Computer memory chipmaker Micron Technology (MU) delivered blowout Q3 earnings on Wednesday, lifting the entire AI memory sector, AI-related stocks and even giving crypto a slight boost.

Bitcoin BTC$58,435.85 climbed back above $60,000 after markets closed, but bullish AI sentiment will ultimately pull more liquidity away from crypto.

Micron shares surged 16% in premarket trading on Thursday after the memory chipmaker’s third quarter guidance exceeded Wall Street expectations. Third-quarter revenue came in at $41.5 billion versus estimates of $35.7 billion, while earnings per share (EPS) reached $25.11 compared with expectations of $20.49.

Memory chips have become the backbone of AI infrastructure, particularly high-bandwidth memory (HBM), which is essential for training and running large AI models. CEO Sanjay Mehrotra told analysts there was “no line of sight” to when supply would catch up with demand, with the shortage expected to persist well beyond 2027.

The company also issued strong fourth quarter guidance, forecasting revenue of approximately $50 billion, well ahead of Wall Street expectations of $43.2 billion.

The AI boom has weighed heavily on the crypto market this year, with bitcoin now more than 50% below its October all-time high, trading at the $60,000 level.

As AI floods the internet with convincing fake humans, proving that a user is a real, unique person is becoming one of crypto’s hardest and most valuable problems. This guide explains what proof of personhood is, how the leading approaches work, and why the cure raises concerns of its own.

Proof of personhood is a cryptographic mechanism that lets someone prove they are a real, unique human being, one person counted exactly once, without revealing who they actually are. That combination is what makes it both powerful and difficult: it must guarantee uniqueness, so that a single person cannot register as a thousand, while preserving anonymity, so that proving you are human does not force you to expose your identity. The problem it solves is old, but it has become urgent for a new reason. For most of the internet’s history, telling humans from machines was a minor nuisance handled by simple puzzles. 

Now, with artificial intelligence able to generate text, images, voices, and entire online personas indistinguishable from a real person’s, the open internet faces a verification crisis: bots can flood platforms, manipulate votes, drain airdrops, and impersonate humans at a scale and quality never seen before. This guide explains what proof of personhood is, the attack it defends against, the main approaches to building it, the leading real-world example and its controversies, and why a technology meant to protect humanity raises hard questions of its own.

The reason this topic has moved to the center of crypto and beyond is that “one real human, counted once,” turns out to be a foundational requirement for a surprising range of things. Fair token airdrops depend on it, or a handful of people with thousands of fake accounts will scoop up everything meant for a community. Democratic voting and decentralized governance depend on it, or whoever can spin up the most identities wins. 

Any system that distributes scarce resources to people, from community rewards to the long-discussed idea of a universal basic income, depends on being able to tell one person from a thousand sock puppets. And increasingly, the world of artificial intelligence depends on it, both to keep bots out of human spaces and, in a twist, to let trustworthy AI agents act on behalf of verified humans. 

Proof of personhood sits at the intersection of cryptography, identity, and the defining technological anxiety of the moment, which is why it has become one of the most watched and most contested ideas in the field.

The sybil attack: the problem at the root

To understand proof of personhood, you first have to understand the attack it exists to stop, which is called a sybil attack. The name comes from a famous case study of a person with many personalities, and in computing it describes a single actor creating many fake identities to gain influence they should not have. On a network where one identity equals one vote, one share, or one claim, a sybil attacker who controls a thousand identities controls a thousand times the influence of an honest participant who has just one. Almost every open online system that tries to be fair, every vote, every giveaway, every reputation score, every “one person, one share” distribution, is vulnerable to someone who can cheaply manufacture identities.

Historically, sybil attacks were limited by the friction of creating convincing fake accounts at scale, and by crude defenses like puzzles meant to slow bots down. Artificial intelligence demolishes both limits. Modern systems can generate unlimited unique-looking personas, complete with plausible writing, profile photos, and behavior, and can solve the puzzles that once filtered them out. 

The very technology that makes AI useful, its ability to produce human-like content, is what makes it the ultimate sybil weapon, capable of populating the internet with armies of fake humans cheaply and convincingly. This is the deeper reason proof of personhood has surged in importance: the old, informal defenses against sybil attacks have broken down precisely when the cost of mounting one has collapsed. If you cannot tell a real, unique human from a generated one, then every system that assumed it could is suddenly exposed, and rebuilding a reliable way to prove humanness becomes foundational infrastructure rather than a nice-to-have.

What a good proof-of-personhood system must achieve

Before looking at how anyone builds proof of personhood, it helps to define what success even requires, because the requirements pull against each other, and that tension shapes every design. A strong system needs to satisfy several properties at once. It must guarantee uniqueness, ensuring each real person can obtain exactly one verified identity and cannot register many. It must preserve privacy, so that proving you are a unique human does not force you to reveal your name, your face, or a linkable record of everything you do. It must resist attack, holding up against sophisticated adversaries, increasingly AI-powered, trying to fake or duplicate humanness. And ideally it must scale to billions of people across every country, language, and level of access, without excluding those who lack documents or technology.

The difficulty is that these goals are in tension. The strongest way to guarantee uniqueness is usually to collect something deeply personal and hard to fake, like a biometric, but collecting biometrics is exactly what threatens privacy and raises ethical alarms. The most privacy-preserving approaches, which avoid collecting sensitive data, often struggle to guarantee uniqueness or to resist a determined attacker. 

Scaling to everyone on earth conflicts with the careful, high-assurance verification that strong uniqueness demands. Every proof-of-personhood design is, in effect, a particular set of compromises among uniqueness, privacy, security, and inclusivity, and there is no design that maximizes all four at once. Understanding a given system, therefore, means asking which of these properties it prioritizes and which it sacrifices, because that choice, more than any technical detail, determines what the system is good for and what it puts at risk.

The main approaches to proving humanness

There are several broad families of proof-of-personhood design, each making a different bet about how to balance those competing goals. The first and most discussed is biometric verification, which uses a physical trait of the human body, an iris, a face, that is hard to fake and naturally unique, to guarantee one person equals one identity. The bet here is that specialized hardware reading a unique biological signal is the only approach robust enough to resist an adversarial, AI-saturated environment, because you cannot generate a real human iris with a language model. The strength is powerful uniqueness; the cost is the privacy and ethical weight of collecting biometric data and the need for physical hardware and in-person enrollment.

A second family is the social-graph approach, which builds humanness through webs of trust: real people vouch for other real people, and the network of mutual verification makes it hard for a lone attacker to fake many identities, because each fake one would need real humans willing to vouch for it. This avoids collecting biometrics and leans on human relationships instead, but it can struggle to scale and to resist a well-resourced attacker who infiltrates the graph. A third family relies on credentials and accumulated signals, combining evidence like existing verified accounts, on-chain history, or government documents into a score or a passport that suggests a unique human without a single biometric gatekeeper. 

This is flexible and privacy-conscious but generally offers softer guarantees of uniqueness than a biometric. A fourth, emerging family uses zero-knowledge identity techniques, proving facts about yourself, that you are an adult, that you are a unique holder of some credential, without revealing the underlying data, and increasingly leans on device-based passkeys and similar tools. Each family is a different answer to the same question, and the field has not settled on a winner, because each answer sacrifices something the others preserve.

The leading example: World and the Orb

The most prominent attempt to build proof of personhood at global scale is the project now called World, formerly Worldcoin, created by a company co-founded by the chief executive of a leading artificial intelligence lab alongside other founders, and launched in 2023. World made the boldest possible bet on the biometric approach, and examining it concretely shows both the promise and the problems of the whole field. Its centerpiece is a custom hardware device called the Orb, a polished sphere that scans a person’s iris. 

The reasoning is that the iris is highly unique and extremely hard to forge, so an in-person iris scan is a strong way to guarantee that each verified human is counted exactly once, even against AI adversaries that can fake almost anything made of pixels but cannot fake a living eye on demand.

The privacy design is central to World’s pitch, because iris scanning sounds alarming and the project knows it. According to the project, when the Orb scans your iris it generates a unique cryptographic code, deletes the actual image after processing, and distributes only anonymized fragments of the code across a network to confirm you have not enrolled before. 

The result is meant to be a credential, called a World ID, that proves you are a unique human without revealing your identity or storing your biometric image, with zero-knowledge techniques letting you later prove “I am a verified unique human” to an app without exposing anything else. The project reports a scale no other proof-of-personhood effort has reached, on the order of millions of people verified through Orbs and a widely used identity app, which is a meaningful achievement for a category that has historically struggled to grow. World is, in short, the clearest real-world test of whether the biometric approach can become global infrastructure, and its trajectory, successes and backlash alike, is where the abstract debate over proof of personhood becomes concrete.

The AI age and the pivot to verifying agents

What has thrust proof of personhood from a niche idea into a mainstream conversation is the arrival of capable artificial intelligence, and the relationship between the two is closer than it first appears. The same advances that make AI able to flood the internet with fake humans also make a reliable proof of humanness more valuable, because humanness is becoming the scarce, trustworthy thing in a sea of synthetic content. This is why a figure deeply associated with frontier AI is also behind the leading proof-of-personhood project: one venture helps create the problem of indistinguishable machine-generated humans, and the other proposes the verification layer to manage it. As AI-generated text, images, video, and behavior become impossible to tell from the real thing, a system that can certify “a unique human is behind this” turns into foundational infrastructure for trust online.

There is a striking twist in how the field is now evolving. Proof of personhood started as a way to keep bots out of human spaces, but it is increasingly being repurposed to let AI agents operate responsibly within human systems. As autonomous AI agents begin acting on people’s behalf, making purchases, sending messages, executing tasks, a new question arises: which human is this agent acting for, and is that human real and accountable? Proof-of-personhood projects have begun building tools that tie an AI agent to a verified human principal, so that an agent can prove it represents a genuine, unique person rather than running wild as an anonymous bot. 

The leading project has also pivoted toward enterprise use, selling proof-of-humanity verification to companies, video platforms, and identity providers that want high assurance a user is real, while keeping the service free for the individuals being verified. The through-line is that AI did not just create demand for proving humans are human; it is reshaping proof of personhood into a layer that governs both humans and the machines acting for them.

Where proof of personhood actually gets used

It is easy to treat proof of personhood as an abstraction, so it helps to ground it in the concrete situations where a reliable proof of unique humanness changes what is possible. The most immediate is fair distribution. Crypto projects frequently give away tokens to early users through airdrops, and the entire premise, rewarding a broad community, collapses if a handful of people can each register thousands of identities and vacuum up the supply meant for many. 

A proof-of-personhood gate, requiring each claimant to prove they are a unique human, restores the fairness the airdrop was supposed to deliver, and the same logic extends to any system handing scarce resources to people: community rewards, grants, promotional credits, or the long-discussed vision of a basic income distributed to verified individuals rather than to whoever runs the most bots.

A second arena is governance and voting. Decentralized organizations and online communities increasingly make decisions by vote, and a vote is only meaningful if each person counts once. Without proof of personhood, governance defaults to systems where influence is bought, whoever holds the most tokens or controls the most accounts decides, which concentrates power and invites manipulation. 

A reliable proof of unique humanness opens the door to genuine one-person-one-vote systems online, a building block for fairer collective decision-making that has been technically out of reach. A third arena is the everyday integrity of online spaces: social platforms drowning in AI-generated accounts, review systems gamed by fake humans, and communities overrun by bots all need a way to certify that a participant is a real, unique person, and proof of personhood offers exactly that certification without forcing users to surrender their identities.

The newest and fastest-growing arena is the one created by autonomous AI. As software agents begin acting on people’s behalf, the question of which human stands behind a given agent becomes urgent, both to assign accountability and to keep anonymous bots from masquerading as authorized representatives.

Proof-of-personhood tools that bind an agent to a verified human principal let an agent prove it acts for a genuine, unique, accountable person, which is becoming a prerequisite for trusting agents with real tasks and real money. Enterprises are also adopting proof-of-humanity checks to defend high-value interactions, from video calls to account access, against deepfakes and impersonation. 

Across all these cases, the common thread is the same: wherever a system needs to know that a participant is a real, unique human, and increasingly wherever it needs to know which human is behind a machine, proof of personhood is the missing layer that makes the guarantee possible. That breadth of application, spanning fairness, governance, online integrity, and the entire emerging world of AI agents, is why the idea has drawn so much attention despite its unresolved controversies.

The serious objections

A guide that only described the promise of proof of personhood would be misleading, because the field, and especially its biometric flagship, has drawn intense and substantive criticism that any honest reader should weigh. The first objection is the biometric honeypot problem. Building a system that scans the irises or faces of millions of people creates, by its nature, one of the largest collections of biometric data in the world, and even with deletion and anonymization, critics argue that such a database is an irresistible target and that the consequences of biometric data being compromised are uniquely severe, because you cannot change your eyes the way you change a password. The risk of normalizing mass biometric collection, and of who ultimately controls it, sits at the heart of the unease.

The second objection is centralization. A system built on specialized hardware that the project manufactures and controls creates a chokepoint: a single company decides who can verify, where the devices go, and how the system runs, which sits awkwardly with crypto’s ideals of decentralization and raises the prospect of a private entity becoming a gatekeeper of human identity online. The third objection is regulatory and ethical: the leading project has faced pushback, suspensions, and investigations from data-protection authorities in numerous countries worried about consent, privacy, and whether scanning eyes in exchange for tokens, sometimes in lower-income regions, is exploitative. 

A fourth, more technical critique questions whether a crypto token needs to be attached to identity verification at all, suggesting the financial layer may be unnecessary to the core function. And a fifth points out that large platforms or governments could build competing verification systems with less controversy, or that softer software-only methods might prove good enough, leaving the biometric approach burdened by risks its rivals avoid. None of these objections proves the technology is bad, but together they explain why proof of personhood, despite solving a real and growing problem, remains genuinely contested.

Why it matters and where it goes

Stepping back, proof of personhood is one of those rare ideas whose importance is rising in lockstep with the technology that makes it necessary, and that is the clearest way to understand its trajectory. The case for it is straightforward and getting stronger: as AI erases the line between human and machine online, almost every system that assumed it could tell the difference, fair distribution, honest voting, bot-free communities, accountable AI agents, needs a new foundation, and a reliable way to prove unique humanness is that foundation. The demand is real, it is growing, and it is not going away, which is why serious people and serious money keep flowing toward the problem even after years of difficulty and controversy.

The open question is not whether proof of personhood matters but which approach, if any, will earn enough trust to become a genuine standard. The biometric path offers the strongest uniqueness guarantees and the most scale so far, but carries the heaviest privacy, centralization, and regulatory baggage. The social-graph, credential, and zero-knowledge paths avoid some of that baggage but offer softer guarantees or struggle to scale. It is entirely possible that no single system wins, and that the future is a patchwork of methods suited to different contexts, a biometric proof for the highest-assurance needs, lighter software proofs for everyday ones. 

It is also possible that the privacy concerns prove decisive and the world rejects mass biometric identity altogether, pushing the field toward less invasive designs. What seems certain is that the underlying need, proving a real, unique human in a world full of convincing fakes, is now permanent, and that how society chooses to meet it, and who it trusts to run the infrastructure, will be one of the defining questions where crypto, artificial intelligence, and identity collide. Proof of personhood is the attempt to answer it, and the answer is still being written.

Frequently Asked Questions

This article is educational information, not investment or identity-security advice. Proof-of-personhood projects, their scale, and their regulatory status change quickly, and details reflect reporting available as of June 25, 2026. Consider the privacy and security implications carefully, and verify current information from primary sources before enrolling in or relying on any identity system.

Asset manager 21shares has revised down several of its bullish expectations for the crypto industry in 2026, arguing that while key market infrastructure is improving, weaker price action and slower retail and enterprise participation have dampened momentum.

In its midyear outlook, the firm said sectors ranging from exchange-traded products (ETPs) and stablecoin regulation to tokenization and prediction markets are continuing to mature. Still, it expects that major DeFi security incidents and enterprise adoption that is “slower-than-expected” will make a number of previously planned 2026 targets harder to reach.

Key takeaways

• 21shares says crypto infrastructure is advancing faster than market prices, leaving parts of the industry on track while broader growth is constrained.
• Despite more institutional involvement, 21shares maintains that Bitcoin’s four-year cycle remains intact.
• Prediction markets are highlighted as a standout growth area, with 21shares projecting annual trading volume could exceed $100 billion.
• Crypto ETPs are described as resilient in the long run, even as US spot Bitcoin ETFs have seen about $3 billion in net outflows this year.
• Regulatory clarity in the US is cited as helping convert ETF application backlogs into new launches beyond Bitcoin and Ether.

Bitcoin’s cycle still matters, even with institutions reshaping markets

One of 21shares’ clearest messages is that Bitcoin’s four-year market rhythm continues to play a central role. The firm pointed to Bitcoin’s post-halving behavior and argued that increased institutional involvement has changed how the asset trades during downturns without changing the cycle itself.

21shares said Bitcoin peaked at roughly $126,000 in October 2025 before pulling back sharply, and it has continued to trade in a manner consistent with past post-halving patterns. In its view, institutional ownership has helped limit how violently markets draw down, but the fundamental cyclical behavior has not been disrupted.

The firm’s stance also echoes commentary from former 21shares co-founder Ophelia Snyder, who left the company after its acquisition by FalconX in 2025. In a recent Substack post, Snyder argued that institutionalization makes crypto more entangled with broader financial and macroeconomic drivers. She wrote that the investor base is larger, more institutional, and more connected to the traditional financial system—meaning geopolitical developments and macro shifts can influence crypto pricing more than they once did.

Prediction markets and regulation-driven momentum

While 21shares trimmed some of its broader growth projections, it elevated specific segments where adoption dynamics appear stronger. The firm singled out prediction markets as one of the industry’s best-performing areas, forecasting that annual trading volume could surpass $100 billion this year.

The outlook also ties market development to regulation, particularly in the US. 21shares argued that improving regulatory clarity has helped transform a backlog of crypto ETF submissions into a more continuous stream of new product launches—expanding offerings beyond the initial wave of Bitcoin and Ether-focused vehicles.

In that context, 21shares referenced the Securities and Exchange Commission’s generic listing standards as a mechanism behind the pace of ETF conversions. It also highlighted a single case: Hyperliquid, which the firm described as standing out among newer US spot ETF tracking structures. According to 21shares, US spot ETFs tracking the asset pulled in over $150 million in net inflows in under a month, which it framed as evidence that traditional capital continues to find its way into digital-asset products.

ETPs show durability despite weaker spot inflows

21shares also addressed crypto ETP performance, arguing that short-term flows do not fully reflect investor behavior during weaker market conditions. The firm noted that while US spot Bitcoin ETFs have recorded roughly $3 billion in net outflows this year, the total holdings are still just above 1.25 million BTC—close to an all-time high for Bitcoin holdings inside the category.

That balance matters because it suggests many investors are not rushing to exit after periods of volatility. 21shares said holdings remain supported by investors who either hold through downturns or accumulate strategically even when Bitcoin trades well below earlier highs.

Beyond Bitcoin-only flows, the report’s theme is that the institutional pipeline has not shut off; it has simply become more selective and less reflexive during drawdowns. For market participants, this distinction can be important: outflows can pressure near-term sentiment, but the level of cumulative holdings can point to longer-term positioning rather than capitulation.

Consolidation accelerates across treasuries and scaling ecosystems

Another major thread in 21shares’ midyear outlook is consolidation. The firm said public companies holding crypto on their balance sheets are increasingly diverging, with some smaller treasury players trading below the value of their digital assets. In 21shares’ framing, this gap can intensify pressure on weaker players and make mergers or strategic combinations more likely.

A similar dynamic, the report suggests, is playing out in Ethereum’s layer-2 ecosystem. 21shares said a handful of dominant rollups continue to take market share while many smaller networks struggle to attract meaningful user activity and liquidity. For builders and users, the implication is that network effects and capital efficiency are becoming more decisive differentiators—particularly in a market where growth is harder to come by.

What to watch next

As 21shares moves several 2026 targets out of reach, investors should watch whether regulatory catalysts (especially ETF-related) and segment-specific strength (like prediction markets) can offset the drag from weaker price conditions, security setbacks in DeFi, and slower enterprise adoption.

Boston-based stablecoin issuer Circle Internet Financial announced a partnership on Thursday with Japanese financial conglomerate Nomura Holdings to launch a digital asset settlement business. The firms plan to deploy a corporate payment service in Japan as early as 2027.

The agreement will let Japanese businesses exchange yen for USDC, Circle’s U.S. dollar-backed stablecoin, according to the announcement first reported by Nikkei. USDC is the world’s second-largest dollar-pegged stablecoin, boasting a market cap of $73.8 billion as of this writing.

The Circle stablecoin token can be used for cross-border supplier payments, transfers between overseas affiliates, and foreign exchange settlements.

The business aims at Japan’s import, export, and corporate currency markets. Bank for International Settlements data shows that Japan’s foreign exchange market handled $440 billion in daily transactions as of 2025. Standard bank wires take two to three business days to clear funds between yen and foreign currencies. This blockchain setup can drastically reduce that transfer time.

Bitcoin (BTC) hit new 21-month lows at Thursday’s Wall Street open as high US inflation unsettled stock markets.

Key points:

• Bitcoin returns to its lowest level since September 2024, dropping to $58,000.
• US PCE inflation rocks equities, with the Nasdaq 100 shedding 2% in just 30 minutes.
• BTC’s correction mirrors the price action seen throughout the 2022 bear market.

Crypto liquidations pass $600 million in an hour as BTC price drops

Data from TradingView showed BTC/USD dropping to $58,035 on Bitstamp — a level it last traded at in September 2024.

BTC/USD one-hour chart. Source: Cointelegraph/TradingView

The May print of the US Personal Consumption Expenditures (PCE) index came in at 4.1%, setting a new three-year record.

“From the preceding month, the PCE price index for May increased 0.4 percent. Excluding food and energy, the PCE price index increased 0.3 percent,” a data release from the Bureau of Economic Analysis (BEA) stated. 

“From the same month one year ago, the PCE price index for May increased 4.1 percent. Excluding food and energy, the PCE price index increased 3.4 percent from one year ago.”

US PCE one-month % change (screenshot). Source: BEA

Stocks reacted with volatility, with the Nasdaq Composite Index down 0.5% at the time of writing, while the S&P 500 managed to eke out a gain.

The Nasdaq 100, meanwhile, saw a larger snap decline of 2% in just 30 minutes at the open.

“What a chart,” trading resource The Kobeissi Letter responded on X.

Bitcoin itself sparked considerable long position liquidations, with CoinGlass putting the cross-crypto liquidation total at $600 million over a single hour.

Crypto liquidation history (screenshot). Source: CoinGlass

Commenting, market participants suggested that price moves were being artificially managed to squeeze positions.

“$BTC is in the manipulation phase,” pseudonymous trader Killa told X followers. 

“Every time $BTC trades sub-$60K, that is our manipulation beneath the significant $60K swing low on the weekly and quarterly. Precisely the reason why the orderbook is stacked below us.”

Source: Killa/X

Niels Klaver, cofounder of crypto platform STABL Agency, suggested that BTC/USD “seems to be going for its final leg down of this bear market.” 

“$55K remains the target,” he added, referring to an increasingly popular short-term price goal.

BTC/USDT one-week chart. Source: Niels Klaver/X

Bitcoin analysis sees new resistance near $65,000

As BTC price action attempted a modest rebound, trader and analyst Rekt Capital had already described $60,000 support as “clearly weakening.”

Related: BTC price four-year trend calls for $76K as analysis says Bitcoin ‘not broken’

“Once June Monthly Closes, we’ll know from which price July will be able to potentially spring into a post-breakdown relief rally,” an X post read.

BTC/USD one-month chart. Source: Rekt Capital/X

Rekt Capital maintained that the market was acting similarly to 2022, with the 50-month exponential moving average (EMA) tipped to become new resistance next.

BTC/USD one-month chart. with 50EMA. Source: Cointelegraph/TradingView

South Korean cryptocurrency exchange Bithumb was order to pay a $136,000 fine after it was found to have breached personal information protections rules when it sent user data overseas.

In a Thursday notice, the country’s Personal Information Protection Commission (PIPC) said that its investigation into Bithumb found that the exchange had “transferred personal information overseas without the separate consent of the data subjects during the process of order book sharing and virtual asset transfer with overseas virtual asset exchanges.”

The incident was connected to Bithumb sharing its Tether (USDT) order books between September and November 2025 with BingX, despite obtaining consent to share the data with Stellar, as well as sharing user information with 13 overseas exchanges.

“The Personal Information Protection Commission determined that there is a necessity to provide personal information for anti-money laundering purposes when transferring virtual assets to other exchanges, but regarding the overseas transfer of personal information and the data subject’s right to self-determination, it was determined that, as this is a closely related matter, it is necessary to strictly comply with the requirements and procedures stipulated in the Protection Act,” the notice said, in translation.

Source: PIPC

One of the largest crypto exchanges in South Korea, Bithumb has been subject to intense scrutiny from authorities. 

The country’s financial watchdog imposed a six-month suspension of the exchange’s activities in March over alleged violations of South Korea’s Financial Information Act, but a court reversed the decision in April. Earlier this month, police reportedly raided Bithumb’s offices as part of an investigation into alleged nepotism involving South Korean lawmaker Kim Byung-gi.

Related: SBI to acquire Bitbank in $289M deal creating Japan’s biggest crypto exchange

South Korean crypto tax set to take effect in 2027

South Korea’s Finance Ministry confirmed in May that a 22% tax on cryptocurrency gains would be imposed beginning in January 2027. The tax has faced several delays in implementation after initially expected to go into effect in 2025, but will likely affect many South Koreans who hold crypto.

According to the Yonhap news agency, about 16 million South Koreans were invested in digital assets as of March 2025.

Earlier this month, Chainalysis said that it signed a memorandum of understanding with the Korean National Police Agency (KNPA), aimed at building investigative capability within South Korea’s law enforcement. 

One of the driving factors behind the pact is to better combat North Korea-linked crypto attacks, with South Korea’s police “at the forefront” of tackling these threats. 

Magazine: Japanese pension fund tips 1% in crypto, G7 urges action on NK hackers: Asia Express

TLDR:

• Kulechov firmly denied reports of selling AAVE at a 70% discount, calling the media framing inaccurate.
• All Aave Protocol, GHO, and product revenue flows entirely to the AAVE token under the Aave Will Win proposal.
• Aave Labs is designing Aavenomics 3.0, featuring a new automated and non-discretionary AAVE buyback mechanism.
• Aave targets the entire financial asset market, including real-world assets, beyond the crypto-native TAM.

Aave co-founder Stani Kulechov has moved to address circulating discussions about AAVE token sales and the protocol’s revenue model.

In a post on X, Kulechov pushed back on what he called inaccurate media framing surrounding Aave Labs and its token allocation.

He confirmed that all protocol and GHO revenue flows to the AAVE token while teasing a new automated buyback mechanism. The protocol currently generates $134 million in annualized revenue.

Kulechov Rejects Discount Sale Reports, Outlines Revenue Framework

Kulechov was direct in dismissing reports suggesting AAVE tokens could be sold at a steep discount. Addressing the claim head-on, he wrote, “There is NO WAY we’d sell AAVE at a 70% discount lol.”

He then moved to clarify the structure governing all revenue flows within the Aave ecosystem. The Aave Will Win (AWW) proposal, already passed by the DAO, forms the backbone of that structure.

Under AWW, 100% of Aave Protocol and GHO revenue is directed to the AAVE token. Kulechov confirmed the framework also covers all product revenue streams. “AWW also applies to all product revenue, including the Aave App, Aave Pro, and Swaps,” he stated. None of that revenue flows to Aave Labs, which operates solely as a service provider to the DAO.

He also addressed Aave Labs’ own AAVE token allocation separately. Kulechov noted that “multiple market participants have discussed purchasing, directly or indirectly, through deeper long-term partnerships.”

That allocation is distinct from the DAO’s revenue framework and does not alter how protocol earnings are distributed to token holders.

On intellectual property, Kulechov was equally clear. He confirmed that “all intellectual property, including the Aave brand and any software built for Aave, belongs to AAVE.” Token holders, not Aave Labs, hold rights over these core assets under the current governance structure.

Aavenomics 3.0 and Aave’s Broader Financial Ambition

Beyond correcting the revenue narrative, Kulechov pointed to a coming upgrade. He revealed that “the Aave team is designing Aavenomics 3.0, which includes a new automated and non-discretionary buyback mechanism.” He noted that further details would follow in a later announcement, keeping the specifics close for now.

The planned buyback builds on a strong revenue foundation. Aave is generating $134 million in annualized revenue, all of which flows to the Aave DAO.

That base positions the DAO to sustain meaningful token buybacks without relying on discretionary decisions from any single party.

Kulechov also broadened the scope of Aave’s stated ambitions. He said Aave is “building not only for the crypto TAM, but for the entire finance asset TAM, including RWAs.” That framing places Aave alongside traditional finance infrastructure rather than solely within the DeFi space.

He closed his remarks with a pointed statement on organizational alignment. “Everyone at Aave Labs and Aave DAO works for AAVE,” he wrote.

That statement was directed at reassuring token holders that commercial and governance structures remain oriented around their interests above all else.

A new 2026 ranking highlights beginner-friendly trading platforms based on simplicity, reliability, and trustworthiness for first-time investors.

For those who are new to investing, the hardest part is not placing a trade — it is choosing where to trade in the first place. Search for the best trading platform for beginners, and dozens of names come up, conflicting reviews, and interfaces that look like an airplane cockpit. For a beginner, that complexity is intimidating, and complexity is exactly what causes costly mistakes.

So we ranked the five best trading platforms for beginners in 2026 using three priorities that actually matter when somoen is starting out: simplicity (how easy it is to begin), stability (how well it holds up when markets fall, not just when they rise), and credibility (whether someone can trust it with their money). Whether someone wants a traditional broker or a hands-off automated option, there is a fit here for everyone.

How these platforms were ranked

Every platform below was measured against the same beginner-focused criteria:

• Simplicity: Can a complete beginner start in minutes without a finance background or coding?
• Stability: Does the platform — or its strategies — hold up during a one-sided market downturn, or does it only work when prices rise?
• Credibility: Is the company transparent about fees, withdrawals, and risk, with a real track record?
• Supported markets: Can assets be accessed whenever the user wants and diversify as they grow?
• Cost to start: Is there a low barrier, free trial, or demo to learn without risking much?

One honest note before the list: no platform removes market risk, and none guarantees profit. The best beginner platform is the one that keeps things simple and protects its users while they learn.

1. SaintQuant — Best overall for hands-off beginners

Best for: Beginners who want automated, stable trading without learning to read charts.

SaintQuant tops the list because it removes the single biggest barrier for newcomers: no need to know how to trade. There is no configuration, no coding, and no chart-watching. Users pick a pre-built, pre-optimized strategy, launch it in a few clicks, and the platform handles execution, strategy management, and 24/7 market monitoring automatically.

On simplicity, it is hard to beat — the entire experience is built for people who want results without complexity. On stability, it stands apart from typical beginner platforms: rather than only profiting when prices rise, SaintQuant runs quantitative strategies designed to pursue steady, rules-based returns across market conditions, with risk controls structured directly into each strategy to help manage volatility and one-sided downturns. On credibility, it is transparent about how it works and supports cryptocurrencies, stocks, and futures from a single account.

New users also get a $99 free starter trial credit to experience live strategies before depositing, plus a $7 instant cash bonus at registration with no hidden conditions — a low-pressure way to see how it performs before committing real money.

Watch a live review of SaintQuant in action:

Pros: Truly no-code, designed for stability in down markets, multi-market support, free trial credit. 

Cons: Pre-built strategies favor simplicity, so advanced users may eventually want more granular controls.

2. eToro — Best for social and copy trading

Best for: Beginners who want to learn by following experienced traders.

eToro built its reputation on an approachable interface and copy trading, which lets newcomers mirror the moves of more experienced investors. For a beginner who learns best by watching others, that lowers the intimidation factor considerably.

The trade-off is that copy trading still leaves users exposed to the market’s direction and the choices of whoever they copy. It is simple to start, but results depend heavily on who they follow.

Pros: Beginner-friendly interface, copy trading, broad asset access. 

Cons: Copying does not remove risk; outcomes depend on the trader someone follows.

3. Webull — Best free stock trading app

Best for: Beginners who want a clean, commission-free stock app.

Webull offers commission-free trading with a tidy mobile experience and useful learning tools, making it a popular entry point for new stock investors. Paper trading lets beginners practice before risking real funds.

It leans toward self-directed trading, so users still make every decision themselves. That suits people who want to learn actively, but it offers little protection during a downturn beyond personal discipline.

Pros: Commission-free, clean app, paper trading. 

Cons: Fully self-directed; no built-in downturn protection.

4. Fidelity — Best for long-term credibility

Best for: Beginners who prioritize a trusted, established institution.

Fidelity is a long-established name with a strong reputation, broad research tools, and excellent customer support. For beginners who value credibility and stability of the institution above all, it is a safe, respected choice.

The platform is more oriented toward long-term investing than active or automated trading, and its depth can feel like a lot for an absolute beginner. But few names inspire more trust.

Pros: Highly credible, strong support, great for long-term investing. 

Cons: Less suited to automated or active trading; feature depth can overwhelm.

5. Robinhood — Best for ultra-simple first trades

Best for: Beginners who want the simplest possible first trade.

Robinhood popularized commission-free, frictionless trading with an interface so simple anyone can place a trade in minutes. For sheer ease of starting, it is among the simplest options available.

That same simplicity has drawn criticism for encouraging impulsive trading, and it offers little to protect beginners when markets fall. Simple to start is not the same as stable.

Pros: Extremely simple, commission-free, fast onboarding. 

Cons: Minimal downturn protection; simplicity can encourage impulsive trades.

Quick comparison at a glance

How to choose the right platform

The best choice comes down to what kind of beginner someone is:

• Want it fully hands-off and stable in any market? Start with an automated platform like SaintQuant.
• Want to learn by following others? A copy-trading platform like eToro fits.
• Want a trusted institution for the long term? Fidelity is hard to beat on credibility.
• Just want the simplest first trade? Robinhood or Webull get started fast.

Whatever is chosen, apply the same beginner discipline: start small, understand the fees, and never invest money a user cannot afford to lose.

The Bottom line

For most beginners in 2026, the best trading platform is the one that is simple to start, stable when markets turn, and credible with money. That balance is why SaintQuant leads this list — it pairs genuine no-code simplicity with quantitative strategies designed to hold up during downturns, not just rallies.

New users can claim a $99 free trial package plus a $7 instant cash bonus with no deposit and no strings attached, making it easy to experience stable, automated trading before committing personal capital.