology**: Woolley argues that the technology and assets for grid flexibility already exist, and that the slow part is the process used to prove and deploy them. They contrast a conventional utility path of scoping, procurement, an 18-month pilot, a report, and maybe a program with using AI to model how a program would perform before a single driver is enrolled and simulate grid impact before a contract is written. In their framing, the pilot existed to reduce uncertainty; AI reduces it faster. They ask utilities and regulators whether institutions will use it that way.
- **The grid already owns flexible capacity; it needs coordinating**: This is Woolley's founding belief and the reason they give for starting ev.energy eight years before the posts: the answer to the grid's biggest challenges was already sitting in driveways, on rooftops, and in garages, and only needed coordinating. Their argument is that supply-side answers take roughly half a decade, while flexible resources such as EVs, batteries, and smart charging can shift load and absorb pressure much sooner. Coordinating them, they say, produces lower-carbon and cheaper energy for households and businesses, not just for industry.
- **Build the AI operating model once, then put the same engine in customers' hands**: Woolley describes becoming an AI-first company as a progression: principles for working with AI, a playbook that turns them into operations, a company-wide hackathon building one agent each, and then an agentic operating model where multiple agents run a whole function end to end while people steer. They say the move from one clever agent to something systemised is what almost nobody talks about. Their distinctive claim is reuse: the same engine built for internal functions sits inside customer tooling, so the company's operational gains and its product are the same design.

## Work to read

- [Introducing Eve: the multi-DER platform](https://www.linkedin.com/posts/nickwoolley_eight-years-ago-i-started-evenergy-https-activity-7468323563209674752-Xsz9)
- [What 'AI-native' means for Eve, and what it does not](https://www.linkedin.com/posts/nickwoolley_we-describe-eve-as-ai-native-heres-exactly-activity-7472602458456924160-kXdL)
- [AI is hungry and the grid is struggling to keep up](https://www.linkedin.com/posts/nickwoolley_ai-is-hungry-and-the-grid-is-struggling-activity-7447277171431211008-VnV7)
- [Four phases of becoming an AI-first company](https://www.linkedin.com/posts/nickwoolley_at-the-start-of-this-year-we-set-out-to-activity-7478131201354956800-2AIW)

## Brand relationships

- **ev.energy** (owned_business)

## Partnerships

Woolley's four posts do not describe a creator sponsorship offer: there is no media kit, rate card, package list, audience-deliverable specification or sponsorship contact. They focus on company and product communication for ev.energy, the business they say they started eight years ago, and Eve, its platform for running utility flexibility programs. Brands looking for a sponsored post, paid appearance or co-marketing package will not find terms, deliverables or a negotiation route. The visible commercial relationship is software: ev.energy sells a platform that utilities and energy companies use to plan, enrol and operate flexible-energy programs. One post says the same engine sits inside customers' own tooling. That is a vendor relationship, not a creator partnership. Ask for pricing, contracting terms, deliverables and a partnership contact; the posts provide none.

## Alternatives

- Marcelo Campos: Choose Campos when the utility's AI question is really about buildout and supply, not software governance. Campos writes about the physical infrastructure that AI-driven demand depends on. One post reads a utility capital plan as a supply-chain signal, laying out $65.7 billion over five years, with $11.5 billion in 2026 rising to $15.6 billion by 2029; data centres at 28 percent of Virginia Power's electricity sales; PJM's projection of 5.4 percent average peak load growth in Dominion's territory over the next decade; and spending across offshore wind, dispatchable gas, transmission, solar and battery storage. The conclusion is a supplier's argument: transformers, insulators, switchgear and cable are the bottleneck, US manufacturing capacity is a competitive moat, and policy signals from the Defense Production Act, the IRA and state approval processes all point the same way. A second post is an internal quarterly review from Insulation Technology Group, describing record sales, an expanding portfolio of suspension bells, station posts, hollows and long rods, and North American team investment. The writer describes their company as the largest ceramic insulator manufacturer in the world. For a utility deciding whether it can procure and deliver against a rising load forecast, that mix of demand numbers and manufacturing detail is more relevant than a platform pitch. The question shifts from how to embed AI in grid management to what AI-driven load forecasts mean for equipment lead times, supplier qualification and domestic content.
- Aleksandar Rodic: Choose Rodic when the utility's question is where AI compute should sit and whether power and grid connections can support it, framed for a board rather than an engineering team. One post covers Anthropic's first Australian data-centre lease, a planned 2.16-gigawatt campus about 250 kilometres from Brisbane, expected to come online in 2027 and used for inference rather than training. It also covers Nvidia's separate plans with Australian partners for up to 2 gigawatts of AI capacity by 2027, against Australia's roughly 1.6 gigawatts of computing capacity today. Rodic turns those plans into board-level questions: where data sits, where inference happens, who controls the infrastructure, which laws apply and whether electricity supply can support it. A second post applies the same lens to Europe, noting gas storage around 67 percent full, Gulf disruption removing Qatari and UAE volumes, industry estimates near $40 per mmBtu for Asian spot LNG in a cold winter, and the argument that energy security now constrains industrial strategy, defence production and AI deployment together. A utility weighing a large new load, a data-centre interconnection request or a jurisdiction decision gets strategic framing and an exposure list for a board paper, including which markets gain and lose power and what to watch next. Rodic addresses the shared buyer task through siting, power availability and regulatory certainty, rather than operating models.
- Claudia Asare: Choose Asare when the buyer's problem is comprehension rather than strategy: explaining how the grid behaves to people outside the engineering team, or building energy literacy inside an organisation before it commits to an AI program. The Energy Desk posts are worked teaching pieces. One uses a street-level scenario to explain selectivity and coordination, then covers ANSI device numbers under ANSI/IEEE C37.2; why 51 denotes a protection function rather than a physical relay; how a single digital relay or IED can run several functions; how time overcurrent settings are graded so the device closest to a fault trips first and an upstream device acts as backup; why 50N and 51N ground overcurrent functions are tuned separately because ground fault currents can be much smaller; and how 67 directional overcurrent adds direction on ring-fed networks or near embedded generation. The second explains why a fan feels cooling without lowering room temperature: moving air removes the warm boundary layer and speeds sweat evaporation, helping the body shed heat. Running one in an empty room adds motor heat with nobody to benefit. A utility can share this material with a council, a regulator's non-technical staff, a customer panel or new hires who need the concepts behind grid reliability and efficiency. Here, the buyer is choosing someone who can make the technical stakes of an AI-in-grid decision legible to the people who must approve or live with it, rather than an adviser.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/