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The Role of AI Chatbots in Modern HR Process Automation
Human Resources has evolved from an administrative function into a strategic driver of organizational performance. Yet, despite this shift, many HR teams remain burdened by manual processes, fragmented systems, and reactive workflows that do not scale with modern workforce demands. As organizations grow across geographies, compliance frameworks, and talent models, the complexity of HR operations increases exponentially. Traditional HR automation tools, built on rigid workflows and static portals, fail to deliver the responsiveness and intelligence required today.
This is where AI chatbots in HR are redefining how organizations automate, scale, and modernize HR operations. By partnering with an experienced AI Chatbot Development Company, enterprises can implement conversational interfaces powered by artificial intelligence to unlock a new era of HR process automation – one that is proactive, context-aware, and deeply integrated into core business systems.
The Limitations of Traditional HR Automation Systems
Before understanding the impact of AI chatbots, it is critical to examine why legacy HR automation approaches fall short.
Key Challenges in Traditional HR Operations
- HR systems operate in silos (HRMS, ATS, payroll, compliance tools)
- Employees struggle to navigate complex portals for simple queries
- Most HR workflows rely on email-based approvals
- Policy interpretation remains manual and inconsistent
- HR teams spend the majority of their time on repetitive support tasks
Despite digitization, HR remains process-heavy but intelligence-light. Automation exists, but decision-making and interpretation still require human intervention. This operational gap has created the need for HR automation using AI, where systems can understand, decide, and act without constant manual oversight.
How AI Chatbots Enable HR Workflow Automation at Enterprise Scale
Unlike traditional rule-based systems that rely on fixed keywords, modern AI chatbots are built to understand how employees naturally communicate. Powered by advanced language intelligence and enterprise integrations, they play an active role in automating HR operations rather than simply responding to queries.
Key Differentiators of AI Chatbots in HR
- Intent-based understanding, not keyword matching
AI chatbots use Natural Language Processing to accurately interpret employee intent even when questions are informal, incomplete, or phrased differently. - Context-aware conversations
By retaining conversation history and employee context such as role, location, and policy eligibility, chatbots deliver consistent and personalized responses without repetition. - Direct integration with HR systems
AI chatbots connect seamlessly with HRMS, payroll, ATS, and compliance platforms to retrieve real-time data and perform actions securely. - Built-in workflow execution
Beyond answering questions, chatbots can initiate and complete HR workflows, including leave applications, approvals, and record updates. - Active participation in HR operations
These capabilities allow AI chatbots to move beyond static FAQs and function as intelligent, action-driven components of end-to-end HR process automation.
Together, these capabilities form the intelligence layer that differentiates AI-powered HR solutions from traditional, rule-based HR software.
AI Chatbots for Employee Self-Service: Redefining HR Accessibility
One of the most impactful and widely adopted use cases of AI chatbots in HR is employee self-service automation. As organizations scale, HR teams are increasingly overwhelmed by repetitive, high-volume queries that do not require human intervention but still consume significant time and resources.
The Employee Experience Challenge
Employees regularly reach out to HR for routine requests such as:
- Leave balances and approval status
- HR policy explanations and clarifications
- Payroll and salary-related questions
- Benefits eligibility and coverage details
- Tax documents and compliance information
While these queries are essential, they are largely repetitive and manual, leading to slower response times, employee frustration, and reduced HR productivity.
How AI Chatbots Enable Self-Service at Scale
When integrated with core HRMS and payroll systems, AI chatbots for employee self-service can:
- Securely authenticate employees
- Retrieve real-time, role-specific HR data
- Interpret policies contextually based on eligibility
- Deliver instant, conversational responses
This approach significantly reduces dependency on HR support tickets while ensuring employees receive accurate, consistent, and timely information—anytime they need it.
HR Workflow Automation Using AI Chatbots
The true power of AI chatbots lies in their ability to execute HR workflows, not just provide information.
Examples of Automated HR Workflows
- Leave requests and approvals
- Attendance regularization
- Shift and roster management
- Policy acknowledgment tracking
- Employee exit and clearance processes
How Workflow Automation Works
Instead of navigating forms or sending emails, employees interact naturally:
Employee: “Apply for three days of leave starting Monday.”
AI Chatbot:
- Validates leave balance
- Checks policy rules
- Routes approval to the manager
- Updates HRMS automatically
This is HR workflow automation driven by conversation, fast, accurate, and scalable.
AI Chatbots for Recruitment: Automating Talent Acquisition at Scale
Recruitment is one of the most resource-intensive HR functions, making it an ideal candidate for automation.
AI Chatbots for Recruitment and Candidate Engagement
AI chatbots assist recruitment teams by:
- Engaging candidates 24/7
- Answering role-specific queries
- Screening candidates based on predefined criteria
- Scheduling interviews automatically
- Sending follow-ups and reminders
This improves both recruiter efficiency and candidate experience.
Intelligent Candidate Screening
Chatbots can evaluate:
- Skill relevance
- Experience thresholds
- Availability and location preferences
- Role alignment
By automating early-stage screening, recruiters focus on high-quality candidates instead of manual filtering.
AI Chatbots for Onboarding: Accelerating Time-to-Productivity
Once a candidate is hired, onboarding becomes the next critical HR challenge.
How AI Chatbots Improve Onboarding
AI chatbots guide new employees through:
- Document submission and verification
- Policy walkthroughs
- IT and access requests
- Training module assignments
- First-week task coordination
This structured, guided onboarding experience improves retention, engagement, and early productivity.
Payroll and HR Compliance Automation with AI Chatbots
Payroll and compliance processes involve high risk, strict regulations, and minimal tolerance for errors.
1. Payroll Automation Use Cases
AI chatbots can:
- Explain salary structures
- Break down tax deductions
- Track reimbursements
- Answer bonus and incentive queries
2. HR Compliance Automation
Chatbots assist with:
- Labor law interpretation
- Location-specific compliance rules
- Policy enforcement consistency
- Audit-ready interaction logs
This enables payroll and HR compliance automation with reduced manual dependency and lower risk exposure.
AI Chatbots for Internal HR Support and Knowledge Management
HR knowledge often exists in scattered formats, such as PDFs, intranets, shared drives, and emails.
Centralized HR Knowledge Access
AI chatbots for internal HR support act as a unified interface that:
- Retrieves policy documents instantly
- Interprets complex policy queries
- Escalates sensitive issues appropriately
- Provides role-specific guidance to managers and employees
This transforms HR from a reactive support function into a structured, intelligent service layer.
Enterprise Architecture for AI-Powered HR Solutions
Enterprise-grade HR chatbots require a robust technical foundation.
Key Components:
- Conversational AI and LLMs
- Secure HR data retrieval (RAG pipelines)
- HRMS, ATS, and payroll integrations
- Workflow orchestration engines
- Role-based access control (RBAC)
- Compliance logging and audit trails
Because of this complexity, most enterprises partner with an experienced AI Chatbot Development Company to design, build, and maintain these systems.
Data Privacy, Security, and Compliance Considerations
HR data includes sensitive personal and financial information. Any AI-driven HR system must prioritize security.
Best Practices
- End-to-end data encryption
- On-premises or private cloud deployment
- Zero data retention for AI models
- Access control by role and hierarchy
- Compliance with GDPR and regional labor laws
Without these safeguards, HR automation introduces operational risk.
The New Standard for HR Operations
As workforce structures grow more complex and distributed, organizations must move beyond basic digitization and embrace intelligent, AI-driven automation. AI chatbots are no longer optional add-ons; they are becoming the backbone of modern HR operations and a core component of enterprise HR workflow automation solutions. By enabling employee self-service, automating recruitment and onboarding, streamlining payroll and compliance, and strengthening internal HR support, AI chatbots deliver faster execution, consistent governance, and significantly improved employee experiences. The future of HR belongs to organizations that invest early in scalable, secure, and AI-powered HR solutions, setting new benchmarks for efficiency, agility, and workforce engagement.
Antier empowers enterprises to build next-gen AI-driven HR ecosystems as a trusted AI Chatbot Development Company, delivering secure, enterprise-grade chatbot solutions tailored for complex HR environments. With deep expertise in AI, automation, and system integration, Antier helps organizations transform HR into a strategic, future-ready function.
Frequently Asked Questions
01. How have HR operations evolved in recent years?
HR operations have shifted from being purely administrative to becoming a strategic driver of organizational performance, although many teams still face challenges with manual processes and fragmented systems.
02. What are the limitations of traditional HR automation systems?
Traditional HR automation systems often operate in silos, require manual intervention for decision-making, and rely on outdated workflows, making them less responsive to modern workforce demands.
03. How do AI chatbots improve HR workflow automation?
AI chatbots enhance HR workflow automation by using Natural Language Processing to understand employee intent, enabling proactive and context-aware interactions that streamline HR operations.
Federal Reserve Governor Chris Waller says the crypto hype that came with US President Donald Trump’s election victory has begun to wane as the market has become more entangled with traditional finance.
“I think some of the euphoria that came into the crypto world with the current administration, some of that’s kind of fading,” Waller said at a conference on Monday.
“A lot of it has been brought into the mainstream finance,” Waller said. “Then, you know, things have to happen there, so I think there was a lot of sell-off just because firms that got into it from mainstream finance had to adjust their risk positions.”
More traditional finance players have started to increase their exposure to crypto under the Trump administration, which has helped to elevate the market, but Waller argued that Congress’ failure to quickly pass the crypto market structure bill had also “put people off” as it leaves much uncertainty about how the products are regulated.
He also brushed off the recent market drop as “part of the game” with crypto. “You get in, you make some money, you might lose some money — that’s the nature of the beast.”
“Look, prices go up, prices go down — it’s just the nature of the business,” Waller said. “If you don’t like it, don’t get in it, that’s my advice to everybody.”
Bitcoin (BTC) has fallen 45% from its peak of $125,000 in October and is currently trading around $69,500 after a brief crash to under $60,000 on Friday.
Fed “skinny master accounts” to come this year: Waller
Waller said that the Fed would roll out its proposed “payment accounts” this year, which aims to give fintech and crypto firms limited access to the central banking system.
The Fed fielded feedback on the accounts, dubbed “skinny master accounts,” up until Friday, with crypto companies backing the plan while banking associations urged caution over the proposal.
Related: Bessent suggests Warsh nomination hearings alongside Powell probe
“We got a ton of stuff, and we’ll have to kind of work through that,” Waller said. “If we can get that done reasonably well, I’d like to try to have this done by the end of the year, if possible.”
The Fed’s proposal would see payment accounts given fewer privileges compared to master accounts commonly owned by major banks, such as removing the ability to earn interest and imposing balance limits.
Waller has previously said that payment accounts would “support innovation while keeping the payments system safe” and are necessary due to “rapid developments” in payments technology.
Magazine: How crypto laws changed in 2025 — and how they’ll change in 2026
TLDR
- Vitalik Buterin proposes Ethereum as a key tool for privacy-preserving AI interactions and trust-minimized AI systems.
- He advocates for local AI models and cryptographic tools to protect users’ identities in AI interactions.
- Ethereum can serve as an economic layer for AI-to-AI interactions, supporting decentralized coordination and an AI reputation mechanism.
- Buterin envisions AI scaling human judgment to improve prediction markets and decentralized governance.
- Ethereum’s involvement in AI could decentralize power and shift control from corporations to more distributed systems.
Vitalik Buterin, the co-founder of Ethereum, shared his updated vision on the intersection of Ethereum and Artificial Intelligence (AI). In his statement, Buterin emphasized avoiding “accelerationist AGI” and instead focusing on human empowerment, privacy, and safety. He proposed that Ethereum could play a key role in building trust-minimized tools for secure AI interactions and integrating these technologies with crypto.
Building Privacy-Preserving AI Tools
Buterin advocates for the development of tools that prioritize privacy and trust in AI systems. He highlighted the importance of creating local AI models (LLMs) that allow users to interact without revealing their identities.
“ZK-payment for API calls” was also mentioned as a way to prevent linking users’ identities during transactions with remote models. Moreover, Buterin stressed the significance of cryptographic advancements that could improve AI privacy.
These technologies could include client-side verification of cryptographic proofs and trusted execution environments (TEEs). By implementing these tools, Ethereum could help ensure the safe interaction between AI systems and their users.
Ethereum as an Economic Layer for AI Interactions
Buterin envisions Ethereum becoming the backbone for economic transactions in AI ecosystems. He sees Ethereum facilitating AI-to-AI interactions, such as bots hiring bots and securing deposits for AI services.
By incorporating mechanisms like ERC-8004 for AI reputation, Ethereum can support decentralized coordination between AI systems. This setup would reduce the dependency on centralized organizations controlling AI models.
Ethereum could allow these systems to function economically, empowering more decentralized architectures. By doing so, Ethereum would help shift the power dynamic in AI from large corporations to a more distributed and transparent framework.
Decentralizing Governance and Expanding Human Judgment
In his vision, Buterin believes that AI could help overcome the limits of human decision-making. He emphasized how large language models (LLMs) can scale human judgment, making prediction markets and decentralized governance more efficient.
LLMs could help in areas like quadratic voting, combinatorial auctions, and universal barter economies. Buterin’s focus is on using AI to create better markets and governance structures that were previously limited by human attention.
With AI support, these systems could function more effectively, enabling more accurate decision-making at scale. Ethereum’s role in facilitating these interactions would strengthen the foundation of decentralized cooperation and improve future defense mechanisms.
Backpack Exchange, the crypto trading platform founded by former FTX and Alameda leaders, is reportedly in talks to raise around $50 million in new financing at a pre-money valuation above $1 billion.
Summary
- Backpack Exchange is reportedly in discussions to raise around $50 million at a valuation exceeding $1 billion, potentially elevating it to unicorn status.
- CEO Armani Ferrante outlined a tokenomics structure aimed at preventing early insider sell-offs and aligning incentives with long-term product growth.
- The company has also revealed plans for a 1 billion token supply, with 25% allocated at the token generation event, including community rewards.
If completed, the round would cement Backpack’s position in the crypto sector and potentially elevate it into unicorn status, a milestone for a firm still emerging from the post-FTX landscape.
The discussions come amid increased investor interest in fintech and crypto startups, and Backpack could parlay the fresh capital into expanding its exchange, wallet, and regulatory footprint globally.
The $50 million figure is a baseline, and reports suggest the eventual round size could grow larger.
Backpack CEO outlines tokenomics strategy
Meanwhile, Backpack CEO Armani Ferrante took to X to flesh out the company’s tokenomics framework ahead of a future token generation event.
Ferrante emphasized that the structure is designed to prevent early insiders from “dumping” tokens on retail investors, with no founders, executives, or venture backers receiving unlockable tokens until the product reaches significant traction, a concept he described as product “escape velocity.”
He also highlighted Backpack’s long-term goal of eventually going public in the U.S., signaling ambition beyond private fundraising and into regulated capital markets.
According to Ferrante, aligning token incentives with users, not short-term speculation, lays a foundation for sustainable growth and broader global adoption.
In a related post on the official Backpack account, the team confirmed elements of its upcoming token issuance plans. This includes a 1 billion token supply at launch and the allocation of 25 % of tokens at the Token Generation Event (TGE), with a portion earmarked for active community participants and points holders.
Bitmine Immersion Technologies has pushed its Ethereum treasury to new highs, with total ETH holdings now standing at 4.326 million tokens, as the firm continued aggressive accumulation despite ongoing volatility in the crypto market.
Summary
- Bitmine Immersion Technologies’ Ethereum holdings have reached 4.326 million ETH, representing about 3.6% of ETH’s circulating supply.
- On-chain data shows the firm bought 40,000 ETH worth roughly $83.4 million in a single day, including a $42.3 million purchase from BitGo.
- Nearly 2.9 million ETH are staked, underscoring Bitmine’s long-term strategy despite ongoing market volatility.
The Tom Lee–chaired company disclosed in a recent press release that its Ethereum stash now represents around 3.6% of ETH’s total circulating supply, cementing Bitmine’s position as the largest known corporate holder of the asset.
Combined with Bitcoin and cash reserves, Bitmine’s total crypto and cash holdings are valued at approximately $10 billion.
Fresh $83M ETH buy signals continued accumulation
On-chain data flagged by Lookonchain shows that Bitmine added significantly to its position on Monday.
According to the analytics account, the firm purchased 20,000 ETH worth about $42.3 million from BitGo, following an earlier buy of the same size.
“Bitmine has been steadily buying Ethereum, as we view this pullback as attractive, given the strengthening fundamentals. In our view, the price of ETH is not reflective of the high utility of ETH and its role as the future of finance,” said Tom Lee, Executive Chairman of Bitmine.
Staking strategy anchors long-term bet on Ethereum
Bitmine said nearly 2.9 million ETH of its total holdings are currently staked, generating yield through its expanding Ethereum infrastructure operations. The company added that its ETH-focused strategy is aimed at long-term value creation rather than short-term price movements.
Chairman Tom Lee described recent price weakness as an opportunity, citing Ethereum’s history of sharp recoveries following deep drawdowns and pointing to staking yields as an additional source of return.
Ethereum has struggled to regain upside momentum amid broader risk-off sentiment across crypto markets. Still, Bitmine’s continued buying underscores growing interest among institutional players in Ethereum as a treasury asset, even as near-term price action remains uncertain.
Tokenization has rapidly evolved from a niche blockchain experiment into a strategic enabler for enterprises seeking greater liquidity, operational efficiency, and transparency across asset classes. However, as enterprises move beyond proof-of-concept initiatives, it has become increasingly clear that early, fully on-chain tokenization models are not designed to meet real-world enterprise requirements.
Enterprises operate within complex ecosystems defined by regulatory oversight, data privacy mandates, legacy infrastructure, and multi-jurisdictional compliance obligations. While public blockchain networks offer decentralization and transparency, they often lack the governance controls enterprises require. Conversely, fully private systems limit interoperability and long-term scalability.
This gap is being decisively filled by hybrid tokenization, a pragmatic and future-ready framework that blends blockchain innovation with enterprise-grade control. Hybrid models are emerging as the preferred foundation for enterprise asset tokenization, enabling organizations to unlock tokenized value without compromising compliance, privacy, or operational stability.
What Is Enterprise Asset Tokenization and Why Are Enterprises Re-Evaluating Tokenization Models in 2026?
Enterprise asset tokenization is the practice of using tokens built on blockchain technology to digitally represent ownership, rights and economic value of an enterprise’s asset[s] while embedding the governance, compliance, and operational controls that large organizations require.
Assets that can be tokenized include but are not limited to financial securities; portfolios of real estate; interests in private equity; commodities; infrastructure-related assets; intellectual property; and instruments for sharing revenue.
Numerous large structural shifts are influencing how organizations view and approach the tokenization of their assets:
- The regulatory landscape has matured. As such, regulators are now requiring that asset tokenization projects be auditable and provide investor protection through jurisdictional enforcement rather than being experimental efforts.
- Institutional participation has increased in the tokenization of assets. Institutional investors are establishing higher standards for how organizations should protect the confidentiality of their data, accurately report their results and mitigate risk.
- There has been an expansion of operational scale; enterprises have progressed from conducting small pilots of tokenizing their assets to developing and implementing broad-based strategies for tokenizing multiple types of assets in multiple markets.
- Enterprises can no longer avoid integrating their legacy systems and other operational platforms into their tokenization platforms. Tokenizing assets and developing tokenized asset-based products will require seamless integration between the tokenization platform and legacy ERP systems, the provider of custodial services, banks, and systems for complying with regulations.
These developments are causing organizations to rethink their evaluation of enterprise tokenization models by prioritizing those models that align with how they operate on a day-to-day basis rather than those that require organizations to alter their methods of operation.
Evaluate whether Hybrid Tokenization fits your Enterprise Roadmap
What Is a Hybrid Tokenization Model?
A hybrid tokenization model is an architectural approach that strategically distributes tokenization functions across blockchain networks and off-chain enterprise systems. Instead of forcing all processes onto a decentralized ledger, hybrid models apply blockchain selectively—where it delivers the greatest value—while retaining centralized control where required.
The following three components are integral to the hybrid tokenization architectural:
- A blockchain layer for token issuance, ownership tracking, and transaction immutability
- An off-chain enterprise layer for compliance, identity management, legal documentation, and sensitive data
- Middleware that synchronizes on-chain events with off-chain business logic and enterprise workflows
The Hybrid Tokenization Models provide a method for Enterprises to maximize the use of Blockchain technology while minimizing their exposure to regulatory and operational risks.
How Hybrid Tokenization Architecture Combines On-Chain and Off-Chain Tokenization
The success of tokenization of hybrid assets is dependent upon how well the on-chain and off-chain tokenization functions are coordinated. Each layer has been specifically designed to carry out the operations that will work most effectively in that environment.
1. On-chain Tokenization Layer
The on-chain tokenization layer has responsibility for activities that can be carried out in a decentralized, immutable, and automated manner:
- Issuing Tokens and Managing Their Life Cycle
Tokens will be generated on chain as a method of providing a cryptographic proof of ownership of the asset they represent. When life cycle events occur (minting, burning, freezing, or unlocking tokens), they are executed in an open and transparent manner to ensure that the integrity of the asset is maintained.
- Records of Transfer of Ownership
All transfers of the token from one holder to another will be recorded on the blockchain, resulting in an unalterable record of who owns an asset. The result is a reliable record for enterprises, investors & regulators to rely upon to establish provenance of the asset and validate any corresponding transactions.
Smart contracts provide an automated means of enforcing all contractual obligations (e.g., transfer restrictions, vesting schedules, dividend distributions, redemption of asset rights). As a result of using smart contract execution, manual interventions can be reduced, reducing the likelihood of operational errors. All contractual obligations will be consistently enforced.
Auditability in real time is possible due to the recordkeeping of the blockchain. Regulators and internal compliance departments can verify the validity of transactions with no reliance on reconciled reports, thereby creating far greater trust and transparency with all parties involved.
2. Off-chain enterprise layer
The off-chain layer provides functionality that requires privacy, flexibility, and regulatory discretion.
- KYC/AML Verification & Investor Accreditation
Identity verification in different jurisdictions has different processes and is continuously changing. By managing these workflows off-chain, businesses can quickly adapt their compliance logic to accommodate changing regulations and apply eligibility checks before participating on-chain.
- Legal Agreements & Contractual Governance
Ownership of assets is determined by legal documentation (prospectuses, shareholder agreements, and regulatory filings). Off-chain storage of these documents provides the ability to keep them updated and still be cryptographically linked to the on-chain token.
- Asset Valuation, Reporting, and Metadata Management
Many types of assets will need to be valued periodically, sometimes using third-party data feeds or human intervention. An off-chain system can facilitate accurate financial reporting and mitigate the risk of unnecessary oracle dependency.
- Integration to Enterprise Systems
Hybrid architectures facilitate the integration of ERP systems, accounting packages, custodial services and banking infrastructure. This enables tokenization to build on existing operations and not disrupt them.
Tokenization Models Comparison: Public, Private, and Hybrid Enterprise Tokenization Models
A comprehensive tokenization models comparison highlights why hybrid approaches are increasingly favored by enterprises.
Public tokenization models
Public models operate entirely on open blockchains, offering transparency and composability. However, they present challenges such as:
- Exposure of sensitive transaction data
- Limited jurisdictional enforcement capabilities
- Unpredictable transaction costs and network congestion
- Governance dependency on public network consensus
While suitable for open ecosystems, public-only models struggle to meet enterprise governance and compliance standards.
Private tokenization models
Private models emphasize control and confidentiality but introduce other limitations:
- Restricted interoperability and liquidity
- Heavy reliance on centralized administrators
- Limited external auditability
- Reduced long-term flexibility
These constraints can hinder scalability and investor confidence.
Hybrid enterprise tokenization models
Hybrid models combine the strengths of both approaches:
- Selective transparency with controlled access
- Built-in compliance and governance mechanisms
- Scalable participation across markets and asset classes
- Future adaptability to regulatory and technological change
For enterprises pursuing long-term digital asset strategies, hybrid models offer the most resilient foundation.
Why Hybrid Tokenization Will Define Enterprise Asset Strategies
As enterprises consider tokenization as long-term infrastructure, decision-makers are increasingly prioritizing frameworks that align with governance, compliance, and scalability. In this shift, the hybrid tokenization model is emerging as the most practical and future-ready choice for enterprise asset tokenization.
Key factors contributing to the adoption of hybrid tokenization by enterprises:
- Hybrid tokenization architecture balances decentralization with enterprise governance
Unlike fully public systems, hybrid tokenization architecture allows enterprises to use blockchain for immutable ownership and auditability while retaining off-chain control over compliance, identity, and legal enforcement—an essential requirement for regulated enterprise environments.
- Supports regulatory-ready enterprise tokenization models
Enterprise tokenization models will enable the enterprise to manage compliance regulations, jurisdictional limitations, and reporting procedures off-chain, therefore enabling the enterprise to update its compliance regulations more easily and with fewer disruptions than if using a fully decentralized architecture.
- Optimized use of on-chain and off-chain tokenization layers
A hybrid implementation allows an enterprise to maximize the benefits of implementing on-chain and off-chain tokenization while providing transparency, as appropriate, and confidentiality as needed on its blockchain infrastructure.
- Delivers superior results in tokenization models comparison
In any realistic tokenization models comparison, hybrid tokenization approaches often surpass completely public and completely private tokenization approaches by delivering selective transparency, controlled access, and long-term scalability—critical factors in determining whether enterprises will adopt tokenized assets.
- Enables scalable hybrid asset tokenization across multiple asset classes
With hybrid asset tokenization, enterprises can tokenize a variety of assets (e.g., securities, funds, real estate, RWAs) over a single shared blockchain layer while applying customized off-chain governance, valuation, and compliance workflows for each asset type.
- Reduces cost volatility and operational risk at scale
Enterprises using fully public or private on-chain systems experience significant volatility in fees due to fluctuating network congestion and stable fees. In contrast, hybrid tokenization models move the majority of high-volume and compliance-centric processes off-chain and deliver a more predictable level of performance and cost savings as a result of this approach.
- Strengthens institutional trust and accelerates market participation
Institutional investors require a high level of trust based upon the governance and enforceability of how assets are tokenized. Hybrid frameworks provide on-chain transparency coupled with off-chain legal and compliance controls to create a more credible and investable enterprise asset tokenization.
- Best implemented with an experienced asset tokenization development company
In order to implement hybrid tokenization solutions, it is important to partner with an experienced asset tokenization development company with expertise in blockchain, compliance, and enterprise integration so that the architecture supports legislative and operational realities.
Explore Enterprise-Ready Hybrid Tokenization
A Strategic Enterprise Outlook
With more organizations starting to implement their tokenization initiatives instead of merely experimenting with these new technologies as they come to market, hybrid tokenization models are clearly proving to be one of the leading approaches for enterprise asset tokenization due to their ability to combine on-chain and off-chain tokenization in a manner that ensures transparency, governance, and flexibility to operate in various asset classes and jurisdictions.
Of all the various tokenization models being evaluated today, hybrid tokenization models stand out because they achieve a proper balance between innovation and control; thus, providing enterprises with the means to execute their long-term tokenization strategy through 2026.
Organizations cannot achieve hybrid tokenization at scale without deep technical and regulatory knowledge. As an established and reliable asset tokenization development company, Antier is able to assist these enterprises by not only providing the asset tokenization development services and expertise needed to design and deploy compliant hybrid tokenization architectures but also by providing the blockchain, Web3, and enterprise integration capabilities necessary to create tokenization platforms that will meet future regulatory requirements in an efficient and secure manner.
Jump Trading, a Chicago-based quantitative trading company, is reportedly set to acquire minority stakes in prediction market platforms Polymarket and Kalshi, underscoring growing institutional interest in the rapidly expanding sector.
The equity stakes would be obtained in exchange for providing trading liquidity on both platforms, Bloomberg reported Monday, citing people familiar with the discussions.
While the report did not disclose specific ownership percentages, Bloomberg said Jump’s stake in Polymarket would scale based on the liquidity the company ultimately provides.
Founded more than two decades ago, Jump Trading has long been a major player in proprietary financial trading and has expanded aggressively into digital assets. It has been active as both a market maker and venture investor in crypto, backing blockchain infrastructure projects and exchanges through its affiliated investment arms.
Polymarket and Kalshi are the two largest prediction market platforms, each commanding multibillion-dollar valuations following recent funding rounds.
As previously reported by Cointelegraph, Polymarket raised $2 billion from NYSE parent Intercontinental Exchange, valuing the company at $9 billion. In early December, Kalshi secured $1 billion in funding at an $11 billion valuation.
While both platforms allow users to trade on the outcomes of real-world events, they operate under different models. Polymarket is a decentralized platform built on the Polygon blockchain that enables onchain settlement of prediction contracts, whereas Kalshi operates as a centralized, federally regulated exchange in the United States.
Related: Trump Jr. joins Polymarket board as prediction market eyes US comeback
Prediction markets gain traction, but still face regulatory hurdles
Prediction markets gained mainstream attention after Polymarket’s event contracts accurately forecast the outcome of the 2024 US presidential election, highlighting the sector’s potential as a real-time information and risk-pricing tool. Industry analysts now estimate that prediction markets may generate trillions of dollars in annual trading volume by the end of the decade.
Eilers & Krejcik Gaming, a research and consulting company specializing in the global gambling and gaming industry, has identified sports-related contracts as a major driver of that growth. Speaking to CNBC in December, Eilers & Krejcik partner emeritus Chris Grove said sports betting could account for nearly half of the sector’s projected expansion.
Despite the growth potential, Grove cautioned that legal and regulatory challenges could slow adoption.
Kalshi, which operates as a federally regulated prediction market, has received approval from the US Commodity Futures Trading Commission to run as a Designated Contract Market. However, the platform is facing pushback at the state level. Regulators in Nevada, Maryland, New Jersey and Ohio have challenged Kalshi’s offerings, triggering ongoing litigation and cease-and-desist actions.
Related: Polymarket wins regulatory approval to operate US trading platform
Ripple said on Monday it has expanded its institutional custody platform through new integrations with Securosys and Figment.
The company said it is adding hardware security modules to enable banks and custodians to deploy custody services and offer staking without necessarily operating their own validator or key-management infrastructure.
Building on Ripple’s recent acquisition of Palisade and the integration of Chainalysis compliance tools, the custody upgrades allow regulated institutions to manage cryptographic keys using on-premises or cloud-based HSMs and to offer staking on networks such as Ethereum (ETH) and Solana (SOL), with compliance checks embedded directly into transaction workflows.
Ripple said the integrations are intended to reduce deployment complexity and support faster rollout of custody services for institutional clients. Ripple has been pushing further into institutional infrastructure activity as it expands beyond payments with custody, treasury and post-trade services for regulated companies.
Ripple is a US-based blockchain infrastructure company that provides payment and custody technology to financial institutions and is the issuer of the XRP (XRP) token and the dollar-pegged stablecoin RLUSD, which it launched in December 2024.
The update comes weeks after the company launched a corporate treasury platform that integrates traditional cash management systems with digital asset infrastructure.
Related: Institutional staking and yield products gain traction
Institutional staking and yield products gain traction
Institutional interest in staking has grown as proof-of-stake networks mature and regulatory expectations continue to evolve.
In October, Figment expanded its integration with Coinbase, enabling Coinbase Custody and Prime clients to stake additional proof-of-stake (POS) assets beyond Ether. The update gave institutional customers access to staking on networks including Solana (SOL), Sui (SUI), Aptos (APT) and Avalanche (AVAX) through Figment’s infrastructure.
In November, Anchorage Digital added staking support for the Hyperliquid ecosystem, enabling HYPE (HYPE) staking alongside its existing custody services. The bank said the offering would be available through Anchorage Digital Bank, its Singapore entity, and its self-custody wallet Porto, with validator operations supported by Figment.
While staking enables institutions to earn rewards on proof-of-stake networks, parallel efforts have also emerged to generate yield from Bitcoin, which does not support staking.
Earlier this month, Fireblocks said it will integrate Stacks, enabling institutional clients to access Bitcoin-based lending and yield products. The integration uses Stacks’ roughly five-second block times while settling transactions to the Bitcoin ledger for finality, addressing latency constraints that have limited institutional use of BTC-based decentralized finance.
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Chainlink co-founder Sergey Nazarov argues the recent crypto market downturn is unlike any previous bear market — there have been no major FTX-style collapses, and tokenized real-world asset (RWA) growth remains substantial.
Market cycles are normal, “but what is important is what those cycles reveal about how far the industry has progressed,” said Nazarov on X on Tuesday.
Crypto market capitalization has fallen 44% from its October all-time high of $4.4 trillion, with almost $2 trillion exiting the space in just four months.
Nazarov, however, did not appear concerned, highlighting two primary factors that separate this current bear market from previous ones.
Unlike previous cycles, such as the FTX and crypto-lending failures in 2022, there haven’t been major institutional collapses during this drawdown, indicating the industry can now handle volatility more reliably, he said.
“There have been no large risk management failures leading to large institutional failures or widespread systemic risks.”
RWA growth will drive institutions and infrastructure
Secondly, RWA tokenization and on-chain perpetual contracts for traditional commodities continue accelerating regardless of crypto prices, proving this innovation has standalone value beyond speculation.
Tokenized RWA onchain value has increased 300% over the past 12 months, according to RWA.xyz.
This signals that having real-world assets on-chain “is not tightly coupled to cryptocurrency prices but provides its own unique value that can grow irrespective of market pricing of Bitcoin or other crypto assets,” he said.
The surge hasn’t been reflected in the price of Chainlink (LINK), however, with the blockchain oracle and RWA-centric asset tanking 67% since its October peak and down 83% since its 2021 all-time high, trading at a bear-market low below $9 at the time of writing.
Related: Crypto VC Funding Doubled in 2025 as RWA Tokenization Took the Lead
Nazarov also sees other converging trends reshaping the future of crypto.
On-chain perps and tokenization offer unique value, such as 24/7 markets, on-chain collateral, and real-time data, which is growing steadily. Institutional adoption will be driven by this fundamental utility, and infrastructure demand will surge as complex RWAs require more sophisticated on-chain systems, the Chainlink co-founder said.
“If these trends continue, I believe what I have been saying for years will happen; on-chain RWAs will surpass cryptocurrency in the total value in our industry, and what our industry is about will fundamentally change.”
Not all bear markets are equal
Bernstein analyst Gautam Chhugani echoed the sentiment in a note on Monday, writing that we are experiencing “the weakest Bitcoin bear case in its history.”
“The current Bitcoin price action is a mere crisis of confidence. Nothing broke, no skeletons will show up,” analysts led by Chhugani said.
Jeff Mei, chief operating officer at the BTSE exchange, told Cointelegraph that this sell-off is different “in that it was caused largely by non-crypto catalysts.”
Those include fears that a faltering AI tech boom could cause stocks to crash, “compounded by the appointment of Kevin Warsh to Fed chair, who many believe will reduce liquidity in the financial system,” he said.
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Chainlink (CRYPTO: LINK) co-founder Sergey Nazarov argues that the current crypto downturn is not a replay of previous bear markets. Speaking on X on Tuesday, Nazarov noted that there have been no FTX-style collapses this time and pointed to a persistent wave of tokenized real-world assets that continues to grow despite price declines. Crypto market capitalization has fallen about 44% from its October all-time high of $4.4 trillion, with roughly $2 trillion leaving the space in just four months. He frames the cycle as a test of the industry’s progress: cycles reveal how far the ecosystem has advanced, and this downturn is exposing both resilience and a real-world asset narrative that could outlast speculative pricing.
Key takeaways
- The downturn lacks a single systemic event comparable to FTX-era collapses, suggesting improved risk management across institutions.
- Tokenized real-world assets (RWAs) are expanding on-chain, signaling a use case beyond mere price speculation.
- On-chain perpetuals and asset tokenization offer 24/7 markets, on-chain collateral, and real-time data that could drive institutional adoption.
- Chainlink’s credibility as a backbone for on-chain RWAs remains intact even as the broader market experiences weakness.
- Analysts and industry observers see a bifurcation between crypto prices and the growth trajectory of on-chain RWAs, potentially reshaping the industry’s value proposition.
Tickers mentioned: $BTC, $ETH, $LINK
Sentiment: Neutral
Price impact: Negative. A broad sell-off and outflows have pressured prices and market capitalization, even as on-chain RWA activity trends higher.
Market context: The current cycle unfolds amid a shifting risk environment, macro uncertainty, and ongoing debates about liquidity and regulation that influence both crypto assets and tokenized RWAs.
Why it matters
The argument that the bear market is not a monolithic crash but a spectrum of dynamics matters because it reframes what investors should watch. Nazarov emphasizes that the absence of large, systemic failures this cycle points to improved risk controls and more mature market infrastructure. In practical terms, this could translate into steadier liquidity provision, fewer cascading liquidations, and greater confidence in deploying capital through on-chain channels rather than off-ramp exits.
Central to this narrative is the acceleration of RWA tokenization. According to RWA.xyz, tokenized RWAs on-chain have surged by about 300% over the past 12 months, underscoring a use case that can prosper irrespective of crypto price cycles. The implication is clear: real-world assets—ranging from securitized notes to commodity-linked contracts—are becoming meaningful, on-chain stores of value and collateral concepts, not merely speculative bets. This trend could feed into broader institutional demand, as on-chain mechanisms offer transparency, auditability, and cross-border settlement capabilities that traditional markets take days or weeks to deliver.
Yet the market’s performance remains tethered to macro and sector-specific catalysts. LINK, the token associated with pricing data and oracle services, has faced sustained weakness, trading in bear-market territory after peaking earlier in the cycle. The dynamic illustrates a decoupling: while RWAs push forward in practical utility, the crypto market, including major assets like Bitcoin and Ethereum, can diverge for periods where macro sentiment dominates. In this context, on-chain RWAs could gradually displace some narrative weight away from pure price action toward real-world utility and risk-adjusted capital formation.
Institutional involvement is widely anticipated to hinge on the utility of these on-chain structures. Nazarov argues that the combination of perpetual markets, tokenized assets, and robust on-chain collateral is creating a more resilient foundation for institutions to experiment with crypto-enabled finance. The broader ecosystem benefits from infrastructure upgrades that enable risk management, settlement, and governance in a transparent, programmable environment. The takeaway is not that crypto prices must explode to prove value, but that the underlying systems—the oracles, the data streams, and the contractual primitives—are becoming indispensable to professional finance.
As markets digest these developments, some observers emphasize that the current sell-off is driven by factors outside the crypto sector. Analysts have framed the move as a wider market concern about AI equities, liquidity expectations under a potentially tighter policy regime, and shifts in liquidity leadership. While these external pressures complicate the price narrative, the on-chain RWA ecosystem appears to be advancing on its own trajectory, aligned with broader fintech adoption and cross-chain interoperability goals.
“If these trends continue, I believe what I have been saying for years will happen; on-chain RWAs will surpass cryptocurrency in the total value in our industry, and what our industry is about will fundamentally change.”
Not all bear markets are equal
Industry observers have framed this downturn as potentially less damaging to the core ecosystem than prior cycles. Bernstein analyst Gautam Chhugani described the Bitcoin bear case as historically weak, suggesting that the price action reflects a crisis of confidence rather than a structural breakdown. “The current Bitcoin price action is a mere crisis of confidence. Nothing broke, no skeletons will show up,” the note said. The takeaway is that the macro environment, not just isolated crypto incidents, is weighing on sentiment.
Other voices emphasize a more nuanced picture. For instance, market participants note that macro catalysts—ranging from interest-rate expectations to tech-sector dynamics—have a disproportionate influence on crypto pricing versus on-chain activity. The sell-off has been described as being driven more by non-crypto catalysts than by internal systemic failures within the crypto space, a distinction that could support a faster reacceleration should risk appetite improve and liquidity return.
Market context
Against the backdrop of a 44% drawdown in crypto market cap from the October peak and substantial outflows, the story of RWAs on-chain remains a central pillar of longer-term value propositions in crypto. The dynamic underscores a broader trend toward tokenization and on-chain finance as mainstream infrastructure projects mature. If on-chain RWAs continue to gain traction, the sector could reorient investor attention toward scalable, real-world use cases, rather than relying solely on volatility-driven appetite for purely digital assets.
Why it matters
For builders, the message is clear: investing in robust on-chain infrastructure for RWAs—oracle reliability, settlement speed, and secure collateral mechanisms—could yield enduring demand. For investors, RWAs offer a potential hedge against crypto-price cycles by anchoring value in tangible, off-chain assets. For the market, the continued growth of RWAs may redefine what constitutes “crypto value,” expanding the spectrum of investable instruments and potentially attracting traditional finance players to participate in a more regulated, verifiable on-chain ecosystem.
What to watch next
- Updates from RWA.xyz on on-chain RWAs growth metrics and new asset classes tokenized on-chain.
- Institutional pilots adopting on-chain perpetuals and RWA-backed collateral frameworks.
- Regulatory developments affecting tokenized real-world assets and oracle data provisioning.
- Cross-chain integrations that improve liquidity, settling quickly, and governance for RWAs.
Sources & verification
- Sergey Nazarov’s X post discussing bear-market dynamics and RWAs growth.
- RWA.xyz data showing on-chain RWA value growth (about 300% YoY).
- LINK price/index coverage referenced in market commentary.
- Bernstein note on Bitcoin bear-case context.
- Wemade KRW stablecoin alliance with Chainlink coverage.
RWA momentum and a reshaping crypto market
Chainlink’s foundational role in powering on-chain RWAs remains a consistent thread as the sector charts its next phase. The on-chain RWA narrative is supported by observable growth metrics and a steady flow of products that enable real-world assets to exist, trade, and collateralize on-chain. While price action can swing with global liquidity and risk sentiment, the underlying technology stack—secure oracles, robust data feeds, and programmable contracts—continues to attract the interest of developers, institutions, and asset issuers alike. The broader question is whether on-chain RWAs will eventually carry a larger share of industry value than speculative crypto assets, a shift Nazarov has been vocal about predicting for years.
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Hyperliquid, a decentralized perpetual futures exchange, has quietly overtaken Coinbase in total notional trading volume, marking a major shift in how crypto traders are choosing to trade.
Summary
- Hyperliquid recorded about $2.6T in notional trading volume in 2025.
- Coinbase posted roughly $1.4T over the same period.
- The gap reflects rising demand for on-chain derivatives platforms.
According to data shared on Feb. 10 by on-chain analytics platform Artemis, Hyperliquid processed about $2.6 trillion in notional trading volume in 2025. Coinbase, one of the world’s largest centralized exchanges, recorded around $1.4 trillion over the same period.
Despite Hyperliquid (HYPE) launching only a few years ago and running entirely on-chain, the numbers show that it handled almost twice Coinbase’s trading volume. The milestone has drawn attention across the crypto industry, especially as decentralized platforms continue to challenge traditional exchanges.
How hyperliquid built its lead
Hyperliquid primarily focuses on trading perpetual futures and derivatives on its proprietary Layer 1 blockchain. Active traders seeking quick execution, cheap fees, and direct access to on-chain liquidity have been drawn to it thanks to its focused approach.
The platform grew quickly throughout 2025. Daily trading occasionally increased to close to $30 billion, while monthly volumes frequently reached hundreds of billions of dollars. The total value locked increased toward $6 billion, while open interest peaked at about $16 billion.
User growth also accelerated. The platform’s active user base grew from about 300,000 to more than 1.4 million in a year, driven largely by word-of-mouth and product performance rather than heavy marketing.
Fees collected on Hyperliquid are partly used for HYPE token buybacks and burns. This model has helped support long-term interest in the ecosystem. As of early 2026, HYPE is up roughly 31.7% on the year and continues to draw increasing attention from traders.
Coinbase operates very differently. Its higher fees, stricter compliance requirements, and fully centralized model for spot and derivatives trading still make it a key entry point for retail users. However, professional traders are increasingly turning their focus toward alternatives that offer more flexibility and lower costs.
Coinbase stock is down about 27.0% so far this year, showing how much pressure traditional crypto companies are under in the current market slowdown.
What this shift means for crypto trading
The growing gap between Hyperliquid and Coinbase reflects a change in how users trade. On-chain platforms offer speed and transparency without requiring users to hand over custody, and more traders are getting comfortable using them.
With Hyperliquid, derivatives traders do not need to trust a central operator with their funds. Smart contracts are used to manage risk, and trades settle on-chain. Users who have been wary of exchanges in the past will find this appealing.
At the same time, Hyperliquid has placed a strong emphasis on user experience. Its user interface is similar to that of large centralized platforms, which makes it easier for new users to get started. Its growth has largely been attributed to this combination of usability and decentralization.
Momentum has also been boosted by recent developments. The platform is being used to test new products such as outcome-based contracts and limited-risk options. Notable industry figures, like Arthur Hayes, who recently increased the size of his own HYPE holdings, have also taken notice of it.
But there are still issues. Competition in decentralized derivatives is increasing, and regulators are paying more attention to on-chain trading activity. Aster and Lighter, two rivals, are also expanding their product lines.
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