Firebird AI factory in Armenia comes amidst a new AI funding market
YEREVAN, August 11. /ARKA/. The global artificial intelligence boom is beginning to transform not only the technology but also the financial markets. Against this backdrop, the Firebird AI factory project in Armenia can be viewed not only as a major technology investment but also as an example of a new class of infrastructure assets, the attractiveness of which is increasingly dependent on long-term demand for computing power and the ability to generate predictable cash flow.
On August 10, 2026, NVIDIA announced a partnership with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to create financial platforms that are expected to attract over $500 billion in third-party capital for AI infrastructure. As NVIDIA itself emphasizes, the agreements at this stage are formalized as memoranda of understanding (MOUs)—that is, they are partnerships subject to further development and the conclusion of final agreements, rather than signed, binding deals.
The specific investment commitments of the participants and the timeframe for raising the full amount have not yet been disclosed. NVIDIA itself, according to company CEO Jensen Huang, can provide backstop support for potential deals worth up to $125 billion, or up to 25% of their volume.
Thus, AI data center financing is gradually expanding beyond the capital budgets of major tech companies. Institutional investors are beginning to view computing infrastructure as a standalone asset capable of generating long-term income.
What makes an AI factory attractive to banks
For a bank or investor, it's not just the cost of servers and the number of GPUs that matter. The key question is whether the facility can consistently generate enough income to service the capital raised. Globally, data center financing can be secured by real estate, equipment, customer contractual rights, associated invoices, and project cash flows.
Long-term capacity lease or computing resource purchase agreements are particularly important. The longer the contract and the more reliable the customer, the more predictable the infrastructure facility's future revenue.
One variation of this model is compute offtake—an upfront, long-term commitment by the customer to purchase a certain amount of computing capacity.
A telling example, although not entirely comparable in scale and jurisdiction, is Hut 8, a public company listed on the Nasdaq and TSX. In July 2026, Hut 8 signed a second 15-year contract for its Beacon Point campus in Texas worth $9.8 billion with an existing investment-grade customer, after which the entire 1 GW site was commercially contracted (the total contract value for the campus increased to $19.6 billion). Unlike Firebird—a private project in an emerging market without a public listing and without disclosed counterparty investment grades—Hut 8 operates in the mature and much more liquid US data center financing market, so a direct comparison of risk levels between projects would be inappropriate. Nevertheless, the mechanics themselves—risk compensation through long-term contracted demand—illustrates the logic banks use when evaluating such assets.
For a lender, such a contract is much clearer than the assumption that demand for computing power will "definitely appear" after the facility's construction.
How Firebird is being financed in Armenia
The Firebird project already has a significant amount of attracted funding (data provided as of March–August 2026). In March 2026, six Armenian financial institutions—Ardshinbank, Acba Bank, Evocabank, Fast Bank, C-Quadrat Ampega Asset Management Armenia, and Amundi-Acba Asset Management—joined forces to provide $300 million in syndicated financing to Firebird AI. Ameriabank also announced an additional $60 million in financing for the project that same month.
In addition, the Armenian government signed a five-year agreement with Firebird to purchase high-performance computing resources worth a total of $25 million for startups, research organizations, universities, and other users.
This agreement creates a pre-contracted demand for computing resources for Firebird. However, published data does not allow for an assessment of the payment structure, capacity utilization schedule, or other financial terms of the agreement.
Some parameters of Firebird's bank financing have not yet been publicly disclosed: the collateral structure, loan terms, interest rates, covenants, and possible minimum commercial occupancy requirements.
The full scope of long-term commercial contracts with private clients, which could be used to estimate the project's future cash flows, is also unknown.
When an AI factory becomes bankable
Global experience shows that a large number of GPUs alone does not make an AI project attractive to banks.
A fully-fledged investment model requires a combination of several factors: access to electricity + established infrastructure + necessary permits + reliable clients + long-term contracts + projected cash flow.
Amid the AI boom, banks are simultaneously becoming more interested in such projects and more cautious in their evaluation. Large international lenders are increasingly carefully analyzing the availability of electricity and water, permitting documentation, and the risks associated with local community attitudes toward data center construction. Therefore, for Firebird, the next stage of project evaluation will be not so much the number of installed NVIDIA GPUs, but the economics of their operation.
Key metrics will include the actual utilization of computing power, the number and quality of clients, contract duration, the cost of provided computing power, electricity costs, and the ability of the resulting revenue to service the raised financing.
The Firebird project in Armenia already has significant bank financing and a government contract for computing resources. However, based on publicly available data, it is not yet possible to fully assess the commercial utilization of the Armenian AI factory, the structure of the collateral for the raised financing, its value, and the ability of the project's future cash flows to service the debt.
It is the emergence of a predictable long-term cash flow that transforms the AI factory from a large technology project into a full-fledged infrastructure financial asset.-0-
