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Motion to Vacate Alleging Fraud Upon the Court in SEC v Reggie Middleton et al
March 17, 2025
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Will the SEC Defend Its Alleged Fraud?

Motion to Vacate Puts Crypto Oversight on Trial

On March 13, 2025, Reginald Middleton, founder of Veritaseum, filed a Motion to Vacate the Consent Order and Judgment in SEC v. Reggie Middleton et al., alleging fraud upon the court by the SEC. A letter from his attorney, Franklin Jason Seibert, requested a delay in the SEC’s briefing schedule—originally set for opposition papers by March 14 and replies by March 21—until after the motion’s ruling, with new deadlines two weeks and one week post-disposition, respectively.
 
The modified schedule order (DOC-106) required filings as follows:
  • March 14, 2025: opposition papers, if any, are served on the SEC
  • March 21, 2025: reply papers, if any, must be served by the SEC
The revised scheduling order, as stipulated, would be as follows:
  • Two weeks after disposition of Defendants’ FRCP 60(d)3 motion to vacate Consent Order and Judgment (DOC-61) for Fraud Upon the Court: opposition papers, if any, are served on the SEC;
  • One week later: reply papers, if any, must be served by the SEC

The question now becomes, will the SEC defend "Fraud Upon the Court?"

Digital Asset Securities

The SEC “regrets any confusion” caused by its characterization of these tokens as “crypto asset securities” and “no longer uses the shorthand term,” according to the Sept. 12 filing. Yet, this term was used to claim jurisdiction over the crypto industry raising questions over past cases, including that of Reggie Middleton.

"...by using imprecise language we've been able to suggest the token itself is a security, apart from that investment contract, which has implications for Secondary Sales, it has implications for who can list it...We've fallen down on our duty as a regulator not to be precise. So, tucking into a footnote that yes we admit that now that the TOKEN ITSELF IS NOT A SECURITY..." ~ SEC Commissioner @HesterPeirce

Tom Emmer @GOPMajorityWhip would later introduce the "Security Clarity Act" further questioning the SEC's jurisdiction over some cases.
 
What's even more suspicious, is the VERI token was mentioned about 150 times in the SEC's original complaint but the VERI Token was not mentioned once in the Final Judgment, which begs the question. Is the SEC deliberately hiding any reference to the VERI Token, just as they hid "The SEC is not referring to the crypto asset itself as a security" in a footnote of the Binance case? This becomes a little more questionable when the SEC refused to issue a written reply to the No Action Letter submitted by Jeremy Hogan and the VeriDAO.
 

The SEC's Smoking Guns: Fraud on the Court Allegations

1 - Falsely Claimed Patents were "not novel", "stalled" and would never be granted, claiming Reggie "misled investors about the status of Veritaseum’s IP". A total of 7 patents have since been granted with 3 in the US (US11196566B2, US11895246B2, US12231579) and 4 in Japan (JP6813477B2, JP7204231B2, JP7533974B2, JP7533983B2). These patents titled "Devices, systems, and methods for facilitating low trust and zero trust value transfers" are foundational to DeFi, Tokenized Assets, NFT's, Stablecoins, Proof of Stake and Proof of Work.

Coinbase filed a IPR2023-00751 in an attempt in invalidate these patents. The USPTO upheld the patents denying the IPR challenge based on "lack of merit" further strengthening the validity of the patents
 
2 -VeADIR Platform Functionality - a live demonstration was performed in front of SEC staff and days later Reggie was told to shut it down, Tenreiro then claimed the platform was not functional. VeTest Channel on YouTube has videos that prove the functionality but as shown in his affidavit, the owner was threatened by Tenreiro "...the line of questioning quickly turned aggressive, abusive and threatening" and told to cease making videos "...through threats of multiple felony charges against me for supporting Mr. Middleton, testing his software and publicizing the results through my YouTube Channel".
 
3 - Misrepresented Ownership of Kraken Corporate Account as Personal - Jorge Tenreiro failed to correct the record after expert witness Patrick Doody corrected his statements "I understand now that the account is titled in the name of Veritaseum LLC", found on the last page of his 2nd declaration. Also detailed on page 20 of the SEC RICO Dossier
 
Reggie Middleton a NY resident points out that Kraken is not licensed to do business in NY making it impossible for him to have a personal Kraken account as found on Krakens Support page under Geographic Restrictions.
 
4 - Misrepresentation of Asset Flow - by falsely alleged vast sums of money were flowing into Middleton’s personal account, misleading the court about asset misappropriation of funds. This point becomes moot as the account is proven to be a Corporate account as evidenced in point #3 and also in a 423 page reply to the TRO.
 
5 - False Allegation Regarding Agreements - alleging the defendants were merely negotiating deals with the Jamaican Stock Exchange (Memorandum of Understanding) and Nigerian Stock Exchange(Joint Venture Agreement), when signed agreements were already in place. The SEC's aggressive and actions caused the cancellations of these agreements. FOIA request have been submitted seeking communications between the SEC and the JSE.
 
6 - Misrepresenting Trading Activity on Etherdelta - as manipulation when it was publicly announced prior as a liquidity test of the new platform also found on page 49 Veritaseum's reply to the TRO Testing EtherDelta as a method of distributing post-Offering Veritas tokens. Anyone interested in buy VERI please visit https://etherdelta.github.io and let me know”
 
7 - Misrepresentation of CEO Payments - falsely misrepresented that $1.7 million in periodic payments to Middleton over 27 months (about 2 and a half years), was dissipation of assets, misleading the court about CEO compensation. this is detailed on page 55 of the SEC RICO Dossier.
 
8 - Nature of International Payments -The SEC's TRO action misrepresented payments to overseas contractors as asset dissipation. Daneillo would later correct her findings to show they were in fact payments to overseas contractors. The SEC continued to imply that the payments were part of an effort to hide assets to thwart judgment relief, which is clearly a disingenuous characterization (SEC Memo of Law in Further Support of TRO).
 
9 - Unethical Conduct in No-Action Letter Request - Involved himself unethically in a No Action Letter (NAL) request meeting, breaching the SEC’s ethical separation as found in the Bar Complaint against Jorge Tenreiro.
 
10 - Harassment of VERI Token Holders - aggressively pursued VERI token holders to coerce them into giving evidence against Middleton, despite them stating they were not victims of Fraud. Victims of harassment have either come forth with notarized affidavits (Lloyd Cupp, John Doe) explicitly and verbosely describing the coercion, or have indicated fear of retaliation due to their treatment after interaction with Mr.Tenreiro.
 
11 - The sanctions against the SEC for lying to the Court to issue a Temporary Restraining Order in the Debtbox case further exemplifies the SEC's tactics in issuing TRO's. Quoted from a letter by Senator at the time JD Vance to Gary Gensler “It is difficult to maintain confidence that other cases are not predicated upon dubious evidence, obfuscations, or outright misrepresentations”. Parallels of the TRO issued in the Debtbox and that of Veritaseum.
 

Timeline of Events

Aug 19, 2019: All allegations against Reggie were addressed and rebutted in a strong
423 page reply to the SEC emergency TRO but days later the SEC would ignore the evidence provided and the TRO was granted regardless forcing a Consent Order and Final Judgment
 
March 10, 2021: Jorge Tenreiro argued the SEC's case against Ripple’s Christian Larsen for aiding and abetting unregistered securities sales was valid, highlighting Tenreiro's aggressive enforcement approach.
 
Oct 13, 2022: SEC v Middleton Case Information claiming he harmed investors yet no token holders came forth as witnesses for the SEC.
 
March 2024: “Gross Abuse of Power” US Court SEC for Misrepresenting Evidence to obtain a TRO against Debtbox. US Court Memorandum Decision and Order. An analysis comparing this to the SEC's TRO against Veritaseum can be found on page 42 of the SEC RICO Dossier.
 
Sept 2024 - SEC v Binance - Footnote states Token itself is not a security.
 
Oct 4, 2024: A Bar Complaint was filed against Jorge Tenreiro by the VERI Community.
 
Oct 31, 2024: A 96 page SEC RICO Dossier supported by over 1800 pages of evidence was also released by the VERI Community.
 
Dec 6, 2024: The Attorney Grievance Committee forwarded the Bar Complaint back to the SEC OGC. The VERI Community issues a letter to the AGC asking it to reconsider investigating the complaint.
 
Jan 2025: SEC admits in a footnote "that a token itself is not a security" revealing that "Digital Asset Securities" is a made up term used to claim jurisdiction over digital assets https://x.com/SovereignRiz/status/1881316167987388904
 
March 9, 2025 - Tom Emmer introduces the "Securities Clarity Act" stating Tokens are separate from an investment contract.
 
Feb 5, 2025: Tenreiro has since been reassigned to the IT Dept. WSJ Article "SEC Ousts Top Litigator Who Battled with Crypto Giants"
 
 
 

Other Articles exploring this topic in more detail

 
 
 

Source links:

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Finally, any statements regarding individuals, entities, or organizations are not intended to malign, defame, or harm the reputation of those mentioned. Any resemblance to real individuals or incidents is purely coincidental, unless otherwise explicitly stated, and the authors urge readers to exercise caution and discernment when interpreting the information presented.
 
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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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