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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September 07, 2023
Polkadot 2.0 and the future of DOT, explained
What is Polkadot 2.0?
The Polkadot network has grown significantly over the past couple of years due to its unique utilitarian features. And now, a new vision for the network's future, which we can call Polkadot 2.0, has been laid out.
Initially announced in June 2023 by Polkadot founder Gavin Wood, the new network will change the way Polkadot assigns its resources. Speaking during the Polkadot Decoded 2023 event held in Copenhagen, Wood took the time to delve into the ideological concepts of the new system.
Wood said Polkadot 2.0 would use a new system for allocating blockspace. The system would be more flexible than the current lease model, allowing developers to buy blockspace as needed, either in bulk or on demand. This, according to the crypto network developer, would make it easier for new projects to enter the Polkadot ecosystem. He explained that the changes would also make Polkadot more attractive to Web2 businesses adopting Web3 frameworks.
The core concept driving the evolution of Polkadot 2.0 centers on the introduction of elastic cores, catering to adaptable computational capabilities. Presently, parachains function akin to fixed CPU cores within the Polkadot supercomputer. However, the upcoming system will allocate resources like Relay Chain security flexibly, responding to real-time needs. This innovation holds the promise of significantly enhancing efficiency throughout the ecosystem.
Another significant change in Polkadot 2.0 revolves around its strategy for coretime allocation. Coretime signifies the time needed for validation and consensus on the Polkadot Relay Chain. In the new version, coretime will be purchasable as block time. This will be achieved using an auction and a pay-as-you-go model with a fixed price.
The table below highlights the transition from fixed slot leasing in Polkadot 1.0 to a more dynamic and tradable coretime asset in Polkadot 2.0, allowing for greater flexibility and customization in coretime allocation.
According to Wood, the design of the new system would be based on the emergent needs of developers to avoid future design problems. He also noted that the new system could increase the liquidity of Polkadot (DOT) tokens by reducing token lockup periods.
Introducing the new model would be a significant milestone for Polkadot, marking a shift away from the current parachains model where blockspace is allocated primarily through an auction process and a fixed lease period. The new system is expected to make Polkadot more accessible and affordable for developers, fostering innovation in the Web3 ecosystem.
conventional blockchains, which frequently exist in isolated silos, incapable of mutual communication. It makes the Polkadot network ideal for crafting decentralized applications utilizing data from multiple blockchains.
As Polkadot is already established as the foundational layer, it alleviates many of the problems programmers face when working with rigid layer-1 chains by providing a more adaptable base infrastructure. Governance of the Polkadot network is carried out directly by holders of the DOT token, whereby token holders actively participate in a voting process to vote on all proposals aimed at making changes to the network. The democratic approach, launched earlier this year and known as OpenGov, grants every token holder a voice in shaping the platform’s evolution.
When it comes to transaction validation, Polkadot employs a nominated proof-of-stake (NPoS) mechanism to select its validator set, focusing on enhancing chain security. Validator nodes are responsible for block production, parachain block validation, and finality assurance, while nominators can support specific validators with their stake, backing trusted candidates with their tokens.
About DOT Polkadot’s native token
DOT, Polkadot’s native token, fulfills three integral roles within the ecosystem, each contributing to the network’s functionality and growth.
The first role of the DOT token is governance. DOT holders can participate in network governance proposals, encompassing critical decisions such as network upgrades and fee adjustments. This participatory function is a cornerstone of Polkadot’s long-term viability. By enabling DOT holders to influence network operations, Polkadot fosters a transparent and democratic governance structure that ensures fairness.
DOT tokens also have a vital operational purpose by bolstering network security and transaction processing through the staking process. Nominators use some of their DOT holdings to select validators, who are responsible for validating transactions and appending blocks to the blockchain. Their engagement in this process is rewarded with additional DOT tokens, incentivizing their contribution to the network’s operation.
This operational facet is essential for the day-to-day functioning of Polkadot, as DOT’s utilization ensures a secure and efficient platform for developers to construct their applications.
Lastly, DOT tokens are used in slot auctions. The current Polkadot blockspace auction model allows projects to obtain a parachain by bonding DOT tokens. The process is pretty straightforward: the project that pledges the highest number of DOT tokens in an auction obtains the parachain slot. Projects can bid for a slot using their own DOT holdings or through a crowd loan. A crowd loan allows projects to raise DOT from their communities and supporters to bid for a parachain slot.
The DOT tokens submitted as collateral during an auction are effectively tied up for the entire duration of the lease. Consequently, the project cannot utilize these DOT tokens for any other purpose during this timeframe.
Following the finalization of the auction, the winning project can link its parachain to the Polkadot Relay Chain. Subsequently, the parachain becomes operational and benefits from the Polkadot network security and scalability attributes. The slot durations are limited to two years.
The future of DOT and Polkadot
The enhanced utility of the Polkadot network is expected to boost the value of DOT tokens if Polkadot 2.0 is well-received and gains traction.
That’s because projects will need DOT tokens to acquire coretime. Additionally, DOT — the native token of the Polkadot network and the primary token for settling network fees — could benefit from increased demand, subsequently driving up its value.
To benefit holders and boost DOT value, entities with surplus coretime may sell it. As a result, the secondary market value of DOT might grow. Additionally, decentralized finance services on Polkadot offer earning potential to DOT holders, which raises the token’s use, value and liquidity.
Moreover, revenue from coretime sales will be channeled into the Polkadot Treasury. DOT tokenholders decide how Treasury funds are distributed through OpenGov. Treasury spend is fluid, and tokens not assigned to projects bidding for funds are periodically burnt.
The burning mechanism creates deflationary pressure on DOT, which is nominally an inflationary token.This balances the overall circulating supply of the token, which could be something to take into account when considering the future value of DOT.
However, potentially more important aspects to consider in this regard are market dynamics, overall adoption and developments within the Polkadot network, such as the arrival of Polkadot 2.0.
https://cointelegraph.com/explained/polkadot-20-and-the-future-of-dot-explained
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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👇
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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.
🧠 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 ...
🖥️ Targon launches Bare Metal and Sandboxes for dedicated and ephemeral compute 🖥️
Targon has expanded its decentralized cloud marketplace with Bare Metal servers and disposable Linux Sandboxes, giving users more options for AI, software development, agents, and testing.
🔑 Key points
🔹 Bare Metal provides full machines: Customers receive direct access to physical servers without a hypervisor, container runtime, or noisy neighbors.
🔹 Full hardware control: Users can manage kernels, drivers, firmware-level GPU settings, and specialized inference stacks.
🔹 Dedicated compute targets demanding workloads: Bare Metal is suited for training, inference, benchmarking, custom drivers, and workloads requiring predictable performance.
🔹 Operators are KYC-verified: Targon says Bare Metal providers are verified to support workload security without requiring a trusted virtual machine.
🔹 Sandboxes are short-lived environments: Users can create isolated Linux environments, run tasks, and discard them ...
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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