Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents. |
Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request. The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies. Revolut claims that derived biometric face data was not. The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency. The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself. Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not. Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities. Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen. The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.  The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones. One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons. This means that even if you close your account, your identity documents don’t disappear. And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked. This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals. It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss. The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.
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June 14, 2023
Credit Unions Gear Up to Build Instant Payments Ecosystems as FedNow Looms
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Credit Unions Gear Up to Build Instant Payments Ecosystems as FedNow Looms
BY PYMNTS
JUNE 13, 2023
July 2023 will mark the second time in six years that a new real-time payments rail will go live in the United States.
And with it comes a lot of uncertainty about whether the Federal Reserve will move as “fast” in adapting these rails to the use cases that matter for credit union members. But forward-thinking CUs can leverage members’ trust to forge new instant payments ecosystems.
Kelly Botti, CEO of TruMark Financial Credit Union; Tansley Stearns, president and CEO of Community Financial Credit Union; and Chuck Fagan, president and CEO at PSCU, told Karen Webster that credit unions have been moving cautiously over the last several years as faster payments creep ever closer to reality.
Although it’s true that RTP rails have been in the U.S. since 2017, when The Clearing House launched, credit unions had hoped that a FedNow launch would help to bring broad swaths of the U.S. financial system closer to ubiquity — eventually — as thousands of financial institutions (FIs) opt in.
From PSCU’s vantage point, said Fagan, as part of the FedNow pilot and the advisory group helping to steer the launch, a few observations emerge. For many years, he said, credit unions had not been able to have a direct-to-the-Fed account. Juniper Payments, which was acquired by PSCU just over a year ago, allows PSCU and its owner credit unions to participate more fully in faster payments innovation by fostering that connectivity. But getting there will be a process, as credit unions traditionally have been laggards when it comes to payments innovation.
Changing the Brand
Fagan, Botti and Stearns noted that credit unions see instant payments rails as a way to create their own faster payments ecosystems, fostering new use cases for consumers and small business clients alike.
“The opportunity here is to change the very brands of credit unions,” said Fagan. “That FI-to-FI connectivity will benefit our CUs … but that hesitancy around just how reliable and strong of a technology is in place remains a question — and something that I’m hopeful the Fed will answer in a positive way.”
Webster asked what PSCU’s portfolio of CUs — spanning the breadth of the industry — is seeking in terms of instant payments and the FedNow rollout.
“It’s pretty consistent,” Fagan said, adding that most of PSCU’s credit unions are “considering ‘receive, send and request’ as the main components.”
That’s the general order of things, he said, noting that some credit unions will be more aggressive than others in bringing instant payments to market.
Regardless of progression, said Fagan, CU members should not think about how their money is moved; money movement should happen in as expedited a manner as possible.
And with that guiding principle in place, the low-hanging fruit is ripe to be plucked by CUs, serving members who trust them to help improve their financial well-being. Instant payments can help consumers and families send money to recipients who need money to deal with financial stressors rather than waiting days for funds to settle.
Faster payments can also be leveraged beyond the confines of peer-to-peer (P2P) transactions to help even the youngest clients foster a sense of financial responsibility. Stearns recounted how her CU has a partnership with Greenlight to offer debit cards for kids. Stearns’ daughter wanted to get tickets to see Taylor Swift.
“The question [to my daughter] became, ‘How much money have you saved through your allowance through Greenlight?’” Stearns said. “And now we’re having a financial literacy conversation.”
Younger consumers, the panelists noted, are ever more adept with technology and will want to see their money move when they want it to move.
Partnerships Matter
No matter the use case, said Stearns and Botti, partnerships with the right providers have mattered.
“We have not been fast enough to be able to respond to what our members need,” said Stearns.
And as Botti echoed, “we started our journey around instant payments about four years ago … as we’re starting to look at FedNow, one of the things that we really had to do was right-size our operations.”
TruMark, for its part, will likely embrace inbound instant payments before moving to outbound transactions, where funding (member loans, etc.) will be seeing considerable demand. TruMark, continued Botti, serves low-income members, and instant payments can help salve the pressures of living paycheck to paycheck.
“We’re going to take it one step at a time so we can meet the market,” said Botti.
Both Community Financial and TruMark will find demand from small- to medium-sized businesses (SMBs), which will want faster access to payments, as cash flow, no surprise, is critical.
Artificial intelligence? No conversation would be complete without a discussion of AI, but as Botti and Stearns said, the world needs to stop and catch its breath before throwing AI at every use case in financial life.
“When the Elon Musks of the world are saying we should slow down,” remarked Stearns, “the answer is [to do that].”
Looking ahead, said the trio, faster payments may cannibalize at least some credit and debit spend, as well as ACH, but they will certainly take a bite out of cash. Botti predicted that as costs come down, instant payments will gain ground against other payments modalities.
“We’re going to [be] leveraging third parties and pulling them into our ecosystem so we can offer better products and services for our members that capitalize on this,” said Botti.
The benefits will accrue to the leading-edge CUs, said Fagan, which can act as the central hubs for how money is moved in consumers’ or small businesses’ financial lives because those FIs will wind up being the providers of choice, even, in Botti’s telling of it, the “wallet” for end users.
Added Fagan, “the interest level and confidence is going to grow … the faster pace of moving money is something that starts, and it never slows down.”
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Tokenization is moving from issuance to utility 🔧
Stellar gives institutions the controls they need to issue assets onchain and the infrastructure to put them to work across a growing financial ecosystem.
Hear more from @DenelleDixon on @DefiantNews👇
#RWAFI
00:01:23
🚨 JUST IN — CONST AT EXPLOIT SUMMIT— 🖕The Cabal
@Const of Bittensor ($TAO) just delivered an absolute mic-drop speech on decentralized intelligence and true freedom! 🔥
Key takeaways from the stage:
1️⃣ Mind Outside of the State: 🌐
"Bitcoin was money outside of the state. Bittensor said, let’s build artificial intelligence that’s not controllable by China and the United States."
2️⃣ Fighting Centralized Control: 🛡️
The goal is to build an independent, sovereign intelligence outside the cabal to safeguard against potential impending totalitarianism.
3️⃣ A Message to Regulators:
"And the fact that we exist is a massive middle finger to anybody trying to regulate AI and slow this down."
4️⃣ Harnessing the Swarm: 🤖
Bittensor embraces hyper-intelligent bot swarms to optimize tech, creating open systems where anyone can contribute and have a say.
5️⃣ Empowering Everyone: 🌍
From a core team to 25 validators, to $dTAO holders, and ultimately—everyone on Earth!
🚀 Rule #1: Don't become the cabal. Decentralize ...
00:01:47
🚨 Chutes is being framed as a Hyperliquid-style breakout for decentralized AI inference, with live revenue, verified GPU infrastructure, and a direct challenge to centralized cloud AI 🚨
Chutes is gaining attention as a decentralized AI inference platform that claims to combine real usage, cryptographic verification, confidential computing, and open-source infrastructure into a working production system. The thesis is simple: instead of trusting Big Tech clouds with AI workloads, users get a distributed compute layer built around verification and privacy.
🔑 Key points
🔹 Chutes is live in production and reportedly scaled to more than 1,170 active GPU nodes, including large numbers of Nvidia H200s and Blackwell-class hardware.
🔹 The platform says it has processed nearly 38 trillion tokens since launch across 53 deployed applications and more than 700,000 registered users.
🔹 The team reportedly cut unprofitable usage programs, reduced total token volume, and still improved revenue efficiency, with revenue per GPU rising sharply after removing subsidized traffic.
🔹 Chutes is using post-quantum cryptography, trusted execution environments, and Nvidia confidential ...
🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
A new clash is emerging between legacy finance and crypto legislation after JPMorgan CEO Jamie Dimon reportedly warned that the CLARITY Act could let crypto firms offer bank-like products without bank-level oversight. The dispute is quickly turning into a larger fight over regulation, competitiveness, and who controls the future architecture of digital finance in the United States.
🔑 Key points
🔹 Jamie Dimon reportedly called the CLARITY Act a threat to the financial system, arguing it could allow crypto firms to offer yield-like products while avoiding the capital, reserve, and oversight burdens traditional banks face.
🔹 Senator Cynthia Lummis pushed back publicly, framing the issue as a global strategic race and warning that if the U.S. does not set digital asset standards, other powers will.
🔹 The core tension is whether the bill creates legitimate regulatory clarity or simply opens the door to regulatory arbitrage for crypto platforms operating outside the traditional banking...
👉 Coinbase just launched an AI agent for Crypto Trading
Custom AI assistants that print money in your sleep? 🔜
The future of Crypto x AI is about to go crazy.
👉 Here’s what you need to know:
💠 'Based Agent' enables creation of custom AI agents
💠 Users set up personalized agents in < 3 minutes
💠 Equipped w/ crypto wallet and on-chain functions
💠 Capable of completing trades, swaps, and staking
💠 Integrates with Coinbase’s SDK, OpenAI, & Replit
👉 What this means for the future of Crypto:
1. Open Access: Democratized access to advanced trading
2. Automated Txns: Complex trades + streamlined on-chain activity
3. AI Dominance: Est ~80% of crypto 👉txns done by AI agents by 2025
🚨 I personally wouldn't bet against Brian Armstrong and Jesse Pollak.
🌎 Schumann Resonance Today 10/1 🌎
Right now the Schumann resonance fundamental sits at 7.83 Hz, with geomagnetic activity quiet (Kp 1.0).
Flat Kp, Slow Wind, Solid Earth Below
With a Kp index of 0.00 and solar wind crawling at 294 km/s, geomagnetic conditions are about as settled as they get.
🧠 How Bittensor Subnet 53 (engy) Solves the "Silent Downgrade" Problem in AI 🧠
As open-source AI models close the performance gap with proprietary giants, a massive infrastructure challenge has emerged: How do you actually trust the API serving your model?
When you call a centralized AI inference provider (like Together AI or Fireworks AI), you pay for a specific model at a specific precision—say, a 70B parameter model in FP16. But under the hood, you have zero cryptographic proof that the provider didn't quietly swap in a 4-bit quantized version or a smaller 14B model to slash their GPU and electricity costs.
This silent failure mode is called Model Swapping, and Subnet 53 (engy) on Bittensor ($TAO) is built specifically to kill it.
🔍 1. The Core Innovation: Verifiable Inference
Instead of forcing users to rely on a provider's promise, engy (developed by hanlin.ai) turns open-model inference into a trustless, cryptographically checkable marketplace.
• Merkle Root Pinning: Every model’s exact weights, parameters, ...
☁️ Hippius positions S3-compatible storage as a way to eliminate cloud egress fees ☁️
Hippius is promoting its S3-compatible storage as an alternative for teams that want to keep using AWS tools while avoiding recurring data-transfer charges.
🔑 Key points
🔹 S3 tools remain compatible: AWS CLI, rclone, boto3, AWS SDKs, MinIO clients, Cyberduck, Duplicati, and Nextcloud can connect through standard S3 settings.
🔹 Egress fees are the main target: Amazon S3 charges published internet-transfer rates, while Hippius says data downloads are included.
🔹 10 TB of monthly egress can become expensive: At Amazon’s listed rate of $0.09 per GB after the free allowance, a 10 TB workload could add roughly $913 before storage and request charges.
🔹 Hippius advertises $0 egress: The platform says its plans include data transfer, requests, and retrieval without additional egress fees.
🔹 Pay-as-you-go pricing is available: Hippius lists pricing at approximately $6 per TB per month, billed hourly without a ...
Revolut Leak Shows the Cost of Constant ID Collection
This Is The Income A Family Needs To Live Comfortably In Every US State
Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:
In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.
So… how much does a family of four need in your state?
This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.
The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.
And as Visual Capitalist notes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.
Hawaii follows at $313,165, while California ranks third at $302,682.
Rank State Income needed for family of four (2026)
- 1 - Massachusetts - $329,555
- 2 - Hawaii - $313,165
- 3 - California - $302,682
- 4 - Connecticut - $298,189
- 5 - New Jersey - $295,110
- 6 - New York - $291,533
- 7 - Colorado - $283,213
- 8 - Washington - $281,798
- 9 - Oregon - $280,966
- 10 - Vermont - $280,384
- 11 - Alaska - $272,064
- 12 - New Hampshire - $267,904
- 13 - Rhode Island - $264,659
- 14 - Minnesota - $263,078
- 15 - Maryland - $257,837
- 16 - Maine - $250,931
- 17 - Montana - $249,434
- 18 - Pennsylvania - $247,936
- 19 - Illinois - $244,109
- 20 - Virginia - $242,944
- 21 - Nevada - $242,278
- 22 - Indiana - $241,696
- 23 - Wisconsin - $238,451
- 24 - Arizona - $236,870
- 25 - Utah - $235,789
- 26 - Delaware - $228,134
- 27 - Ohio - $226,221
- 28 - Idaho - $226,054
- 29 - Florida - $223,392
- 30 - New Mexico - $223,142
- 31 - Nebraska - $223,059
- 32 - Missouri - $217,734
- 33 - Georgia - $214,573
- 34 - Michigan - $214,323
- 35 - South Carolina - $212,909
- 36 - North Carolina - $212,410
- 37 - Wyoming - $212,410
- 38 - Oklahoma - $211,910
- 39 - North Dakota - $210,496
- 40 - Kansas - $207,917
- 41 - Iowa - $204,422
- 42 - Texas - $203,424
- 43 - West Virginia - $202,592
- 44 - South Dakota - $201,760
- 45 - Alabama - $198,931
- 46 - Louisiana - $197,933
- 47 - Tennessee - $197,267
- 48 - Arkansas - $195,437
- 49 - Kentucky - $194,854
- 50 - Mississippi - $187,533
Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.
Colorado and Vermont Make the Top 10
As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.
However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.
Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.
Just Six States Come in Below $200,000
Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.
Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.
The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.
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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
Now that AI is moving into the physical world, many are asking a bigger question:
Will these same companies end up controlling robotics too?
It's a valid concern.
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
That movement is decentralized AI.
Why Decentralized AI Exists
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
Why This Matters for Robotics
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
This is where decentralized systems become interesting.
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
Imagine a future where:
- Warehouse robots contribute operational data.
- Delivery robots contribute navigation data.
- Factory robots contribute manipulation data.
- Developers contribute models.
- Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
That vision is beginning to emerge.
Bittensor's Move Toward Physical AI
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
In other words, the pieces are starting to appear.
Not a decentralized robot network yet.
But the infrastructure that could support one.
Beyond Bittensor: The Rise of Physical AI Networks
Bittensor isn't alone.
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
The goal is not simply decentralization for its own sake.
The goal is resilience.
If one server fails, the system continues.
If one company disappears, the network survives.
If one participant leaves, innovation continues.
But Here's the Reality
Decentralized AI faces the same challenge every decentralized technology faces.
Big Tech has resources. A lot of resources.
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
The challenge isn't just decentralizing intelligence.
It's decentralizing intelligence while maintaining performance.
That's much harder.
The Most Likely Outcome
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
The companies building robots may use NVIDIA hardware.
Train on Azure.
Run foundation models from OpenAI.
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
The Bigger Question
The real question isn't whether decentralized AI can eliminate Big Tech.
It can't.
At least not anytime soon.
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
Because the battle for the future of robotics is no longer about hardware.
It's about who owns the intelligence.
And that battle is just getting started.
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