FingerMotion outlines plan to build modular AI compute infrastructure in North America

FingerMotion (Nasdaq: FNGR) plans to build modular, behind-the-meter AI data centers in Western Canada, committing capital in stages tied to secured customer demand. The company holds a 9.9% stake in Lyken AI Computing and will require signed contracts before major expenditures.

Published on: Aug 25, 2026
FingerMotion outlines plan to build modular AI compute infrastructure in North America

FingerMotion (Nasdaq: FNGR) said it will expand into modular AI compute infrastructure in North America, building smaller data centers at sites where power already exists rather than constructing hyperscale campuses. The company, which operates mobile payment and recharge services in China, said it will commit capital in stages and only after securing customers or commercially supportable demand.

The announcement came in a shareholder letter from Jolie Kahn, who became chief executive officer earlier this month. Kahn said the company will evaluate behind-the-meter facilities in Western Canada, where electricity can be generated and consumed on site near natural-gas resources, avoiding grid-interconnection delays.

Why modular infrastructure

"Artificial intelligence is driving demand for computing capacity faster than the power grid can deliver," Kahn wrote. "Conventional data centers take years to build and interconnect. FingerMotion intends to address this gap with modular, behind-the-meter compute infrastructure in North America-capacity built in stages, sited where power already exists, and matched to visible demand."

The company's first step was acquiring a 9.9% interest in Lyken AI Computing Inc., which provides outsourced cloud-compute capacity and is developing an integrated enterprise offering spanning compute infrastructure, secure storage, private low-latency networking, and deployment support. FingerMotion said it may increase that position over time under the terms of the transaction.

Kahn said the company targets enterprise customers who need more specialized infrastructure than retail colocation provides but lack the scale to contract directly with hyperscale operators. The company said it will not pursue growth merely to announce larger projects.

Milestones and capital discipline

FingerMotion outlined specific operating and commercial milestones, including completing technical, legal, and financial due diligence on the initial project; negotiating site, equipment, hosting, power, and construction agreements; and confirming network connectivity, permitting, and regulatory requirements before committing substantial capital.

The company recently completed financing transactions intended to provide additional working capital and said it will deploy that capital in stages. Chris Polimeni joined as chief financial officer, bringing more than 35 years of financial leadership across mergers and acquisitions, capital raising, SEC reporting, auditing, and financial planning.

Kahn's background includes serving as chief executive officer of a digital asset treasury company, general counsel to one of the largest publicly traded Bitcoin mining companies in North America, and interim chief financial officer to several Nasdaq-listed companies.

For executives weighing similar moves, the letter offers a template for entering capital-intensive AI infrastructure markets: tie spending to visible demand, secure power before promising capacity, and measure progress against defined milestones rather than press releases. The company's stated approach - smaller facilities, staged deployment, and contractual commitments before major expenditures - reflects the discipline that infrastructure investors increasingly expect. Executives evaluating AI compute strategies can apply the same framework to their own capital planning: AI Learning Path for CEOs covers how to assess these decisions. For a broader view of how AI strategy affects leadership roles, see AI for Executives & Strategy.

Why this matters for executives and strategy

FingerMotion's plan reflects a broader shift in AI infrastructure: companies are seeking alternatives to hyperscale data centers that face years-long interconnection queues and power constraints. The behind-the-meter model, if it works, could shorten deployment timelines and give operators more control over energy costs. Executives should watch whether the company converts its stated milestones into definitive agreements and customer contracts, and whether the staged capital approach holds as project costs become clearer.


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