Ohio’s AI Boom: Risks & Rewards for Data Centers, Energy, and Economy

The $2.3 billion investment from Google is indicative of a broader — and accelerating — trend in Ohio: data center development increasingly driven by artificial intelligence workloads. Google’s funding spans its Columbus, Lancaster, and New Albany sites and aims to bolster its capacity to support both Google Cloud and AI functions. This builds upon an already substantial base of investment, suggesting high confidence in Ohio’s infrastructure, energy availability, and workforce.

Other major investments reinforce this pattern. AWS’s pledge of another $10 billion through 2030 will bring its total investment in Ohio to over $23 billion — projects contingent upon securing long-term energy contracts and managing site selection carefully for grid capacity. Meanwhile, smaller but strategically significant investments are being made by Vultr and SoftBank. Vultr’s $1 billion Springfield cluster (50 MW of AMD hardware, 24,000 accelerators) positions the company to compete as a lower-cost alternative to hyperscalers. SoftBank’s conversion of the Lordstown plant into a modular data center equipment manufacturing hub (purchased for $375 million) ties into the national Stargate initiative, helping anchor supply-chain activity domestically.

Despite momentum, serious headwinds exist. Microsoft’s decision to slow or pause a $1 billion data center project in Licking County points to recalibration in expected demand, while regulatory updates in Ohio address concerns around special energy rates and the burden placed on non-data center ratepayers. Also, projects like the 800 MW Cologix campus in Johnstown will test the limits of energy availability, permitting, workforce supply, and logistical capacity.

Strategically, Ohio is balancing opportunity and risk. For investors or developers, major opportunities lie in AI-oriented compute capacity, fabrication/manufacturing of AI infrastructure, and local service ecosystems (construction, power, grid, technical roles). Risks include regulatory pushback, energy cost volatility, grid capacity constraints, and possible oversupply or demand softening. Key open questions involve whether projected demand for AI/ML compute will sustain, how energy policy will evolve, whether utilities can keep pace, and how competing states may challenge Ohio’s advantages.