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The Evolution of Blockchain for Bank Payments Modernization
May 30, 2023
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Blockchain: 1985-2014

The idea of cryptocurrency first emerged in the late 1980s.  But it was only in 2008, when Satoshi Nakamoto published the white paper Bitcoin – A Peer to Peer Electronic Cash System, that Bitcoin, the first cryptocurrency, was realized. Cryptocurrencies were designed to be anonymous and have no central authority. Therefore banks, as regulated entities, could not use cryptocurrencies.

In 2012, Ripple was launched. The system was built upon a distributed open-source protocol, and using XRP, a Ripple-issued cryptocurrency. While Ripple had the aspiration that banks would adopt the system, the reliance on XRP was a nonstarter for banks. Ripple eventually introduced RippleNet, separating the Ripple messaging service but without the use of XRP or tokenization.

Where Ripple was a centralized, permissioned payment network, the Stellar network was launched in 2014 as an open, semi-permissionless network. Stellar Lumens, another cryptocurrency, act as a medium of exchange. In 2017, IBM announced that they would attempt to use the Stellar protocol to build a payment network for banks, but this was never completed.

The reliance on cryptocurrency as a medium of exchange for interbank payments was a hurdle banks could not get past. It wasn't until the focus on bank solutions shifted from cryptocurrency to the use of blockchain and distributed ledger technology to build bank payment infrastructure that blockchain gained traction with banks.

Generation 1: The blockchain consortium

The first of these initiatives was R3. Rather than focusing on the issuance of a cryptocurrency, R3 started as a consortium funded by HSBC and others in 2017.  In 2018, they received additional investment from another 44 banks. The idea was for each bank to not only invest in R3 but to use the R3 technology to enable payments. However, the early R3 model assumed that the banks had the in-house technical resources to develop payment systems using the R3 technology. This proved to not be the case. Conceding this, R3 has changed its business model to partner with consulting firms like Accenture to take the R3 technology and build custom implementations for banks.  

UBS funded some early research on what was called the Utility Settlement Coin Project. By 2019, this evolved into a consortium renamed Fnality International. Members included 15 multinational banks. Fnality has yet to launch a service.

The original concept of a consortium of banks building interoperable systems using common R3 or Fnality technology appears to have fallen by the wayside. None of the bank investors in R3 or Fnality appear to be actively supporting their activities.

The rise and fall of Facebook Libra/Diem is a fascinating illustration of the challenges facing a consortium. The initial Libra Association included Facebook, Visa, Mastercard, Stripe, PayPal, and several other technology companies. (Interestingly, neither digital wallet providers Apple and Google nor any banks joined the Libra Association.) Beginning in May 2018, this consortium funded the development of a digital coin tied to a basket of fiat currencies and securities. The project ran into regulatory headwinds and, in November 2020, was downsized to a set of single-currency stablecoins and later, only a USD denominated stablecoin. In January 2022, after a number of members left the Diem Association, it was decided to wind down operations and sell the intellectual property to Silvergate, a crypto-focused bank.

Generation 2: The megabank blockchain projects

With the demise of the consortium model, blockchain projects became the province of a handful of very large multinational banks. Perhaps the best known of these projects was the efforts of JP Morgan Chase and the launch of their JPM Coin in February 2019 (subsequently rebranded Onyx Coin Systems). This involves the creation of a USD stablecoin issued by JPMC.

The New York Times has reported that in 2020, “Bank of America filed the biggest number of patent applications in the bank’s history, including hundreds involving digital payments technologies. It’s unclear how exactly the bank plans to use its technology, but it was partly driven by the desire to keep customers within the bank’s systems rather than lose them to scrappy cryptocurrency start-ups that allow them to transfer money free.”

These megabank projects suffer from a number of limitations:

  1. They are costly. Hence limited to only the largest multinational banks with a user base large enough to generate (or at least theoretically support) the necessary return on investment.

  2. Blockchain engineers are generally not attracted to banks. While large banks may employ engineers, these are traditional engineers with backgrounds in relational database technology and conventional transaction processing. Blockchain experts are typically trained at blockchain companies and often leave to form new blockchain companies.   

  3. In some cases, large banks developing technology may be unwilling to license their technology to competitors.

  4. Conversely, other banks may be reluctant to use the developing bank’s product. For example, it is hard to imagine a Citi or Bank of America using a JPMC-issued stablecoin and connecting to JPMC systems to process their payments.

Generation 3: The independent blockchain-aaS

Recognizing the limitations of a megabank project and the opportunities it creates, we argue that it is time for a new generation of providers. These providers would be independent of any single bank and technology-driven with expertise in both cutting-edge blockchain and the esoteric world of conventional payments.

Benefits

The Gen 3 provider enables:

  • Creation of a network of banks like Gen 1, but without individual banks having to do the development.

  • Faster time to market. The services would be delivered in a SaaS model.

  • Shared cost. Development costs could be capitalized and recovered across multiple bank customers.

  • Ease of integration into existing infrastructure. The new system would employ open APIs to enable easy integration into existing bank systems. These APIs could be integrated with key banking systems from vendors like FIS, FiServ, and ACI Worldwide. This means banks utilizing these systems would have an easier adoption path.

Implications for product

The emergence of a Gen 3 solution that can be shared across a network of banks has implications for the product/service:

  • Need for high throughput and low latency to easily serve today’s use cases and scale to tomorrow’s use cases. Target: 1M transactions per second at < 500ms latency.

  • Private permissioned DLT for speed and security with byzantine fault tolerant (BFT) consensus algorithm to deliver the highest system resilience and robustness.

  • Banks and payment service providers connect using APIs.

  • Payment information transparency for banks to meet regulatory obligations.

  • Optionally, provide regulators direct access to transaction information.

Implications for operations

It is likely that the Gen 3 provider will be a nimble, technology-first company. Exactly the kind of company that banks and regulators will have concerns about running critical financial infrastructure. As a result, we envision:

  • Trusted third-party operators will operate the Gen 3 platform - a local operator for each ledger. One ideal candidate for an operator would be the Payment Clearing House in a country. The Clearing House is often (a) owned jointly by the largest banks in the country, (b) has experience running key financial market infrastructure, (c) is already connected to the banks, (d) is trusted by the regulators, and (e) meets the data sovereignty requirements of many countries.

  • The role of issuing banks is unchanged in this model. The Gen 3 service acts as a parallel payment system. Issuing banks initiate payments (or requests for payments), hold customer funds, extend credit, provide customer service and reporting, and are responsible for KYC, AML, CFT, and sanction screening.

Implications for partnerships

Gen 3 providers may face challenges to engage decision makers at central banks and commercial banks. We predict that they would do best in establishing partnerships with leading technology suppliers who already provide software and services to banks. These suppliers are trusted and can stand behind the new Gen 3 provider. Some implications for these partnerships:

The message to Gen 3 providers and partners is: "don’t get greedy". There is enough for everyone and the opportunity is massive. Collaboration will lead to better interoperability and a higher chance of success as a result.

Thank you @MrmanXRP for sending us this Link

 

 

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​🚨 BREAKING: The Final Clarity Act Bill Text is Official! 🇺🇸🔥

​After more than a year of back-and-forth, the final draft is here—incorporating 126 last-minute amendments requested by Democrats just 24 hours before the vote. 🤯

​Key updates in the final text:

​Strict Ethics Oversight: Expanded restrictions now cover federal officials, judges, and spouses, with Senator Lummis noting Trump opted in voluntarily.
​Banking Safeguards: Treasury gains authority to step in if high-yield stablecoins start draining liquidity from community banks.

​Builder Protections: Civil safe harbor provisions have been strengthened to explicitly cover crypto miners and network validators.

​Market Integrity: Added guardrails target conflicts of interest and affiliate trading while leaving state consumer protection laws intact.

​Does it have enough momentum to secure 60 votes tomorrow? 👀

00:00:09
September 13, 2026
RFK Jr: "The Pandemics are coming from labs. ALL OF THEM... Lyme, COVID, RSV, HIV & Spanish Flu came out of a vaccine lab." ☠️ 💉

"Gain-of-Function Vaccine research has created the worst plagues in our history."

"We can go down the whole list of diseases... It’s just a disaster. It’s given us no benefits. It’s given us everything from Lyme disease to Covid, and many many other diseases. RSV, which is now one of the biggest killers of children, came out of a vaccine lab."

"There’s strong evidence that even Spanish flu came from vaccine research."

"There’s plenty of evidence that HIV also came from a vaccine gain-of-function lab program. "

"The 'PANDEMICS' are coming from labs... ALL OF THEM."

00:04:00
September 13, 2026
🚀The industry has gotten incredible at teaching robots

🚀The industry has gotten incredible at teaching robots to move, sprint, and imitate body dynamics. But as Michael Parker (@bittensormax) points out in The UMI Thesis, there’s still a massive missing piece in Physical AI: Motion Understanding.

✨ Key Takeaways:

🔹Looking Human vs. Understanding Humans: Robots can execute impressive physical feats, but they still struggle to reliably read non-verbal human cues in context.

🔹Motion is Meaning: A gesture, hesitation, or glance changes completely depending on posture, timing, and surrounding context.

🔹Beyond Pixels: True intelligence requires mapping human intent and sequence across time—not just processing raw frames.

🔹The UMI Intelligence Layer: As robots enter hospitals, factories, homes, and stores, Bittensor’s SN78 @umi_sn78 UMI (Universal Motion Intelligence) aims to own the critical layer that translates human movement into real meaning.

The future of robotics isn't just about how machines move—it's about how ...

00:04:54
🚨 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 ...

🚨 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 🚨
🚨 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...

🚨 JPMorgan’s criticism of the CLARITY Act is fueling a fresh power struggle over who gets to write America’s crypto rules 🚨
👉 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.

👉 Coinbase just launched an AI agent for Crypto Trading

Taiwan equities are now live on Pvth Pro

Pyth Pro is Pyth's real-time market-data service, giving exchanges, fintechs, trading platforms, and financial applications one consistent way to access prices across asset classes, regions, and local market sessions.
This launch is the next step in Pyth Pro's broader Asian equities expansion, bringing six Taiwan-listed companies into the same market-data laver used across Pyth Pro's cross-asset catalog:

• TSMC
• Foxconn
• Quanta Computer
• Wistron
• MediaTek
• Unimicron Technology

Each feed follows Taiwan's local market schedule, sc applications can access Taiwan equity data during regular trading hours through the same integration used across Pyth Pro.

For teams building global products, this makes it simpler to add Taiwan market data alongside other assets without creating a separate workflow for every new market
Taiwan, in real time

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⚙️ Refinery turns Bittensor Subnet 125 into an optimizer research market ⚙️

Refinery (SN125) is creating a competitive marketplace where miners develop optimization techniques for AI systems, while validators evaluate how much those improvements increase speed, efficiency, or model performance.

🔑 Key points

🔹 Optimization is the product: Miners compete to improve models, workloads, algorithms, and infrastructure rather than simply producing larger systems.

🔹 Multiple objectives can be tested: Optimizers may target speed, cost, memory usage, accuracy, energy consumption, or hardware efficiency.

🔹 Validators measure real gains: Submissions must be evaluated against consistent workloads to determine whether improvements are genuine.

🔹 Competition encourages discovery: Independent contributors can explore optimization strategies that a centralized research team may overlook.

🔹 Results can benefit other subnets: Better optimization could improve inference, training, robotics, scientific ...

⚡ While frontier labs debate AI’s pace, Bittensor is accelerating through open competition ⚡

As major AI labs debate timelines, safety, and the limits of scaling, Bittensor is taking a different path: allowing independent subnet teams to build, test, and deploy specialized AI systems in parallel.

🔑 Key points

🔹 Parallel experimentation: Bittensor allows multiple teams to work on inference, compute, robotics, cybersecurity, scientific research, data, and agent systems at the same time.

🔹 No single roadmap controls the network: Progress does not depend entirely on one company deciding which research direction deserves funding.

🔹 Subnets specialize: Each subnet can target a narrow problem and compete using its own evaluation rules and incentives.

🔹 Real products are emerging: Recent subnet activity includes AI models, GPU rentals, autonomous drones, confidential computing, scientific research tools, and security services.

🔹 Competition accelerates iteration: Miners and developers are ...

September 13, 2026
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Revolut Leak Shows the Cost of Constant ID Collection
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.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

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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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 notesMassachusetts 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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