Psy Protocol Achieves 521,000 TPS on a Live Proof-of-Work Network, Offers $100,000 Bounty to Anyone Who Can Prove the Results Invalid

marsbit2026-02-14 tarihinde yayınlandı2026-02-14 tarihinde güncellendi

Hong Kong | February 11, 2026 — Psy Protocol today announced that it has achieved 521,000 transactions per second (TPS) on a live, on-chain verifiable Proof-of-Work (PoW) network. The team stated that this result, achieved without sacrificing decentralization, security, or privacy, surpasses all publicly benchmarked performances of mainstream Proof-of-Stake (PoS) and Proof-of-Work chains to date.

The stress test was conducted on thousands of Google Cloud instances to simulate a high-concurrency, internet-scale operating environment. Every transaction in the benchmark is backed by cryptographic proof, and the complete dataset has been published for independent verification.

To demonstrate confidence in the results, Psy Protocol has publicly posted a $100,000 (US$100,000) bounty, to be awarded to any individual or team that can invalidate this throughput result based on the published proof materials.

"This is not a devnet demo, nor a theoretical projection," said Carter Feldman, Founder and CEO of Psy Protocol. "Every single result is verifiable. If we are wrong, the math will show it—and we will pay $100,000 to the person who shows it."

Why 521,000 TPS Matters Now

High-throughput benchmarks are not uncommon in the crypto industry. Psy Protocol believes this result is structurally significant because of the use-case context it addresses.

Blockchains were originally designed around human transaction patterns: sporadically occurring activities, manual approvals, and low concurrency. However, this model is becoming increasingly mismatched as millions of autonomous AI agents transact, collaborate, and settle at machine speeds continuously in the future.

When networks designed for human-scale usage encounter sustained machine-grade demand, the result is often network congestion, soaring fees, and cascading bottlenecks. Psy Protocol views 521,000 TPS as the "baseline" infrastructure required for a machine-native economy, not a performance ceiling.

How the Architecture Works

Most blockchains require every node to re-execute every transaction, a design that imposes a hard architectural ceiling on throughput. Psy Protocol removes this bottleneck through four synergistic design choices:

  • Parallel State Architecture (PARTH): Each user operates in isolated state partitions. This eliminates global state contention, allowing thousands of state transitions to be processed simultaneously without conflict.
  • Client-Side Proof Generation: Transaction execution and proof generation are performed on the user's device, with sensitive data always remaining under the user's control. Miners are only responsible for verifying and aggregating proofs, not re-executing transactions, thus eliminating redundant computation across the entire network.
  • Recursive Zero-Knowledge Proofs: Individual transaction proofs are recursively folded into a single succinct proof per block. As transaction volume grows, verification costs grow logarithmically rather than linearly, meaning massive throughput increases do not require proportional increases in resources.
  • Horizontal Scaling via Realms: The network scales by adding parallel processing domains ("Realms") and proof aggregation capacity. Throughput increases linearly with added infrastructure, rather than being limited by a fixed architectural cap.

Psy Protocol states that 521,000 TPS reflects the result in the current test configuration; higher throughput is achievable by scaling parallel proof generation capacity.

Verification: Open Data, Verifiable on Consumer Hardware

Because verification relies on succinct recursive proofs rather than full re-execution, Psy Protocol states that any combined proof from the benchmark can be independently verified on consumer-grade hardware—including limited-performance devices like a Raspberry Pi.

The complete test methodology, ZK circuit data, and combined proofs have all been open-sourced and are available at:

https://st8.psy.xyz/explorer

Applications Feasible at This Scale

With sustained throughput exceeding 500,000 TPS, the following new types of on-chain activity become feasible:

  • High-frequency micropayments between autonomous agents
  • Continuous clearing markets with real-time settlement replacing discrete batch settlement
  • High-density coordination among large-scale AI agent swarms operating without human intervention
  • Keyless agent execution via programmable signature circuits, eliminating reliance on human-controlled private keys or third-party custody

About Psy Protocol

Psy Protocol is building a Proof-of-Work smart contract platform for the "agentic internet." Its architecture combines the security and decentralization benefits of Proof-of-Work with the throughput and fee efficiency historically associated with Proof-of-Stake systems. Psy employs a "Proof-of-Useful-Work (PoUW)" consensus model, where miners perform cryptographically productive work—namely, aggregating and verifying zero-knowledge proofs—rather than arbitrary hash puzzle computations.

İlgili Sorular

QWhat is the TPS (Transactions Per Second) achieved by Psy Protocol in a real-world Proof-of-Work network, and what is the bounty offered for disproving this result?

APsy Protocol achieved 521,000 TPS in a real-world, verifiable Proof-of-Work network. They have offered a $100,000 bounty to anyone who can prove this result is invalid based on the published cryptographic proofs.

QAccording to the article, why is achieving 521,000 TPS particularly significant in the current context of blockchain technology?

AIt is significant because it addresses the future need for a 'machine-native economy.' Traditional blockchains designed for sporadic human transactions are inadequate for the continuous, high-speed transactions of millions of autonomous AI agents. This result represents a baseline for the infrastructure required to support such activity without congestion or high fees.

QName two of the four key architectural designs that Psy Protocol uses to remove the throughput bottleneck found in most blockchains.

ATwo of the four designs are: 1. Parallel State Architecture (PARTH), which isolates state partitions to eliminate global state contention, and 2. Client-side proof generation, where users execute transactions and generate proofs on their own devices, so miners only verify and aggregate proofs instead of re-executing transactions.

QHow can the results of Psy Protocol's benchmark test be independently verified, and what kind of hardware is sufficient for this verification?

AThe results can be independently verified using the complete test methodology, ZK circuit data, and composite proofs that have been open-sourced. This verification can be performed on consumer-grade hardware, including limited devices like a Raspberry Pi, because it relies on verifying succinct recursive proofs rather than fully re-executing all transactions.

QWhat type of consensus model does Psy Protocol use, and what useful work do the miners perform instead of solving arbitrary hash puzzles?

APsy Protocol uses a Proof-of-Useful-Work (PoUW) consensus model. Instead of performing arbitrary hash calculations, miners perform cryptographically productive work by aggregating and verifying zero-knowledge proofs.

İlgili Okumalar

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit8 dk önce

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit8 dk önce

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit1 saat önce

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit1 saat önce

İşlemler

Spot
活动图片