The Problem

Centralization, Security Vulnerabilities, and AI's Insatiable Compute Demand

1. The Quantum Threat to Digital Assets

Quantum computing introduces an existential risk to global financial systems, digital identities, and blockchain security.

• Shor’s Algorithm. Once scalable quantum machines are available, classical public-key cryptography can be broken. Wallets, blockchains, and online transactions that rely on these algorithms could be compromised instantly, enabling theft, forged signatures, and systemic breaches.

• Systemic risk. A successful quantum attack on digital infrastructure could cause widespread disruption across financial markets, blockchains, and global communications.

• The PQC challenge. Moving to post-quantum cryptography requires adopting new, more complex algorithms with larger key sizes. This migration is costly, disruptive, and in some cases may require hard forks in blockchain networks. The uncertainty around evolving NIST standards complicates adoption.

• Critical gap in wallets. While blockchains and enterprises are beginning to plan PQC upgrades, consumer wallets remain the weakest link. Billions of dollars in assets stored today could be harvested and decrypted later, once quantum hardware matures.

• Legacy risks. Existing wallets and custody platforms face expensive, disruptive upgrades. Without quantum-native solutions, they remain exposed.

2. Centralization in Quantum Computing Access

Quantum computing today remains restricted to a handful of corporations and governments, creating systemic concentration of power.

• Astronomical costs. Industrial-grade systems cost $10M–$50M, while even basic superconducting setups start at $1M–$2M.

• Specialized requirements. Operating a quantum system demands extreme conditions: dilution refrigerators near absolute zero, ultra-high vacuum chambers, electromagnetic shielding, vibration isolation, and teams of highly trained specialists.

• Centralized cloud models. Providers like IBM Quantum and Amazon Braket offer access, but only under centralized terms. Users face vendor lock-in, limited hardware options, censorship risks, and single points of failure.

• Stifled innovation. Innovation slows when access is limited to elite institutions. The decentralization ethos of Web3 is undermined by control concentrated in the hands of a few.

3. AI’s Exponential Compute Demand: The “Classical Ceiling”

Artificial intelligence is colliding with the physical and economic limits of classical computing infrastructure.

• Explosive growth. Compute requirements for frontier AI models double every few months, with training times stretching into months across thousands of GPUs.

• PetaFLOP scale. Modern AI workloads already run at quadrillions of operations per second, driving unprecedented demand for compute.

• Classical bottlenecks:

  • Energy: Data centers consume massive amounts of electricity, and AI magnifies this burden.

  • Costs: High-performance GPU clusters are prohibitively expensive.

  • Scarcity: GPU and TPU shortages inflate prices and cause project delays.

  • Innovation limits: Teams cap model size and complexity due to compute ceilings.