Preface

AI is undoubtedly one of the hottest fields globally today, with both cutting-edge startups like OpenAI in Silicon Valley and domestic players such as Moonshot and Zhipu Qingyan joining the AI revolution. Not only is AI leading trends in technology, but it is also one of the standout sectors in the cryptocurrency market this year. Despite recent market turbulence, AI leader Bittensor (TAO) remains at the forefront, delivering over 5x returns compared to other new tokens this year. As AI technology continues to advance and be applied, the importance of data as the cornerstone of AI development becomes increasingly evident.

Under the current tide of the AI era, the importance and potential value of data have reached unprecedented heights. Statistics show that mainstream AI large model companies need to process and consume billions of datasets annually, with the effectiveness and accuracy of these datasets directly impacting the training outcomes of AI models. However, the cost of data acquisition is rising, presenting a significant challenge for AI companies.

Performance optimization relies on the growing volume of data consumption. For example, OpenAI used approximately 45TB of text data to train the GPT-3 model, with GPT-4 training costs reaching up to $78 million; Google’s Gemini Ultra model’s computing costs are around $191 million. This enormous data requirement is not unique to OpenAI; other AI companies like Google and Meta also need to handle massive amounts of data when training large AI models.

The effectiveness of data needs to be addressed. Effective data must be high-quality, unbiased, and rich in feature information to ensure accurate predictions by AI models. For instance, OpenAI used diverse sources for GPT-3, including books, articles, and websites, to ensure data diversity and representativeness. However, data effectiveness depends on more than just its source; it involves data cleaning, annotation, and preprocessing, which require significant manpower and resources.

Economic considerations cannot be ignored. The costs of data collection and processing are often underestimated but can be substantial. Data annotation itself is time-consuming and costly, often requiring manual labor. Once data is collected, it must be cleaned, organized, and processed for effective use by AI algorithms. According to McKinsey, the cost of training a large AI model can reach millions of dollars. Additionally, building and maintaining data centers and computing infrastructure is a significant expense.

Overall, training large AI models relies heavily on high-quality data, where the quantity, effectiveness, and acquisition costs directly impact the performance and success of AI models. In the future, efficiently acquiring and utilizing data will be a key competitive factor for AI companies.

In this context, DIN (formerly Web3Go), as the first modular AI-native data preprocessing layer, has emerged. DIN aims to lead a data economy trend where everyone can monetize personal data through decentralized data validation and vectorization processing, and businesses can acquire data more efficiently and economically. DIN has already secured $4 million in seed funding from Binance Labs and an additional $4 million in pre-listing funding from other institutions, communities, and KOL networks, with a current valuation of $80 million. This reflects the market’s high recognition of its potential and future development. Its partners include Polkadot, BNB Chain, Moonbeam Network, and Manta Network.

DIN’s Data Preprocessing Node – Chipper Node

DIN’s market positioning is clear, aiming to build a decentralized data intelligence network in the AI and data fields. The Chipper Node plays a crucial role in the DIN ecosystem, handling data validation, vectorization processing, and reward calculation, making it a core component of DIN’s data preprocessing layer. To promote the data economy more broadly, DIN has opened public sales of Chipper Nodes to encourage more users to participate in the network’s development and maintenance while earning rewards, creating a positive feedback loop that fosters the growth of the DIN ecosystem and the data economy.

As a new token issuance method, the node selling model has quickly gained popularity in the cryptocurrency market due to its unique advantages. Compared to traditional public sales, it offers investors greater flexibility and potential returns. The core of this model is that by selling nodes, project teams can better incentivize early participants while ensuring network decentralization and maximizing economic benefits.

DIN’s node selling plan will proceed in stages, including pre-sale, whitelist sale, and public sale, each with different participation conditions and reward mechanisms. The distribution and unlocking rules for node tokens are carefully designed to ensure market price stability and long-term investor returns. By purchasing and operating DIN’s Chipper Node, users can not only engage in data validation and vectorization but also earn substantial $DIN token rewards.

With the continuous development of the AI and data markets, DIN is poised to become a leader in this field. The following sections will delve into the Chipper Node’s sales model and its unique advantages in the market, analyzing return rates and payback periods to reveal its future investment potential and growth prospects.

Analysis of Expected Return Rates and Payback Period

DIN’s node selling plan will proceed in phases, including pre-sale, whitelist sale, and public sale, each with different participation conditions and reward mechanisms. The distribution and unlocking rules for node tokens are carefully designed to ensure market price stability and long-term investor returns. By purchasing and operating DIN’s Chipper Node, users can participate in data validation and vectorization processes and earn $DIN token rewards from node mining. Below is a detailed analysis of the expected return rates and payback periods for DIN nodes.

DIN sales plan

1. Node Token Reward Distribution Plan: DIN allocates 25% of its node tokens, with 50% unlocked in the first year. In addition to the node mining rewards, extra $DIN tokens will be airdropped to $xDIN holders, unlocking 100% at the Token Generation Event (TGE). Chipper Node holders will also receive a 13% token airdrop, with linear unlocking over six months following the TGE. This distribution plan helps maintain token market price stability and reduces price volatility caused by a large influx of tokens in a short period.
2. DIN’s Node Sale Phases: The node sale is divided into three stages: pre-sale, whitelist sale, and public sale. Each stage has different pricing and conditions to attract various types of investors. The pre-sale is targeted at early product users and core community contributors; the whitelist sale is aimed at specific institutions, community partners, and KOLs; and the public sale is open to the general public.
3. Invitation Mechanism: DIN has introduced an invitation mechanism where existing users can invite new users to purchase nodes, and both parties will receive additional token rewards. This mechanism effectively expands the user base and increases community engagement and loyalty.

The price and return periods for different node rounds are as follows

With a total supply of 100 million $DIN, and using io.net—another DePIN project that also had node sales and raised $10 million before TGE, with a current FDV of $1.5 billion—as a benchmark, we assume a $15 price for $DIN post-TGE and 50% operational nodes. Pre-sale Tier 1 nodes are offered for free to eligible xData Chip NFT holders and some community contributors, so there is no break-even concern. Participants can start mining early and convert their wafer into $xDIN airdrop points to secure a share of the $DIN airdrop. In the whitelist sale Tier 2, nodes are priced at $99, with an expected first-year reward of 106 $DIN worth $1,590, and investors will break even in 27 days according to the release rules. The public sale is divided into two phases: Tier 3 nodes are priced at $149, providing a first-year reward of 133 $DIN valued at $1,995, with a break-even period of 36 days. Tier 6 nodes are priced at $300, offering a first-year reward of 265 $DIN valued at $3,975, with a break-even period of 3 months.

Compared to other recent mainstream projects like Aethir and CARV, DIN’s node sales offer advantages in price, unlock speed, and reward mechanisms. Aethir’s node tokens are unlocked over four years, leading to a longer payback period, while CARV, despite using a multi-round sales strategy, offers an overall return rate lower than DIN. Meanwhile, DIN’s node sales provide faster unlock speeds and a flexible reward mechanism, allowing investors to see returns in a shorter period while maintaining market price stability and reducing investment risks.

DIN’s technical strength and market potential

Technical strength

DIN stands out as the first modular AI data preprocessing layer, demonstrating notable technical innovation and unique advantages. Its core technology involves decentralized data validation and vectorized processing, offering efficient and reliable data preprocessing services. This approach not only enhances data processing efficiency but also ensures data security and privacy. Additionally, DIN’s Chipper Node nodes have significant advantages in data validation and reward calculations, allowing node holders to directly participate in the network’s operation and maintenance, further strengthening the network’s decentralization and robustness.

Market Potential

The vast potential of the AI and data markets is a key driver for DIN’s development. With the rapid advancement of artificial intelligence and big data technologies, the demand for high-quality data is growing. DIN, with its innovative technology and business model, provides efficient data preprocessing services for AI models, significantly reducing data acquisition and processing costs. This positions DIN advantageously in the competitive market, with substantial market potential and growth prospects.

Capital Background

DIN’s strong capital backing and supporters enhance its market competitiveness. The project has completed $4 million in seed funding and $4 million in pre-IPO funding, with a current valuation of $80 million. Notably, DIN has received support from top investment institutions like Binance Labs, providing ample financial security and robust resources and network support for its future development.

Conclusion

Despite recent shocks to global capital markets and the subsequent plunge in the cryptocurrency market, the current panic in the secondary market has not fully subsided. However, participating in node sales might offer higher odds of return during market turmoil, providing more reliable node reward returns compared to the secondary market. DIN, with its detailed node token reward distribution and flexible sales approach, offers investors higher returns and a shorter payback period. As macroeconomic conditions stabilize and interest rate cut expectations materialize, a bull market is anticipated to return in the latter half of the year. With its integrated approach to modularization, DePIN, and AI narratives, DIN is poised to lead a wave in the private data economy amidst rapid AI development, and its performance in the future market is worth looking forward to.

statement:

1. This article is reproduced from [GO2MARS’ WEB3 Research], the original title is “New Paradigm of AI Data Economy: Looking at DIN’s ambitions and node sales from the perspective of modular data preprocessing”, the copyright belongs to the original author [ D^2Labs], if you have any objection to the reprint, please contact Gate Learn Team, the team will handle it as soon as possible according to relevant procedures.

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