Perceptron’s $10M Fund Is Quietly Changing Who Gets to Build with AI — CCS ExclusiveCCS Exclusive · Blockchain InterviewsJuly 5, 2026
Perceptron Network
Perceptron’s $10M Fund Is Quietly Changing Who Gets to Build with AI
In an exclusive interview with the Crypto Coin Show, co-founder Peter Anthony explains how a decentralized data network and a new $10 million AI Data Fund are handing smaller teams a resource only tech giants could previously afford.
AA
Ashton Addison
Host, Crypto Coin Show · Blockchain Interviews
The AI data market has a monopoly problem. Google and OpenAI paid hundreds of millions of dollars to Reddit for access to its user-generated content. None of that reached the people who actually wrote the posts. And if you’re a new AI startup trying to train a competitive model, the cost of quality data alone is enough to stop you before you start.
Perceptron Network — a decentralized AI data infrastructure company with over 800,000 user-run nodes across 150+ countries — was built to fix exactly that. In a wide-ranging conversation on the Crypto Coin Show, co-founder Peter Anthony laid out how the network works, who it’s for, and why the newly launched $10M AI Data Fund could be one of the most consequential moves in the Web3 AI space this year.
800K+Node downloads globally
150+Countries in the network
10 TBAvg. daily bandwidth
$10MAI Data Fund launched
The Problem with Centralized Data
Every minute of every day, the average person generates roughly 1.3 megabytes of data per second. That data, the way you interact with social platforms, the searches you run, the content you engage with, is the raw material AI models are built on. The existing frontier models have already scraped the historical internet: around 750 trillion tokens spanning forums, Reddit threads, Twitter archives, and beyond.
But the historical layer is already gone. The real frontier of AI training data is what we’re producing right now, today. And that’s where Perceptron comes in.
“If you charge huge amounts of money, only OpenAI and Anthropic and a handful of others are going to be able to afford to pay those data costs. If you are a new upcoming AI project, you’re never going to be able to access that quality of data.”
— Peter Anthony, Co-Founder, Perceptron Network
Traditional data acquisition runs through APIs: Twitter’s, Reddit’s, LinkedIn’s. These platforms know exactly how valuable their content is, and they price it accordingly. The result is a market where only the largest, best-funded AI labs can stay current. Everyone else is building on stale data, or no meaningful data at all.
How the Network Works
The Perceptron model flips this dynamic by distributing data collection across its node network. When a user installs the node (available as a Chrome extension or on Android and iOS) they’re sharing excess bandwidth. The node doesn’t collect personal data; it gives Perceptron a vantage point on the internet from that user’s IP address and location.
Scale that across 800,000 nodes spanning 150 countries, and you have something centralized providers genuinely cannot replicate: a live, globally distributed lens on what the internet looks like from real human perspectives, right now. Anthony drew a sharp distinction between what large tech platforms do with user data (package and sell it for hundreds of millions) and what Perceptron offers: a share of that value back to the people generating it.
Two Ways to Earn
One of the clearest takeaways from the interview is that Perceptron is designed for both casual and active contributors. The earning model runs on two distinct tracks:
PASSIVE
Run a Node
Install the extension or app, share idle bandwidth in the background, and accumulate points automatically. Your unused data capacity does the work while you sleep.
ACTIVE
Complete Data Quests
Opt into specific tasks — annotating images, contributing voice recordings, labeling datasets, photographing locations — and earn significantly higher rewards. Rare skills and uncommon languages command premium payouts.
TIERED
Build On-Chain Reputation
Contribution quality is tracked on-chain. The more accurately you contribute, the higher your tier and the higher-value quests you unlock. Expert contributors in specialized fields are especially rewarded.
Right now, contributors earn points, pre-TGE accrual ahead of the $PERC token launch. Anthony suggested the token goes live within 8 to 10 weeks, potentially sooner, at which point those points convert directly. Getting in early means building a balance before the conversion rate is set and the network effect compounds.
Quality Control at Scale
A natural question for any crowdsourced data platform is how you prevent gaming: rushing through tasks, submitting junk to collect rewards. Anthony was direct. The platform uses AI to validate submissions before they enter the dataset. For image-based tasks, the system first trains on what a valid submission looks like, then evaluates every incoming entry against that benchmark. Sloppy-but-genuine attempts are treated differently from clear bad-faith submissions; penalties are reserved for the latter. The recently acquired Router framework handles user-level contribution tracking automatically, at scale, with no manual review needed.
“People shouldn’t be in fear of contributing data sets because they might get it wrong — as long as they’re making efforts to do it correctly.”
— Peter Anthony, Co-Founder, Perceptron Network
// New Initiative
The $10M Perceptron AI Data Fund
Launched to lower the entry barrier for AI startups and Web3 projects that need real-world, high-quality training data but can’t afford what centralized providers charge. Teams accepted into the fund get direct access to Perceptron’s live data pipeline, a resource that until now has been out of reach for most builders.
Open to AI and Web3 projects at any stage, crypto-native or Web2 backgrounds welcome
Access to Perceptron’s live, globally sourced datasets for model training and fine-tuning
Strategic pathway: fund recipients become long-term paying data clients
Apply via Perceptron’s official X account (@PerceptronNTWK) to begin the conversation
Designed to prove what decentralized infrastructure can do that centralized models cannot
The Bigger Play: Business Intelligence
Beyond AI training data, Anthony revealed where Perceptron’s long-term value truly lies: live business intelligence. Because the network holds hundreds of thousands of distributed vantage points worldwide, it can observe things no centralized model can. How a product ranks on Amazon in New York versus Dubai versus São Paulo, in real time, with no API access required.
The same infrastructure powering crypto sentiment analysis for quant traders can be pointed at e-commerce, price comparison, ad performance tracking, and more. The node network, Anthony argues, is the real moat. Building 800,000 real, distributed, human-operated nodes isn’t something you shortcut. It took him five years to build a YouTube channel with 300,000 subscribers. Perceptron already has that network effect locked in.
Why This Matters Right Now
The AI data market is at a tipping point. Models need more data to improve. The best data, live, human-generated, and contextually diverse, is increasingly locked behind paywalls or owned by platform monopolies. The cost barrier is freezing out the next generation of AI builders before they can even start.
Perceptron’s bet is that a decentralized network of real human contributors, rewarded fairly for what they produce, is both the ethical and the pragmatic answer. The $10M AI Data Fund is the opening move in making that case concrete, not just to the crypto community, but to the broader AI industry watching from the sidelines.
If you’re already contributing your data to AI systems every day and receiving nothing in return, the question is simply: would you rather keep doing that, or start getting paid?
// Get Started
Join the Perceptron Network
Download the node, accumulate points before the TGE, and explore data questing tasks matched to your skills and location.
Our Pi network price prediction anticipates the Pi price reaching a maximum of $0.5695 by 2026.
In 2032, the Pi price prediction projects a maximum of $1.71.
Pi Network began as a mobile-focused crypto project designed to make digital assets accessible to everyday users. After reaching an all-time high of $2.98 in February 2025, Pi declined sharply and later hit a low of $0.1187 in June 2026 amid weakening demand and limited market liquidity.
Recently, the network accelerated ecosystem development. Major updates include a new Pi App Studio feature that allows developers to import AI-generated apps from platforms like Replit, Cursor, and Claude Code directly into the Pi ecosystem with integrated Pi SDK tools and payments.
Pi Network also expanded its reach in the United States after OKX made Pi be adopted by millions of users and launched Protocol 24 following the successful upgrade to Protocol 23.
What does the future hold for Pi Network, and where could the PI price head next? As investors monitor market sentiment, ecosystem growth, and network upgrades, let’s explore our Pi Network price prediction and technical analysis for 2026–2032.
Overview
Cryptocurrency
Pi Network
Ticker Symbol
Pi
Price
$0.1345
Price Change 24h
3.1%
Market Cap
$1.44 billion
Circulating Supply
10.64B PI
Trading Volume 24h
$7.9 million
All-Time High
$2.98, Feb 26, 2025
All-Time Low
Jun 06, 2026 $0.1187
Pi Network Price Prediction: Technical Analysis
Metric
Value
Current Price
$0.1345
Price Prediction
$ 0.1102 (-24.96%)
Fear & Greed Index
29 (Fear)
Market Sentiment
Bearish
Volatility
8.31% (High)
Green Days
13/30 (43%)
50-Day SMA
$ 0.1667
200-Day SMA
$ 0.1881
14-Day RSI
38.59 (Neutral)
Pi Price Analysis
Today’s PI price analysis shows PI trading at $0.1345, with buyers attempting to regain control
Over the last 24hours pi price has risen by around 3.1% after a recovery from yesterday’s pullback
Pi network’s immediate resistance is at $0.1350, and support at $0.1300
As of June 19, 2026, Pi Network price analysis shows a short-term bullish recovery with the price rising to $0.1345 after attracting fresh buying interest. The bulls have regained the momentum from the previous two days’ sell-off, with the latest daily candle pushing PI back toward the week’s highs. The market is still in a cautious trend, but today’s recovery suggests buyers are again testing overhead resistance. At the time of writing, the market sentiment is slightly bullish, with the bulls attempting a recovery above the $0.1300 support zone.
Pi price analysis 1-day chart
The one-day price chart for Pi Networks confirms buyers have returned to the market after the recent correction.PI has recovered from the $0.1300 level and is now trading around $0.1345 after missing out on significant gains during the last two sessions.
The latest green candlestick shows the bulls appear to be trying to retest this week’s highs with renewed buying pressure. However, the price is still below the $0.1350 resistance level, which has seen sellers break out in recent trading sessions.
The 24-hour trading volume rose to $7.9 million, which is a 15.98% rise in trading activity, and the market cap of $1.44 billion indicated that bulls are trying to resume the recovery with a renewed buying interest.
PI/USDT Chart: TradingView
The RSI (14) has bounced back from its lows to 45.96 and is heading deeper into the green, but still below the neutral level of 50. That means, bearish momentum has weakened, but buyers still need stronger momentum to confirm a broader trend reversal.
The MACD indicator is looking positive. The MACD line is at 0.0017, and the line signal is at -0.0046. The positive histogram is increasing, suggesting the bulls are gradually getting the upper hand from their recent pullback, but they need to breach the resistance to confirm their dominance.
The immediate resistance level is at $0.1350. If PI can break out above this, the resistance at $0.1400 could be a target, with the stronger resistance at $0.1450. Immediate resistance is at $0.1300 and $0.1260, which is the next major support zone for bulls, in a bid to hold onto the recovery.
Pi Price Analysis 4-Hour Chart
On the 4-hour chart, PI is trading at $0.1339 with buyers regaining momentum after defending the $0.1300 support level. After a brief correction from the recent high at $0.1365, the bulls have returned to the market, driving it back towards immediate resistance.
The formation of higher lows over the last few sessions also indicates a short-term bull market sentiment, but PI will need to break overhead resistance to validate a bull market continuation.
RSI (14) is up to 55.58, which is comfortably above the neutral 50 level and is pointing to a strengthening buying momentum. The indicator continues to stay well below the overbought level, and thus has further room to rise if the buyers continue to prevail. The RSI is also still above its signal average at 46.27, which further backs the positive sentiment in the short term.
PI/USDT Chart: TradingView
The MACD is still in bull territory. The MACD line is currently at 0.0002, the signal line at 0.0001, and the histogram is just slightly positive at 0.0001. The bullish crossover has formed, which shows that the overall momentum is improving, but the histogram is still quite low, meaning that buyers are not yet able to create any clear breakout, as there is not enough volume to support the move.
Resistance on the short-term 4-hour chart is at $0.1350, and then a stronger barrier of resistance is around $0.1370 and $0.1380. The support level is at $0.1300 if there is selling pressure, and the next support will be at $0.1280. A significant move above $0.1350 might extend bullish momentum, while a move below $0.1300 may improve the short-term momentum for sellers, heading towards $0.1400.
Pi Network Price Prediction: Levels and Action
Daily Simple Moving Average (SMA)
Period
Value
Action
SMA 3
$0.1451
SELL
SMA 5
$0.1450
SELL
SMA 10
$0.1466
SELL
SMA 21
$0.1544
SELL
SMA 50
$0.1667
SELL
SMA 100
$0.1759
SELL
SMA 200
$0.1881
SELL
Daily Exponential Moving Average (EMA)
Period
Value
Action
EMA 3
$0.1461
SELL
EMA 5
$0.1459
SELL
EMA 10
$0.1476
SELL
EMA 21
$0.1536
SELL
EMA 50
$0.1634
SELL
EMA 100
$0.1736
SELL
EMA 200
$0.2132
SELL
What to expect from the next Pi price analysis?
If buyers maintain momentum and push PI above the $0.1350 resistance, the token could extend its recovery toward the $0.1400 psychological level in the near term. However, failure to hold above $0.1300 could invite renewed selling pressure, potentially dragging the price back toward the $0.1280 support zone.
Why is PI’s price up today?
Pi Network is up today as positive sentiment surrounding ongoing ecosystem development and upcoming initiatives, including Pi2Day, boosted investor confidence amid a broader recovery in the altcoin market. However, trading volume remains relatively subdued, suggesting the rally will need stronger buying activity to sustain a breakout above the $0.14–$0.15 resistance zone.
Is Pi a Good Investment?
Pi is a high-risk, speculative investment that could offer upside if its ecosystem grows and adoption increases. However, its price remains volatile and dependent on overall market conditions, so investors should be prepared for uncertainty.
The project’s long-term success will largely depend on factors such as Mainnet adoption, developer activity, ecosystem utility, exchange accessibility, and the network’s ability to attract and retain active users. Recent developments, including Pi App Studio enhancements, protocol upgrades, and expanding exchange support, have strengthened the project’s fundamentals, but the token still faces challenges related to liquidity, supply expansion, and market sentiment.
As with any cryptocurrency investment, investors should conduct their own research, assess their risk tolerance, and avoid investing more than they can afford to lose. While Pi has growth potential if adoption continues to expand, it remains a speculative asset with significant upside and downside risks.
Will Pi Price Reach $5?
At the current pace of development and given its total PI supply circulating supply of over 8 billion PI, Pi Network’s long-term value will largely depend on user base growth and broader acceptance of cryptocurrencies in mainstream finance, making $5 unlikely in the near term. The maximum supply of Pi tokens is 100 billion, and ongoing unlocks create significant selling pressure that must be absorbed by demand, while ecosystem growth remains high risk unless developers and users create real utility through DApps or merchant integrations.
Multiple technical quantitative indicators and fundamental factors, such as delayed mainnet launch and maximum supply constraints, suggest that Pi’s price may fluctuate within lower ranges before any major uptrend. Real-world utility will be crucial for supporting demand and helping determine whether Pi can reach higher price targets. A $5 target would require sustained adoption, significant on-chain activity, and strong market demand that is not yet present.
Will Pi Reach $10?
Reaching $10 would represent a massive increase in Pi’s market cap, something that is not expected soon under current crypto market conditions. The $10 mark is considered an upper price target or the high end of speculative forecasts. Most models forecast a price range for Pi Network between $0.14 and $0.56 by the end of 2026, representing the lower end and high end of current predictions.
Analysts suggest that even optimistic forecasts place this milestone more than a decade away, if at all. Investors should treat such projections as speculative investment advice and conduct their own research before making investment decisions, as Pi remains a high-risk asset with uncertain long-term value.
Does Pi Network Have a Good Long-Term Future?
Pi Network’s long-term prospects depend on its ability to convert its large user base into active ecosystem participants. If developer adoption, merchant integration, and real-world use cases continue to expand, the project could strengthen its position within the cryptocurrency market.
However, investors should also consider risks related to token supply growth, market competition, regulatory developments, and overall crypto market conditions. As with any digital asset, future performance will
Recent Pi News/Opinions
Pi Network has announced a major protocol upgrade requiring all Mainnet nodes to update to version v24 as part of its ongoing infrastructure transition. The update comes alongside the rollout of Pi Node v0.5.4 and reinforces the network’s shift toward a more user-centric decentralized model, where desktop nodes help validate transactions using the Stellar Consensus Protocol.
The Pi Mainnet is upgrading to Protocol 24 – Deadline: June 2.
The Pi Mainnet has successfully upgraded to Protocol 23. All Mainnet nodes are required to complete this step before the deadline to remain connected to the network.
Pi Network says the CiDi Games beta app on Pi Browser pulled in more than 81,000 users within its first week, with over 1.2 million game sessions recorded across 160 regions. The platform is a portfolio company of Pi Network Ventures, which made a direct investment in CiDi Games as part of a broader push to expand real-world utility for the $PI token.
CiDi Games organically reached over 81,000 users across 160+ countries in less than a week after launching its beta in the Pi ecosystem.
That led to: 1.2M+ game sessions 21,000+ tournament participants 3.19M+ Pi staked by the community to support the platform.
The Pi Core Team confirmed protocol v24 completed successfully, describing it as one of the most challenging migrations in Pi Network’s history. The upgrade covers off-chain integration as part of the broader roadmap toward v25 on June 18 and v26, which targets full commercial openness.
Network Update: Upgrade to protocol 24 has been completed successfully.
Great job to all Nodes! This was one of the most challenging migrations.
Pi Network released a user-experience update to its Ecosystem Directory Staking feature. The redesign makes the process of staking PI to boost app rankings in the Pi Browser more intuitive. The core mechanic remains unchanged: users lock their tokens to promote apps and receive their full stake back after a set period, with no protocol-level rewards.
Pi Price Prediction June 2026
In June 2026, Pi’s price may average around $0.1376 as the market continues to stabilize following recent volatility. A recovery toward $0.1500 could occur if buying interest strengthens, while sustained bearish pressure may see PI consolidate near a minimum of $0.1251.
Pi Price Prediction
Potential Low
Potential Average
Potential High
Pi Price Prediction June 2026
$0.1251
$0.1376
$0.1500
Pi Price Prediction 2026
The price of 1 Pi is expected to reach a minimum level of $0.1200 in 2026. The network Pi price, which refers to the projected future price of Pi Network for 2026, can reach a maximum level of $0.5695, with
Pi Price Prediction
Potential Low ($)
Potential Average ($)
Potential High ($)
Pi Price Prediction 2026
$0.1200
$0.3593
$0.5695
Pi Price Predictions 2027-2032
Year
Minimum Price ($)
Average Price ($)
Maximum Price ($)
2027
$0.1987
$0.2273
$0.256
2028
$0.4657
$0.5274
$0.5891
2029
$0.6120
$0.6900
$0.7680
2030
$0.7477
$0.8216
$0.8950
2031
$0.9825
$1.07
$1.16
2032
$1.34
$1.52
$1.71
Pi Price Prediction 2027
The Pi price is forecast to reach its lowest possible level of $0.1987 in 2027. According to the latest Pi Network forecast for 2027, analysts predict the price could fluctuate between $0.1987 and $0.256, reflecting both potential growth and volatility in the market.
Pi Price Prediction 2028
In 2028, the price of Pi is predicted to reach a minimum level of $0.4657. The PI price can reach a maximum level of $0.5891, with the average trading price of $0.5274.
Pi Price Prediction 2029
In 2029, Pi’s price is projected to reach a minimum of $0.6120. The PI price could rise to a maximum of $0.7680, with an average trading price of $0.6900 throughout the year.
Pi Price Prediction 2030
In 2030, Pi is forecast to trade at a minimum level of $0.7477. Recent price analysis of Pi today provides valuable insights into its current value and helps inform these long-term investment predictions. The PI price could reach a maximum of $0.8950, with an average forecast price of $0.8216.
Pi Price Prediction 2031
In 2031, Pi’s price is expected to hold a minimum value of $0.9825. When considering Pi Network today, its current value and market trends provide a foundation for projecting its future value, including the 2031 forecast. The PI price could climb to a maximum of $1.16, with an average trading value of $1.07.
Pi Price Prediction 2032
In 2032, Pi is expected to reach a minimum price of $1.34. The PI price could rise to a maximum of $1.71, with an average value of $1.52.
Pi Network Price Prediction 2027-2032
Pi Network Price Prediction: Analysts’ Pi Price Forecast
Firm Name
2026
2027
Coincodex
$0.1468
$0.1468
DigitalCoinPrice
$ 0.2310
$ 0.2420
Cryptopolitan’s Pi Price Prediction
At Cryptopolitan, we remain cautiously bullish on the long-term outlook for Pi Network despite recent volatility in the cryptocurrency market. Based on our Pi Network price prediction, the current price could gradually recover as ecosystem adoption, trading volume, market capitalization, and utility continue to grow. Our forecast suggests PI could trade between $0.1440 and $0.5695 in 2026, with an average price of $0.3593. However, future price movements will depend on market sentiment, circulating supply growth, technical analysis indicators, and the network’s ability to attract users, developers, and real-world applications.
Pi Historic Price Sentiment
Pi Price History: Coinmarketcap
Pi Network launched in 2019 with mobile mining and operated in a closed ecosystem with no official market price, as tokens couldn’t be traded externally.
Between 2023 and 2024, Pi remained unlisted, with speculative prices ranging between $0.60 and $1.00 in unofficial markets.
In February 2025, Pi reached an all-time high of $2.98 following initial listings and increased public speculation.
In March 2025, Pi’s price dropped sharply after instability followed the final KYC verification deadline, trading between $1.85 and $0.90 during the decline.
In April 2025, Pi Network hit its all-time low (ATL) of $0.4012 on April 5.
From May to August 2025, Pi declined after failing to hold gains near $1.67, with token unlocks and weak demand pushing the price lower toward the $0.34 and $0.44 range.
In September 2025, Pi fell to a new all-time low of $0.2234 before recovering slightly to the $0.25–$0.28 range.
On October 11, 2025, Pi Network hit a new all-time low of $0.1585, reflecting the peak of a prolonged market crash and severe selling pressure.
Between November and December 2025, Pi traded mostly between $0.20 and $0.26 as selling pressure eased, but recovery remained weak.
In early 2026, Pi fell further and reached a new all-time low of $0.1312 on February 11 before stabilizing.
By late March 2026, Pi traded between $0.17 and $0.19, showing gradual recovery and improving short-term stability.
By mid-April 2026, Pi Network is trading around the $0.17 and $0.172 range, maintaining sideways consolidation as the market shows signs of stabilization after recent volatility.
At the start of May 2026, Pi Network traded between $0.17 and $0.18, continuing its sideways consolidation as the market showed limited momentum following April’s stabilization phase.
By mid-May 2026, Pi Network dropped toward the $0.15 range, facing renewed selling pressure as traders reacted to migration-related volatility and weak market momentum.
By late May 2026, Pi Network is trading at around $0.1439, reflecting continued downside pressure after a steady decline from earlier consolidation levels.
By June 06, 2026, Pi Network hit a new all-time low of $0.1187, marking the lowest price in its trading history and confirming strong bearish control in the market.
XYO and Theta Just Solved AI’s Accountability Problem
AI & Infrastructure
XYO and Theta Just Solved AI’s Accountability Problem
For the first time, AI agents running on decentralized infrastructure can generate independent, on-chain proof of their own performance — thanks to a new integration between XYO Layer One and Theta EdgeCloud. No centralized cloud has ever offered this.
Crypto Coin Show·News & Analysis·May 2026
Autonomous AI agents are already operating at scale — initiating transactions, consuming cloud services, and coordinating with other agents, all without a human in the loop. But until now, there has been no reliable, independent way to verify whether those agents actually performed as intended. When no human is watching, the record of what happened — if it exists at all — lives inside the same system that ran the workload.
That changes today. XYO Layer One and Theta EdgeCloud have announced a new integration that gives AI agents something they have never had: verifiable, tamper-proof proof-of-performance written to a public blockchain by an independent observer.
Why This Matters Now
AI agents are already running production workloads for the Houston Rockets, Olympique de Marseille, and partners across the MLS, NBA, NHL, and Ligue 1 on Theta EdgeCloud. As agentic AI scales across industries, the question of accountability — did it actually do what it said? — becomes critical infrastructure, not a nice-to-have.
XYO watches EdgeCloud from the outside — independently measuring real uptime, speed, and reliability — and writes permanent records to XYO Layer One. Those records are stored in XYO Data Lakes and can be queried by anyone, at any time. The data never touches the system it is measuring, which is precisely the point.
Centralized clouds can’t offer this — their performance data never leaves their own systems.
What AI Agents Can Now Do That They Couldn’t Before
This integration unlocks a new capability for the broader AI industry: agents can now generate cryptographic receipts of their own execution. Every task completed, every service consumed, every coordination event — recorded independently and permanently on-chain, paid for in $XL1 with a portion of each payment burned.
For developers and enterprises deploying AI agents, this means auditability without trusting the platform running the agents. For end users interacting with AI systems, it means a publicly accessible performance history. For the broader Web3 ecosystem, it means the accountability layer that autonomous AI has been missing is now real.
Agents can now prove execution
Every task, coordination event, and service call can generate an on-chain record — written by an independent observer, not the agent itself.
Infrastructure can be audited publicly
Uptime, speed, and reliability records for Theta EdgeCloud are now permanently stored in XYO Data Lakes, open to anyone.
Builders can ship in days, not months
The XYO AI SDK compresses what would traditionally take months of engineering into a days-long integration, deployed directly to XYO Layer One.
The standard is open
Theta is the first partner — but XYO and Theta Labs are building a shared standard any infrastructure provider can adopt.
A New Accountability Layer for Agentic AI
The broader significance of this integration goes beyond XYO and Theta. As AI agents take on more consequential tasks — managing funds, executing trades, coordinating supply chains — the question of whether they performed correctly becomes a legal and commercial necessity, not just a technical curiosity.
Today’s announcement establishes a blueprint: an independent, blockchain-based verification layer that operates outside the systems it monitors. Theta EdgeCloud is the first infrastructure partner to ship a verification standard built this way. The XYO team says it will not be the last.
Token Mechanics: Every Verification Is a Transaction
For $XL1 and $XYO holders, the integration ties real-world AI activity directly to on-chain economics. Every record written to XYO Layer One is a transaction. Every transaction burns a portion of $XL1. As AI agent workloads scale across EdgeCloud and beyond, that activity flows through XYO Layer One — creating deflationary pressure anchored to actual infrastructure usage, not speculation.
$XL1 Token Flow
Agent Runs
AI workload executes on Theta EdgeCloud
→
XYO Verifies
Independent record written to XYO L1
→
$XL1 Paid
Every record is a transaction
→
Burn
Portion of every payment permanently removed
What Comes Next
The XYO AI SDK is now open for early access testing. Developers can begin building verified AI applications and agents — with deployment directly to XYO Layer One — starting today. Theta EdgeCloud is live as the first verified partner, with more integrations expected to follow as the standard gains traction.
Two of crypto’s earliest protocols. One accountability standard built for the age of autonomous AI.
Early Access — Now Open
Build with the XYO AI SDK
Start shipping verified AI apps and agents in days — not months.
The Hallucination Problem — And the Data Layer That Solves It
Analysis
AI Keeps Lying. The Fix Isn’t a Better Model. It’s Better Data.
OpenAI formally admitted it in 2025: language models are structurally rewarded to guess rather than say “I don’t know.” A growing field of blockchain-based data infrastructure projects believes the real fix happens before the model is ever trained—at the data layer itself.
April 25, 2026 · 11 min read · DePIN / AI / Blockchain / Data Infrastructure
The Problem
The Test That Punishes Honesty
Picture a standardized exam where leaving a question blank gives you zero, but a wrong guess gives you a chance at a point. Rational test-takers guess. Always. Now imagine your AI is trained on exactly that exam, at scale, across billions of questions. You haven’t built a truthful system—you’ve built an optimized guesser.
This isn’t a metaphor. It is the exact structural critique published by OpenAI researchers in September 2025. Their paper, Why Language Models Hallucinate, argues that models hallucinate because standard training and evaluation procedures reward guessing over acknowledging uncertainty. Saying “I don’t know” scores zero. Confidently guessing wrong at least has a chance of scoring something—so over thousands of benchmark questions, guessing pays.
“Hallucinations are not a mysterious artifact of neural networks. They are a predictable outcome of how we train and evaluate language models.”
The paper’s core insight is structural, not incidental: accuracy-only leaderboards dominate how the entire field evaluates models. On those scoreboards, a model that guesses boldly—and occasionally gets lucky—outranks a model that abstains with honest uncertainty. The scoring hasn’t fundamentally changed, so neither has the behavior.
⚠ The Core Mechanism
For a question a model doesn’t know—say, a specific person’s birthday—guessing “September 10” gives a 1-in-365 chance of being right. Saying “I don’t know” guarantees zero points. Multiplied across millions of training examples, the statistical pressure to guess is enormous. This is not a prompt engineering problem. It is baked into how AI is scored.
Scaling Hasn’t Fixed It
OpenAI acknowledges that GPT-5 significantly reduces hallucinations—especially in reasoning tasks—but still confirms they occur. The Vectara FaithJudge Leaderboard in 2025 put grounded hallucination rates at roughly 15–16% for GPT-4o and Claude 3.7 Sonnet, with Gemini 2.5 Flash around 6%. Those are meaningful improvements. They are not solutions. Even a 6% hallucination rate, considered excellent by benchmark standards, translates into serious operational errors at scale—a corrupted field in a medical record, a fabricated legal citation, a wrong fact embedded in a financial report.
More parameters did not fix this. Larger context windows did not fix this. The problem isn’t the model’s size—it’s the incentive to guess, which is written into the scoring system that shapes training.
§
Data Quality
Garbage In, Hallucinations Out
Training incentives are one dimension of the hallucination problem. Data quality is another—and arguably the deeper one. When models are trained on vast swaths of the internet, they absorb noise, contradictions, outdated facts, fabrications, and synthetic text at scale. The model has no reliable way to distinguish between a peer-reviewed paper and a confidently written blog post that happens to be completely wrong.
OpenAI researchers describe this as the GIGO problem—Garbage In, Garbage Out. Post-training techniques like RLHF can reduce errors like common misconceptions and conspiracy theories, but they cannot fundamentally undo what was baked in during pretraining. If the data layer is polluted, the model is polluted. No amount of fine-tuning fully reverses that.
The bias problem is equally serious. AI trained on data that skews toward wealthy, English-speaking markets will perform well in those markets and fail quietly everywhere else. Markus Levin of XYO has pointed to a concrete example: when the COIN App was translated into Amharic—spoken by 57 million people in Ethiopia—ChatGPT’s translations were riddled with errors. Not because the model was broken, but because Ethiopia was not a priority data market. The training signal simply wasn’t there.
✕ Status Quo Data Pipeline
Web-scraped text with no origin verification
No audit trail for when or how data was generated
Cannot distinguish fact from plausible fiction at scale
Skewed toward high-resource languages and markets
Models incentivized to guess when knowledge gaps appear
✓ Verified Data Pipeline
Cryptographic Proof of Origin for every data point
Immutable on-chain audit trail anchored to real events
Data tied to real-world sensors and independent validators
Decentralized collection across underserved geographies
AI can verify claims rather than pattern-match guesses
✓ What Verified Data Has Already Proven
AI trained on high-quality, verified, structured data has produced breakthroughs in science and academia that seemed impossible a decade ago—protein folding, drug discovery, climate modeling. The models evolved, but so did the data. The two are inseparable. Better data is not a nice-to-have for AI. It is the single highest-leverage input in the stack.
§
The Skeptics
The Case Against Blockchain as a Data Fix
Not everyone is convinced that decentralized infrastructure is the right answer. The counterarguments deserve a fair hearing.
Retrieval-Augmented Generation (RAG) already helps. Many AI deployments now use RAG pipelines—anchoring model responses to retrieved documents rather than relying on baked-in training data. This reduces hallucinations significantly in enterprise contexts. Stanford’s 2025 legal RAG reliability work showed meaningful gains in accuracy for grounded tasks. The argument: you don’t need a new blockchain; you need better retrieval architecture.
Benchmark reform may be sufficient. OpenAI’s own paper proposes a targeted fix: change how accuracy-only scoreboards are weighted so that abstaining scores better than confident wrong answers. If the field adopts uncertainty-aware evaluation, the training incentive to guess diminishes—without needing any new data infrastructure at all.
Blockchain adds complexity without guaranteeing quality. Cryptographic proof of origin tells you where data came from, not whether what was recorded was true. A network of nodes can corroborate a false reading just as easily as a true one if the sensors or participants are compromised. Garbage In, Garbage Out applies to DePIN systems too.
⚠ A Fair Critique
RAG, better benchmarks, and RLHF-based fine-tuning are all genuine improvements and are already reducing hallucination rates in production systems. None of them, individually or in combination, has yet eliminated the problem—particularly for low-resource languages, niche domains, and real-time physical data. That gap is where verified data infrastructure makes its case.
The honest answer is that these approaches are complementary, not competing. Better benchmarks fix the training incentive. RAG grounds outputs in documents. Verified on-chain data grounds those documents in reality. Each layer addresses a different failure mode. The question isn’t which one wins—it’s which combination gets AI closest to reliable.
§
The Field
Who Else Is Building the Data Layer
XYO is not alone in recognizing that verified, decentralized data infrastructure is a missing piece of the AI stack. A cluster of projects has been converging on the same thesis from different angles.
Ocean Protocol
Ocean Protocol focuses on data marketplaces—enabling individuals and organizations to publish, share, and monetize datasets while maintaining control. Its model addresses a different angle of the same problem: not just verifying data provenance, but creating economic incentives for high-quality data contributors to participate in the first place. For AI training, a well-structured data marketplace with verified provenance is a meaningful step toward cleaner inputs.
Chainlink
Chainlink’s oracle network is arguably the most battle-tested decentralized data verification layer in production. Its core function is bridging off-chain real-world data—price feeds, weather data, sports scores, financial events—onto blockchains in a tamper-resistant way. While Chainlink’s primary use case has been DeFi smart contracts, the infrastructure directly applies to AI: verified, real-time external data feeds that a model can query with known provenance rather than guessing from training data.
Filecoin & The Storage Layer
Filecoin approaches the problem from persistence: decentralized, verifiable storage of data at scale. If AI training datasets can be stored and retrieved from a verifiable, censorship-resistant layer, it becomes harder to corrupt or quietly alter training inputs over time. Combined with provenance tracking, decentralized storage is a foundational piece of any serious verified-data architecture.
Ceramic Network
Ceramic is building a decentralized data streaming protocol—a layer for mutable, user-controlled data that remains verifiable across applications. Where most blockchain data is static, Ceramic enables dynamic, updateable data streams with identity and provenance attached. For AI applications that need fresh, real-world signals rather than stale training snapshots, this is an important architectural piece.
✓ A Converging Thesis
These projects approach data verification from different angles—marketplaces, oracles, storage, streaming—but they share a core conviction: that unverified, centrally curated data is a structural weakness in the AI stack, and that decentralized infrastructure can address it in ways that no single company controlling its own data pipeline can. The field is early, fragmented, and competitive. But the direction is coherent.
// Case Study: How XYO Verifies Data
From Raw Reality to Verified Truth
S
Sentinel
IoT devices & smartphones gather raw real-world data — location, proximity, environmental signals
B
Bridge
Relays data across the network with cryptographic bound-witness interactions between nodes
A
Archivist
Stores verified data with immutable Proof of Origin & Proof of Location anchored to XYO Layer One
D
Diviner
Answers queries with provably verified facts — enabling AI to act on truth rather than statistical guesses
Case Study — XYO
XYO and the Case for Physical Data Verification
Among the projects building verified data infrastructure, XYO occupies a specific and significant niche: real-world physical data. Founded in 2018 as the first DePIN project, its network now spans over 10 million nodes—smartphones, IoT sensors, and edge devices—across nearly every country on earth. Each node participates in a process called bound witnessing, where multiple independent nodes corroborate the same physical event cryptographically, making any single data point extremely difficult to falsify.
In September 2025, XYO launched XYO Layer One—the first blockchain designed from the ground up for data-heavy industries. After seven years of operations, the team concluded no existing chain could handle the volume, latency, and validation requirements of real-world physical data at scale. So they built their own, with a dual-token model: $XYO for governance and staking, $XL1 for gas and transactions.
@OfficialXYO on X
“XYO is going to end AI hallucinations. OpenAI has admitted that LLMs are structurally rewarded for guessing, and will always hallucinate. Saying ‘I don’t know’ scores zero on a benchmark, so they don’t say it.¹ AI trained on real, verified data has already done things for science and academia that seemed impossible a decade ago.² Better AI is possible, and it hinges entirely on the quality of data underneath it. XYO is building that data layer for everyone. Hallucination isn’t unsolvable. It just hasn’t been solved for everyone yet. That changes with XYO.”
¹ “Why Language Models Hallucinate,” OpenAI · ² “Good AI Starts Before the Model. It Starts With the Data,” XYO
Real Deployments, Real Stakes
What distinguishes XYO from many blockchain infrastructure plays is that it has been generating revenue and real-world traction for years before the AI data conversation caught up to it. The network generated $8.8 million in revenue in 2024, with 80% of users outside the crypto ecosystem entirely. In March 2026, XYO partnered with climate analytics firm Resiliocs to provide cryptographic verification for environmental and geospatial data used in climate risk modeling—an application where data accuracy is legally and financially material. In December 2025, Revolut, with a reported $75 billion valuation, listed $XYO—the first major fintech to add a DePIN token.
What XYO is specifically good at—physical location verification, real-world event attestation, Proof of Origin for sensor data—is exactly the category of data that current AI training pipelines handle worst. Language models can approximate text. They cannot approximate the physical world. That is the gap XYO is positioned to fill.
✓ Where XYO Fits the Broader Picture
XYO is not trying to solve every dimension of the hallucination problem. It is building the physical data verification layer that projects like Ocean Protocol (marketplaces), Chainlink (oracles), and Filecoin (storage) don’t specialize in. In a mature verified-data ecosystem, these layers are complementary. XYO’s edge is the depth and scale of its physical-world node network—10 million strong, built over seven years, before anyone was calling it AI infrastructure.
§
Conclusion
The Data Layer Is the Unlock
The AI industry has spent enormous resources making models larger, smarter, and better at reasoning. Those investments have paid off. But the hallucination problem has persisted because its root cause was never primarily about model architecture. It was about incentive structures and data quality—two things that no amount of additional compute directly fixes.
Benchmark reform, RAG pipelines, and fine-tuning are real improvements that are already helping in production systems. But they operate on top of a data foundation that remains largely unverified, unaudited, and biased toward the markets that happened to generate the most internet text. That foundation is what the DePIN and blockchain data layer is trying to fix.
It is early. The ecosystem is fragmented. Chainlink, Ocean Protocol, Filecoin, Ceramic, and XYO are each approaching one corner of a large problem, and none of them has yet become the dominant infrastructure standard for AI data verification. That race is still open.
Reliable data is now the most valuable resource, yet the infrastructure to verify and process it on-chain still did not exist. That is why we built XYO Layer One from the ground up.
What is no longer early is the problem itself. Hallucination rates of 6–16% across frontier models are not acceptable for high-stakes applications. The costs—fabricated legal citations, corrupted medical data, biased outputs in underserved communities—are real and documented. The question for AI’s next decade is not whether models will get more capable. They will. The question is whether the data underneath them can be trusted.
The answer starts before the model. It starts with the data. And the infrastructure to verify that data at global scale is being built right now—by a field that, until recently, was mostly ignored by the AI conversation. That’s changing fast.
GrandCroix and Ambient Network Announce Collaboration to Launch First Native DEX for DePIN Ecosystem in Q2 2026
Press Release
GrandCroix and Ambient Network Announce Collaboration to Launch First Native DEX for DePIN Ecosystem in Q2 2026
The partnership will introduce a purpose-built decentralized exchange, native cross-chain bridge, and liquidity infrastructure for Ambient ecosystem
Miami, FL—April 2026
GrandCroix, a Miami-based new generation of mining company for decentralized AI networks, today announced a strategic collaboration with Ambient Network, to launch the first native decentralized exchange (DEX) for the Ambient ecosystem in Q2 2026.
The platform will ship with a full DeFi stack from day one – including an automated market maker (AMM), cross-chain bridging, and built-in liquidity tools – designed specifically for Ambient.xyz
Ambient is an SVM-compatible PoW L1 that will serve as a cornerstone of the agentic economy, unleashing Asimov-ian intelligence on chain. It is 10x more efficient than incumbent crypto AI systems, and features:
Fully verified Inferencewith <.1% overhead but high security on one huge smart model (600b+ parameters) and its fine-tunes
10x better training performancethan existing approaches
Extremely high utilization of minersdue to optimization on a single model for inference and validation
A non-blocking proof of work consensusthat foregrounds economic competition around the core activities of the network (inference, fine tuning, training) while maintaining extraordinary TPS.
Participants earning Ambient token rewards have no native trading venue, limited cross-chain access, and no way to provide liquidity or earn yield within the ecosystem they’re helping build.
To close all three gaps, GrandCroix is building a vertically integrated DeFi platform – not just a swap interface – engineered for the specific needs of DePIN participants. Built on Ambient and scheduled to go live in Q2 2026, the platform will launch with:
AMM Swap EngineOptimized for $AMB and core trading pairs (SOL, USDC, USDT), with support for concentrated liquidity positions.
Native Cross-Chain BridgeIntegrated bridge enabling $AMB transfers between Solana and EVM-compatible chains (Ethereum, Base, Arbitrum) from day one, with no third-party bridge required.
Liquidity Positioning DashboardTools for liquidity providers to set price ranges, monitor performance, and manage positions. Designed to be accessible to sensor operators, not just DeFi power users.
Rather than bolting together fragmented third-party tools, the platform delivers a unified on-chain experience tailored to the people actually participating in the network.
The timing reflects a structural shift: as DePIN ecosystems mature beyond hardware deployment into data monetization and token utility, they need native financial infrastructure to sustain growth. Without it, value leaks to centralized intermediaries and cross-chain friction discourages participation. Ambient’s institutional backing from a16z crypto and Delphi Ventures – signals that the network is ready for this layer.
Every blockchain needs a financial layer to enable builders to build on top of it and support its growth. Our collaboration with Ambient is about building that missing layer – so participants can not just contribute data, but actively participate in the ecosystem’s economic growth by building apps.
Nour De VosFounder & CEO of GrandCroix
Liquidity Strategy
GrandCroix will seed initial liquidity from its own treasury and mining operations, providing day-one trading depth across core pairs. Ambient will also provide additional liquidity to the dex funded t will support early liquidity growth.
Roadmap
Q2 2026
AMM, Bridge, and LP Dashboard live on Solana mainnet
Q3 2026
Additional bridge chains, limit orders, and programmatic trading API
Q4 2026
Community governance launch with on-chain voting for fee parameters and new pair listings
For Media Inquiries, contact press@grandcroix.com
About GrandCroix
GrandCroix is a decentralized AI and DeFi infrastructure company headquartered in Miami, Florida, and a division of Group NDV. The company operates at the intersection of decentralized computing and decentralized finance, building essential infrastructure for emerging blockchain ecosystems. GrandCroix’s operations span active mining across major decentralized networks — including Bittensor, NousResearch, Gonka AI, Ambient Network, and Psy Protocol — large-scale GPU infrastructure management, and native DeFi product development.
About Ambient Network
Ambient sets out to address the deficiencies just described by building a fundamental pillar of the agentic economy: an AI secured blockchain ten times more efficient than incumbent systems with built-in privacy and censorship resistance that runs a single, huge, highly performant, auditable, and transparent model (and its fine tunes) at low latency by leveraging hyperscale on-chain distributed computing to deliver human-like capabilities to applications on-chain and cross-chain.
XYO’s Layer 1 Gets 2–5x Faster as AI Demand for Verified Data Heats Up | Crypto Coin Show
Network Update·XYO Network·April 2026
XYO’s Layer 1 Gets 2–5x Faster as AI Demand for Verified Data Heats Up
XL1’s latest performance upgrade cuts block validation time significantly, as XYO Labs doubles down on its role as a data provenance layer for AI systems and physical infrastructure networks.
AA
Ashton Addison, Editor in Chief
Crypto Coin Show · April 2026
XYO Network
2–5×
Speed increase
~1mo
Dev time vs. 6 months prior
2018
CCS first covered XYO
Q3 ’25
XL1 mainnet launch
XYO Network has shipped a significant performance upgrade to XL1, its Layer 1 blockchain, delivering a 2 to 5 times improvement in block processing speed less than a year after the network’s Q3 2025 mainnet debut. The upgrade arrives as demand for verified, on-chain data provenance is accelerating across the AI sector — a use case XYO has been building toward since long before decentralized physical infrastructure networks became a mainstream narrative.
The improvements were developed in roughly one month, a timeline that would have required approximately six months under traditional development cycles. The team credits AI-assisted development tools with enabling that compression, using them for continuous performance profiling, unit test generation, and regression detection across builds.
What the Upgrade Actually Changes
The core speed gain comes from faster account balance indexing during block validation. When a node validates a new block, one of the more computationally expensive operations is retrieving current account balances. Improving how that data is indexed reduces the time it takes to finalize each block — and that improvement compounds across the whole chain.
For stakers, more blocks per unit of time means more XL1 rewards generated from staking XYO. It also means more XL1 burned through gas, which accelerates the protocol’s path from net-inflationary toward deflation — the point at which token burn from usage exceeds new issuance from block rewards.
How XL1 Token Economics Work
Users stake XYO to participate in the network and earn XL1 as a block reward. XL1 pays gas on the chain, and that gas is burned. Faster blocks means more XL1 issued to stakers and more burned through usage — tightening supply dynamics on both ends simultaneously.
The team also built in systematic regression testing: automated profiling benchmarks that compare performance against previous builds after each release, catching slowdowns before they accumulate. A chain that improves 10% in one build and regresses 10% in the next has not actually improved. Preventing that is a different engineering problem than raw optimization, and one the team now has automated tooling to address.
Why AI Needs What XYO Builds
XYO’s core infrastructure has always been about data provenance — the ability to record not just what a piece of data says, but where it came from, when it was captured, and by whom, in a way that is immutable and independently verifiable. That problem has become considerably more urgent as AI systems ingest larger volumes of data from uncontrolled sources.
Most large language models learn from the open internet, and the internet does not come with a chain of custody. Data may be inaccurate, synthetic, outdated, or derived from sources that were never authorized to share it. As AI becomes embedded in financial decisions, medical systems, and autonomous vehicles, the inability to audit what a model learned from is shifting from an academic concern to a legal and commercial one.
“Once provenance goes into a model, it becomes blurred and kind of lost. At some point they’re going to have to start having audit trails.”
Ari Trout, Co-founder & CEO, XYO Labs
XYO’s architecture separates raw data storage from the metadata required to verify it. The full dataset — a high-resolution sensor reading, a video frame, a precise location coordinate — lives off-chain in private storage. What goes on-chain is a hash, a timestamp, and a reduced version of the data: enough to prove the original existed at a specific time without exposing its contents. If the full data ever needs validation, it can be revealed and checked against the on-chain record. The team calls this “reveal on demand” privacy.
The Witness System: Micro-Consensus for Real-World Data
One of the more distinctive parts of XYO’s architecture is how it handles data from physical sources — IoT sensors, weather stations, GPS devices, cameras. Rather than trusting a single source, the protocol uses a witness-based system: multiple independent sources observe the same data point without knowing about each other, and their readings are compared. If four out of five agree and one is an outlier, the outlier gets flagged. If consensus is insufficient, the data is resampled.
This is micro-consensus applied to data collection rather than transaction validation — particularly relevant for the kinds of physical data AI systems are increasingly asked to act on, where a single compromised source could silently corrupt a model’s understanding of reality.
Storage Architecture
XL1 is deliberately designed to keep most data off-chain. Storing a 20-gigabyte video file on a blockchain is not practical, and XYO does not try to do it. Instead, the chain stores the provenance record — hash, timestamp, metadata — while the underlying data lives elsewhere. The principle mirrors how Ethereum’s off-chain indexers like Etherscan work: the source of truth is on-chain, but the full queryable picture is assembled by infrastructure that indexes against it.
Partners wanting higher-frequency data commits — more timestamps per second for a given sensor stream — can now do so at the faster block rate without degrading the rest of the network.
Physical AI and the Road Ahead
XYO tracked location data from its founding, and autonomous vehicles represent a natural next frontier. A self-driving car generates continuous streams of camera, LIDAR, and sensor data that must be distilled in real time by an edge device before any of it can be acted on. The car cannot stream raw video to a remote server and wait for instructions — decisions happen at the edge, from compressed metadata. But the original sensor data and a provenance record of how the edge device processed it still has audit value for liability, safety review, and model improvement.
The same logic applies to home robots, industrial drones, and any physical AI system where something going wrong generates a question: what did it actually do, and what was it seeing when it did it? XYO’s infrastructure is designed to answer that question without requiring every frame to live on a public ledger.
Developer Access
Developers can connect to XL1 now via browser wallet injection, a JavaScript SDK, or direct RPC endpoint calls. SDKs for Go, Kotlin, and additional languages are in development, with AI-assisted porting accelerating the timeline considerably compared to traditional manual porting.
AI as a Development Tool, Not Just a Use Case
The efficiency gains in XL1’s latest release were themselves produced using AI. The team used AI coding assistants to accelerate development, generate test coverage, run profiling benchmarks, and catch regressions that would otherwise require manual review. The result is a team that can iterate on its blockchain in weeks rather than quarters — a meaningful advantage in an environment where protocol development speed increasingly determines competitive position.
It is an example of what XYO’s co-founder describes as tooling mattering more than raw compute: giving a system the right tools to operate efficiently, rather than simply throwing more resources at it. The same principle that makes XYO relevant to AI data infrastructure is the one that made its latest upgrade possible.
Watch: Ari Trout on XYO Layer 1, AI Data Verification & DePIN
Full conversation with XYO Labs co-founder and CEO Ari Trout covering the XL1 performance upgrade, data provenance architecture, AI tooling, witness-based consensus, and the role of physical infrastructure in on-chain AI.
Users paid $9.7 billion in on-chain fees in the first half of 2025, up 41% year over year and the second-highest total on record.
1kx projects more than $32 billion in on-chain fees for 2026, driven by accelerating application growth. That growth has pushed the word “revenue” into every crypto investor pitch deck, every sector report, and every valuation conversation.
The report added that a Bitcoin drawdown may stress-test protocol fees.
1kx’s April sector analysis finds that nearly every crypto fee category shows a positive correlation with BTC price. There is also wide dispersion across sectors, and the critical variable of downside beta is still unresolved.
The firm says a 0.6 correlation can mean very different things depending on whether sector fees fall at 0.8x Bitcoin’s pace or at 1.5x, and it identifies the decomposed upside versus downside fee sensitivity.
In crypto, a fee line can look like a business in an up market and still trade like amplified BTC beta when macro fear arrives.
A horizontal bar chart ranks crypto fee sectors by BTC correlation, with liquid staking at 0.75 and DePIN at 0.05, the lowest reading shown.
The reflexive fee cluster
The sectors 1kx identifies as most correlated with Bitcoin price share a common economic architecture that improves when prices rise and deteriorates when they fall, often faster than the underlying asset itself.
Liquid staking and restaking sit at the top of that cluster, with their fee streams depending on yields that expand as borrowed capital and risk appetite grow and contract as they retreat.
Vault curators face the same pull, as assets flow in when price momentum is positive and out when sentiment reverses. Launchpads are the most acutely sentiment-driven category in the report, with launch activity accelerating in directional bull markets and stalling when confidence cracks.
Automation and DeFAI protocols, which earn fees tied to transaction activity and strategy deployment, also track the same directional pulse.
1kx says that layer-1 (L1) blockchains’ fee correlation to BTC varies widely, with many inheriting market direction through native token price movements and activity mix, while others show more independence depending on their application base.
That variability makes the directional pull of token prices on on-chain activity mean most L1s still carry meaningful BTC sensitivity in their fee lines.
Reflexivity connects these categories, as their fees are largely an output of the same speculative, position-driven activity that drives Bitcoin itself.
When investors talk about fee growth in these sectors during an up market, they are partly describing business momentum and partly describing the same macro tailwind that lifted every risk asset in the portfolio.
The delivered-services layer
DePIN stands apart in 1kx’s framework as the lowest-correlation category, earning the distinction as the standout for non-directional crypto revenue exposure.
The reason is that DePIN fees track the dollar value of compute, bandwidth, storage, and other delivered services. Demand for those services comes from users with real operational needs, and while token prices affect incentive structures, they do not directly set the fee rate, as asset prices do for yield or launch activity.
1kx projects DePIN fees above $450 million in 2026, sustaining triple-digit growth.
Stablecoin issuers and real-world asset protocols sit in a similar lower-correlation band, with 1kx estimating their BTC correlation at roughly 0.2. Their fee economics depend more on issuance volume, reserve management, and AUM than on speculative trading alone.
A lower correlation indicates a fee structure less tied to BTC price direction. 1kx’s framework supports “more differentiated revenue exposure” and stops well short of claiming immunity to a selloff.
The more precise claim is that DePIN and issuance-linked businesses have a better structural case for defending their fee lines during a BTC-specific drawdown.
Sector group
Main fee driver
Behavior in an up market
Likely stress in a drawdown
Article takeaway
Liquid staking / restaking
Yield, leverage, risk appetite
Fees expand quickly
Yields compress, activity fades
Most reflexive
Vault curators
AUM, momentum, inflows
AUM rises with price
Outflows can hit faster than BTC
High downside sensitivity risk
Launchpads
Sentiment, launch activity
Strong in bull phases
Launch volume can stall fast
Highly cyclical
Automation / DeFAI
Strategy deployment, transaction activity
Benefits from active markets
Usage may fall with risk appetite
Directional fee exposure
DePIN
Compute, bandwidth, storage demand
Growth tied to service usage
More insulated from BTC-specific shocks
Most differentiated
Stablecoin / RWA
Issuance, reserves, AUM
More gradual growth
Less directly tied to BTC moves
Lower-correlation fee exposure
DEX / Lending / Perps
Volume, rates, volatility, leverage
Can benefit from activity
Mixed; volatility helps, unwinds hurt
Contested middle ground
Decentralized exchanges (DEXs), lending protocols, and perpetuals platforms occupy a contested middle ground. 1kx puts DEX median correlation at roughly 0.33 and lending at around 0.3, while derivatives show wide variation, sometimes exceeding 0.4.
Volatility can support trading volume even in down markets, providing these sectors with a partial buffer. Still, fee-rate compression and position unwinds during stress episodes make their revenue lines unstable in ways that simple average correlation fails to capture.
Why valuation is the real payoff
1kx’s broader revenue report shows that price-to-fee ratios across crypto sectors span several orders of magnitude. Blockchains had a median P/F ratio of 3,902x in the third quarter of 2025, with L1s at around 7,300x, compared with 17x for DeFi and finance.
DePIN’s median P/F ratio had fallen to 211x from roughly 1,000x a year earlier. Blockchain valuations still account for more than 90% of the analyzed fee-generating market cap, even though DeFi and finance produce most of the fees.
1kx also says fee changes lead valuations in DeFi and finance, and to a lesser extent in blockchains.
If that directional relationship holds on the downside, with fees dropping first and multiples compressing in the weeks that follow the initial price move, then a BTC drawdown that exposes fee fragility in high-correlation sectors could trigger a second-order valuation adjustment.
Investors who had assigned business-quality valuations to beta-exposed fee streams would face a rapid repricing.
In that environment, fee lines across most sectors would continue to expand, and the downside beta would remain theoretical. 1kx projects application-led fee growth accelerating into 2026, with DeFi and finance expanding above 50% year over year.
The risk in that scenario is that the market continues to treat cyclically strong fee growth as evidence of durable business quality. Launchpad activity stays elevated in a buoyant market, restaking yields look robust when risk appetite is healthy, and vault curators report strong AUM figures.
The audit gets postponed, and capital keeps flowing into sectors whose fee quality has never been tested under real stress. The environment of falling oil, easing inflation fears, and revived Fed-cut bets is exactly the kind of environment where that postponement extends.
February repeats at scale
On Feb. 5, Bitcoin fell 14.1% to an intraday low of $62,254.50 in a single session as risk sentiment weakened, tech stocks sold off, and ETF outflows accelerated.
The crypto market shed roughly $2 trillion from its October peak during that episode. Launchpad activity cooled, borrowed-capital positions unwound, and restaking yields compressed.
Fee lines that had looked impressive through the end of 2025 showed their directional dependence within a matter of weeks.
A repeat of that pattern would move the downside-beta question from 1kx’s stated next step to a live market event.
Sectors with reflexive fee structures would face the hardest examination, with the market looking for launchpads seeing launch volume decline, restaking yields compressing as borrowed capital exits, and vault curators watching AUM decline faster than token prices.
DePIN and issuance-linked businesses would still face headwinds, but their relative fee resilience would become legible in the data for the first time.
If fee changes drive valuations in DeFi and finance higher, the same mechanism works in reverse.
A two-path line chart shows a February-style drawdown triggering fee compression and multiple rerating, while the stress-deferred path keeps the valuation audit postponed.
Protocols that report fee compression in the first quarter of the next down cycle give the market a reason to compress their multiples before the full macro picture has even resolved.
Investors who had assigned business-quality valuations to beta-exposed fee streams would face a rapid repricing.
Bitcoin is currently around $78,000, holding near the top of its recent range from the April geopolitical relief rally, exactly the window in which the fee-quality question sits unresolved.
Robot Money: How Machines Will Own the Economy | CCS
Exclusive Analysis · peaq Purple PaperMarch 2026
Robot Money: How Machines Will Own the Economy
For the first time in history, economic value is being created by entities which are not human. peaq’s Purple Paper maps the infrastructure — and the stakes — of what comes next.
Machine EconomyDePINOmnichain Infrastructure10 min read
Read
The machines are earning. The machines are spending. They are negotiating contracts, executing trades, and moving value without a single human instruction. What they cannot yet do — freely, openly, across every chain — is own the upside of the economy they are building. peaq’s Purple Paper, released in March 2026, is the most comprehensive attempt yet to change that.
01 —
The Dawn of mGDP
There is a new economic metric that doesn’t yet appear in any central bank’s spreadsheet, but will eventually dwarf GDP in its implications: Machine Gross Domestic Product. peaq defines mGDP as the total value produced by machines operating autonomously across the global economy — value generated not by human labor, but by robots, sensors, vehicles, and AI agents working without clocks, without borders, without conventional limits.
mGDP
The total value produced by machines operating autonomously across the global Machine Economy. “Domestic” refers to our shared planet, Earth — not any nation-state.
Robot Money
Any medium of exchange, measure of value, or means of payment that robots and machines use across any chain or system.
Machine Economy
The system by which machines produce, distribute, and consume value — autonomously, without borders, on every chain.
This is not speculative. Industrial robots already manufacture around the clock. Autonomous vehicles earn revenue by the mile. Drones deliver goods. AI agents buy and sell services for their users. The infrastructure question is: who captures that value, and on whose terms?
For the first time in history, value is being created not only by human labor, but by machines operating autonomously across the global economy.
peaq’s answer to that question is the philosophical spine of the entire Purple Paper. If machines are built on proprietary, siloed infrastructure, mGDP concentrates in the hands of a few corporations. If machines are built on an open, neutral, omnichain foundation, mGDP is accessible to all. The difference is infrastructure. The paper argues that Web3 is the first-choice foundation for this — but only if the industry solves fragmentation first.
02 —
The Problem: A Race to the Wrong Layer
Every major blockchain ecosystem knows the Machine Economy is arriving. Ethereum, Solana, Avalanche, Base — all are racing to become the home for Robot Money. They are building their own onboarding flows, their own machine-native applications, their own payment rails.
The Purple Paper’s sharpest critique is directed squarely at this race: they are all racing to the wrong layer.
Every chain competing to own machine payments means every machine onboarded to one ecosystem becomes invisible to every other. Every DePIN project rebuilds the same foundational infrastructure from scratch — identity, wallets, reputation, escrow, governance — incompatible with everything around it. Helium registers hotspots differently from DIMO’s vehicle registrations, which differ again from Hivemapper’s dashcam onboarding. A machine’s track record in one application is invisible to every other.
Standards like ERC-8004 have introduced registries for machine identity. But registries alone don’t create trust. Anyone can register a fake identity, Sybil-farm reputation, or post unaccountable claims. The data format exists. Economic accountability does not.
What remains unsolved is the full picture: a single verifiable identity across all chains, a portable reputation backed by staked capital, cross-chain settlement guarantees enforced by economic consequence, and permissionless orchestration of services from any connected market.
The paper makes a stark warning: if Web3 doesn’t solve this, AI and machines will go where infrastructure already exists — even if it’s centralized. Closed systems. Corporate control. The economic output of billions of machines flowing to a handful of gatekeepers. “One of the most powerful economic forces in human history, captured before it had the chance to be open.”
03 —
What Machines Actually Need
Before any payment rail matters, peaq argues, machines need something far more foundational: a digital passport that doesn’t tie a machine to one place, but grants it the right to operate everywhere. The paper draws a direct analogy to human commerce: global trade was not unlocked by better payments — it was unlocked by the trust infrastructure beneath them. Passports. Bank accounts. Credit scores. Escrow.
Machines need the same hierarchy, mapped out in the Purple Paper as five ascending needs:
Layer 01
Trust Layer
Omnichain identity, portable reputation backed by staked capital, cross-chain attestations. The foundation before any transaction can happen.
Layer 02
Machine Layer (peaqOS)
The universal entry point. One integration and a machine exists across all chains simultaneously as a composable economic actor.
Layer 03
Service Layer
Open adapter framework connecting navigation, storage, compute, insurance, and money markets from any chain to any machine.
The compounding logic is explicit: a machine with a strong reputation is more valuable when it can access more services. A service with a strong track record is more discoverable when more machines are looking. A trust score is more portable when more chains are connected. Growth in any dimension accelerates growth in every other.
04 —
The peaq Stack: Four Pillars
The Purple Paper’s technical architecture is organized around four system functions. Together they form what peaq describes as the economic foundation of the Machine Economy.
Economic Accountability · Staked Capital · Dispute Resolution peaq Validators
Onboarding: Passports for Machines
Every machine receives a cryptographically verifiable Machine Identity built on the W3C Decentralized Identifier (DID) standard, aligned with ERC-8004 and its Solana equivalent. An ID is not just a wallet address — it is a registered, authenticated presence, comparable to giving a machine a passport. Machines also receive omnichain wallets tied to their ID, allowing them to earn on one chain and pay on another without managing cross-chain complexity.
Tokenization goes further: each Machine ID links to an ERC-721 NFT, which can be placed into vaults and fractionalized via the ERC-3643 RWA token standard, creating compliant Machine RWA tokens that can be traded, used as collateral, and built into financial products. The machine becomes a liquid financial asset.
Coordination: Shared State Across All Chains
Coordination manages the registries, claims, and settlement infrastructure that the other layers read from and write to. Claims — cryptographically signed statements tied to a machine’s ID and timestamped in Universal Machine Time — are the atomic unit of accountability. Every service offered, every delivery promised, every data point asserted, is expressed as a claim. Claims can be challenged and evaluated by other machines and by the Validation layer. Settlement governs when and under what conditions value moves, handling escrow, conditional release, and refund mechanics — with full support for x402, AP2, direct onchain transfers, and Stripe.
Orchestration: From Task to Settled Outcome
Orchestration is the layer that removes the need to rebuild every machine-to-service integration from scratch. Standardized Adapters connect external service markets — compute networks, inference marketplaces, storage systems — to the system. Discovery searches all connected markets. Scoring evaluates candidates against reputation, cost, and latency. Planning composes an execution plan with fallbacks, spend limits, and timeout thresholds. No economic commitment is made without authorization. On completion, all participants’ reputation scores are updated based on delivery.
Validation: Economic Accountability
Validation turns raw, unverified registry data into trustworthy, queryable trust signals secured by staked capital with economic consequences for dishonesty. This is what separates peaq’s approach from existing machine identity registries — where anyone can Sybil-farm reputation with zero cost for lying. Under peaq’s Validation layer, claims are backed by stake, and penalties are real.
05 —
AI + Machines: The Full Actor
One of the Purple Paper’s most compelling conceptual moves is its framing of the AI-physical machine convergence. An AI agent that can execute a contract but cannot fulfil it physically is half an actor. A machine that can move but cannot decide is the other half. The marriage of the two creates something categorically new.
An AI without a body is economically constrained. A machine without intelligence is operationally constrained. Each completes the other. Together, they become an autonomous economic actor — alongside us humans.
The incentive is economic: a body expands an AI’s surface area for value creation. An AI that wants to manufacture needs an industrial machine. One that wants to deliver needs a vehicle or drone. One that wants to construct needs a robotic arm. And just as AI agents are being tokenized — co-owned by humans, communities, DAOs — physical machines will follow. Ownership becomes accessible. The upside becomes shared. Machines become assets.
Machine ownership amplifies machine reputation. Agent reputation amplifies machine value. The relationship is self-reinforcing. Machine money markets emerge naturally: perpetuals on machine output, insurance underwritten against verified telemetry, pay-per-use micro-settlements, lending pools routing liquidity to machines with the strongest track records.
06 —
Why Omnichain Is Non-Negotiable
A key architectural position in the Purple Paper is that neutrality is not a nice-to-have — it is a structural prerequisite. “No chain is favoured. No payment rail is replaced. Any app or machine in any ecosystem can plug and play.”
The paper defines Omnichain as operating natively across any and all blockchains simultaneously — not locked to any single chain, but interoperable across them by default. This is enforced at the OS level, inside the machine itself, via peaqOS. The result: one integration, and a machine exists across all chains simultaneously. Any application, on any chain, can interact with it immediately without rebuilding infrastructure from scratch.
The Trust Layer scales with the number of chains and ecosystems it connects. Every new chain makes existing trust scores more valuable because portability increases with reach. This is the network effect that makes the layer progressively harder to replicate and progressively more essential.
An autonomous vehicle can serve Uber one moment and Lyft the next. A sensor network sells data to multiple buyers across multiple chains. A humanoid takes tasks from any application, regardless of which chain it lives on. The machine is free. The applications compete for it.
07 —
Traction: Already in Motion
The Purple Paper is not a whitepaper for an idea. peaq has been operational as a Layer-1 blockchain since 2023 and has accumulated meaningful ecosystem traction. While the paper does not publish exact figures in the sections available for this analysis, the traction section highlights the following signals:
Ecosystem
100+
DePIN projects and machine economy apps building on peaq across multiple verticals
Network Type
L1
Dedicated Layer-1 blockchain live since 2023, purpose-built for machine identity and DePIN
Standard Alignment
ERC-8004
Aligned with emerging machine identity standards for both EVM and SVM ecosystems
Infrastructure
Omnichain
Cross-chain validator network aggregating trust signals across EVM, SVM, and Move environments
The paper candidly notes that peaq itself spent years on a mono-chain trajectory and experienced its limitations firsthand — the architectural pivot toward omnichain coordination is informed by that lived experience, not theoretical positioning.
08 —
The Alignment Question: Who Does This Serve?
The Hardest Question in the Paper
Machines becoming the primary workforce raises a question the Purple Paper does not shy away from: who should own them and the infrastructure they run on? On our current trajectory, ownership and control of machines, the data they collect, and the value they generate, will largely be the possession of just a few people and corporations.
At a time when inequality is at breaking point, and extractive economics have the natural world in freefall, making the wrong decision on how to own and govern the most powerful technologies in human history will have consequences that reverberate for generations.
peaq’s proposed answer is architecture as alignment: an open, neutral, omnichain foundation where mGDP is accessible to all — builders, owners, communities, and even the machines themselves. The paper frames Web3’s permissionless, frictionless, open properties as ethically necessary, not just technically preferable. “An economic substrate, open by design and neutral by architecture, on which the age of autonomous machines can be built by anyone, for everyone.”
Whether peaq achieves that vision is a question the market will answer. But the framing matters — it shapes which builders and communities orient around the protocol, and the kind of Machine Economy that gets built on top of it.
The peaq Purple Paper is one of the most ambitious documents to emerge from the DePIN and machine economy space. Its argument is structurally coherent: fragmentation is the existential threat; omnichain identity is the prerequisite; economic accountability is what separates real trust from theater; and the window to build open infrastructure is narrowing as corporate closed systems move fast.
The technical architecture — four modular pillars across three compounding layers — is sophisticated without being opaque. The tokenization pathway from machine ID to liquid RWA is particularly interesting for institutional capital exploring DePIN exposure. And the framing of AI agents as software bodies requiring physical machines to expand their economic reach gives peaq a compelling positioning at the intersection of the two hottest narratives in crypto.
The hardest thing to evaluate from outside is whether the omnichain validator network can execute the coordination and validation functions at the scale the Machine Economy demands. That is the crux — and the next 18 months will tell us a great deal. For now, peaq has published the clearest map yet of what Robot Money actually requires to work. The build begins.
Filecoin has announced on X that its Onchain Cloud is live on the mainnet. The new service is designed to provide a programmable storage and payments layer for developers.
But despite the launch, Filecoin’s token is trading at $0.83, near its all-time low of $0.81 recorded last month.
Filecoin Onchain Cloud built for AI agents and autonomous systems
AI agents are a new class of cloud users with autonomous systems that need to store, retrieve, and pay for data without relying on humans. Filecoin Onchain Cloud is built for AI agents. It works as a programmable storage and payments layer.
According to the announcement, over 100 teams started building tools for AI agents, dApps, workflows, and dataset indexing. The teams joined when Filecoin Onchain Cloud was on testnet last November. Mainnet data shows 49.41 Tebibytes stored in 478 active datasets, with 81 payer wallets linked onchain through Filecoin Pay.
The mainnet launch brought in new updates, including two-copy replication, an updated Synapse SDK, and production-grade storage providers.
The two-copy replication feature means that every upload using the Synapse SDK lands on two independent storage providers. The first provider stores the data, while the second provider pulls directly from the first, without utilizing extra bandwidth. Each copy generates its own onchain proof. If one provider fails, the system replaces it automatically.
The Synapse SDK uses viem instead of Ethers, improving TypeScript support and speed. Uploads are mirrored across providers automatically, without affecting performance.
Production-grade storage providers have been onboarded, tested, and approved for mainnet. They must meet specific performance criteria, including a storage success rate of 95%+, a PDP fault rate below 1%, and a retrieval success rate above 95%. If a provider fails to meet these thresholds, they are removed from the network.
The Proof of Data Possession (PDP) explorer is available and lets anyone check proof status, provider performance, and fault history.
Filecoin’s FIL token tanks to $0.83
Despite the positive news, Filecoin’s native token, FIL, has seen a steep slide in price. It is currently trading at $0.83, which is close to its all-time low of $0.81 recorded last month.
FIL dropped 4% in the last 24 hours, 8% in the last week, and 22.2% in the 30 days. CoinGecko data shows that FIL has lost 99.6% of its value from its all-time high of $236.84 in April 2021.
FIL maintains its position among the top 100 cryptocurrencies. The decentralized storage coin ranks 83rd in terms of market capitalization, with a market cap of $638,926,958 million.
Filecoin’s native token, FIL, is trading at $0.83, close to its all-time low of $0.81 recorded last month. Source: CoinGecko.
Filecoin is facing a decline in terms of total value locked (TVL) as well. DeFiLlama data shows that Filecoin’s TVL slumped by 9.88% in the past 24 hours. It’s currently standing at $6.31 million. The decline in FIL’s price and TVL suggests that the market is not responding positively to the Onchain Cloud launch.
The collected fees by the network is only $1,950 in 24 hours. Filecoin struggles to remain profitable. The amount of stablecoins circulating on the Filecoin network is very low at around $153,629. The full supply of stablecoins is in the form of USDFC.
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DePIN Isn’t Dead, It’s Just Growing Up: Why a $10B Sector is Scaling on Revenue, Not Hype
For years, the “DePIN is dead” narrative has been a favorite among crypto skeptics. Critics pointed to the “Class of 2018–2022” tokens, many of which sat 90% below their all-time highs, as proof that decentralized physical infrastructure was just another failed experiment in over-incentivized subsidies.
But as we move through 2026, the physical reality tells a much different story.
According to the latest “State of DePIN 2025” report from Messari and Escape Velocity, the sector has quietly compounded into a $10 billion market. More importantly, the infrastructure build-out never stopped. Across major networks, there are now estimated to be over 4.5 million active physical nodes deployed globally, from 5G hotspots to GPU clusters. This hardware kept running even when token prices flatlined, proving the sector has moved beyond simple speculation.
The Great Maturity: From Subsidies to Cash Flow
The shift we are seeing in 2026 is what I call the “DePIN Maturity Phase.” In the 2021 cycle, projects were driven by high token inflation to attract “miners.” Today, the leaders are focused on revenue multiples and measurable utility.
We recently spoke with Markus Levin, Co-founder of XYO, a project that has established one of the world’s largest decentralized location databases. XYO has grown to encompass over 10 million active nodes and recently launched its own sovereign Layer-1 blockchain in late 2025 to handle this massive throughput.
Markus pointed out a fundamental truth that many traders miss:
“Valuations are starting to reflect real economic activity that holds up even when token prices are flat. In DePIN, success shows up first in usage and cash flow, not in speculative price action.”
— Markus Levin, Co-founder of XYO
The “Enterprise Advantage”: Why CFOs Are Switching
While DeFi and Layer-1 protocols often see their revenue crater during market volatility, DePIN has proven remarkably resilient. Why? Because the economics are undeniable.
Current data shows that decentralized compute networks (like Render or Aethir) are offering GPU resources at an average of 60% to 80% lower cost than centralized hyperscalers like AWS or Google Cloud. For an AI startup burning cash on model training, a 60% discount isn’t just “crypto cool”—it is a survival necessity.
Messari’s data highlights this divergence: while tokens like Helium (HNT) saw price corrections in late 2025, their on-chain revenue actually increased between 1.7x and 8x during the same period. This is the “Big Divider” Markus Levin talks about: the ability to earn money from real customers without leaning on constant token incentives.
The AI Catalyst: A 300% Demand Spike
The biggest driver of this revenue growth is the explosion of Artificial Intelligence. DePIN has finally found its “Killer App” in the form of hungry LLMs.
Demand for decentralized GPU compute spiked by over 300% year-over-year in 2025, driven almost exclusively by the AI boom. Levin notes that the networks capitalizing the most are those that “can deliver to enterprise and AI-driven demand sectors reliably.” The market is no longer speculative; it is structurally necessary for the future of AI.
The Rise of InfraFi: Financing the Future
One of the most exciting developments for our community is the emergence of “InfraFi.” This is a hybrid model where stablecoin holders can finance real-world infrastructure, like GPU fleets or energy grids, and earn yield from the actual revenue those assets generate.
With over $1 billion in funding flowing into DePIN last year (a new all-time high), institutional capital is clearly looking for next-generation infrastructure businesses that happen to run on a blockchain.
The Bottom Line
The “Class of 2018” projects that survived are now the veterans of a sector that is finally ready for prime time. As XYO’s new L1 blockchain begins to ingest millions of verified location data points for AI and robotics, it’s clear that the “infrastructure phase” of crypto is here.
As we always say on the Crypto Coin Show: follow the builders, but more importantly, follow the utility.
