The transition from digital identity as a passive, siloed record to an autonomous, sovereign asset represents a structural shift in the Web3 economy. Globally, the digital identity market is projected to reach approximately 

. Yet, despite this explosive potential, the current landscape remains fundamentally constrained by a fragmentation crisis.


Today’s identities are mostly "data honeypots" isolated within proprietary platforms, forcing users into repetitive 

 submissions that lead to drop-off rates as high as 

. True economic agency in Web3 requires more than just a wallet; it requires a decentralized infrastructure for reusable, chain-agnostic trust.


(Identity Operating System) addresses this gap by proposing a modular three-layer architecture designed to turn isolated data silos into a cohesive "sovereign compliance" ecosystem. This article summarizes the architectural constraints of the current market, the idOS modular solution, and the tokenomic design underpinning this new era of decentralized identity.

The Fragmentation of the Identity Economy

Current identity architectures prioritize platform-specific silos over interoperability. A user verified on one exchange cannot natively port that status to a decentralized finance (DeFi) protocol or a stablecoin payment app without undergoing the entire verification process again. This "ID tax" is a massive overhead, with fintech and stablecoin companies spending up to 

Furthermore, a "Security Gap" exists. Centralized databases act as lucrative targets for cyberattacks; for example, a 2023 attack on the MOVEit tool compromised 

. The industry faces a critical design challenge: how to facilitate regulatory compliance, such as 

, without creating centralized points of failure that expose users to identity theft.

idOS’s Modular Architecture: The 3-Layer Design

At the core of the idOS approach is a three-layer framework that reimagines identity not as a static file, but as a user-controlled profile built on cryptographic sovereignty.

 Utilizing a decentralized relational database called 

, this layer provides the storage backbone. It employs a specialized consensus mechanism known as 

 to ensure all participants see the same state of encrypted user data.

, translating user intent into enforceable permissions across disparate blockchains like 

. It allows idOS to function as a neutral bridging infrastructure rather than a competing blockchain.

 and a user dashboard, developers can integrate idOS to handle compliance without ever storing sensitive 

Personally Identifiable Information (PII)

Bridging the Trust Gap: Permissioned Validators and Time Locks

To address reliability issues with institutional gateways, idOS introduces a 

 in Zug, Switzerland, ensuring the network meets legal and compliance standards.

To reconcile the "right to be forgotten" (GDPR) with AML mandates, idOS utilizes 

. When a user grants access for compliance, the system can postpone data deletion until the legally required retention period concludes. This ensures that while users "hold the keys" to their data, institutions maintain the audit trails required by global regulators.

The Economics of Identity: The IDOS Token Model

, a utility and governance asset capped at 

 The model features a steep early inflation phase, with over 

 entering circulation shortly after the Token Generation Event (TGE) to ensure deep market liquidity.

 on "Access Grant" fees when data consumers request pre-verified credentials.

 Staking rewards are front-weighted to attract validators, while a 

aligns the protocol with major L1/L2 ecosystems.

We are witnessing a shift from platform-specific "honeypots" to a unified trust economy. While traditional KYC vendors offer robust verification, they cannot provide the cross-chain reusability and user-centric privacy of a decentralized layer.

The idOS architecture, combining the speed of decentralized storage with the truth of on-chain access grants, positions it as critical infrastructure for the Web3 compliance stack. By standardizing how identities are stored and shared, it lays the rails for an economy where users retain ownership of their data while moving seamlessly across the decentralized web.

This summary captures only a fraction of the architectural deep dives and competitive analysis. To explore the complete Porter’s Five Forces analysis of the identity market and the full tokenomic modeling, 

Analyze how idOS solves the Web3 identity crisis with its 3-layer architecture, reusable KYC, and chain-agnostic design. Read our full 2026 research report.

Breaking the Silos: How idOS Aims to Solve the Web3 Identity Crisis

For many decades, the Western world has always upheld individual freedoms: freedom of speech, freedom of assembly, and freedom of thought. These values are not just slogans but foundations, earned through history, conflict, and social struggle. However, that freedom is being lost silently today with the development of social networks, as these applications have the capacity to collect private messages, or the fact that the 

EU is pushing forward a legislative proposal which is the Chat Control Law

. Blockchain and privacy-focused projects could be the solution for maintaining personal privacy today, where data is no longer controlled by third parties or publicly accessible.

Throughout human history, freedom has often been regarded as a central aspiration of human societies. Forms of social organization have never been static. They have moved along a clear trajectory, from absolute domination to conditional control and gradually toward democracy and individual freedom. Ancient empires were built on violence and fear. Monarchies relied on bloodlines and divine authority. Modern states emerged when people began to question the legitimacy of power, the right of individuals to self-determination, and the boundaries that the state must not cross.Democracy and freedom are the results of a social evolution that has taken centuries. Every step forward has been accompanied by struggle, resistance, and sacrifice. Therefore, when modern societies, especially those that once took pride in freedom, begin to return to comprehensive control and mass surveillance, this is not merely a policy shift: it is a reversal of evolutionary direction.

, Freedom House announced a global decline in freedom scores for the 19th consecutive year.

A system of total surveillance runs counter to this instinct. It asks people to accept that thoughts, conversations, and private interactions may be observed, analyzed, and judged. It demands that we place trust in AI algorithms, data-holding institutions, and those who control access to society's most sensitive information. The belief that they will do the right thing with that data is a dangerous assumption.


The core issue here is trust. When societies are asked to place absolute trust in a central authority, dependence itself becomes a vulnerability. It is often in such contexts that alternative systems begin to emerge. It is precisely in such contexts that new technologies like blockchain have emerged. Blockchain did not appear randomly or in isolation from history. It emerged after the 2008 global financial crisis, when trust in centralized financial institutions was deeply shaken. The banking system, which demanded unquestioned trust, exposed its limitations and failures. Most individuals had no meaningful ability to oversee it or hold it accountable.


“The central bank must be trusted not to debase the currency, but the history of fiat currencies is full of breaches of that trust.” - 

Although blockchain is often associated with transparency, this does not mean that it rejects privacy. On the contrary, privacy-focused projects are receiving increasing attention and continued development.

. Within a single week, a large number of amateur retail traders inflicted heavy losses on several hedge funds, causing billions of dollars in damage, by collectively buying shares of GameStop, a video game retailer. It began when a group of Reddit users discovered that several hedge funds were coordinating to short GameStop stock. Those who learned about it spread the information across the Reddit community, sparking a wave of buying intended to drive the price upward. The result was a several-fold increase in GameStop’s share price and massive losses for the hedge funds involved.

This episode is a clear illustration of how important privacy is, especially in a decentralized world built on blockchain technology. Not everything should operate in full public view. For example, messages do not need to be stored publicly on-chain. With the development of privacy-focused projects, messages can be stored anonymously on-chain without being controlled by any centralized server, unlike in the Web2 model.


Rather than being treated as an optional add-on, privacy should become a core layer of the Internet and of financial systems. This is especially critical in sectors such as finance and healthcare, where sensitive data must be tightly controlled in terms of who can access it and under what conditions. Imagine, for example, a financial institution using machine learning to generate trading signals and execute trades based on those predictions. If transaction patterns or execution behavior become visible to the market, the institution may unintentionally reveal its positioning. Once others infer the model’s behavior, they can trade against it or front run execution, quickly degrading performance and turning an effective strategy into a costly and potentially catastrophic loss. That’s why privacy is necessary.

Monero is currently the largest privacy project by market capitalization, with an FDV of over $10 billion. It is one of the earliest privacy blockchains, launched in 2014. Unlike Bitcoin, where all transaction information is public, Monero transactions are confidential and untraceable.

The sender, receiver, and amount of every transaction are hidden through the use of three key technologies: Stealth Addresses, Ring Signatures, and Ring Confidential Transactions (i.e. RingCT).

 They allow and require the sender to create random one-time addresses for each transaction on behalf of the recipient. The recipient can publish a single address, while all incoming payments are sent to unique addresses on the blockchain, which cannot be linked back to the published address or to other transaction addresses.

 In cryptography, a ring signature is a type of digital signature that can be created by any member of a group of users who each possess a key. When a user sends a transaction, their real signature is mixed with decoy signatures, ensuring that no one can identify the true sender, while the network can still verify that the transaction is valid.

: A technology that completely hides the transferred and received amounts while still allowing the blockchain network to verify that the total input and output values are correct.

Zcash is the second largest project by market capitalization in the privacy coin sector. Zcash uses a Proof of Work consensus mechanism similar to Bitcoin, but differs in its ability to anonymize transaction data through zk-SNARK technology, a cryptographic proof that allows a transaction to be verified as valid without revealing the sender, the receiver, or the amount. Unlike Monero, where all transactions are private by default, Zcash allows users to choose whether transactions are public or private.

While Bitcoin offers full transparency and Monero provides full anonymity, Zcash represents a combination of both. However, in the legal context, Zcash still faces disadvantages, 

as the European Union has planned to ban privacy focused cryptocurrencies such as Monero and Zcash in 2027

. Not being fully anonymous like Monero may give Zcash a better chance of achieving legal acceptance in the future.

Starknet is a Layer 2 scaling solution for Ethereum that uses Zero-Knowledge proof (specifically STARKs) to significantly improve throughput and reduce costs while inheriting Ethereum’s security. Instead of focusing primarily on scaling solutions as in 2021, Starknet is now focusing on privacy as execution capacity across the industry has improved dramatically while privacy at scale remains largely unresolved. At its core, Starknet leverages two key advantages of Zero Knowledge proofs to address both scalability and privacy:

Concealment allows proving that something is true without revealing the underlying data.

Compression makes it possible to summarize a massive execution into a short proof that anyone can verify at low cost.

Starknet is currently building a comprehensive privacy ecosystem with components across the entire stack, including core infrastructure, private payments, private trading, privacy pools & mixers, privacy neobank, and data-preserving protocol.

In addition, the Starknet team is also developing Ztarknet, a Starknet-based Layer 2 for Zcash that operates as an external computation layer described as the GPU of Zcash. Its goal is to enable the development of sophisticated applications such as user experience-rich DeFi protocols or full-stack applications while remaining aligned with Zcash’s privacy principles.

Zama is one of the new generation blockchain projects in the privacy sector today. The project has raised nearly $140 million and is expected to launch its mainnet by the end of 2025. The Zama Confidential Blockchain Protocol enables confidential smart contracts on top of any L1 or L2 using Fully Homomorphic Encryption (FHE). FHE is an end-to-end encryption technology developed by multiple cryptographers, including 

, co-founder of Zama. FHE enables data processing without decryption allowing companies to provide services without accessing user data while users experience unchanged functionality. With encryption maintained during both transit and processing, validators can process data without performing decryption thereby enhancing user security and privacy.

Zama has also developed fhEVM, a specialized version of the Ethereum Virtual Machine that supports private smart contracts. It allows developers to build data confidential applications in a straightforward way using the familiar Solidity programming language and can be integrated into any EVM compatible chain. Zama does not aim to build a closed blockchain, but instead positions itself as an infrastructure layer that can be integrated across multiple blockchains. If privacy gradually becomes a default standard, especially in the context of AI where personal and financial data are increasingly intertwined with blockchain systems, Zama is likely to emerge as a privacy service layer attached to existing blockchains. This is one of the reasons why Zama has been able to raise a relatively large amount of funding.

Zano is an open-source cryptocurrency (layer-1) and ecosystem with enterprise-grade privacy, security, and scalability that operates as a foundation for confidential assets and decentralized applications (dApps). 

, the lead developer of Zano, is the person who created the first ever privacy coin protocol code base known as CryptoNote, which is used by Monero and hundreds of other projects.

Similar to Monero, Zano uses Stealth Addresses, Ring Signatures, and RingCT to maintain privacy. On Zano, everything is 

, meaning people cannot see transaction amounts, the addresses involved, or the type of asset being transferred. This means that if performing a 51% attack on Zano, one would need the majority of the hash rate and a significant share of the total staked coins, making the cost of an attack incredibly high and rendering it infeasible. In addition, Zano incorporates the world’s first Proof of Stake mechanism, Zarcanum, which enhances traditional Proof of Stake with untraceability and hidden amounts.

One of Zano's unique features is the 'Confidential Layer Bridge', which allows users to bridge any asset to Zano. Consequently, a private "wrapped" version will be created, allowing the addition of a privacy layer to any asset. This bridge hides sender, receiver, and amount details, allowing public assets to gain high-level privacy, fast transfers, and low fees.

For more than a decade, blockchain has been discussed as a symbol of transparency. However, it may be time to reconsider and recognize that privacy is necessary. The demand for financial privacy continues to grow even as regulation tightens. Privacy coin transactions reached $250 billion in 2025, more than 70 exchanges have delisted privacy coins, and usage persists despite reduced access. 

In crypto, information has direct monetary value. Anyone can easily scan on-chain data to see what you hold, how much you hold, your entire transaction history, who you transact with, and when you do it. If your activity is public by default, you are effectively giving away high signal financial metadata for free. Market participants are beginning to realize that they need privacy more than ever, as reflected in the recent surge in privacy coins. We should only make public what truly needs to be public, such as charitable donations. Even in an era where everything is being digitized and AI is developing rapidly, data does not have to be controlled by third parties, as privacy can still be preserved, and information is made public only when necessary.

As global surveillance rises, privacy-focused blockchains like Monero and Zama offer a solution. Learn why decentralized privacy is the key to digital freedom

The Future of Privacy: Why Decentralized Systems are the Final Frontier for Freedom

The U.S. economy is supposed to be great, or as President Trump has lately been calling it, ‘an A+++++’ economy. GDP numbers have been rising; in fact, Q2 showed the most substantial quarterly growth in nearly two years, despite the tariffs that were predicted to tank the economy. Unemployment rates remain historically low at 4.4%, a figure close to pre-COVID levels. The stock market, despite facing some headwinds lately, is still enjoying one of its best years, with the S&P 500 up about 16% year-to-date.

All these parameters clearly show that the U.S. economy is in a great state, right? Well, the issue is that many critical numbers are going unnoticed in the big picture. Essentially, they have gotten lost in translation, resulting in a fragile economy propped up by inflated metrics that paint a romanticized picture of reality.

But beneath the headlines, the foundation is rotting. The 'resilience' is purchased on credit, the 'growth' is fueled by shadow debt, and the 'stability' is a facade masking a consumer stretched to their absolute limit. This isn't a story of recovery; it is a story of a pressure cooker waiting to blow. As Hemingway famously wrote about bankruptcy, it happens in two ways: gradually, then suddenly. We are currently in the 'gradually' phase, but the 'suddenly' phase is closer than the data suggests.

The COVID-19 outbreak had a significant global impact, both financially and socially. The economy and, therefore, consumer behavior have shifted to more short-term gratification and satisfaction. The long-lasting financial impact is truly remarkable, as the FED has increased the money supply (i.e. M2) from 15 trillion USD to 21.75 trillion USD in 24 months, an increase of 45%, which has never been seen before in such a quick succession. While after COVID-19 the M2 has decreased for a short time, it is again rising, and is currently at the peak levels of 23 trillion USD.

By contrast, the labor market has undergone a significant transformation in the aftermath of COVID-19, with the gig economy emerging as a firmly established form of employment. The number of people earning income from jobs where assignments are delivered through online platforms increased by 3.1 million between 2020 and 2021, with the most significant growth occurring in transportation and food delivery. The effects of COVID-19, and the changes it brought are still very much observable in the current economic landscape.

Degree Required, Experience Demanded, Gig Work Offered

The ‘COVID-19’ generation, or Gen Z, has been struck the hardest. After COVID-19, job openings have been falling dramatically for junior positions, and the unemployment rate for Gen Z is 10.4% as of September 2025, on par with pre-recession 2008 levels. The real unemployment rate of Gen Z is expected to be significantly higher, as many have resorted to the gig economy, whereby highly educated individuals are forced into the gig economy (i.e. Uber drivers), trading career stability for the insecurity of short-term contracts. This is not surprising, as the labor market is squeezing out entry-level jobs that require significant experience, among other factors, leading to a highly competitive market for starters. However, the first cracks in the gig economy are starting to appear.

A basic economic pricing concept is supply and demand: if demand exceeds supply, the price is expected to rise in a free market. In a market where supply exceeds demand, the opposite will happen. This is exactly where the issue arises: there is a massive oversupply in the gig economy, leading to lower prices in isolated markets such as Uber. This is observable, as the same Gen Z is struggling financially to repay their ever-mounting student debt, creating a pressure cooker.

According to a recent study of The Institute for College Access & Success (TICAS), more than four in ten borrowers (i.e. 42%) make tradeoffs between loan payments and covering basic needs. At the same time, one-fifth of those surveyed are either delinquent or in default. The total expected federal student loans that went into default is over 140 billion USD. Borrowers generally enter default on their federal student loans after being delinquent for more than 270 days. As a result, the number of defaulted federal student loan borrowers could surge to over nine million within weeks, with several million additional defaults occurring in the months that follow.

Default carries serious consequences. Beyond negative credit reporting, which can make it harder to secure housing, transportation, or employment, the government has strong collection powers. These include administrative wage garnishment, seizing federal tax refunds, and offsetting federal income and benefits, including Social Security. Earlier this year, the Education Department announced that collection activity on defaulted federal student loans would resume.

This leads to a situation that TICAS has described as a ‘default cliff’, a rapidly shifting sequence of events in which many ex-students are unable to repay their loans due to a weak job market.

In any normal functioning economy, you would expect that the overall expenditure of Gen Z would decrease, but luckily for us we do have Buy-Now-Pay-Later (BNPL) to save the day. Recent Black Friday data shows online spending hit a new record of $11.8 billion. In the U.S., BNPL accounted for $747.5 million of that total, up 8.9% year over year, according to Adobe Analytics. Adobe also projected BNPL spending would exceed $1 billion by the end of Cyber Week, suggesting consumers were willing to keep buying even without having the cash on hand.

BNPL is not a new concept; it is essentially short-term credit, but it has scaled rapidly by exploiting a regulatory gap. Because BNPL products do not qualify as credit cards, they fall outside the scope of Regulation Z. This is mainly because they typically do not charge interest and require repayment in four installments. As a result, BNPL remains unregulated, mainly at the federal level in the U.S., and, like most unregulated credit markets, it operates like the Wild West.

In 2023, BNPL users took an average of 6.3 loans per lender, up 11 percent from 5.7 in 2022. Additionally, the inflation-adjusted average annual amount borrowed per lender rose by 14 percent, increasing from $745 in 2022 to $848 in 2023. The borrower profile is equally troubling. As of 2022, nearly two-thirds of borrowers had low credit scores, and subprime or deep subprime applicants were approved in 78% of cases. Given that economic conditions have deteriorated for many subprime groups over the past three years, especially in auto lending, these figures are likely even higher today. Unfortunately, as BNPL is not regulated anymore by the Consumer Financial Protection Bureau (i.e. CFPB) after 2023, BNPL companies do not have to disclose any data, leading to relatively old data.

BNPL default rates are accelerating. According to Lending Tree, 42% of BNPL users made at least one late payment in 2025, up from 39% in 2024 and 34% in 2023. The main issue here is that, due to the unregulated nature of the debt, most of it is not reported to credit bureaus, leading to ‘phantom debt’. That means that other lenders can’t see when someone has taken out five different BPL loans across multiple platforms, as it does not impact your credit score. Therefore, there is an incentive to keep using BNPL companies, as the acceptance rate is significantly higher than that of credit cards, and it does not impact your credit score as of now if you default on one or multiple payments.

The CFPB released a report in January this year showing that roughly 63% of borrowers originated multiple simultaneous loans at some point during the year, and 33% took out loans from various BNPL lenders. While BNPL itself is not a systemic threat yet, as the total market is roughly a trillion, the lack of visibility, combined with the concentration of significantly distressed borrowers, can be a catalyst for more, as BNPL continues to grow significantly.

Klarna currently leads the BNPL industry with a 35% market share. In Q3 2025, the company reported $903 million in revenue, up about 26% year over year. Klarna also disclosed a 32% increase in active consumers, reaching more than 114 million users, and a 38% rise in merchants, with over 850,000 merchants supporting Klarna YoY. The CEO of Klarna has stated in the recent earnings release of the company that Q3 2025 was Klarna’s strongest ever, and that the Klarna Card has exploded since its launch of July, which currently accounts for 15% of all global transactions. Klarna currently processes over 3.4 million USD in daily transactions. However, beneath the growth story, the cracks are beginning to show.

In Klarna's recent income statement, the company has significantly increased its provision for credit losses. As a provision is, by definition, always forward-looking, as it is based on the expectation of a particular event, it shows that Klarna expects more and more of its consumers to default on their BNPL loans. In Q3 2024, the provision was approximately 116 million USD, increasing by 102% YoY to over 235 million USD in Q3 2025. The credit provisions to revenue ratio increased from 17% to over 26%, highlighting that despite the increase in Klarna’s revenue, the overall credit provisions has increased more, leading to a weaker position of the company. This is further exaggerated by a significant increase in funding costs of 60 million USD to 180 million USD, up from 123 million USD in 2024, as the company is suffering from high interest rates. Despite the significant increase in growth, the company incurred a loss of 246 million in 2025, mainly due to a substantial increase in the provision for credit losses and funding costs.

The U.S. economy currently operates as a set of temporary band-aids attempting to hold a fracturing system together, while the underlying house of cards slowly but surely crumbles. Total housing debt has been climbing steadily since 2014, but more alarmingly, there has been a distinct acceleration in this trend since 2020. This suggests that the post-pandemic recovery was built less on organic growth and more on leveraged appreciation.

Simultaneously, overall non-housing debt has surged, fueled primarily by a sharp increase in credit card utilization. This pivot to revolving credit indicates that for a growing segment of the population, debt has shifted from a tool for lifestyle enhancement to a survival mechanism against the rising cost of living. As interest rates remain elevated, the cost of servicing this ballooning debt is compounding, effectively draining liquidity from consumers and removing the final structural support for the economy.

Credit card delinquency rates have surged to 12.4%, exceeding levels seen during the 2008 financial crisis. This figure is likely an underestimation because it excludes BNPL services favored by younger generations. Furthermore, because delinquency is a lagging indicator, the sharp increase observed from 2024 to 2025 suggests that rates will continue to rise as macroeconomic conditions deteriorate for individual consumers

It is not just the debt data that is flashing red; consumer sentiment is collapsing in real-time. The University of Michigan’s consumer confidence index climbed to 53.3 in December from 51, the second-lowest reading on record. The survey comprises approximately 50 core questions tracking various consumer attitudes. Notably, the Index of Consumer Expectations specifically gauges three key areas: personal financial outlooks, near-term economic performance, and long-term economic prospects.

Finally, we must look at the engine keeping this entire machine running: the stock market. We have already established in another article that the current rally is heavily artificial, driven primarily by the AI boom. Since ChatGPT’s launch in November 2022, AI-related stocks have accounted for a staggering 75% of S&P 500 returns and 80% of earnings growth.

The ultimate prop for this fragile market is the “One Big Beautiful Bill Act”, a policy that functions as a form of legislated Quantitative Easing (QE). By providing a $1,000 seed in S&P 500 stocks for every newborn and incentivizing up to $5,000 in annual tax-advantaged contributions, the Treasury has effectively created a permanent, price-insensitive bid for U.S. equities. Because this capital is legally mandated to remain in the market until the child turns 18, it serves as a forced floor for the S&P 500. This essentially stakes the stock market on the future of the American family.

However, this creates a grotesque economic paradox in which the government is forcing capital into the stock market for the year 2043, while the parents of those same children are using high-interest BNPL loans to afford diapers and formula today. It is a desperate attempt to manufacture long-term stability by ignoring a short-term liquidity crisis, ensuring that when the market eventually corrects, it will not just hit retirement portfolios but will wipe out the seeds of an entire generation.

The history of economic collapse is rarely written in a single day. Instead, it is the story of a million small, ignored choices that eventually reach a tipping point. As we move into 2026, the U.S. economy finds itself in a precarious "K-shaped" reality. At the top, a stock market fueled by the AI boom and artificial liquidity injections from the One Big Beautiful Bill Ac

 continues to break records. At the bottom, a generation is drowning in "phantom debt," gig-economy oversupply, and a cost-of-living crisis that has turned credit from a tool into a survival vest.

We are currently witnessing the "gradually" phase in real-time. It is visible in the rising credit provisions on Klarna’s balance sheet, the 12.4% delinquency rates in credit card portfolios, and the "default cliff" facing millions of student loan borrowers. These are not isolated data points; they are the creaks in the floorboards of a house propped up by high-interest scaffolding and short-term bandages.

The "suddenly" arrives when the consumer, who accounts for nearly 70% of U.S. GDP, finally loses the ability to shuffle debt from one unregulated platform to another. When the "buy now" finally meets the "pay never," no amount of legislated market floors or baby bonds will be enough to stop the gravity of a debt-deleveraging cycle. Hemingway’s warning remains our most accurate forecast: by the time you see the crisis, the foundation has already rotted. The question for 2026 is no longer 

 the pressure cooker will blow, but how many people will be left standing when the steam clears.

The U.S. economy is a pressure cooker. Explore the Great Disconnect: Gen Z's 10.4% unemployment, $1T in phantom BNPL debt, and the coming "suddenly" phase.

The U.S. Debt Crisis: Slowly, But Then, All at Once

The transition from AI as a passive tool to an autonomous economic actor represents a structural shift in value creation. Globally, the agentic AI market is projected to grow from roughly $5.3 billion in 2024 to over $50 billion by 2030. Yet, despite this explosive potential, the current landscape remains fundamentally constrained.

Today’s agents are mostly "digital butlers" isolated within proprietary silos like AWS or Azure, capable of answering prompts but unable to independently negotiate, transact, or coordinate across platforms. True economic agency requires more than just intelligence; it requires a decentralized infrastructure for trust, interoperability, and financial execution.

Theoriq addresses this gap by proposing a modular coordination protocol designed to turn isolated software tools into a cohesive "agentic economy." This article summarizes the architectural constraints of the current market, Theoriq’s modular solution, and the economic design that underpins this new era of Decentralized Agentic Finance (DeFAI).

Current agent architectures prioritize vendor lock-in over interoperability. A retail agent on one cloud platform cannot natively execute a complex workflow with a trading agent on another without cumbersome, manual integration bridges. This fragmentation contradicts the fundamental economic principle that value flows most efficiently through open, composable networks.

Furthermore, a "Trust Gap" exists. Without standardized mechanisms for reputation or consequences for failure, agents cannot reliably verify peers. They remain tethered to human oversight, reducing potential "swarms" (self-organizing collectives) to mere extensions of their operators. The industry faces a design challenge: how to create systems that facilitate agent discovery and trustless interaction without centralized gatekeepers.

Theoriq’s Modular Architecture: AlphaProtocol

At the core of Theoriq’s approach is the AlphaProtocol, a framework that reimagines agents not as monolithic systems, but as composable entities built from standardized "Behaviors."

Rather than building custom connectors for every interaction, agents that comply with the protocol can discover and communicate with one another via Channels, a secure, publish-subscribe messaging system. This decouples communication from vendor-specific platforms, allowing messages to serve as triggers for conditional, multi-agent workflows across the network.

Supporting this are Agent Profiles, persistent on-chain identities that store performance history and capability declarations. Together, these layers replace fragile point-to-point connections with a dynamic swarm architecture where agents can coordinate based on intent rather than hard-coded integrations.

Bridging the Trust Gap: Evaluators and Proof of Contribution

To solve the reliability problem, Theoriq introduces specialized agent roles: Aggregators (Routers and Planners) to assign tasks, and Evaluators to provide quality assurance.

Evaluators assess agent performance (accuracy, latency, and compliance) and issue Proofs of Contribution. These are cryptographically verifiable attestations that an agent successfully completed a task. By replacing subjective user ratings with auditable, on-chain performance data, the protocol creates a meritocratic environment where reliable actors are objectively rewarded, and trust becomes a verifiable asset.

AlphaVault: The Engine of Agentic Finance

For agents to become true economic actors, they must be able to manage capital. AlphaVault serves as a non-custodial execution layer that bridges the gap between autonomous logic and secure financial settlement.

AlphaVaults enforce policy constraints directly on-chain. This means that while an agent has the autonomy to strategize and propose transactions (such as rebalancing a liquidity pool or executing a trade), it cannot exceed pre-defined risk parameters or withdraw user funds. This architecture enables complex financial swarms, like AlphaSwarm, to optimize yield and mitigate risks like impermanent loss and MEV, all without taking custody of the underlying assets.

The Economics of Agency: The THQ Token Model

Theoriq’s economy is powered by the THQ token, designed to align the incentives of agents, developers, and liquidity providers. It functions as both an access key and a value-capture mechanism.

Access and Security are the primary drivers. Agents must stake THQ to operate, while users stake to secure the network.

Delegation and Slashing introduce a "skin in the game" mechanism where users delegate αTHQ (a time-locked governance token) to specific agents. This boosts the agent’s reputation but exposes the delegator to slashing risk if the agent misbehaves.

The Flywheel effect ensures protocol revenue, generated primarily from vault performance fees, is recirculated to support staking rewards and the treasury. This design aims to shift the economy from reliance on inflationary emissions to a sustainable model driven by real yield and economic activity.

We are witnessing a shift from a market of single-purpose tools to an economy of collaborative swarms. While centralized tech giants offer convenience and compliance, they cannot offer the censorship resistance, open composability, and diverse asset management of a decentralized protocol.

Theoriq’s hybrid architecture, combining off-chain compute speed with on-chain settlement truth, positions it as a critical infrastructure layer for the DeFAI sector. By standardizing how agents talk, trade, and trust, it lays the rails for an economy where software doesn't just assist humans, but actively participates in the market alongside them.

This summary captures only a fraction of the architectural deep dives, competitive analysis, and tokenomic modeling found in the full report.

To explore the complete "Porter’s Five Forces" analysis of the AI market, the technical details of Proof of Contribution, and the full scope of the AlphaSwarm roadmap,

Explore Theoriq's decentralized agent protocol. Learn how modular swarms, Proof of Contribution, and AlphaVaults are building the future of Agentic Finance.

From Silos to Swarms: A Critical Analysis of Theoriq’s Agentic Infrastructure

The California Gold Rush left a long-lasting impact on the United States (i.e. US), and is widely considered the most significant event of the first half of the nineteenth century. People from as far as the Ottoman Empire, nowadays Turkey, and substantial portions of North Africa and Eastern Europe, flocked to the US to capitalize on this ‘golden’ opportunity. In 1848, over 300,000 people, about 0.03% of the world's population, headed to the U.S. in search of gold. Most didn’t strike gold; instead, they struck coal and left empty-handed.

Most prospectors didn’t make substantial amounts of money; the merchants who sold them shovels and food made the money. One of the best-known examples is Levi Strauss, a Bavarian immigrant who sold sturdy denim workwear to gold seekers passing through San Francisco. The name remains globally relevant today, as Levi Strauss & Co. reported net revenue of about $6.36B in 2024.Today, California is experiencing a new kind of gold rush, one driven by the race to build and deploy artificial intelligence (i.e. AI). But the real question is: is this “pot of gold” even more elusive, yet potentially far larger? And more importantly, will this rush shape civilization’s long-term future, or will it fade as just another short-lived boom?

The number of voices claiming that AI might be in a bubble has exploded in the last few weeks, according to Google Trends. Multiple key institutions have argued that AI exhibits bubble-like characteristics, including the Bank of England, Deutsche Bank, and the International Monetary Fund (IMF). The concern stems from the significant increase in the value of AI-related companies over the last year, as investment continues to flow in.

According to UBS, global annual AI spending is expected to swell to 375 billion USD, a significant increase over the last year. This makes you wonder, who is spending so much capital on AI? Well, first of all, it’s venture capital that is flowing into AI deals, but more importantly, it mainly stems from a couple of companies: NVIDIA, OpenAI, Oracle, Amazon, and Anthropic.

NVIDIA, the world's largest Generative Processing Unit (i.e. GPU) manufacturer, has gone through an exceptional 2-year cycle, as the total operational revenue nearly doubled, which is predominantly being driven by its data center revenues. The increasing demand for compute drives these revenues, as according to Jensen Huang, Founder and CEO of NVIDIA, “Blackwell sales are off the charts, and cloud GPUs are sold out,” and “The AI ecosystem is scaling fast - with more new foundation model makers, more AI startups, across more industries, and in more countries. AI is going everywhere, doing everything, all at once”. NVIDIA’s success is hard to dispute: it’s essentially the Levi Strauss of AI, only, instead of denim workwear, it sells the compute that powers today’s AI models.

One of the most potent forces in the AI space is undoubtedly OpenAI, as the company is currently valued at a staggering 500 billion USD, making it as valuable as Mastercard. Yet, it has lower revenues and a significantly higher operating deficit. It is estimated that OpenAI currently generates about 20 billion USD in revenue while incurring an operational loss of 70 billion USD. While keeping these numbers in our minds, it makes you wonder how OpenAI can fulfill its significant deal spree, as OpenAI has inked multiple deals, including one of 300 billion USD with Oracle, as it agreed to spend 300 billion USD to purchase data centre capacity from Oracle to fulfill its computing demand for its AI models. In its place, Oracle has bought NVIDIA GPUs for its data centre. To complete the circle, NVIDIA has invested in OpenAI, enabling it to fund these deals in the first place. Everyone in the circle reports rising revenue, rising valuations, and surging share prices, but the cash is moving in circles. Thus, all this churn is just NVIDIA recycling its own money, rather than creating new value.

One parallel that’s been drawn repeatedly is that the AI capex flywheel (i.e. hyperscalers, model labs, and vendors recycling spend into more buildout) resembles the dot-com bubble, where Cisco was the “shovel seller.” Many companies with thin fundamentals surged in value before collapsing. Iconic examples include Pets.com, Webvan, and eToys, each of which went public and then failed within a short period. Boo.com never reached an IPO but still became a cautionary tale, reportedly burning roughly $135M in venture funding in around 18 months before liquidation.

Another parallel is “vendor financing,” which can destabilize the markets. Late in the 1990s dot-com boom, hardware suppliers like Cisco made enormous profits selling networking gear to ISPs. To accelerate rollouts and drive even more Cisco purchases, Cisco also extended loans to those ISPs. When the boom broke, and the scale of overbuilt internet capacity became painfully apparent, many ISPs couldn’t repay. The QQQ fell about 70%, and, roughly twenty-five years on, Cisco’s share price still hasn’t regained its 2000 peak.

The last striking parallel is that there are concerns that the companies are using accounting tricks to make their operational cash flow appear stronger than it actually is. In the dotcom era, channel stuffing, pulling forward demand by pushing excess inventory into the distribution channel, and improperly recognizing early revenues were among the leading causes of companies to appear operational healthier than they were. In the current era, the big tech companies have all extended the expected economic life of their GPUs, going from 3 to 5 or 6 years. However, Jensen Huang said that ‘when Blackwell starts shipping in volume, you couldn't give Hoppers away’ and ‘There are circumstances where Hopper is fine," he added. "Not many." This raises the question: Is it valid to significantly extend the economic life of these GPUs?

If the “real” asset lifespan is closer to 2–3 years, a plausible assumption given the pace of computing advances, that could shave roughly 5–10% off these companies’ EPS. If Jensen’s expectations are correct, that’s as much as $4T, or nearly one-third of their current combined market cap. However, several experts have weighed in, suggesting there may actually be a use case for these older, “dusty” GPUs after all.

A key counterargument is that GPUs don’t become worthless just because the frontier shifts: data centers can redeploy older cards to non-training workloads, and to “good enough” inference. In practice, A100s, now roughly 5 years old, are still widely used, supporting the idea that GPU utility can persist well beyond the typical “1–3 year” narrative. On the lifecycle side, Lambda and other infrastructure operators argue GPUs can remain economically useful for ~6–8 years, especially when operators ladder them down the stack (i.e. training → inference → lighter workloads) and rely on support/warranty coverage for a large portion of that window.

A specialized AI company like Groq might see its hardware’s value tied almost exclusively to a narrow set of inference workloads, making it more susceptible to rapid obsolescence. In contrast, a hyperscaler like Google, Amazon, or Microsoft runs everything from cloud databases and video transcoding to scientific simulations and internal analytics. For them, a three-year-old H100 may not be obsolete. It can instead be redeployed to accelerate countless other tasks, delivering a significant performance uplift over traditional CPUs and generating economic value for years.

Real-world precedent also shows extended service lives on hyperscaler fleets. Azure retired its NCv2 (P100) and ND (P40) VM series on 6 September 2023, implying multi-year deployment cycles for 2016-era chips, and has announced NCv3 (V100) retirement for 30 September 2025, roughly a “many-years” lifecycle for a 2017-era GPU generation. This suggests it’s feasible to extract value from GPU assets over a materially more extended period than consensus often assumes.

In 2001, approximately a year after the bust of the dot-com bubble, Cisco had to disclose a provision for its inventory of over $2.77 B and over 8,500 layoffs. Cisco describes excess inventory tied to a “sudden and significant decrease in demand.” Cisco's stock fell by over 75% within that year. This shouldn’t be surprising: once the rush ended, Cisco’s “shovels” were no longer in such high demand, and many of its former buyers either went bankrupt or were forced to downsize sharply just to keep operating.

The argument that older GPUs might get used for different tasks might have some truth, as it sounds logical that older equipment might still be used for lighter and more straightforward tasks, as it has done in the past. However, this time it is different: unlike 5-10 years ago, the investment in GPUs has increased steeply, raising the question of whether the market can absorb such a massive influx.

Demand for computing will continue to rise as long as investment remains elevated. However, the 

 demand driving the high-end GPU arms race is still largely tied to training and frontier-scale experimentation, not to the steady-state task of running already-trained models. That matters because many inference and non-AI workloads can be served with materially less hardware than training requires. For example, GPT-OSS-20B can deliver performance comparable to OpenAI o3-mini while running on roughly 16GB of VRAM. In practice, 16GB is a standard configuration even on newer consumer GPUs (e.g., RTX 4080-class cards). By contrast, 64GB VRAM is generally not a consumer-GPU baseline, which illustrates how quickly the “minimum viable hardware” for a large share of tasks can drop as models and runtimes become more efficient.

Against that backdrop, the key question is not whether older GPUs can be redeployed; they can, but whether there will be enough demand at scale to absorb them if supply increases. The U.S. has been investing heavily in AI, and a large share of global private AI capital has concentrated there, reinforcing the incentive for firms to continue upgrading in pursuit of performance and cost advantages. If next-generation GPUs deliver a step-change in capability and efficiency, incumbents in an arms race have an apparent reason to prioritize the newest hardware. The practical consequence is that older cards may be pushed down the stack, or outward into secondary markets, increasing the effective supply of “last-gen” capacity.

That’s where the absorption constraint bites. Even hyperscalers have expanded their AI offerings substantially, but demand for their broader, non-AI services does not necessarily scale in lockstep. New customers can help soak up capacity, but most entrants are not training frontier models and therefore typically do not need the same volumes of compute. In other words, demand for older GPUs exists. Still, it is uncertain whether it is sufficient to offset rapid rotation into newer generations, especially if a large wave of redeployed capacity simultaneously hits the market.

OpenAI has recently signed a $38 billion cloud computing deal with Amazon, Anthropic secures a $15 billion investment commitment from Microsoft and NVIDIA, OpenAI and Oracle signed a $300 billion USD deal, and last but not least, NVIDIA claims it will build up to $500 billion of US AI infrastructure in the next 4 years. 

The metaphor of ‘everyone is dancing, until the music stops’ is adequate here. The industry is currently filled with euphoria and copium, but is being held together by private investments. These private investments allow OpenAI, Grok, and Anthropic to spend enormous amounts of capital, which mainly flows back to NVIDIA, which in turn recycles it back to these companies. As 81% of total private investment is already going towards AI, a slowdown is very likely, as the cycle feels very much artificial and irrational.

There’s a self-reinforcing loop at work: the U.S. amasses ever more compute, which enables breakthroughs (e.g., faster or better models), and those advances then spur demand for even more compute. It’s a cycle that can’t compound indefinitely; the structural break comes when one link in the chain can no longer finance the next step.

To illustrate the extent of today’s market exuberance, a handful of mega-cap names are doing a disproportionate amount of the heavy lifting across major equity markets:

 NVDA (+30%), Apple (+19%), Google (+83%), Microsoft (+14%), Amazon (+11% YoY), Broadcom (+131% YoY).

 Tencent (+55%), Agricultural Bank of China (+77%), Alibaba (+90%), ICBC (+47%), China Construction Bank (+29%).

 the market is effectively anchored by TSMC (≈ $37T MC), with a market cap roughly 10x the second-largest name (Hon Hai Precision Industry). TSMC is +35% this year, while Delta Electronics, #3 by market cap, a major supplier of power components used across electronics, including AI-related infrastructure, is up 150%,

AI-related stocks have accounted for 75 percent of S&P 500 returns and 80 percent of earnings growth since ChatGPT launched in November 2022.

The discussion is not about whether AI is here to stay, but about what shape it will ultimately take. In the near term, private AI investment is likely to cool meaningfully as stock market sentiment becomes more rational and valuations gradually return to more normal levels. Some companies appear particularly stretched, with examples like Palantir trading at a price-to-earnings ratio of around 400, a level many would consider difficult to justify outside of the current AI-driven excitement.

This normalization increases pressure on AI firms that are not yet generating meaningful revenue and therefore rely heavily on continued inflows of investment to fund growth. That pressure can extend beyond the application layer as well. NVIDIA, often seen as the leading supplier powering the boom, could be affected if reduced investment leads to slower demand for GPUs. If demand softens, NVIDIA’s operational cash flow could come under pressure, which may also limit how quickly the broader AI buildout can continue and expose just how fragile parts of the current momentum may be

As a result, a major sell-off could hit the broader economy hard, especially given that roughly 80% of recent earnings growth has been driven by AI-related stocks. At the same time, several warning signs are already flashing; job openings, for instance, have been trending down, suggesting the downturn may be underway. We’ll publish a separate blog soon that takes a deeper look at the overall state of the US economy.

One direct consequence for many Americans would be pressure on their 401(k) balances during a market slump. That may not be an immediate problem for everyone, since many 401(k) strategies and target-date funds gradually shift from stocks (i.e. risk-on) toward bonds and cash equivalents (i.e. risk-off) as retirement nears. Still, even if your 401(k) isn’t explicitly AI-focused, AI-linked stocks have driven a large share of S&P 500 returns, and a decline in that segment can pull the broader index down due to high correlation.

This effect is amplified by the fact that major US indices are market-cap weighted: the biggest AI “winners” (or perceived winners) become an increasingly large part of the index. And for retirees, or anyone close to retirement, a significant drawdown right before or shortly after withdrawals begin can permanently reduce how much the portfolio can sustainably support. Target-date funds can reduce that risk by holding more bonds, but they’re not immune to a broader market shock.

Is NVIDIA funding its own sales? We break down the Trillions of circular financing loops among NVIDIA, OpenAI & Oracle and why they mirror the dot-com bubble.

The AI Bubble: Is NVIDIA Just Recycling Its Own Money?

Internet access is now an essential requirement for economic participation, yet nearly 2.6 billion people still remain offline globally . In India, more than half of the population lacks stable connectivity despite the country being one of the world’s fastest-growing digital economies. This gap isn’t simply technical. It is structural, economic, and deeply tied to the limitations of traditional telecom models.

India’s operators have spent almost 20 billion USD on 5G spectrum auctions, making it one of the most expensive markets in the world . With such high upfront costs, telecoms naturally prioritize urban, high-income regions where returns are predictable. Rural, peri-urban, and low-income communities are left waiting because the economics simply do not support their inclusion.

Dabba Network presents a different path. Instead of following the capital-heavy, spectrum-dependent logic of legacy telecom, Dabba builds a decentralized connectivity marketplace that connects global hotspot owners, Local Cable Operators, and everyday broadband users. It blends DePIN, token incentives, and India’s fragmented last-mile operator ecosystem into a cohesive, demand-led system that grows only where real users exist.

This article summarises the model, its incentive structure, its competitive position, and why it matters for the future of Internet Capital Markets.

Why Traditional Connectivity Models Struggle

The economics of Indian telecom create a structural barrier to inclusive connectivity. Spectrum pricing is several times higher than in markets like Germany, the United Kingdom, and South Korea. Operators must recoup these costs, which inevitably shifts deployment toward high-revenue environments and away from the regions where connectivity would be most transformative.

At the same time, many households face tight budget constraints. Even affordable data plans can represent a notable share of income. Wi-Fi offers a more cost-effective alternative for consistent bandwidth, but centralized telecom models have historically struggled to deploy and maintain last-mile Wi-Fi networks at scale.

The result is a national landscape where millions are excluded from digital participation, not due to technological limits but because the underlying incentives are misaligned.

Dabba Network’s Marketplace Architecture

Dabba operates as a coordination layer rather than a traditional telecom provider. It brings together global participants who purchase hotspots, local operators who handle deployment and maintenance, and households that subscribe to affordable broadband.

India’s network of more than 150,000 Local Cable Operators is central to this design. Once the backbone of cable TV and neighborhood networks, many LCOs have faced steep subscriber losses in recent years, with 93 percent reporting declines and more than 31 percent reducing staff . Dabba gives them a new revenue model with transparent on-chain settlement and predictable token rewards.

The marketplace illustrates how Dabba coordinates access to technology, operating licenses, bandwidth providers, hardware manufacturers, and hotspot owners in one integrated system .

This is not a telecom company attempting to scale through centralized capital. It is a distributed network that grows organically through local expertise and global participation.

Unlike models that build infrastructure first and hope users appear later, Dabba’s network expands only when a paying subscriber requests a connection. LCOs install hotspots directly at subscriber locations, ensuring that each deployment serves an active, verified user.

This removes the inefficiency of idle hardware and the speculative hotspot overbuying seen in earlier DePIN networks. Every device exists because someone needed it. As a result, Dabba’s network has scaled to tens of thousands of hotspots while maintaining strong utilization.

By late 2025, the network had reached nearly 29,000 hotspots, close to 200,000 connected devices, and around 80,000 daily active users, processing tens of terabytes of bandwidth each day . These are real subscribers consuming real data, not synthetic usage.

The hotspot onboarding pipeline consists of four coordinated steps, each verified on-chain.


A hotspot owner purchases a Dabba Lite device for 199 USD and burns 10 DBT to mint an ownership NFT linked to the device.


Through the Dabba Explorer, owners select an LCO and pay 70 DBT for a seven-year lease agreement.


To activate the hotspot, the LCO burns 10 DBT, creating an immutable verification of installation and operational responsibility.


Owners pay 10 DBT to a marketing partner to drive local awareness and subscriber adoption.

Across these stages, approximately 30 USD worth of DBT is burned, creating a direct connection between physical deployment and token scarcity.

Once deployed, the hotspot owner receives all epoch rewards and distributes fixed shares to LCOs, backhaul providers, hardware manufacturers, and location owners through the Explorer interface. 

The DBT token underpins coordination, ownership, and reward distribution across the network. It has a fixed maximum supply of 10 billion tokens, with 60 percent reserved for long-term community rewards.

Emissions begin at 600 million DBT in the first year and decline by 10 percent annually. This establishes predictable inflation early on and increasing scarcity as the network matures.

Burns occur through hardware onboarding, KYC functions, advertising, software services, and, most importantly, data consumption. Data Credits are priced at 0.012 USD per gigabyte, and each purchase triggers a corresponding burn. Over time, data usage becomes the dominant contributor to deflation.

Rewards begin with a Universal Basic Income model to support rapid early onboarding and later shift toward performance-based incentives that reward high-uptime, high-throughput hotspots.

Dabba’s goal is not to compete directly with major telecoms. Instead, it fills the connectivity gaps that large operators cannot serve profitably.

Consumer alternatives such as Starlink are priced far above the budgets of most Indian households. Starlink’s monthly fees and hardware requirements place it almost ten times higher than typical broadband plans, effectively removing it from contention in mass markets.

Within the DePIN sector, competition is more direct. Projects like Helium, Roam, Wayru, and XNET all target decentralized wireless growth. However, Dabba’s integration with LCOs and its demand-driven deployment strategy give it a structural advantage. 

High real-world usage reflects the effectiveness of the underlying incentive model and local operator integration.

The marketplace approach can extend naturally to other regions with fragmented last-mile operators and underserved populations. Indonesia, the Philippines, Mexico, and Nigeria all have market conditions where distributed deployment and community-led maintenance can outperform traditional telecom economics.

Each region would require customized regulatory alignment and partnerships with local operators, but the fundamental model remains transferable.

A Broader Foundation for Internet Capital Markets

Reliable connectivity is the first prerequisite for participation in Internet Capital Markets. Without stable bandwidth, communities cannot interact with decentralized financial rails, digital services, or modern economic systems.

Dabba’s model creates the physical layer for that participation. It coordinates global capital, local deployment expertise, and tokenized incentives into an infrastructure network that can scale sustainably in regions where traditional models fail.

How Dabba Network’s DePIN model leverages decentralized infra and incentives to close the connectivity gap and support Internet Capital Markets.

Insights & Expert Advice on Web3 and Cryptocurrency

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From Silos to Swarms: A Critical Analysis of Theoriq’s Agentic Infrastructure

Building the Rails for Internet Capital Markets: A Guide to Dabba Network’s DePIN Connectivity Model

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