Zuckerberg Predicts AI Will Replace Mid-Level Developers in 2025

Posted on by Editorial Team

Meta CEO Mark Zuckerberg believes artificial intelligence is quickly advancing to the point where it can handle the work typically done by mid-level software developers – potentially within the year.

Speaking on The Joe Rogan Experience podcast, Zuckerberg noted that Meta and other major tech companies are developing AI systems capable of coding at a mid-tier engineer’s level. However, he acknowledged current limitations, such as AI occasionally generating incorrect or misleading code – commonly known as “hallucinations.”

Other tech leaders are equally optimistic. Y Combinator CEO Garry Tan has praised the rise of “vibe coding,” where small teams leverage large language models to build complex apps that once needed large engineering teams.

Shopify CEO Tobi Lütke has gone as far as requiring managers to justify new hires if AI could perform the same tasks more efficiently. Anthropic co-founder Dario Amodei has made a bold prediction: within a year, AI will be capable of writing nearly all code.

At Google, CEO Sundar Pichai recently revealed that over 25% of new code is now AI-generated. Microsoft CEO Satya Nadella reported a similar trend, with a third of the company’s code produced by AI.

Despite the enthusiasm, some experts urge caution. Cambridge University AI researcher Harry Law warns that over-reliance on AI for coding could hinder learning, make debugging harder, and introduce security risks without proper human oversight.

LinkedIn Replaces Keywords With AI, Enhancing Job Search Efficiency

Posted on by Editorial Team

LinkedIn has unveiled a transformative update to its job search functionality, phasing out traditional keyword-based searches in favour of an advanced AI-driven system. This shift promises to deliver more precise job matches by leveraging natural language processing, fundamentally changing how job seekers and employers connect.

AI-Powered Job Matching

Gone are the days of rigid keyword searches. LinkedIn’s new AI system dives deeper into job descriptions, candidate profiles, and skill sets to provide highly relevant matches. According to Rohan Rajiv, LinkedIn’s Product Manager, the platform now interprets natural language queries with greater sophistication, enabling users to search with conversational phrases rather than specific job titles or skills.

For instance, job seekers can now input queries like “remote software engineering roles in fintech” or “creative marketing jobs in sustainable fashion” and receive tailored results. This intuitive approach eliminates the need to guess exact keywords, making the process more accessible and efficient.

Enhanced Features for Job Seekers

The update introduces several user-centric features designed to streamline the job search experience:

  • Conversational Search: Users can describe their desired role in natural language, and the AI will interpret and match based on context, skills, and preferences.
  • Application Transparency: LinkedIn now displays indicators when a company is actively reviewing applications, helping candidates prioritise opportunities with higher response potential.
  • Premium Perks: Premium subscribers gain access to AI-powered tools, including interview preparation, mock Q&A sessions, and personalised presentation tips to boost confidence and performance.

A New Era of Job Search Philosophy

LinkedIn’s overhaul reflects a broader mission to redefine job searching. With job seekers outpacing available roles, mass applications have overwhelmed recruiters. The platform’s AI aims to cut through the noise by guiding candidates toward roles that align closely with their skills and aspirations, fostering quality over quantity.

“AI isn’t a magic fix for employment challenges, but it’s a step toward smarter, more meaningful connections,” Rajiv said. By focusing on precision matching, LinkedIn hopes to reduce application fatigue and improve outcomes for both candidates and employers.

Global Rollout and Future Plans

Currently, the AI-driven job search is available only in English, but LinkedIn has ambitious plans to expand to additional languages and markets. The company is also exploring further AI integrations to enhance profile optimisation and career coaching features.

This update marks a significant leap toward a more intelligent, user-friendly job search ecosystem, positioning LinkedIn as a leader in leveraging AI to bridge the gap between talent and opportunity.

The Future of Blockchain Efficiency: Maximising Throughput Using Rollups

Posted on by Editorial Team

Author: Asutosh Mourya is Engineering Manager at Trili Tech – a blockchain research and development hub focused on the open-source Tezos blockchain.

Blockchain technologies are already part of our daily lives, even if we’re not consciously aware of them. They are applied in such areas as secure online transactions, supply chain management, identity verification, and even in creating and trading digital assets like non-fungible tokens (NFTs). Understanding how exactly blockchain works can shed light on its practical implications for enhancing efficiency and transparency in different aspects of our daily interactions.

In the article, I will share my views on how to maximise blockchain efficiency using rollups. Join me on this journey.

What is Blockchain

In terms of functionality, blockchain can be described as a series of data blocks linked in an uneditable digital chain. These blocks are stored in a decentralised environment, where each block’s information is verifiable by all participating computers. The decentralised structure ensures trust, validity, and usability, departing from traditional hierarchical systems.

Simply put you can think of a blockchain as a digital necklace made up of individual beads, with each bead representing a piece of information. These beads are linked together to form the necklace, just like blocks are linked to form the chain. Each bead holds details about transactions, who sent money and when it was sent.

In a blockchain, blocks not only contain transaction data but also a crucial element known as a hash. These cryptographic hash functions are fundamental to the blockchain’s operation. Hashes are represented by a unique series of characters like:

X23G9K1H4P8Q6L2V5

They act as the digital signature for a block, generated from its data. A key feature is that each block includes the hash of the previous block, forming a linked chain. This interconnectedness through hashes ensures the integrity of the blockchain network. Any attempt to modify the content within a block would alter the hash, signalling potential tampering to the network.

The incorporation of hashes creates a self-regulated network in the blockchain, eliminating the need for intermediaries. This design prevents third parties from monitoring or interfering with transactions, bolstering the security and reliability of the system.

What Makes Blockchain Special

Blockchains set themselves apart from other digital databases in distinct ways. Firstly, they operate as distributed databases, spreading data across multiple servers situated in various physical locations. This decentralisation enhances reliability, performance, and transparency compared to traditional databases. 

What is more, blockchains employ open-source software, allowing the entire network community to scrutinise the underlying code collaboratively. This transparency facilitates the detection and resolution of bugs, glitches, or flaws. 

Notably, once verified, new information can only be added to the blockchain; it cannot be altered. Security and trustworthiness are upheld by requiring majority consensus from network participants, promoting a shared responsibility model instead of relying on a single, central entity.

Current Limitations 

Though blockchain is getting widely adopted, it faces some challenges. One of them is scalability. In blockchain, it means handling more transactions without making things slower or less secure. To understand the grounds of this problem we need to get acquainted with the following concepts.

Block Time

This term refers to the average time it takes for a new block to be added to the blockchain. Different blockchain networks have varying block times. For example, Bitcoin has a block time of around 10 minutes, while Ethereum aims for a shorter block time of around 15 seconds. The challenge is to strike a balance: a shorter block time can lead to faster transactions, but it may also increase the likelihood of forks (divergent branches in the blockchain) and reduce security.

Transactions per Second

This metric indicates the number of transactions a blockchain can process in one second. The TPS varies widely among different blockchain networks. Traditional payment systems like Visa can handle thousands of transactions per second, whereas many blockchain networks, especially those focused on decentralisation and security, may have lower TPS.

For Bitcoin, the most used blockchain, the problem is its small 1MB block size, limiting the number of transactions and making fees higher. They suggested a solution called Segregated Witness (SegWit), but it’s not widely adopted yet. Ethereum, another big blockchain, has a 15-second block time, which is faster but also limits transactions per block. They plan to fix this with Ethereum 2.0, using Proof of Stake (PoS) and sharding. Other blockchains explore ideas like side chains or off-chain methods (like Lightning Network) to handle more transactions without slowing down the main blockchain. Solving scalability is crucial for making blockchains work better.

What are Rollups

Blockchain rollups are a Layer 2 solution for scaling cryptocurrencies, involving the consolidation of multiple transactions on a secondary blockchain (Layer 2). These transactions are then bundled into a unified piece of data, broadcasted onto the primary blockchain (Layer 1). 

In simpler terms, rollups extract transactions from the main blockchain, process them off-chain, compile them into a single data unit, and reintegrate them onto the primary chain. This process is why rollups are often referred to as ‘off-chain scaling solutions.’

At the most fundamental level, layer 1 scaling involves enhancing the scalability of the primary blockchain. On the other hand, layer 2 scaling entails relocating transactions from the main blockchain layer to a distinct layer that can interact with the primary chain.

Why Blockchain Rollups

Typical blockchain blocks have limited space, causing delays and increased costs as networks grow busier. Blockchain rollups solve this by consolidating transactions into one data piece off-chain, making processing more efficient.

How Do Blockchain Rollups Work?

Blockchain can store two types of information: transactions and data. While processing transactions on-chain is heavy, data resulting from transactions is lighter. Rollups merge transactions off-chain, submitting consolidated data to the mainnet, reducing the burden and enabling multiple transactions in one data piece. This enhances blockchain scalability.

A Step-by-Step Rollups Mechanics 

  1. Conducting Off-Chain Transactions

   – Engage in transactions directly on the rollup chain, acting as a blockchain platform.

   – Transaction processing occurs on the rollup chain, overseen by the “sequencer,” who validates, constructs L2 blocks, and submits transaction data with proofs to the primary L1 chain.

  1. Aggregating Batched Transactions

   – The sequencer organises multiple transactions into batches, collectively presenting them to the main L1 chain.

   – Batched transactions not only streamline processing but also reduce gas fees, providing a cost-effective experience for end-users.

  1. Ensuring On-Chain Security

   – Post-batching, the rollup chain delivers transaction data to a dedicated smart contract on the L1 chain.

   – Upon finalisation of the L1 block containing rollup transactions, the data becomes immutable, safeguarded against modification or censorship, ensuring constant data availability for verification.

  1. Generating Verifiable Proofs

   – Some rollups enhance transaction data with cryptographic “summaries” or “proofs.”

   – These proofs, serving as cryptographic assurances, are deposited on the L1 chain, validating the successful execution of the designated batch of transactions by the rollup.

Types of Blockchain Rollups

1. ZK Rollups (Zero-Knowledge)

 – ZK-SNARK: Uses short proofs for quick transaction processing and enhanced security but has vulnerabilities to certain hacks.

 – ZK-STARK: More scalable and transparent than ZK-SNARK, with larger proof sizes, providing improved security.

2. Optimistic Rollups

 – Assume all transactions are valid by default, approving them to the mainnet without extensive validation.

 – Use fraud-proving mechanisms to identify illegitimate transactions and penalise validators accordingly.

 – Depend on Ethereum mainnet for security, making them easier to implement and cost-effective compared to ZK-rollups.

Here are examples of operational rollup blockchains that simplify complex blockchain technology:

  • Optimism: an Ethereum optimistic rollup with a TVL of $700 million in November 2023. Optimism stands out for its standardised and open-source development stack, the OP Stack. Developers use it to launch their blockchains, and the native token is known as OP.
  • Base: An Ethereum optimistic rollup developed by Coinbase, one of the world’s largest crypto exchanges. Base does not have its own native token.
  • StarkNet: An Ethereum ZK-rollup leveraging zero-knowledge technology (STARK) for transaction computation and verification. The native token is called STRK.
  • Polygon Hermez: Polygon provides a suite of ZK-rollup solutions, Polygon Hermez is one of them, it employs Proof of Efficiency (PoE) consensus, allowing anyone to be a sequencer or aggregator.  Sequencers compile transactions, while aggregators validate and provide proofs to Ethereum. Polygon Hermez incentivizes honest behaviour and boasts decentralisation.

Benefits of Rollups

High Throughput

As can be seen from above, rollups, as a scaling solution for blockchains, deliver a remarkable boost in throughput. By efficiently processing and bundling multiple transactions together, they significantly enhance the overall capacity of the network. This surge in throughput ensures that a more extensive volume of transactions can be seamlessly handled, contributing to a smoother and more efficient blockchain experience.

Reduced Wait Time

Another prominent benefit ushered in by rollups is the substantial reduction in transaction wait times. Through the aggregation of transactions into batches, users experience quicker confirmation and processing.

Limitations of Rollups

Layer 2 blockchain solutions, while improving transaction speed and lowering costs, still face some important limitations. One concern is the risk of fraud by validators in Layer 2. Additionally, these solutions often sacrifice a bit of decentralisation for efficiency. Withdrawing from Layer 2 can be slow, as seen in plasma chains, and might involve added costs. 

Moreover, implementing Layer 2 solutions demands substantial computational power, making certain options less cost-effective for scenarios with lower activity. Despite these challenges, ongoing development in Layer 2 solutions remains vital for addressing blockchain scalability issues, playing an important role in the blockchain ecosystem’s future growth.

Microsoft: AI Now Constitutes 30% of Company Code, Estimated to Reach 95% by 2030

Posted on by Editorial Team

The coding landscape at Microsoft is undergoing swift change owing to the evolving application of artificial intelligence. As outlined by Satya Nadella, the company’s CEO, AI makes up about 20 to 30 percent of the code within company repositories, and that figure could jump to 95% by 2030, especially for AI’s Python Language.

During the ‘LlamaCon’ conference in a dialogue with Mark Zuckerberg, Nadella also remarked on AI’s increasing prominence in software engineering task automation. He pointed out that Python retains the lead in AI-generated code, while languages such as C++ tend to lag far behind due to complexities in adoption.

Microsoft’s Chief Technology Officer Kevin Scott shares this view, predicting a long-term shift where AI will substantially dominate code writing, calling this an inevitable change in development workflows.

A Broader Industry Trend  

Microsoft isn’t the only one to experience this change. Just last week, Google’s CEO Sundar Pichai said that over 30 percent of Google’s code is also being AI generated. Neither of the tech companies, however, provided any insight on how those numbers are calculated, which opens them up to some interpretation.  

The concern with not measuring the contributions of AI accurately is that AI code generation is not uniform. Equality could be measured by how companies measure contributions—whether that’s by lines committed, code accepted, pull requests merged, etc.

The Main Takeaway

Although it’s possible to argue about the precise figures, one thing is clear: AI is increasingly becoming integrated within software engineering at leading tech companies. If the current trends continue, it seems we may be heading towards a time in the future where human developers engage more with problem-solving and design while AI does most of the coding.

Observability at Scale

Posted on by Editorial Team 
Authored by Muhammad Ahmad Saeed, Software Engineer

This article has been carefully vetted by our Editorial Team, undergoing a thorough moderation process that includes expert evaluation and fact-checking to ensure accuracy, and reliability.

***

In today’s digital world, businesses operate on complex, large scale systems designed to handle millions of users simultaneously. What is the challenge one might wonder? Keeping these systems reliable, performant, and user friendly at all times. For organizations that rely on microservices, distributed architectures, or cloud native solutions, downtime can have disastrous consequences.

This is where observability becomes a game changer. Unlike traditional monitoring, which focuses on alerting and basic metrics, observability offers a deeper understanding of system behavior by providing actionable insights from the system’s output. It empowers teams to diagnose, troubleshoot, and optimize systems in real time, even at scale. When it comes to engineers, observability isn’t just a tool for them , it’s rather a lifeline for navigating the complexity of modern infrastructure.

What Is Observability?

Observability is the ability to deduce the internal states of a system by analyzing the data it produces during operation. This concept, originally derived from control theory, which focuses on the principle that a system’s behavior and performance can be understood, diagnosed, and optimized without directly inspecting its internal mechanisms. In the realm of modern software engineering, observability has transformed into a foundational practice for managing complex, distributed systems. In order to fully understand observability, let’s unpack its three pillars:

  1. Logs: Logs are immutable, time stamped records of events within your system. They help capture context when errors occur or when analyzing specific events. For example, a failed login attempt might produce a log entry with details about the request.
  2. Metrics: Metrics are quantitative measurements that indicate system health and performance. Examples include CPU usage, memory consumption, and request latency. These metrics are great for spotting trends and anomalies.
  3. Traces: Traces map the journey of a request through a system. They show how services interact and highlight bottlenecks or failures. Tracing is especially valuable in microservices environments, where a single request can touch dozens of services.

Collectively, these components provide a view of the entire behavior of a system, making it possible for teams to be able to address important questions, such as why a certain service is slower than it should be, what triggered an unexpected rise in errors, and whether certain identifiable patterns have led up to system failures.

While observability can significantly improve reliability, achieving it at scale presents some  challenges. Since as systems grow in size and complexity, so does the volume of data they generate. Therefore, managing and interpreting this data effectively requires robust strategies and tools to address several key challenges, some of which are presented next.

One major hurdle is the massive volume of data produced by large scale systems. Logs, metrics, and traces accumulate rapidly, creating significant demands on storage and processing resources. Without efficient aggregation and storage strategies, organizations risk escalating costs while making it increasingly difficult to extract meaningful insights.

Another challenge arises from context loss in distributed systems. In modern architectures like microservices, a single request often traverses numerous services, each contributing a piece of the overall workflow. If context is lost at any point, whether due to incomplete traces or missing metadata, debugging becomes an error prone task. 

Finally, distinguishing the signal from the noise is a persistent problem. Not all data is equally valuable, and the sheer quantity of information can obscure actionable insights. Also, advanced filtering, prioritization techniques, and intelligent alerting systems are essential for identifying critical issues without being overwhelmed by less relevant data.

Addressing these challenges requires both technological innovation and thoughtful system design, ensuring observability efforts remain scalable, actionable, and cost effective as systems continue to evolve. Let’s take Netflix as an example, which streams billions of hours of content to users worldwide. Their system comprises thousands of microservices, each contributing logs and metrics, so without a robust observability strategy, pinpointing why a particular user is experiencing buffering would be nearly impossible. This streaming platform overcomes this by using tools like Atlas (their in-house monitoring platform) to aggregate, analyze, and visualize data in real time.

Best Practices for Achieving Observability at Scale

As modern systems grow increasingly complex and distributed, achieving effective observability becomes critical for maintaining performance and reliability. However, scaling observability requires more than just tools, it actually demands strategic planning and best practices. Below, we explore five key approaches to building and sustaining observability in large scale environments.

  1. Implement Distributed Tracing
     Distributed tracing tracks requests as they flow through multiple services, allowing teams to pinpoint bottlenecks or failures. Tools such as OpenTelemetry and Zipkin make this process seamless.
  2. Use AI-Powered Observability Tools
     At scale, manual monitoring becomes impractical. AI-driven tools like Datadog and Dynatrace use machine learning to detect anomalies, automate alerting, and even predict potential failures based on historical patterns. 
  3. Centralize Your Data
     A fragmented observability approach where logs, metrics, and traces are stored in separate silos, leads to inefficiencies and miscommunication. However, centralized platforms like Elastic Stack or Splunk enable teams to consolidate data and access unified dashboards.
  4. Adopt Efficient Data Strategies
     Realistically, collecting and storing every piece of data is neither cost effective nor practical. The best approach is to implement data sampling and retention policies to store only the most relevant data, ensuring scalability and cost optimization.
  5. Design for Observability from the Start
     Observability shouldn’t be an afterthought. It is best to build systems with observability in mind by standardizing logging formats, embedding trace IDs in logs, and designing APIs that expose meaningful metrics.

To sum up, observability at scale is not just a good-to-have but an absolute must have in today’s fast moving and complex technical environment. Organizations will be able to ensure seamless performance and rapid problem resolution by following best practices like distributed tracing, AI-powered tooling, centralization of data, efficient strategies, and designing systems for observability. 

The Business Benefits of Observability

Although the journey to robust observability is not easy, improvements in reliability, decreased debugging time, and a better user experience are priceless. Besides the key approaches tackled above, there is also effective observability that extends far beyond technical gains, where it has measurable impacts on business outcomes:

  • Reduced Downtime: Proactive issue detection minimizes the time systems remain offline, saving millions in potential revenue loss.
  • Faster Incident Resolution: Observability tools empower teams to identify and fix issues quickly, reducing mean time to resolution (MTTR).
  • Better User Experience: Reliable, responsive systems enhance user satisfaction and retention.

For example, Slack, the widely used messaging platform, leverages observability to maintain its 99.99% uptime and ensure seamless communication for businesses worldwide. By implementing automated incident detection and proactive monitoring, Slack can identify and address issues in real time, minimizing disruptions. Their resilient microservices architecture further contributes to maintaining reliability and uptime.

Conclusion: 

To conclude, in an era defined by ever evolving large scale systems, observability has shifted from being a luxury to a necessity. Teams must deeply understand their systems to proactively tackle challenges, optimize performance, and meet user expectations. Through practices like distributed tracing, AI-driven analytics, centralized data strategies, and designing systems for observability from the ground up, organizations can transform operational chaos into clarity.

However, the true value of observability extends beyond uptime or issue resolution. It represents a paradigm shift in how businesses interact with technology, offering confidence in infrastructure, fostering innovation, and ultimately enabling seamless scalability. As technology is constantly evolving, the question is no longer whether observability is necessary, but whether organizations are prepared to harness its full potential. 

Reddit secretly launched. AI that pretended to be a victim of violence an opponent of blm and more

Posted on by Editorial Team

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

Posted on by Editorial Team

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.

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

Posted on by Editorial Team

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

Posted on by Editorial Team 

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?

Posted on by Editorial Team

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.