Perplexity, the AI-powered answer engine competing with ChatGPT and Google, is working with Firebird to access computing infrastructure at a newly launched AI factory in Armenia, reflecting how answer engines are increasingly securing dedicated GPU capacity to handle growing demand. The facility will eventually house more than 70,000 NVIDIA Rubin and Blackwell GPUs and 300 megawatts of computing capacity by the end of 2027, providing the raw processing power needed to train, fine-tune, and deploy AI models at scale.

Why Are Answer Engines Seeking Dedicated Infrastructure?

Firebird, an emerging AI cloud company, launched the Commonwealth of Independent States (CIS) region's largest AI factory in Armenia, with Perplexity listed as an early partner working to access the facility's high-performance infrastructure. The timing reflects a critical shift in how AI companies think about computing resources. According to Gartner research, 25% of organic search traffic is predicted to shift from traditional search engine clicks to AI chatbots and virtual assistants by 2026, meaning answer engines like Perplexity face exponentially growing demand for processing power.

By partnering with Firebird, Perplexity gains access to cutting-edge infrastructure without building its own data center from scratch. The facility is designed with energy efficiency in mind, using NVIDIA's DSX platform to run up to 40% more GPUs on the same physical footprint, which directly translates to lower costs per response and better operational margins. This matters because answer engines operate on thin margins and need every efficiency advantage to compete.

How Does Infrastructure Quality Affect Answer Engine Performance?

The relationship between computing power and answer engine quality is direct. More GPUs mean faster response times, the ability to serve more concurrent users, and the capacity to run larger, more capable AI models. Access to NVIDIA's latest Blackwell and Rubin accelerators gives Perplexity a hardware advantage that can translate into faster, more accurate answers and the ability to handle more complex queries than competitors using older infrastructure.

Infrastructure also enables experimentation. Answer engines need the flexibility to test different model sizes, architectures, and optimization techniques. A dedicated facility with thousands of GPUs allows Perplexity to iterate quickly on its core product without competing for resources on shared cloud platforms. This speed of innovation can become a meaningful competitive advantage in a market where response quality and accuracy matter to users.

How to Optimize Content for Answer Engines Like Perplexity

• Structure Content Clearly: AI systems like Perplexity prioritize well-organized content with clear headings, bullet points, and concise answers. According to research from AirOps analyzing citation patterns across ChatGPT, Perplexity, Google AI Overview, and Gemini, structure is one of three variables most reliably predicting whether a page gets cited by AI systems.
• Prioritize Freshness and Accuracy: Answer engines weight recent, factually accurate content more heavily than outdated information, especially for commercial or time-sensitive topics where facts change frequently. This is a key distinction from traditional search engine optimization, where evergreen content can rank indefinitely.
• Build Third-Party Authority: Mentions of your brand on high-authority websites now carry as much weight for AI citation as on-site SEO signals. This reinforces entity status and trust signals that answer engines rely on when selecting sources to cite in responses.

The distinction between traditional search engine optimization (SEO) and answer engine optimization (AEO), also called generative engine optimization (GEO), is becoming critical for content creators and marketers. As industry research notes, SEO gets content indexed and discovered, while AEO determines whether that same content gets selected and cited when AI systems generate answers. A page can rank well in Google's traditional search results and still never appear in a Perplexity response, or vice versa. This means companies need to optimize for both channels simultaneously.

According to AirOps' 2026 State of AI Search Report, three variables most reliably predict whether a page gets cited or skipped by AI systems: structure, freshness, and credible sourcing. This research directly informs how content creators should approach the answer engine era. High-quality, authoritative, clearly structured content that answers specific user questions is now as important as traditional keyword optimization.

What Does Firebird's Expansion Mean for the AI Infrastructure Market?

Firebird's announcement signals that AI infrastructure is becoming increasingly decentralized and regional. The company has announced plans for a 2-gigawatt AI infrastructure roadmap spanning Armenia, Kazakhstan, and additional markets, with support from major investors. NVIDIA intends to invest in Firebird following an earlier investment by CoreWeave, signaling confidence in the company's ability to deliver infrastructure at scale and compete with established cloud providers.

For answer engines like Perplexity, this infrastructure expansion matters because it reduces dependency on a handful of US-based cloud providers. Having multiple options for accessing GPU capacity, especially in different geographic regions, provides flexibility and negotiating power. It also addresses geopolitical concerns about where AI compute happens and who controls access to it.

The Armenia facility was delivered in just over six months, demonstrating Firebird's ability to turn ambitious infrastructure plans into operational capacity quickly. This speed is important because the AI market moves fast. Companies that can secure infrastructure and deploy new capabilities faster than competitors gain meaningful advantages in user acquisition and feature development.