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A New York Court Is About to Rule on the Future of Crypto
The Securities and Exchange Commission’s case against Ripple over the XRP token will establish a critical precedent.
March 22, 2023
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THREE DAYS BEFORE Christmas 2020, the US Securities and Exchange Commission charged Ripple, a company based in San Francisco that provides the infrastructure for cross-border payments, and two of its executives with conducting a $1.3 billion unregistered securities offering by selling a cryptocurrency, XRP. The same day, Ripple announced it would “fight.”

After more than two years of protracted legal conflict, all of the evidence has been heard, and there remains nothing left but for Judge Analisa Torres of the Southern District of New York to issue a verdict. Those with a stake in the outcome, which will reverberate throughout the crypto sector, have been attempting to divine when a judgment might land, based on the judge’s past ruling patterns. Some believe a resolution is only days away. 

In bringing the charges, the SEC has staked a claim to jurisdiction over cryptocurrency. At the center of the suit is the question over whether XRP, the crypto token on which Ripple’s services are based, should be classified as a security—a tradable financial instrument like a bond or derivative—or something else entirely.

If the court rules that XRP is a security, it would follow that almost all other crypto tokens are too, making them subject to the SEC’s supervision. Not only would this impose burdensome registration and reporting requirements on crypto firms, but it also may have legal consequences for entities that have issued tokens or helped people to trade them without SEC approval. Even large US-based exchanges may suddenly find themselves in the crosshairs. 

That, says defense lawyer John Deaton, who supplied expert testimony on the case on behalf of holders of XRP, would be “very bad news” for crypto businesses.

In the absence of legislation that makes clear the classification of crypto assets in the US, the question of whether they should be treated as securities has to be assessed on a case-by-case basis through the application of the Howey test. Under the test, an investment contract (in this context, a security) is defined as “an investment of money, in a common enterprise, with a reasonable expectation of profits, to be derived from the efforts of others.”

When the SEC charged Ripple and its executives, it declared that XRP met these criteria and that, by raising funds through the sale of XRP, the company was in violation of federal securities law. 

Although Ripple is not itself the issuer of XRP, which sits atop the open source XRP Ledger, some of its executives were part of the group that developed the token. The firm had also received a donation of 80 billion XRP in the early 2010s (worth around $30 billion at present) to develop use cases—some of which it sold off.

Ripple is challenging the SEC’s analysis on two fronts: It is arguing that its sale of XRP does not qualify as an investment contract because no contracts were signed when the transactions took place, and separately, that XRP does not satisfy the prongs of the Howey test.

Stuart Alderoty, chief legal officer at Ripple, says the company is certain that XRP does not meet any of the Howey criteria, but that it is particularly confident that there is no common enterprise—a group undertaking that affects the fortunes of XRP investors—among XRP holders, only “common interest.” 

However, the SEC has long said that the majority of cryptocurrencies are securities, because people invest with the goal of turning a profit and, although tokens sit atop decentralized blockchain networks, many projects are in practice sufficiently centralized to meet the definition of a common enterprise.

The SEC declined to comment for this article.

Speaking at a conference in September, SEC chair Gary Gensler called on crypto businesses to register with the agency. “Given that many crypto tokens are securities, it follows that many crypto intermediaries are transacting in securities and have to register with the SEC in some capacity,” he said.

However, US government bodies have disputed the SEC’s right to regulate crypto. In a lawsuit filed on March 9 against crypto exchange KuCoin, New York Attorney General Letitia James alleged that ether (the cryptocurrency of the Ethereum network), among other crypto assets, should be treated as a security. But the Commodities and Future Trading Commission (CFTC), another US financial regulator, contends that ether is a commodity and should therefore come under its purview.

The SEC has been pushing the crypto industry hard over the past four months following the implosion of crypto exchange FTX in November, which took hundreds of millions of dollars in customer funds down with it. Since then, the SEC has launched a series of quickfire actions against crypto businesses serving the US market.

In January, the regulator charged crypto exchange Gemini and crypto lender Genesis Global Capital over a service that allowed US customers to earn interest on their assets, which the agency alleged was an unregistered securities offering. In a Twitter thread, Gemini cofounder Tyler Winklevoss called the charges “a manufactured parking ticket” and announced that “we look forward to defending ourselves,” but neither the company nor Genesis responded to a request for comment.

This was followed in February by a settlement with another exchange, Kraken, which agreed to halt its crypto staking service in the US, and a threat to sue crypto firm Paxos over its BUSD stablecoin. In both instances, the SEC again claimed the parties were in breach of securities laws. In a statement, Paxos wrote that it “categorically disagrees with the SEC.”

However, the agency has suffered setbacks over the past few weeks in bids to block crypto exchange Binance from purchasing the assets of bankrupt crypto lender Voyager Digital, and asset management firm Grayscale from bringing to market a bitcoin exchange-traded fund (ETF).

Because the case is being held in a district court, the outcome will not set a “binding precedent,” says James Filan, a defense lawyer and former federal prosecutor. Therefore, the verdict is not required to be factored into judgments on similar cases moving forward. However, the judgment may establish what’s known as “persuasive precedent,” he says, which could influence the thinking of judges in future cases.

If the SEC were to win, it would be handed the advantage in its “turf war” with the CFTC, Filan says. The crypto industry will not escape supervision in either scenario, but the CFTC is seen by the exchanges (including FTX) as a soft touch by comparison.

If the SEC is established as crypto's main regulator, companies may need to register their US-facing services with the agency. But many crypto firms have had a “hall pass” to operate in gray areas, says securities attorney Aaron Kaplan. An SEC victory would mean they have to disentangle their various business lines to meet regulatory requirements.

“This would be very difficult for many crypto companies to accomplish,” Kaplan says. “As such, [they] could choose to move and operate outside the US … Those that don’t will need to evolve and come into compliance—or die.”

Ripple has already announced it will appeal in the event of a loss. Doing so would send the case to the Second Circuit—and then potentially the Supreme Court. Alderoty does not expect the SEC to appeal, but instead to argue the result was an aberration. However, Filan suspects the agency will feel it has little choice if it hopes to preserve its claim to jurisdiction. 

As a consequence of the lawsuit, Alderoty says, Ripple has been forced to pull back on efforts to expand in the US and focus instead on other territories, like Singapore. Since the charges were brought, the firm has chosen to operate practically “as if the SEC has won,” to ensure the business remains viable no matter the outcome. If Ripple wins the case, it will be able to lean back into the US.

Crypto markets are likely to react to the judgment when it comes, as traders price in either a renewed clarity over the legality of crypto services provided in the US, or the prospect of further enforcement action.

“We know the crypto market will quickly incorporate the verdict, and token prices will almost certainly be affected,” says Katherine Snow, director of legal at crypto research firm Messari.

Nobody knows precisely when the verdict will land; it could be days, weeks, or even months. Until then, the crypto industry must wait, because “anybody trying to predict the outcome,” Filan says, “is either going to be lucky or wrong.”

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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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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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