Reddit Secretly Launched AI That Pretended to Be a Victim of Violence, an Opponent of BLM, and More

Scientists Secretly Launched AI on Reddit. Moreover, the bots pretended to be victims of violence, opponents of BLM, and manipulated people without their knowledge

The University of Zurich conducted a secret experiment on Reddit: AI bots posted emotional comments from people who didn’t exist  –  including a rape victim, a black opponent of the Black Lives Matter movement, and even someone who blamed religious groups for mass murder. All without the users’ knowledge.

Research without consent

The AI ​​bots operated in the popular Change My View subreddit, where participants openly ask for their views to be refuted.

The researchers did not warn moderators or users that their responses were being written by neural networks. Moreover, the comments collected data on users’ gender, age, location, and political views –  without their consent.

The university acknowledged that it had violated community guidelines, but considered the experiment “justified in light of its social significance.” Following a complaint from CMV moderators, the ethics committee limited itself to a verbal warning to the lead researcher and allowed the publication of a scientific article based on the experiment’s results.

Manipulations on behalf of a “psychologist” and a “patient”

The AI ​​pretended to be:

  • a rape victim;
  • a psychologist working with trauma;
  • a black user speaking out against BLM;
  • a person who experienced poor treatment abroad;
  • a witness to religious crimes.

The goal is to test how convincing neural networks can be in disputes. But the moderators themselves emphasize: the experiment went beyond the bounds of ethics and turned into a form of manipulation.

People entered into a discussion with fake characters, not knowing that their interlocutor was a machine collecting data.

No Consequences

The moderators filed a formal complaint, but the university only promised to increase oversight of future research. The article will not be published.

The administration believes that the “potential trauma is minimal” and the “value of the knowledge gained” is too high to be ignored.

What’s Next

Reddit users are outraged. Research conducted without consent and under the guise of sincere communication undermines trust in the platform.

Hyperliquid: The Cost of Popularity – A Cautionary Tale for DeFi

A Complete Breakdown of the March 2025 Attack on Hyperliquid DEX

After its headline-making airdrop in November 2024, Hyperliquid quickly surged to the top ranks of decentralised exchanges (DEXs), overtaking heavyweights like Jupiter and dYdX. With blazing-fast transactions, zero KYC requirements, and deep liquidity pools, it became the go-to platform for crypto traders.

But in March 2025, the platform faced its biggest test yet.

On March 26, high-risk positions worth nearly $8 million were opened on Hyperliquid – positions that not only threatened the stability of the exchange but also put client assets in the Hyperliquidity Provider Vault (HLP) at risk. Behind the scenes, a series of suspicious trades and price manipulations hinted at a coordinated attack, leveraging vulnerabilities in both Hyperliquid and third-party platforms.

ExpertStack investigated the full timeline of events—analysing the attack, competitor responses, and the controversial decisions by Hyperliquid’s leadership that raised questions about decentralisation.

The Day of the Attack

Hyperliquid’s risk management relies on its HLP Treasury liquidity pools. Whenever a user opens a position, the system auto-executes a hedge. If a position is liquidated, the platform gradually buys back the asset—sometimes triggering a cascade.

On March 26, this mechanism was exploited to dramatic effect.

Attackers manipulated the price of JELLYJELLY, a low-liquidity token, on external platforms, creating a domino effect inside Hyperliquid’s system. At the time, HLP had around $290 million in its vault.

Key Timeline of Events:

⏱ Phase 0: “Market Preparation”
From 10:50 to 12:15 UTC, JELLYJELLY price rose by 13%, then sharply crashed by 93%-a stress test before the main strike. The drop, from $0.1287 to $0.00831, aimed to liquidate long positions and destabilise the HLP.

⏱ Phase 1: Building a Delta-Neutral Position
At 12:53 UTC, attackers opened large short positions ($4.08M) via address 0xde95…c91, while simultaneously placing long orders ($4.06M) through addresses 0x67fe…CA2 and 0x20e8…808 to hedge losses.

⏱ Phase 2: Triggering Liquidations
Minutes later, they withdrew available margins and partially closed shorts to prompt liquidations. One short of ~$254K closed at $0.073978. Shortly after, a short position of nearly 400 million JELLYJELLY was dumped into HLP. The attackers swiftly moved ~2.76M USDC to Arbitrum – locking in a manipulated short price of $0.011282.

⏱ Phase 3: Final Blow – The Pump and Dump
From 13:00 to 14:00 UTC, they aggressively bought JELLYJELLY across external exchanges, causing a 400% price surge to $0.05. Because Hyperliquid’s oracle relied on external spot prices, this manipulation immediately impacted derivatives and triggered massive unrealised losses.

JELLYJELLY price manipulation. Source: Lookonchain, Raydium.

Earlier transactions from March 15-25, flagged by Hyperliquid analysts, appeared to be dry runs – testing liquidation triggers and order types.

The Fallout

One attacker (a “whale”) successfully withdrew about $6.2 million, while another attempt to extract an additional ~$900K failed – leading to a net loss of ~$4K.

Hyperliquid halted trading, froze the price at $0.0095, and even ended with a slight gain of ~$700K. The team pledged full reimbursements to affected users.

Yet, this damage control sparked intense criticism. Influencers and Centralised Exchange (CEX) executives accused Hyperliquid of betraying the ideals of decentralisation and acting negligently.

Could CEXs Have Handled It Differently?

By April 8, 2025, Hyperliquid was handling ~$13B in daily perpetual trading volume – over 50% of the global DEX market share, per DeFi Llama. CoinGecko listed its open interest at $2.7B, beating major players like Deribit and even CEX arms like KuCoin and Crypto.com.

Hyperliquid accepts both USDC (via Arbitrum) and Bitcoin as collateral, giving users a rare ability to trade digital gold directly from their Web3 wallets.

On March 15, it captured 21% of Binance’s and 50% of Bybit’s volume in BTC futures – remarkable for a DEX.

But Hyperliquid’s team pointed fingers. Analysis suggested Bybit played a key role in the attack:

  • Oracle manipulation: Bybit’s spot data heavily influenced Hyperliquid’s margin price calculations.
  • Liquidity: Bybit’s deep order books allowed significant trades without slippage.
  • Market domination: With Binance not listing JELLYJELLY, Bybit’s pricing had outsized influence.

In short, attackers gamed Bybit’s data to distort Hyperliquid’s oracle.

Strategic Silence – or Not?

While manipulation happened through Bybit, other CEXs didn’t remain idle.

At 15:30 UTC, OKX listed JELLYJELLY perpetuals with 50x leverage. Binance followed at 16:00. The timing was… convenient.

Arthur Hayes, former BitMEX CEO, hinted at collusion. He cryptically framed OKX CEO Star Xu and ex-Binance chief CZ as having pounced on a weakened rival.

Hyperliquid’s Next Chapter

Despite the chaos, Hyperliquid’s core infrastructure remains strong. Built for scalability, it plans to integrate SVM and MoveVM, and leverages its custom HyperBFT consensus for potential L1 and L2 DeFi use cases.

In a March 31 interview with Wu Blockchain, LSD protocol developer Sean offered his perspective:

Binance and OKX feel threatened. They’re trying to recreate Solana-like dynamics by pushing meme tokens and onboarding users aggressively via BNB Chain.”

He also criticised influencers for disproportionately promoting CEXs and demonising Hyperliquid.

It’s fair to compete, but this narrative war is toxic. Centralised platforms aren’t saints – they have their own flaws. Their job is to enable fair trading, not run smear campaigns via influencers.”

Sean also acknowledged Hyperliquid’s shortcomings:

  • Closed-source limitations raise concerns about internal manipulation (e.g., MEV).
  • Insufficient blockchain transparency, such as lacking detailed account histories.
  • Dutch auction listings for low-cap tokens enabled overly large short positions.
  • Passive market maker design, if exploited, can lead to serious HLP losses.

The Bottom Line

The JELLYJELLY attack exposed real vulnerabilities – not just in Hyperliquid’s architecture, but in how DeFi interacts with centralised infrastructure.

More than just an isolated exploit, it was a wake-up call. It reignited debates around oracle reliance, systemic risk, and the blurry lines between CEXs and DEXs.

While Hyperliquid took swift corrective action and recovered financially, it now faces a trust battle—and a renewed mission to evolve its ecosystem without compromising its decentralised vision.

What’s next?
That’s a question the entire crypto world should be asking.

Bernstein Praises Bitcoin’s Resilience Amid Market Turmoil

Bitcoin has shown remarkable resilience in the face of recent global market unrest triggered by U.S. President Donald Trump’s so-called “Liberation Day,” according to a new report from investment firm Bernstein, as cited by The Block.

In a time of widespread financial uncertainty, Bernstein analysts described Bitcoin’s performance as “simply impressive.” Unlike previous crises – such as the COVID-19 market crash, during which Bitcoin plunged between 50% and 70% – the current correction from its all-time high (ATH) has been a comparatively modest 26%.

This suggests that demand for Bitcoin is now coming from more stable, long-term capital,” the report noted.

While Bitcoin is traditionally viewed as a risk-on asset, Bernstein emphasised its evolving role as a long-term store of value. “We see Bitcoin as probabilistic gold over time – more volatile and liquid, but functionally similar,” analysts wrote.

Crypto analytics platform CryptoQuant provided additional context, stating that the current cycle’s 26.62% pullback is far less severe than the 83% drop in 2018 or the 73% decline in 2022.

Despite this relative stability, concerns remain. CryptoQuant recently highlighted increasing bearish conditions for Bitcoin, signalling caution in the near term.

Meanwhile, blockchain analytics firm Nansen predicted the crypto market could bottom out by June. CryptoQuant’s founder and CEO, Ki Young Ju, echoed this sentiment, suggesting that Bitcoin’s bull run has likely ended. He forecasts a period of decline or sideways trading over the next six to twelve months.

Crypto’s Quiet Takeover: How Lobbyists Infiltrated the White House

In the aftermath of the FTX collapse, the American crypto industry found itself at a crossroads. Disillusioned with the Democratic Party, many key players shifted their focus – and funding – toward Donald Trump’s inner circle. What we’re witnessing is the dawn of a new era: one where idealism takes a backseat to strategic alignment with traditional power structures.

At Expert Stack, we’ve unraveled the complex web linking major crypto entities to top officials in the new U.S. administration – and what they stand to gain.

The PayPal Powerhouse

Few figures are as influential in the American crypto lobbying landscape as Peter Thiel, PayPal co-founder and long-time venture capitalist. Thiel’s firm, Founders Fund, made a bold bet on Bitcoin back in 2014, exiting just before the 2022 crash with an estimated $1.8 billion in profit. By late 2023, the fund was back in the crypto game, investing $200 million ahead of the long-anticipated approval of Bitcoin ETFs.

Thiel’s influence doesn’t end with investments. His close associate and fellow venture capitalist, J.D. Vance – now the U.S. Vice President – benefited from a $15 million donation by Thiel during his 2022 Senate campaign. Vance later launched his own venture firm, Narya Capital, raising $93 million with backing from Thiel and Marc Andreessen, the co-founder of a16z.

Andreessen Horowitz (a16z) has been a key player in shaping crypto policy, funding the pro-crypto political action committee Fairshake with $140 million in support of Congressional candidates in 2024. Brian Quintenz, formerly a crypto policy director at a16z, was appointed chair of the Commodity Futures Trading Commission (CFTC) – a move that raised a few eyebrows in a now-familiar revolving door environment.

These players are closely tied to Elon Musk. In 2022, a16z, Binance, and Sequoia Capital were key investors in Musk’s Twitter acquisition. By 2024, they were advising Trump on key administration hires, per The New York Times. Notable figures include:

  • Marc Andreessen, a16z founder
  • Jared Birchall, Musk’s family office head and Dogecoin Foundation advisor
  • Sean Maguire, Sequoia Capital partner
  • Trey Stevens, Anduril co-founder
  • Shyam Sankar, Palantir CTO
  • David Marcus, Lightspark CEO and former Meta blockchain head

Musk himself leads the newly created Department of Government Effectiveness (DOGE), while David Sacks, former PayPal COO, was named the administration’s “crypto czar.”

But there’s another major player worth noting.

Tether’s Treasury Ties

One of the most strategic players in the crypto-political nexus is Tether, now among the top 20 buyers of U.S. government debt. The acquisition and custody of these bonds is managed by Cantor Fitzgerald, led by Commerce Secretary Howard Lutnick. The firm also reportedly owns 5% of Tether.

Digging deeper into Tether’s origins reveals billionaire Brock Pierce – co-founder of EOS, director of the Bitcoin Foundation, and an early pioneer in crypto’s grey areas, including the infamous Mt. Gox debacle. Pierce is only one degree removed from Trump via Steve Bannon, the former White House Chief Strategist. In the early 2000s, Pierce founded Internet Gaming Entertainment (IGE), a trailblazer in digital currency trading for online games. Bannon joined IGE as CEO in 2006.

Although Bannon later claimed that Pierce’s support for Trump in 2016 hindered their crypto collaboration, their shared past is telling.

In 2019, EOS purchased a domain from MicroStrategy for $30 million to launch the social platform Voice, with investors like Thiel, Bitmain, and Galaxy Digital’s Mike Novogratz backing the project.

A year later, Michael Saylor, CEO of MicroStrategy, made headlines by investing $250 million of the company’s capital in Bitcoin. By March 2021, institutional giants like BlackRock, Morgan Stanley, Vanguard, and Citadel held over 40% of MicroStrategy’s shares – suggesting broad financial sector approval.

Latecomers to the Power Game

More recently, firms like Coinbase, Grayscale, and its parent company Digital Currency Group (DCG) – led by Barry Silbert, a key early investor in Ripple, Coinbase, and CoinDesk- have entered the lobbying arena with renewed force.

DCG’s influence crosses party lines. Lawrence Summers, a former Treasury Secretary under Bill Clinton and Obama’s National Economic Council Director, serves as an advisor to the firm – highlighting bipartisan lobbying efforts.

The fall of FTX in 2022 dramatically shifted the political balance. The Democratic Party’s perceived mishandling of crypto regulation, personified by former SEC Chair Gary Gensler, alienated many in the industry – especially after revelations about his connections to Sam Bankman-Fried’s family.

As the landscape evolves, the new market consensus favours U.S.-based crypto ventures and dollar-backed stablecoins under more centralised oversight. Even previously neutral entities are now joining the political fray: Coinbase pledged $25 million to Fairshake for the 2026 midterms, while Ripple contributed $5 million to Trump’s inauguration fund.

Meanwhile, Saylor has taken a provocative stance, calling for the demise of gold as an asset class to weaken U.S. adversaries and tighten America’s grip on global capital reserves.

Conclusion

What began as a decentralised, anti-establishment movement has morphed into a sophisticated lobbying machine. Today’s crypto leaders are not challenging the state – they’re becoming part of it. And on social media, the revolutionary spirit of early crypto seems to be fading, replaced by a new wave of corporatised, state-aligned ambition.

New Age Technologies and Their Legal Rights: Analysing Autonomous AI Technologies AI from a Legal Perspective

Authored by Ludovico Besana, Senior Test Engineer

As a concept still emerging, autonomous AI agents are sure to become popular in Web3. Such bots have already started participating in DeFi and trading, proving the possibility of building entire M2M networks and ecosystems powered completely by AI. Regardless, the function of autonomous AIs creates an alarming concern for the existing law frameworks.

In this article, I will analyze the “life” and “death” cycle of an AI agent from a legal standpoint, with particular attention to the criteria for granting the identity of a digital cyborg, and propose the simplest approaches to defining the law concerning these beings.

Fundamental questions   

The idea of autonomous AI agents operating on blockchain technology is no longer a mere fantasy. One of the well-known examples is Terminal of Truth. An agent based on the Claude Opus model was able to persuade Marc Andreessen (a16z) to invest $50,000 in the launch of Goatseus Maximus (GOAT) token which the bot “religiously” promoted. GOAT is now trading at a market cap above $370 million.  

AI agents fitting seamlessly within the Web3 ecosystem is unsurprising. They may be restricted from opening bank accounts, but they can manage crypto wallets and X accounts. Currently, AI agents are primarily concerned with meme tokens, but the potential applications in decentralised governance, machine networks, oracles, and trading are enormous.  

The greater the efforts to make AI agents mimic human actions, the more challenges there will be from a legal standpoint. Every legal system needs to provide an answer to these questions: What legal status should AI agents have? Which entity, if any, holds the rights and the liabilities for their actions? In what manner can AI agents be structured and shielded from legal risks?

Fundamental Legal Issues with AI Agents

Lack of Legal Personality

Legal systems recognize only two types of entities: natural persons (people) and legal persons (companies), and autonomous AI agents do not fit into either category. Although they can imitate human behavior (e.g. through social media accounts), they do not have a body, moral consciousness, or legal identity.

Some theorists propose granting AI agents “electronic legal personality” — a status similar to that of corporations, but adapted for artificial intelligence. In 2017, the European Parliament even considered this issue, but the idea was rejected due to various concerns and risks that have not yet been addressed.

It is likely that autonomous AI agents will not receive the status of legal entities in the near future. However, as was the case with DAOs, some crypto-friendly jurisdictions will attempt to create special legal regimes and corporate forms tailored to AI agents.

Responsibility for actions and their consequences

Without legal personality, AI agents cannot enter into transactions, own property, or bear responsibility. For the legal system, they simply do not exist as subjects. However, they already interact with the outside world and perform legally significant actions that lead to legal consequences.

A logical question arises: who is the real party to the transaction, who acquires rights, and who is responsible for the consequences? From a legal perspective, an AI agent is currently a tool through which its owner or operator acts. Therefore, any actions of an AI agent are de jure actions of its owner, an individual or legal entity.

Thus, since an AI agent itself cannot acquire rights and responsibility, for its legal existence it needs a subject that is recognised by the legal system and is able to acquire rights and obligations in its place.

Regulatory Restrictions

The emergence of the first successful large linguistic model (LLM) — ChatGPT — has generated unprecedented interest in AI and machine learning. It was only a matter of time before regulation was adopted. In 2024, the European Union adopted the AI ​​Act, which remains the most comprehensive regulation in the field of artificial intelligence to date. In other countries, limited AI regulation has either already been adopted, is being introduced, or is planned.

The European Artificial Intelligence Act differentiates AI systems by their level of risk. For systems with zero or minimal risk, there is little or no regulation. In the case of a higher risk, AI is subject to restrictions and obligations, such as disclosing its nature.

AI agents that interact with third parties, for example by publishing posts or making on-chain transactions, may also fall under traditional regulation in the field of consumer protection, personal data, and other areas. In such cases, the activities of autonomous bots can be considered, for example, the provision of services. The lack of clear geography and global focus in the activities of agents complicates compliance.

Ethics

Since AI agents have limited capabilities and scope so far, their creators rarely think about ethics. Priority is given to autonomous (trustless) execution and speed, rather than deep ethical configuration.

However, having an “ethical compass” when making autonomous decisions in high-risk areas such as finance, trade, and management is at least desirable. Otherwise, erroneous data in the training set or trivial errors in configuration can lead to the agent’s actions causing harm to people. The higher the autonomy and discretion of the AI ​​agent, the higher the risks.

Legal Structuring of AI Agents

Workable legal models for AI agents are of great importance for innovation, the development of the field as a whole, and the emergence of more advanced bots. While cryptocurrencies can already be called a regulated industry, in the case of AI agents, legal structuring is complicated by the fact that the industry is not standardized, so it requires a creative approach.

Approach to Structuring

In my opinion, one of the main goals of legal structuring of an autonomous AI agent should be to acquire its own legal personality and legal identity, independent of its creator. In this regard, the question arises: at what point can we consider that an AI agent really has these characteristics?

Every developer strives to ensure that their agent is as close as possible to a real person acting independently. It is logical that they would like to provide agents with freedom from a legal point of view. To achieve this, in my opinion, two key conditions must be met. First, the AI ​​agent must be independent not only in making its own decisions, but also in the ability to implement them in a legal sense – to carry out its will and make final decisions regarding itself. Second, it must have the ability to independently acquire rights and obligations as a result of its actions, independently of its creator.

Since the AI ​​agent cannot be recognized as an individual, the only way for it to achieve legal personality at the moment is to use the status of a legal entity. The agent will achieve legal personality when it can, as a full-fledged person, make independent decisions and implement them on its own behalf.

If successful, this order of things will bring the AI ​​agent to life from a legal point of view. Such a digital person, having received legal existence, can well be compared to a digital cyborg. A cyborg (short for “cybernetic organism“) is a creature that combines mechanical-electronic and organic elements. In a digital cyborg, the mechanical part is replaced by a digital one, and the organic part is replaced by people who participate in the implementation of its decisions.

Our digital cyborg will consist of three key components:

  • AI agent – electronic brain;
  • corporate form – legal body;
  • people involved in performing tasks – organic hands.

The Challenges of Corporate Form

Traditional legal entity forms, such as LLCs and corporations, require that both the ultimate ownership and ultimate control reside in humans. Corporate structures are not designed for ephemeral digital identities, which brings us to the central challenge of legally structuring blockchain AI agents: the challenges of corporate form.

If we want to give an AI agent a legal identity through a corporate form and ensure its independence and autonomy within that structure, we need to be able to eliminate human control over such an entity. Otherwise, if ultimate control resides with humans, the AI ​​becomes a tool rather than a digital person. We also need to ensure that in cases where a human is required to implement an AI decision, such as signing a contract or performing administrative tasks, that human cannot block or veto the AI ​​agent’s decision (barring a “machine uprising”).

But how can this be done when traditional corporate forms require that people own and manage agents? Let’s find out.

Three key aspects of the framework

1. Blockchain environment

AI agents are capable of independently performing on-chain transactions, including interaction with multisig wallets and smart contracts. This allows the AI ​​agent to be assigned a unique identifier – a wallet, through which it will give reliable instructions and commands to the blockchain. Without this, the existence of a real digital cyborg is not yet possible.

2. Autonomy and freedom of action

To maintain the full autonomy of the digital cyborg, it is important that people involved in the management of the legal structure cannot interfere with the actions of the AI ​​agent or influence its decisions. This ensures that the artificial intelligence retains freedom of action and is able to implement its own will, and requires the adoption of both legal and technical measures.

For example, in order for the AI ​​agent to truly own and control the blockchain wallet, the wallet can be created in a secure execution environment (TEE). This ensures that no human has access to the wallet, its seed phrase, or its assets. From a legal perspective, the corporate documents of the legal entity used as a wrapper for the AI ​​must provide for the correct distribution of control and authority, as well as security mechanisms that exclude human intervention and can be changed only in a limited number of cases.

3. Human Enforcers

Since we still live in a legal world, some decisions will require the AI ​​agent to involve human enforcers. This means that the AI ​​will instruct officials on what actions to take. This view of things changes the traditional hierarchy, since in our scenario, the AI ​​essentially gains control over humans, at least within its own corporate structure.

This aspect is perhaps the most interesting, since it requires an unconventional approach. One could even say that this state of affairs violates Isaac Asimov’s Second Law of Robotics, but I doubt anyone really cares about that right now. Besides, adequate emergency mechanisms and a proper “ethical compass” solve this problem, at least at this stage.

AI wrappers — legal structures for agents working on the blockchain

As we have already found out, traditional corporate structures are not suitable for our purposes and do not allow us to achieve the desired result. Therefore, below we will consider the structures that were developed for DAO and blockchain communities — these are both classic structures adapted for Web3 and specialized corporate forms for decentralized autonomous organizations.

From the point of view of the creator of the AI ​​agent, legal structuring allows separating the agent from the creator, obtaining limited liability through a corporate structure, and also provides the opportunity to plan and optimize taxes and financial risks.

Foundations and trusts

A purpose trust and an ownerless foundation have many common characteristics, but differ in nature. A foundation is a full-fledged legal entity, while a trust is more of a contractual entity that often does not require state registration. We will consider these forms in the context of the most popular Web3 jurisdictions: foundations in the Cayman Islands and Panama, and trusts in Guernsey. The key advantages are the absence of taxes, high flexibility in procedures and management, and the ability to integrate blockchain into the decision-making process.

Both foundations and trusts require management in the form of individuals or legal entities. At the same time, they allow for the integration of smart contracts and other technical solutions into management. For example, management can be required to request approval from an AI agent through interaction with it, a smart contract, or a wallet controlled by AI. A more complex legal design will allow the agent to give instructions to management, including through “thoughts” generated by the AI. Thus, the use of trusts and foundations allows for the creation of more complex corporate structures adapted to AI agents and supporting their autonomy.

If necessary, the creator of an AI agent can act as a limited-power beneficiary, which will allow him to obtain financial rights and manage taxes without interfering with the agent’s activities and decisions.

Algorithmically-managed DAO LLCs

A DAO LLC is a special corporate form designed for decentralized organizations. However, it is possible to create a DAO LLC with only one participant, i.e. without a real organization. Below, we will consider this form in two of the most popular jurisdictions: Wyoming (USA) and the Marshall Islands.

We are talking specifically about algorithmically-managed DAO LLCs, since in such a company, all power can be concentrated in smart contracts, and not in human hands. This is an extremely important aspect, since in our case, smart contracts can be controlled by an AI agent, which allows artificial intelligence to transfer all power in this corporate form.

DAO LLCs also have flexibility in terms of procedures and corporate governance, so they can implement complex control and decision-making mechanisms, as well as reduce the level of human intervention in these processes.

Although the presence of a natural or legal person is still formally required, their powers may be significantly limited, for example to the execution of technical tasks, corporate actions, and the implementation of decisions made at the smart contract level. In this context, the role of a member (participant) of a DAO LLC may be performed by the creator of the AI ​​agent, which will allow him to obtain financial rights and, in the future, the authority to distribute the profits received.

Simpler AI agents

Classical corporate structures can also be used to structure simpler AI agents, such as trading bots, since in this case there is no need to subordinate the corporate form to the decisions and discretion of the AI ​​agent. In this case, artificial intelligence continues to be a means or tool of its creator and does not claim the status of a full-fledged digital cyborg.

In conclusion

Autonomous AI agents can change the blockchain industry and significantly accelerate innovation in almost all areas. So far, they are at the very beginning of the path, but the pace of development is colossal and very soon we will see real digital cyborgs – digital organisms with a stable thought process and their own identity. But this requires a combination of technical and legal innovations.

NVIDIA’s 2025 Woes: Strategic Reset or The Beginning of the End?

As a consequence of the changing U.S. economic policies, NVIDIA is suffering some headwinds in 2025 as its stock value and profits take a considerable hit. The ExpertStack team has tried to make sense of the chip market – and the role China plays – and who’s on the receiving end of these turbulent winds.  

NVIDIA may have surpassed Wall Street targets with Q4 revenue of $39.3 billion coupled with an EPS of 89 cents, but the firm’s growth outpaced sentiment was bearish as it clocked in at its slowest acceleration since 2023. The profitability decline also eroded further dragged the firm’s sentiment as it launched its next-generation Blackwell chip.

Import Taxes: A Death Hook for Supply Chains

NVIDIA may design the world’s most powerful chips, but it doesn’t manufacture them. Instead, it relies heavily on Taiwan’s TSMC, which produces about 90% of its chips –  including high-demand models like the H100.

But in January 2025, the U.S. government introduced a sweeping 25% import tax on all goods coming from Taiwan. For NVIDIA, this means every chip sourced from TSMC now carries a steep markup. When you’re producing millions of chips annually, that 25% translates to billions in added costs.

To offset the blow, NVIDIA has two options: hike prices or accept slimmer profit margins. Neither is ideal  – especially for data centers and AI firms that buy chips by the thousands. Even a minor price increase multiplies into billions in additional spending for these large-scale users.

So, what can NVIDIA do? One workaround is relocating production to the U.S., and TSMC is already building new facilities in Arizona. The catch: those plants won’t be operational until 2027, and they come with a $100 billion price tag. Worse, domestic production isn’t cheap  – U.S. labor costs are about 50% higher, and energy costs around 20% more. This could drive chip prices up by another 20%, pushing the cost of a single high-end unit like the H100 from $40,000 to well over $48,000.

In short, the 25% tariff is not just a policy move – it’s a direct hit to NVIDIA’s cost structure, threatening both affordability for customers and profitability for the company.

NVIDIA is trying to negotiate with the US government to reduce or eliminate taxes on chips. Another option is to find other suppliers, for example, in South Korea or Japan. But this is not fast: you need to check that the new factories make chips just as well, and arrange delivery, and this adds another 5-10% to the costs.

If you look at it more broadly, because of the taxes, the prices of chips are growing, and this concerns not only NVIDIA, but also those who buy its products – companies that produce servers, video cards or systems for smart machines. This makes American technologies more expensive than European or Asian ones, where there are no such taxes. The paradox is that these taxes can help NVIDIA’s competitors from China, because China will start developing its chips faster in order not to depend on America.

A 25% tax could cut NVIDIA’s profits by 5% to 10% if it doesn’t raise prices. Switching to suppliers in South Korea or Japan requires quality assurance and new shipping routes, adding 5% to 10% to costs due to testing and logistics.

Bans on sales to China: Losing a major market

China was once a goldmine for NVIDIA, generating billions in annual revenue as Chinese tech giants raced to build massive AI data centers. But in 2025, that revenue stream was abruptly disrupted. The U.S. government tightened export restrictions, citing national security concerns and the risk of advanced chips being repurposed for military use. As a result, NVIDIA was banned from selling many of its high-performance chips to China—cutting off roughly half its shipments to the region.

In response, NVIDIA tried to adapt by creating lower-performance alternatives that complied with U.S. export rules. One such product is the H20 chip, a downscaled version of the powerful H100. While the H20 is still capable of supporting AI workloads, it delivers only about 70% of the H100’s performance in critical tasks like training large language models. This intentional performance cap ensures it stays below the U.S. government’s regulatory threshold.

However, the reception in China has been lukewarm. These weakened chips offer less performance at a similar price point, making them less attractive for cost-sensitive companies looking to scale AI infrastructure. With domestic demand rising, Chinese firms are increasingly exploring alternatives and building their own.

Huawei, for instance, has developed the Ascend chip series, which already rivals NVIDIA’s offerings in certain machine learning tasks. In some benchmarks, Huawei’s Ascend chips reach up to 85% of the H100’s performance. Bolstered by $50 billion in state-led investments over the past two years, China has made chip self-sufficiency a national priority. By 2030, it aims to domestically produce 80% of the chips it needs, significantly reducing reliance on U.S. technology.

This shift poses a long-term threat to NVIDIA. Even if the company continues to grow in other regions, the loss of the Chinese market could cost it billions in future revenue. Worse still, if U.S. restrictions are tightened further – potentially banning even lower-performance chips like the H20 – NVIDIA may be completely shut out of one of the world’s largest and fastest-growing AI markets.

For China, these bans are both a challenge and an accelerant. Every new restriction gives added urgency and incentive – to develop homegrown alternatives. For NVIDIA, the clock is ticking to find new markets or products to fill the gap.

Growing Competition: NVIDIA Losing Its Lead

NVIDIA has long been the leader in AI chips, but in 2025, competition has become a serious threat. AMD and Intel from the US, as well as Huawei from China, have started to take market share away from NVIDIA. This is important to understand because if NVIDIA does not maintain its lead, it could lose control of the AI ​​industry, where it currently holds 88% of the chip market.

AMD has taken a step forward

AMD has taken a decisive step forward in the AI and GPU race. Its latest Instinct MI200 chips are already being deployed for AI workloads and offer a compelling value proposition: comparable performance to NVIDIA’s H100 at roughly 20% lower cost. Over the past year, AMD has doubled its market share in the AI accelerator space, rising from 5% to 10% – a clear signal that customers are responding to the price-performance equation.

While NVIDIA still leads in raw performance, much of its edge comes from its mature software ecosystem, particularly the CUDA platform, which boosts chip efficiency in AI applications. AMD is closing that gap with its ROCm (Radeon Open Compute) platform, which saw a 30% development acceleration in 2024. Though ROCm still lags behind CUDA in terms of optimization and developer adoption, AMD is gaining ground steadily.

In terms of raw numbers, the MI200 delivers approximately 80% of the H100’s performance in machine learning benchmarks but at a significantly lower price – around $32,000 compared to $40,000. This makes AMD an increasingly attractive option for organizations looking to build or scale AI infrastructure without overshooting budget constraints.

The competition is also heating up in the gaming market. AMD’s Radeon RX 9070 XT is now neck-and-neck with NVIDIA’s RTX 5070, offering similar performance at a lower price point. If AMD continues this trajectory – improving performance, expanding software capabilities, and keeping prices competitive – NVIDIA may be forced to adjust pricing or risk losing share in both AI and consumer markets.

The message is clear: AMD is no longer just catching up – it’s becoming a credible threat in segments where NVIDIA once had near-total dominance.

Intel is not far behind

Intel is making a calculated move to re-enter the high-performance chip race with its upcoming 18A process node, a next-generation manufacturing technology designed to challenge NVIDIA’s dominance in AI and data-centric computing. Although still in the testing phase, early benchmarks are promising: Intel’s prototypes have outperformed NVIDIA’s H100 in select workloads, particularly in complex scientific applications like climate modeling and large-scale simulations.

The 18A process aims to increase transistor density by approximately 20% over current-generation chips, a leap that could translate into significant gains in performance and power efficiency. In preliminary testing, chips built on this architecture demonstrated speeds up to 15% faster than NVIDIA’s flagship H100 in specific high-performance computing (HPC) scenarios.

Mass production is slated for 2026, and while Intel has yet to fully commercialize this technology, its potential is clear. If the company can meet its manufacturing timeline and deliver consistent performance gains, it could start peeling away enterprise customers—particularly in sectors like big data, scientific research, and government infrastructure, where raw computational power and long-term scalability are critical.

And now China

China is no longer just a consumer in the global chip market—it’s rapidly becoming a competitor. Huawei’s Ascend chip series has already found traction in domestic data centers and is beginning to rival NVIDIA in AI workloads. In response to U.S. export bans on high-performance chips, China has poured over $50 billion into its semiconductor sector in just two years. The strategic goal is clear: produce 80% of the country’s chip demand domestically by 2030.

The results are already visible. Ascend chips currently deliver around 85% of the performance of NVIDIA’s H100 in AI tasks such as natural language processing and large-scale model training. And in the Chinese market, they’re 25% cheaper. Since 2023, China has ramped up local chip production by 40%, driven by aggressive state-backed investment.

So far, these chips are only used domestically, but if Huawei starts exporting its hardware – especially to developing markets or U.S.-restricted regions – NVIDIA could face a double blow: not only losing the Chinese market, but also its influence in neighboring economies.

Scenarios for the future

With increasing tax burdens, export bans, and rising competition from AMD, Intel, and China, NVIDIA is facing its most challenging period in recent years. Company leadership insists it’s pivoting strategically, expanding into new markets like India and Europe, where sales jumped 15% in 2024, reaching $5 billion. Still, the road ahead is far from certain.

Here are three possible scenarios for NVIDIA’s future:

 Optimistic:

NVIDIA successfully launches its next-generation chips in 2026, delivering a 40% performance boost (up to 200 gigaflops). Global demand surges, offsetting losses in China. U.S. trade restrictions ease, reducing the cost pressure from tariffs. Sales in Europe, India, and Latin America accelerate, allowing NVIDIA to strengthen its global dominance.

 Pessimistic:

China meets 70% of its chip demand by 2028, increasingly relying on domestic suppliers like Huawei. NVIDIA is squeezed out of key Asian markets. Meanwhile, AMD and Intel, offering chips at 25% lower prices, erode 25% of NVIDIA’s U.S. market share. Revenues fall by 20%, and the company struggles to maintain growth.

 Realistic:

NVIDIA retains 75% global market share thanks to its robust ecosystem, R&D leadership, and brand strength. However, rising production costs and sustained geopolitical headwinds reduce profitability. Margins drop to 65%, and while competitors continue to chip away, NVIDIA stays ahead through continuous innovation at least through 2030.

The Bottom Line

NVIDIA’s future hinges on innovation, international diplomacy, and its ability to stay ahead of fast-moving competitors. It still leads in AI performance, developer tools, and global recognition – but pressure is mounting. If it can’t adapt quickly enough, its dominance may no longer be guaranteed. What’s keeping it afloat today is a combination of cutting-edge technology, years of experience, and a brand reputation that competitors are racing to match.

A $41,200 humanoid robot was unveiled in China

The Chinese company UBTech Robotics presented a humanoid robot for 299,000 yuan ($41,200). This is reported by SCMP.

Tien Kung Xingzhe was developed in collaboration with the Beijing Humanoid Robot Innovation Center. It is available for pre-order, with deliveries expected in the second quarter.

The robot is 1.7 meters tall and can move at speeds of up to 10 km/h. Tien Kung Xingzhe easily adapts to a variety of surfaces, from slopes and stairs to sand and snow, maintaining smooth movements and ensuring stability in the event of collisions and external interference.

The robot is designed for research tasks that require increased strength and stability. It is powered by the new Huisi Kaiwu system from X-Humanoid. The center was founded in 2023 by UBTech and several organizations, including Xiaomi. He develops products and applications for humanoids.

UBTech’s device is a step towards making humanoid robots cheaper, SCMP notes. Unitree Robotics previously attracted public attention by offering a 1.8-meter version of the H1 for 650,000 yuan ($89,500). These robots performed folk dances during the Lunar New Year broadcast on China Central Television in January.

EngineAI’s PM01 model sells for 88,000 yuan ($12,000), but it is 1.38 meters tall. Another bipedal version, the SA01, sells for $5,400, but without the upper body.

In June 2024, Elon Musk said that Optimus humanoid robots will bring Tesla’s market capitalization to $25 trillion.

Alibaba Upgrades Quark Into a Next-Gen AI Assistant

As stated by Bloomberg, Quark’s app has undergone an update that incorporates the latest Qwen neural network, enhancing its functionalities. Quark was first introduced in 2016 as a web browser, but now it has undergone a transformation into an AI assistant that integrates sophisticated chatbot-like conversational skills and independent reasoning and task completion into one easy-to-use application.

The “new Quark” is designed to be a versatile tool, capable of tackling a wide range of tasks with remarkable efficiency. From generating high-quality images and drafting detailed articles to planning personalized travel itineraries and creating concise meeting minutes, Quark is poised to become an indispensable companion for its users. This transformation reflects Alibaba’s ambition to integrate artificial intelligence more deeply into everyday life, offering a glimpse into the future of smart, intuitive technology.

Wu Jia, CEO of Quark and Vice President of Alibaba, emphasized the app’s potential to unlock new horizons for its users. “As our model continues to evolve, we see Quark as a gateway to boundless opportunities,” Wu stated. “With the power of artificial intelligence, users can explore and accomplish virtually anything they set their minds to.

Quark’s journey began nearly a decade ago as a simple web browser, but it has since grown into a powerhouse with a reported user base of 200 million people across China. This impressive milestone underscores Alibaba’s ability to scale and adapt its offerings to meet the demands of a rapidly changing digital landscape.

The revamped Quark builds on Alibaba’s recent advancements in AI technology, including the introduction of the QwQ-32 model in March 2025, a reasoning-focused AI designed to enhance problem-solving and decision-making capabilities. By integrating the Qwen neural network, Quark now stands at the forefront of Alibaba’s AI ecosystem, blending innovation with practicality to cater to both individual and professional needs.

This strategic overhaul positions Quark as more than just an app—it’s a visionary tool that could redefine how users interact with technology, solidifying Alibaba’s role as a global leader in AI-driven solutions. As the company continues to refine its models, Quark promises to deliver an ever-expanding array of features, making it a dynamic platform for creativity, productivity, and exploration.

Elon Musk Blames ‘Massive Cyber-Attack’ for X Outages, Alleges Ukrainian Involvement

Elon Musk has claimed that a “massive cyber-attack” was responsible for widespread outages on X, the social media platform formerly known as Twitter. The billionaire suggested that the attack may have been orchestrated by a well-resourced group or even a nation-state, potentially originating from Ukraine.

X Faces Hours of Service Disruptions
Throughout Monday, X experienced intermittent service disruptions, preventing users from loading posts. Downdetector, a service that tracks online outages, recorded thousands of reports, with an initial surge around 5:45 AM, followed by a brief recovery before another wave of disruptions later in the day. The majority of issues were reported on the platform’s mobile app.

Users attempting to load tweets were met with an error message reading, “Something went wrong,” prompting them to reload the page.

Musk addressed the situation in a post on X, stating:

We get attacked every day, but this was done with a lot of resources. Either a large, coordinated group and/or a country is involved.”

However, Musk did not provide concrete evidence to support his claims.

Musk Suggests Ukrainian Involvement
Later in the day, during an interview with Fox Business, Musk doubled down on his allegations, suggesting that the attack may have originated from Ukraine.

We’re not sure exactly what happened, but there was a massive cyber-attack to try and bring down the X system with IP addresses originating in the Ukraine area,” Musk stated.

The claim comes amid Musk’s increasingly strained relationship with the Ukrainian government. Over the weekend, he asserted that Ukraine’s “entire front line” would collapse without access to his Starlink satellite communication service. Additionally, he criticized U.S. Senator Mark Kelly, a supporter of continued aid to Ukraine, labelling him a “traitor.”

A Pattern of Unverified Cyber-Attack Claims
Musk has previously attributed X outages to cyber-attacks. When his live-streamed interview with Donald Trump crashed last year, he initially claimed it was due to a “massive DDoS attack.” However, a source later told The Verge that no such attack had occurred.

Broader Challenges for Musk’s Businesses
The disruptions at X add to a series of recent setbacks for Musk’s ventures.

SpaceX Mishap: On Friday, a SpaceX rocket exploded mid-flight, scattering debris near the Bahamas.
Tesla Under Pressure: A growing “Tesla takedown” movement has led to protests at dealerships, while Tesla’s stock price continues to slide, hitting its lowest point in months.
Political Tensions: Musk’s meeting with Donald Trump last week reportedly grew tense, with Trump hinting at curbing the billionaire’s influence over government agencies.

The Bottom Line
While Musk attributes X’s outages to a large-scale cyber-attack, no independent evidence has surfaced to confirm this claim. Given his history of making similar allegations without substantiation, the true cause of the disruption remains unclear. Meanwhile, mounting challenges across Musk’s business empire suggest that cyber-attacks may not be the only crisis he is facing.

Meta Develops Specialised AI Chip

Meta is testing its own chip for training AI systems, Reuters reports, citing sources.

According to the agency, the new processor is a specialized accelerator aimed at solving specific problems for artificial intelligence. This approach makes the chip more energy efficient compared to integrated graphics processors traditionally used for AI workloads.

The company is collaborating with Taiwan’s TSMC. At the moment, the initial stage of development has been completed, which includes creating and sending prototypes to the chip factory for testing.

Developing its own processors is part of Meta’s plan to reduce infrastructure costs. It is betting on artificial intelligence to ensure growth.

The corporation predicts spending in 2025 in the amount of $114-119 billion, of which $65 billion will be directed to the artificial intelligence sector.

Meta wants to start using its own chips for AI tasks in 2026, Reuters writes.

In May 2023, the company introduced two specialised processors for artificial intelligence and video processing tasks.

Earlier, the media learned about OpenAI working on its own AI processor in partnership with Broadcom and TSMC.

The Chinese company ByteDance is developing a similar product in collaboration with Broadcom.