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Jürgen Mayerhofer, co-founder and CEO of enspired and a 20-year veteran of physical energy markets, sits down with Rohit Yadav in this "AI Meets the Physical World" series episode to unpack where AI-driven energy trading and flexibility optimization actually stand today — across battery storage, the flexibility stack, data scarcity, market culture, hyperscaler and data-center demand, EVs, thermal energy storage, and the broader build-out of a more volatile, decentralized grid.
enspired is a fully automated, AI-driven power trading-as-a-service platform that monetizes flexible assets — batteries, pumped storage, CHP, demand-side flex — on short-term power markets. Founded in 2020 in Vienna, the company has grown to around 110 people and extended its Series B to over €40 million to fund global expansion, with backers including Banpu NEXT, Future Energy Ventures, and EnBW New Ventures. It optimizes some of Europe's largest battery projects — including a 103.5 MW / 238.5 MWh asset owned by ECO STOR in Germany — and is expanding beyond Europe into Japan through its partnership with Banpu NEXT. It's a useful backdrop for this conversation: enspired has been running flexibility assets fully automated for nearly seven years, well before AI-induced demand made grid volatility a mainstream concern — and it became the first and only player in its space to publish KPMG-audited monthly portfolio performance.
Chapters:
00:00 — Introduction: the "AI Meets the Physical World" series
00:31 — Jürgen's background: 20+ years in physical energy
01:10 — What enspired is, and the algo-trading DNA from day one
04:05 — Flexibility-as-a-service: how a battery actually gets monetized
05:52 — Fully automated, zero human intervention: 6–7 GW and 100,000+ trades a day
06:53 — Who enspired serves: infrastructure funds, IPPs, and asset owners
07:23 — Scaling with one platform: from Germany to 10 countries 09:38 — The AI stack: from machine learning to reinforcement learning
10:03 — The data problem: why energy is harder than it looks
11:47 — Cybersecurity as the Achilles heel of full automation
12:14 — Why a battery is a "beast" for reinforcement learning
13:17 — The biggest pain point: culture, not technology
15:52 — Why "we'll just build it in-house" almost never works
18:18 — When does trading become fully automated? It depends on the market
21:28 — Where enspired fits in the flexibility equation
22:48 — The founding thesis: more renewables, more need for flexibility
26:45 — Data centers meet the physical grid
27:19 — Real-time pricing of compute as grid optionality
30:24 — Why recurring market disruptions are a feature, not a bug
31:07 — US vs Japan: a volume game versus first-mover markets
35:08 — Flexibility tools: batteries, EVs, and thermal storage
36:17 — The EV smart-charging case: a great idea that hit reality
40:01 — Why thermal energy storage bridges power and heat
43:23 — What happens when more batteries compress the spreads?
47:22 — Walking the talk: KPMG-audited monthly performance
51:47 — Closing thoughts
Links:
Jürgen Mayerhofer: https://www.linkedin.com/in/juergenmayerhofer/
enspired: https://www.enspired-trading.com/
Rohit Yadav: https://www.linkedin.com/in/rohityadav23/
Newsletter: https://yadavrohit.substack.com/
