PRL’s more than 300% rally over the past month has lifted Pearl’s circulating market value above $470 million, but the token’s valuation is now moving faster than the project’s demonstrated connection between its growing AI inference business and demand for PRL itself. The token climbed from roughly $0.33 to about $1.40 after previously falling from a $1.70 peak to as low as $0.23. At the recent price, Pearl’s fully diluted valuation was about $2.9 billion.
The move has focused attention on a network attempting to combine proof-of-work mining with AI computing. Pearl says its layer-1 blockchain allows GPU owners to run AI training or inference jobs alongside block production through a system called proof of useful work, or PoUW. The stated aim is to redirect graphics-processing power from conventional hash calculations toward workloads that can be used by AI applications.
Pearl’s expanding inference products give the project a clearer commercial narrative than a mining-only network, but token issuance remains driven by block rewards. The available information does not establish that revenue from AI services is used to buy PRL, distribute value to token holders, or otherwise create direct token demand.
Block rewards continue to expand supply
Pearl’s block explorer shows that roughly 16.3% of the network’s 2.1 billion PRL supply has been mined. The chain’s average block time stood near 180 seconds, within Pearl’s original 194-second target range, while the reward had declined to 2,262.52 PRL per block.
At that block-reward rate, the network would issue about 1.086 million PRL each day. At a token price near $1.40, that amounts to approximately $1.52 million worth of new tokens entering circulation daily, based on the calculation in the supplied material.
That issuance rate places persistent pressure on the market to absorb newly mined supply. Miners typically face recurring costs for electricity, hardware, hosting and maintenance, meaning a portion of earned tokens may be sold to cover operating expenses. The risk becomes more acute when mining margins narrow, since operators with limited cash reserves have fewer alternatives to selling rewards.
A community miner’s estimate published on October 11 found that renting GPUs to mine PRL remained unprofitable at a $1.40 token price under an electricity assumption of 1 yuan per kilowatt-hour. Separately, Minercompare estimated daily net income of around $10 for an RTX 5090 and $7.69 for an RTX 4090, using electricity priced at $0.10 per kWh.
Those figures suggest that profitability varies sharply depending on whether a miner owns hardware, rents it, and secures low-cost power. They also show why PRL’s market price alone does not determine the health of the mining ecosystem: GPU depreciation, availability and electricity rates can quickly change the economics.
Hashrate growth raises the network’s mining profile
Pearl’s network hashrate rose from about 19 exahashes per second to 50 EH/s, a gain of roughly 2.5 times. The supplied article compared that level with more than 170,000 RTX 4090 GPUs mining, though real-world hardware mixes can vary and hashrate comparisons depend on each device’s performance.
Rising hashrate makes a proof-of-work network harder to attack by increasing the computing resources needed to compete with honest miners. It also intensifies competition for block rewards. If token prices and useful computing revenue do not rise in line with network power, individual miners can see lower returns even while the chain becomes more computationally secure.
Pearl supports solo mining and mining pools. The supplied information identified pearlhash as the remaining stable pool after several earlier ecosystem pools had shut down. A smaller pool ecosystem can concentrate operational reliance among fewer services, particularly for smaller miners that cannot consistently find blocks alone.
Pearl previously offered cloud GPU rental packages, though new capacity was not open at the time covered by the source material. Cloud access could lower the technical barrier for prospective miners, but rental economics are especially exposed to token-price swings and GPU demand from other AI workloads.
AI inference is becoming Pearl’s main commercial pitch
Pearl has increasingly positioned AI inference, rather than mining, as its outward-facing product. The company said it had expanded its serverless inference offering to seven models and designed its APIs to support OpenAI-compatible functions including streaming, tools and JSON mode.
Pricing published by Pearl listed GLM-5.3-Flash at $0.07 per million input tokens. Pearl compared that price with Fireworks AI’s $0.15 per million input tokens for the same measure. Lower listed pricing could help Pearl attract developers testing low-cost model access, although published API rates alone do not show service reliability, capacity, latency or customer usage.
In July, Pearl announced an accelerated inference platform that was accepting registrations through a waitlist. Earlier, in May, the company said it had developed a Gemma-4-31B-it-Pearl endpoint with Together AI. Together AI had completed an $800 million Series C financing round, according to the supplied material.
Pearl also announced on July 15 that it had joined NVIDIA Inception, NVIDIA’s free program for startups working in AI, data science, deep learning and accelerated computing. Pearl said the program had more than 40,000 member companies as of 2026. Membership can provide access to technical resources and ecosystem visibility, although it does not amount to an endorsement of a project’s token or business model.
Protocol changes target more useful computation
On the technical side, Pearl said in mid-September that it had introduced a floating-point PoUW method intended for production deployment. The approach replaces its earlier reliance on integer-based matrix multiplication, a shift aimed at supporting forms of computation closer to the calculations commonly used in machine-learning workloads.
The network also uses zk-SNARKs, a cryptographic proof system that can verify a computation without revealing all underlying data. Pearl has presented these proofs as a method for supporting privacy and verification around computing work associated with block production.
The technical ambition is substantial: the network must show that AI-related workloads can be independently verified, economically useful and compatible with reliable blockchain consensus. Pearl’s challenge now is less about describing that architecture than demonstrating sustained demand for its inference services and a credible economic bridge between those services and PRL.
PRL’s rally has given the project a much larger market valuation and greater attention, while daily issuance continues to add supply. The next tests are likely to come from the durability of AI product demand, miners’ ability to operate profitably, and the market’s capacity to absorb rewards without relying on thin trading conditions.
Curious how AI and crypto intersect? Explore how AI complements blockchain to better understand narratives driving tokens like PRL.
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