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Hoansoo Lee CEO & CFO

Hoansoo Lee holds a Ph.D. in Business Economics from Harvard Business School and previously served at the Council of Economic Advisers (Executive Office of the President) at The White House.

Zachary Bright Research & Development Lead

Zachary Bright has a Ph.D. in Computer Science and was a Visiting Scholar at Princeton University, with prior experience at Ericsson.

Septem Yang Principal Architect

Septem Yang earned an M.Eng. in Electronic Engineering from Dublin City University and previously worked at Microsoft and Snap.

Xander Wu Data Center Partnerships Lead

Xander Wu holds an M.S. in Communication and Information Systems and an M.S. in Management from Stanford, and was formerly with Bitmain.

Mark Fidelman Head of Marketing

Mark Fidelman has a B.A. in Economics from the University of San Francisco and has previous experience with Forbes, EDS (acquired by Hewlett-Packard), and A. T. Kearney.

For Exascale Labs (XLAB), a next-generation AI infrastructure provider offering GPU-as-a-Service and related solutions with an asset-light model, the key risks to the business include:

1. GPU Supply Constraints and Infrastructure Availability

   Exascale Labs' core business relies on procuring high-performance GPU compute capacity from third-party data centers. The global supply chain for GPUs and related infrastructure components faces significant structural challenges, including manufacturing bottlenecks, particularly in advanced packaging like CoWoS technology, and a severe shortage of High-Bandwidth Memory (HBM). This situation is exacerbated by massive capital expenditures from larger hyperscalers that are consuming a substantial portion of available GPU supply, leading to extended lead times (e.g., 36 to 52 weeks for data center GPUs) and increased competition for essential hardware. These supply limitations could directly hinder Exascale Labs' ability to scale its services and meet customer demand.

2. Intense Competition and Execution Risk

   The AI infrastructure market is characterized by rapid technological advancement and the dominance of much larger, well-established competitors, including major cloud providers and tech giants who are increasingly investing heavily in their own "asset-heavy" AI infrastructure. Exascale Labs must effectively execute its strategy, maintain consistent access to GPU capacity, and deliver reliable, scalable services to compete against these powerful rivals. The challenge lies in distinguishing its offerings and gaining customer trust in a highly competitive and evolving landscape.

3. Customer Acquisition, Retention, and Commercialization Risk

   While Exascale Labs has reported an "approximately $300 million qualified customer pipeline," there is an inherent risk that these prospective customer opportunities may not convert into contracted, recurring revenue. The company is relatively new to the public market (having listed in August 2026), and demonstrating consistent conversion of its pipeline into revenue is crucial for its financial performance and investor confidence. Additionally, a significant portion of its broader platform offerings may still be in pre-commercial stages, adding uncertainty to future revenue generation.

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Addressable Markets for Exascale Labs (XLAB) Products and Services

Exascale Labs (NASDAQ: XLAB) operates in the rapidly expanding sectors of AI infrastructure, GPU-as-a-Service (GaaS), and AI-ready data center solutions. The company's main offerings include GPU cloud infrastructure, scalable GPU compute deployments, system integration, GPU procurement and leasing, and full-stack orchestration. The addressable markets for these services are substantial and exhibit strong global growth trajectories. The global market for **GPU-as-a-Service (GaaS)** was valued between USD 4.96 billion and USD 8.60 billion in 2025. Projections indicate significant expansion, with forecasts ranging from USD 26.09 billion by 2031 to USD 162.54 billion by 2034 and even up to USD 106.04 billion by 2035. The compound annual growth rate (CAGR) for this market is anticipated to be between 22.29% and 44.3% during the forecast periods. For the broader **AI Infrastructure Market**, which encompasses the hardware, software, and networking resources essential for AI and machine learning applications, the global market size was estimated to be between USD 58.78 billion and USD 72.02 billion in 2025. This market is projected to reach between USD 223.5 billion by 2030 and USD 518.26 billion by 2035, with a CAGR ranging from 21.5% to 30.4% over various forecast periods. The **AI Data Center Market**, vital for supporting high-performance computing and AI workloads, was valued between USD 39.49 billion and USD 147.3 billion in 2025. It is expected to grow to between USD 152.91 billion by 2031 and USD 810.6 billion by 2033. Some projections suggest that global data center investment fueled by AI could reach as much as USD 7 trillion by 2030. The CAGR for this market is estimated to be between 23.9% and 28.6%.

Expected Drivers of Future Revenue Growth for Exascale Labs (XLAB)

• Expansion of Global Footprint and Investment in Proprietary Technology: Exascale Labs is positioned to expand its global presence and further invest in its proprietary hardware and software solutions. This strategic move aims to enhance its offerings and reach a broader market.
• Growth in the AI Infrastructure Market: The significant projected growth of the AI infrastructure market, expected to reach $394 billion by 2030 from $182 billion in 2025, will fuel demand for Exascale Labs' core business of designing, deploying, and operating GPU clusters and AI-ready data centers.
• Conversion of a Strong Qualified Sales Pipeline: The company has a reported "over $300 million qualified pipeline," indicating a substantial and identified potential for near-term revenue generation from existing opportunities.
• Increased Adoption of GPU-as-a-Service (GaaS) and AI Data Center Optimization: Exascale Labs' primary business revolves around providing GPU-as-a-Service (GaaS), offering both reserved and on-demand access to GPU compute capacity. Additionally, its GPU cluster management and optimization services for AI data center operators are expected to see increased demand across academia, enterprise, and AI-native platforms, driving future revenue growth.

Share Repurchases

• Exascale Labs Holdings reported a 5-Year Share Buyback Ratio of 0.00% as of March 2026 and August 2026, indicating no share repurchases over the past five years.

Share Issuance

• The company's annual additional paid-in capital increased from $0.00 million in 2020 to $0.07 million by June 2024, and further to $0.22 million by June 2025.
• Exascale Labs Holdings Inc. completed a business combination with D. Boral ARC Acquisition I Corp., beginning to trade on Nasdaq under the symbol "XLAB" on August 28, 2026.
• The listing following the business combination valued the company at $500 million.

Inbound Investments

• The business combination with D. Boral ARC Acquisition I Corp. resulted in Exascale Labs having approximately $12 million in cash after shareholder redemptions, effective with its Nasdaq listing on August 28, 2026.
• Prior to its public listing, Exascale Labs raised an undisclosed amount of funding across 23 grant rounds.

Outbound Investments

• Exascale Labs Holdings reported a "Sale Of Investment" of $0.00 million for the trailing twelve months ended March 2026.

Capital Expenditures

• Exascale Labs operates as an AI infrastructure provider, engaged in designing, building, and operating GPU-accelerated data center infrastructure and cloud platforms.
• The core business includes GPU-as-a-Service (GaaS), providing access to high-performance GPU compute capacity and GPU cluster management services for AI data center operators.
• Management plans to expand into larger infrastructure projects, anticipating higher revenue and margins from these initiatives.