Study discovers 40% of web3 gaming accounts are bots using on-chain data

cryptoslatePublicado em 2022-08-31Última atualização em 2022-08-31

Resumo

Levan Kvirkvelia, the founder of Jigger, an anti-bot protection software, has released a report asserting that 40% of the player base of web3 games are bots.

Levan Kvirkvelia, the founder of Jigger, an anti-bot protection software, has released a report asserting that 40% of the player base of web3 games are bots. The study found evidence of 20,000 bots across over 60 web3 games.

Louis Regis, founder and CEO of Xborg, exclusively confirmed to CrytoSlate that “many games artificially boost their player bases with bots and multiple accounts in order to increase their valuations.”

Bots are a problem across most games that contain a method for accounts to acquire value. Outside of web3, people have bought and sold game accounts containing rare items, skins, or high competitive ratings. Investigating the source of the bots in web2 can be highly challenging as data is diverted through VPNs to mask the identity of the bad actors.

However, Kvirkvelia’s study utilizes the transparency of the blockchain to link wallets and create a probabilistic matrix to establish the most likely bot candidates. A screenshot from Kvirkvelia’s company Jigger presents a visualization of the interconnected accounts, shown below.

The report further listed several web3 projects with their respective bot percentage. Some projects had over 80% bots, with even DeFi projects being targeted through the exploitation of referral schemes.

Jigger has also released a gallery of all the results on its website. Users can request a web3 token to be added to the analytics through a form on the page.

CEO of Polkastarter Gaming, Omar Ghanem, told CryptoSlate that he was unsurprised to read the findings in the report.

“This comes as no surprise; the truth is, the data we have in the space shows almost 2M wallets have interacted with gaming dApps – which is far from a good indication of REAL active players.”

Ghanem cited the Sybil problem with the current state of web3 gaming, explaining that “one person can set up multiple wallets and play on multiple accounts, which this research has further proven.”

On a further point, Ghanem blamed the earning mechanics of web3 games for creating the bot issue.

“As long as we continue to have games that are essentially “click to earn” by nature, with token farming through task repetition at the core of their gameplay loop – bots will continue to exist.
This is why our focus at Polkastarter Gaming has been on supporting high quality, skill based & competitive games with no entry barriers”

Indeed, there is a move within the web3 industry to move away from play-to-earn and toward play & earn. Play & earn mechanics often do not require a player to own a crypto wallet to get started and start having fun. Xborg and Polkastarter gaming are two of the leading companies in this space, focusing almost exclusively on gameplay.

The study is an advertisement for Jigger, but Kvirkvelia does not try to hide this. While the data may require closer scrutiny to rule out confirmation bias, the technology is undoubtedly impressive. The ability to track, trace, analyze, and visualize blockchain data to reveal a hidden picture of a dApps’ user base is a powerful tool in the battle against fraud and manipulation.

Leituras Relacionadas

Stablecoins Finally Find Real Yield: An In-Depth Look at On-Chain Reinsurance Re | A Conversation with Re Founder Karan Saroya

Stablecoin Real Yield Found: A Deep Dive into On-Chain Reinsurance with Re's Karan Saroya As stablecoin supply exceeds $170 billion, the search for sustainable, non-speculative yield intensifies. Re, an on-chain reinsurance platform, provides an answer: connecting stablecoin capital to the trillion-dollar traditional reinsurance market. Re operates as a regulated reinsurer, accepting stablecoin deposits as collateral to back US insurance companies. These insurers pay premiums, generating yield that flows back to on-chain depositors. Currently supporting 35 insurers and underwriting $500 million, Re projects scaling to over $1 billion soon. Key insights from a Bankless podcast with founder Karan Saroya and investor Avichal of Electric Capital: 1. **Uncorrelated, Real-World Yield:** Re offers stablecoin holders access to reinsurance returns (targeting 12-14%+), an asset class entirely separate from crypto or equity markets. 2. **Operational Efficiency via Smart Contracts:** Re replaces traditional, labor-intensive capital fundraising with smart contracts, allowing a ~12-person team to compete with industry giants. 3. **Regulatory Leverage:** For every $1 of collateral, regulations allow backing $5-7 in written premiums. This leverage amplifies returns from the underlying risk-free rate. 4. **DeFi Integration:** Depositors receive receipt tokens, which can be used in protocols like Morpho for "looping," potentially pushing yields to 18-20%+. 5. **The "DeFi Mullet" Model:** A compliant front-end (regulated reinsurer) paired with a decentralized back-end (smart contracts, DeFi capital markets). 6. **RE Governance Token:** Modeled on Lloyd's of London, the token governs the central capital pool's allocation, counterparty acceptance, and parameters. 7. **Real Economic Impact:** Capital funds real-world productivity (factories, clinics, businesses) via insurance, moving beyond crypto's internal loops. The discussion highlights a pivotal moment: DeFi's supply-side infrastructure is now met by real demand for productive yield, potentially kickstarting a flywheel where vast on-chain stablecoin capital seeks these real-world returns.

链捕手Há 1h

Stablecoins Finally Find Real Yield: An In-Depth Look at On-Chain Reinsurance Re | A Conversation with Re Founder Karan Saroya

链捕手Há 1h

1996 or 1999? Walsh's First Test is 'How to View AI'

"1996 or 1999? Wall's First Big Test Is 'How to View AI'" Federal Reserve Chairman Wall's initial challenge is not whether to raise or cut rates, but a more fundamental judgment: what kind of boom is the current AI boom? This will determine the Fed's policy path and define his legacy. Economics is split between two opposing views, according to reporter Nick Timiraos. One sees imminent productivity gains that will increase supply and cool inflation, allowing the Fed to hold steady. The other argues that while productivity benefits are distant, demand shocks are here now, and waiting for data confirmation risks missing the intervention window, forcing sharper rate hikes later. Wall has signaled a leaning toward the first view, echoing 1996-era Alan Greenspan, who embraced strong, productivity-driven growth without fear of inflation. However, Wall faces a different macro environment than Greenspan did, with tariff pressures, expanding fiscal deficits, and diminishing globalization benefits, which could force more significant inflation pressures even if AI benefits materialize. Wall's logic, expressed before taking office, is that AI-driven productivity gains won't show in official data for years. If the Fed waits for confirmation, it might mistakenly tighten policy and choke off the very growth that could suppress inflation. This argues for using forward-looking narratives over lagging data. Chicago Fed President Austan Goolsbee presents a key counter-argument. He distinguishes between expected and unexpected productivity booms. A widely anticipated boom, like the current AI wave, can cause people to spend future wealth gains in advance, overheating the economy before productivity actually rises, thus requiring preemptive rate hikes. He cites rising costs for AI data centers as evidence of such overheating. Fed Governor Christopher Waller offers a rebuttal to Goolsbee, noting the "expected spending" mechanism only works if people can borrow against future income, which many households cannot do due to borrowing constraints. Wall also faces a paradox related to his desire to reduce the Fed's use of "forward guidance" (pre-announcing policy moves). This practice was established in 1999 when Greenspan began signaling hikes to avoid market shocks. If the economy follows a less optimistic path, Wall may be forced to choose between using the guidance he wants to abolish or risking market volatility by staying silent. The ultimate question defining Wall's first major test remains: Is this 1996 or 1999?

marsbitHá 2h

1996 or 1999? Walsh's First Test is 'How to View AI'

marsbitHá 2h

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

Ethereum Q1 2026 Report: Fees Down, Users & Transactions Hit New Highs Token Terminal's Q1 2026 report on Ethereum presents a pivotal development: the network achieved record highs in monthly active users (13.2M, +85.9% YoY), total transactions (200.4M, +81.5% YoY), and throughput (25.78 TPS), while transaction fees on the mainnet plummeted by 47.9% quarter-over-quarter. This shift is attributed to the network's strategic move into a "low fees for scale" phase, exemplified by the Fusaka upgrade which increased data capacity and lowered block space costs, releasing pent-up demand (a manifestation of Jevons's Paradox). The report highlights a core narrative shift for Ethereum: from a DeFi-centric blockchain to a global financial settlement layer. It maintains a dominant position in tokenized assets, holding majority market shares among top chains in stablecoins (61.8%), tokenized funds (73.0%), and tokenized commodities (84.0%). Growth in tokenized funds (+73.1% YoY) and commodities (+325.9% YoY) was particularly strong, driven by institutions like BlackRock and JPMorgan entering the space. Contrasting these usage gains, several USD-denominated value metrics declined in Q1: fully diluted market cap fell 30.3% QoQ, total value locked (TVL) dropped 11.0%, and ecosystem transaction volume decreased 24.0%. The report interprets this as Ethereum prioritizing long-term network expansion and cementing its role as the default settlement layer for finance over short-term fee capture. The commentary from Etherealize argues that, much like the early internet, Ethereum's open, permissionless model is poised to win over closed alternatives as institutional tokenization accelerates.

marsbitHá 3h

Ethereum Q1 2026 Report: Fees Decline, Users and Transaction Volume Hit New Highs

marsbitHá 3h

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

Intel CEO Chen Liwu outlines a bold vision for the company's transformation in a podcast interview, targeting a "10x return in 5-10 years." He emphasizes a strategic shift beyond traditional process node scaling, focusing on advanced packaging (like EMIB), new materials (glass substrates, GaN, SiC, InP, synthetic diamond), and system-level integration. Key to this is Intel's foundry business, where he prioritizes building trust through superior yield, defect density, and cycle times. Chen sees strong CPU demand resurgence driven by agent AI and inference workloads. He details the Terafab collaboration with Elon Musk to address semiconductor infrastructure gaps and stresses the strategic importance of U.S.-based advanced manufacturing. Acknowledging Intel is still in the "crawl" phase of his crawl-walk-run framework, Chen believes the company's full potential—extending from its PC base into edge computing and physical AI—will become apparent by 2030-2032.

marsbitHá 3h

Intel CEO Liwu Chen's First Podcast Interview: Our Goal is '10x in 5-10 Years', Betting on Advanced Packaging, Glass Substrate, and Synthetic Diamond

marsbitHá 3h

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

Pete Florence, a former senior research scientist at Google DeepMind and a key contributor to the Vision-Language-Action (VLA) model architecture, is deliberately distancing his startup, Generalist AI, from the trendy "world model" label. He argues that the industry should prioritize concrete goals over buzzwords. His goal is to create robots that can perform a vast range of unseen tasks with high speed and success rates, without needing task-specific training data. Recently, his company raised $400 million (¥2.7 billion) at a $2 billion valuation. Notable investors include NVIDIA's NVentures, Bezos Expeditions, NFDG, as well as Xiaomi co-founder Lin Bin, Zoom founder Eric Yuan, and renowned AI scientist Fei-Fei Li. Florence's approach stems from his academic background at MIT under Professor Russ Tedrake, focusing on understanding the physical world. After joining DeepMind, he developed models like Transporter Network and co-created the VLA framework. He left in 2025 to found Generalist AI. The company has launched two models: GEN-0, which demonstrated that scaling laws apply to physical motion, and GEN-1. GEN-1 was trained on over 500,000 hours of physical interaction data collected via a specialized wearable device. It achieves a 99% success rate on precise mechanical tasks like folding boxes and maintains performance three times faster than its predecessor. Florence believes GEN-1 is reaching a commercial utility threshold similar to the GPT-3 inflection point. The substantial funding round, following GEN-1's release, signifies strong investor confidence in Generalist AI's practical, goal-driven path to creating versatile, useful robots, regardless of the "world model" terminology.

marsbitHá 3h

He Just Raised 2.7 Billion, and Li Fei-Fei Also Invested

marsbitHá 3h

Trading

Spot
Futuros
活动图片

Categorias popularesright-arrow

Etiquetas Popularesright-arrow