When we look up “AI” and “artificial intelligence” in Google Ngram viewer, we see that the narrative for artificial intelligence comes and goes, just as narrative economics describes. Two important peaks, and a third peak forming for AI, and one less for artificial intelligence.

The first peak dates from 1967, and it is no coincidence. In 1956, the term "artificial intelligence" (AI) was officially coined, marking a significant moment in the history of the field. The term was introduced during a seminal conference at Dartmouth College in Hanover, New Hampshire, known as the Dartmouth Conference. The conference, held in the summer of 1956, is often considered the birthplace of artificial intelligence.

The term "artificial intelligence" was proposed by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, who were among the leading figures in the emerging field of AI. McCarthy, in particular, is credited with coining the term. The Dartmouth Conference aimed to bring together researchers interested in exploring ways to simulate human intelligence using machines.

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The introduction of the term "artificial intelligence" in 1956 marked the formalization of the field as a distinct area of research and study. This moment is crucial in the history of AI as it helped shape the narrative and direction of research in the subsequent years. Since then, AI has undergone significant advancements, with periods of growth and periods of reduced enthusiasm, known as AI winters. The term and the Dartmouth Conference played a foundational role in establishing AI as a field of study and research that continues to evolve and impact various aspects of technology, science, and society today.

After a wile, enthusiasm, funding, and public interest in the AI field experienced a significant decline. The so-called first AI winter. There is a perception that the promises and expectations of AI did not match the actual achievements, leading to a cooling off of investment, research, and development in the field. To challenges such as the complexity of problems, limited computational power, and insufficient understanding of AI techniques funding decreased, and many projects were abandoned, resulting in skepticism and reduced support.

After the first AI winter, interest in artificial intelligence (AI) revived due to several factors. Advances in machine learning techniques, such as backpropagation and reinforcement learning, overcame earlier limitations. Expert systems demonstrated practical applications, garnering interest and investment. Government and corporate funding, including support from agencies like DARPA, continued to flow into AI research. Improved computing power enabled researchers to tackle more complex problems, particularly in machine learning and neural networks.

Real-world applications and commercialization of AI technologies gained momentum as businesses recognized their practical value. International collaboration and conferences facilitated the exchange of ideas, contributing to the collective progress of the field. Successes in speech recognition and computer vision showcased the tangible capabilities of AI systems. These combined factors marked the end of the first AI winter and initiated a renewed era of growth and exploration in AI leading to the peak on 1987 on the chart.

The second AI winter was initiated by a resurgence of skepticism and reduced interest in artificial intelligence during the late 1980s and 1990s. This period was characterized by disappointments in the practical applications of expert systems, limitations in the capabilities of existing AI technologies, and a perception that AI had been overhyped. The publication of "Perceptrons" by Minsky and Papert, which highlighted the constraints of certain neural network approaches, contributed to the declining enthusiasm. Funding cuts, a lack of significant breakthroughs, and the absence of clear, tangible results in AI research further fueled the onset of the second AI winter.

The third AI revival was initiated by breakthroughs in deep learning, particularly with the development of powerful GPUs. Advances in handling large datasets and improved data processing capabilities became crucial. Open source collaboration and the sharing of knowledge on platforms like GitHub accelerated progress. Real-world successes in applications like speech recognition and image classification demonstrated AI's practical value, leading to increased adoption by businesses. Entrepreneurial efforts and industry investment, especially in startups, fostered innovation. Progress in natural language processing, driven by models like BERT and GPT, improved language understanding. Developments in autonomous vehicles and robotics showcased AI's potential in real-world scenarios. These factors collectively fueled the third AI revival, marked by unprecedented growth, breakthroughs, and widespread adoption across various industries.

Crypto and AI

Examining Google Trends data for the search term "AI crypto" reveals patterns starting in 2017, marking a significant period in the evolution of the cryptocurrency space: crypto's cambium. During this phase, the crypto landscape witnessed explosive growth, transforming from a limited number of projects to a myriad of initiatives. This expansion not only resulted in increased diversity within the crypto space but also saw the emergence of projects integrating artificial intelligence (AI) technologies. The intersection of AI and crypto became more pronounced, reflecting the innovative endeavors that have come to define this dynamic and rapidly evolving sector.

After a minor peak around the cycle peak in December 2021, “AI crypto” searches declined. However, breakthroughs in AI use cases marked a turning point. In July 2022, Midjourney, and in August 2022, Stable Diffusion, launched their initial public versions, enabling users, including myself, to generate photorealistic images, paintings, and videos at minimal or no cost. AI began fulfilling its promises.

The technology used by Stable Diffusion to translate text into images sparked interest in AI within the crypto community, particularly ChatGPT. The first public version of ChatGPT was released at the end of November 2022, leading people to realize the immense potential of AI. Subsequently, there was a rapid surge in searches for "AI crypto," and the trend remains upward. Some perceive it as a developing bubble, raising questions about its eventual culmination and the factors that might lead to its end.

The purposes of AI in crypto can be read in my newsletter AI-Infused Crypto Projects.

The future

Learning from the history of AI winters provides valuable insights into potential indicators for the conclusion of an AI crypto phase. Similar to previous AI winters, an end could be signaled by overinflated expectations driven by speculative hype rather than tangible developments. Disillusionment may set in if the technology fails to deliver on initial promises, with failed projects and technical limitations contributing to a decline in interest. Currently, individuals are turning to applications such as ChatGPT and Bard as an oracle, attempting to predict future events; however, it's important to note that these applications lack the capability to do so, occasionally generating nonsensical outputs.

Market corrections, akin to those in traditional financial markets, may occur as speculative valuations readjust to more realistic levels. Such corrections can be part of the natural market cycle. Shifts in public perception, influenced by negative narratives or a loss of confidence in the potential of AI crypto, could impact adoption and investment.

While historical patterns offer lessons, each technological cycle is unique, and the AI crypto space may be influenced by a combination of factors. Economic climates and recessions can have complex effects on the development and sustainability of emerging technologies. Monitoring these indicators and maintaining a realistic understanding of the technology's capabilities will be crucial for navigating the evolution of AI in the cryptocurrency space.

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In the rapidly evolving world of blockchain technology, innovation often leads to new standards and protocols aimed at enhancing functionality and usability. One such recent development is ERC-404, an experimental Ethereum token standard that blends the characteristics of ERC-20 and ERC-721 tokens. This article delves into the intricacies of ERC-404, its origins, technical aspects, use cases, community response, and its similarities and differences with traditional ERC standards.

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ERC-404 emerged from the minds of pseudonymous developers "ctrl" and "Acme" under the Pandora project. The goal was ambitious yet straightforward: to create a token standard that combines the fungible nature of ERC-20 tokens with the unique properties of ERC-721 non-fungible tokens (NFTs). The project's inception sparked curiosity and excitement within the Ethereum community, leading to its rapid development and deployment.

At its core, ERC-404 allows for the creation of fractionalized NFTs, enabling multiple wallets to collectively own a single NFT. This is achieved by merging ERC-20 and ERC-721 standards, leveraging the benefits of both to create a versatile and liquid asset class. ERC-404 tokens retain the divisibility and interchangeability of ERC-20 tokens while preserving the uniqueness and ownership rights associated with ERC-721 NFTs.

The potential use cases for ERC-404 tokens are vast and diverse. Fractionalized NFT ownership opens up new avenues for trading, staking, lending, and borrowing within the Ethereum ecosystem. Token holders can participate in NFT markets with smaller investment amounts, diversify their portfolios, and access liquidity previously unavailable to them. Furthermore, ERC-404 tokens pave the way for innovative financial products and services, such as NFT-backed loans and decentralized exchanges.

Benefits and Challenges: ERC-404 offers several advantages, including increased liquidity for NFTs, enhanced accessibility for investors, and expanded utility for token holders. However, challenges such as security concerns, regulatory uncertainty, and compatibility issues with existing protocols must be addressed. As an experimental standard, ERC-404 is still undergoing refinement and iteration to optimize its performance and mitigate potential risks.

The Ethereum community has shown a mix of enthusiasm and skepticism towards ERC-404. While some view it as a groundbreaking innovation with the potential to revolutionize the NFT space, others remain cautious due to its experimental nature and lack of formal recognition. Nevertheless, ERC-404 has sparked lively discussions and collaborations among developers, investors, and enthusiasts, driving further exploration and adoption of the standard.

While ERC-404 shares similarities with traditional ERC standards in terms of technical implementation and compatibility with the Ethereum blockchain, it also introduces unique features and capabilities. Unlike ERC-20 tokens, which are fully fungible and interchangeable, ERC-404 tokens offer fractionalized ownership of NFTs, enabling finer granularity of asset ownership. Similarly, while ERC-721 NFTs are non-fungible and indivisible, ERC-404 tokens allow for divisible and tradable units, enhancing liquidity and accessibility.

Conclusion: In conclusion, ERC-404 represents a bold step forward in Ethereum token standards, offering a novel approach to combining the fungible and non-fungible aspects of digital assets. While still in its experimental phase, ERC-404 holds immense promise for unlocking new opportunities in decentralized finance, digital ownership, and beyond. As the Ethereum community continues to explore and embrace this innovative standard, it is poised to shape the future of blockchain-based asset management and exchange.

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Welcome to our series exploring various use cases, technologies, and solutions within the realm of cryptocurrencies. Throughout this series, we delve into different applications, user cases, and the underlying technologies behind crypto innovations. It's essential to note that this series aims to provide informative insights and analysis, remaining neutral without endorsing any specific solution or technology. Our goal is to present diverse perspectives within the crypto space, showcasing the potential and possibilities while offering a comprehensive understanding of the evolving landscape.

What is Render Network

The Render Network is a decentralized platform reshaping 3D rendering and computational workloads. It utilizes decentralized GPU nodes for parallel processing, making 3D rendering faster and more scalable.

Blockchain technology ensures digital traceability, enabling licensing and provenance for 3D models. All work on the network, including frames and animations, can be tokenized as NFTs. GPU streaming experiences are facilitated through the X.IO framework, allowing direct in-browser distribution and monetization of ultra-high-resolution media.

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Unique selling point

Render Network distinguishes itself by revolutionizing 3D rendering through decentralized GPU nodes. It accelerates rendering processes, supporting various engines like Redshift and integrating AI for tasks like NeRF rendering. Blockchain ensures traceability, enabling licensing for 3D models and tokenizing work as NFTs. The Burn and Mint Equilibrium model manages costs, while Multi-Tier Pricing offers flexibility. GPU streaming experiences and a community-driven governance model further enhance Render Network's unique position in reshaping 3D rendering, computational tasks, and virtual asset creation.

In essence, the Render Network combines decentralized computing, blockchain, and AI to redefine 3D rendering, computational tasks, and virtual asset creation in a secure, efficient, and community-driven ecosystem.

The Token RNDR

RNDR, the native token of Render Network, holds a pivotal role in shaping the decentralized rendering ecosystem. Operating initially on the Ethereum blockchain and later migrating to Solana, RNDR functions as the primary medium for transactions within the Render Network. Its implementation is integral to the Burn and Mint Equilibrium (BME) model, a mechanism designed to maintain equilibrium in a multi-sided decentralized economy.

Within the Render Network, Creators, who seek rendering services, spend RNDR to access the computational power of decentralized GPUs. This expenditure is tied to a transparent and programmatically executed burn process. The burn is initiated after the completion of rendering tasks, creating an immutable on-chain record of the transaction. This meticulous logging contributes to emissions-based rewards, distributed at the conclusion of each epoch.

Furthermore, RNDR serves as a powerful incentive for Node Operators. These operators, responsible for processing rendering tasks, receive rewards in the form of RNDR, creating a symbiotic relationship within the network. The incentives are twofold: Availability Rewards, encouraging consistent node operation, and Job-to-be-done Completion Rewards, offering compensation for successfully executed rendering tasks.

Liquidity Providers, another key player in the Render Network, are also rewarded with RNDR. Their role involves contributing staked RNDR to liquidity pools on partnered exchanges. This liquidity is essential for the Burn and Mint Equilibrium system, ensuring smooth operations and availability of RNDR for transactions. Liquidity Providers receive rewards proportionate to their contributions, reinforcing liquidity in the network.

Overall, RNDR acts as the lifeblood of the Render Network's economic model, aligning the interests of Creators, Node Operators, and Liquidity Providers. Its utility extends beyond a mere transactional token, playing a crucial role in incentivizing and sustaining the decentralized rendering ecosystem.

Legal structure

Render Network operates as a decentralized and community-driven platform, and its legal structure is designed to align with these principles. The Render Network Foundation, a not-for-profit entity, assumes a central role in overseeing and maintaining the core protocol. As a foundation, its primary objectives include fostering community growth, facilitating governance through the Render Network Protocol (RNP) system, and setting strategic priorities for the network.

The decentralized nature of Render Network is further emphasized by the governance processes outlined in the RNP system. This system allows community members to actively participate in proposing and implementing changes to the network. Render Network Protocol (RNP) proposals undergo a structured process, from initial proposal to implementation, ensuring transparency and community involvement.

Render Network, with its foundation and governance structure, aims to create a decentralized rendering ecosystem that empowers its participants and adapts to the evolving needs of the community. This commitment to decentralization is reflected not only in the technical aspects of the platform but also in its legal and organizational framework.

Principles of the Austrian school

Render Network aligns closely with the principles of the Austrian School of Economics, emphasizing decentralization and minimizing central authority. In its structure, Render Network operates as a decentralized platform with a community-driven approach. The Render Network Foundation, a not-for-profit entity, plays a central role in overseeing the protocol, but its function is to facilitate governance rather than act as a central authority. This design ensures that decisions and changes to the network are driven by community consensus rather than a top-down approach.

The platform embraces the Austrian School's focus on minimizing government intervention, particularly in the free market. Render Network's governance processes, outlined in the Render Network Protocol (RNP) system, underscore the commitment to decentralized decision-making. This system allows community members to actively participate in proposing and implementing changes, fostering an environment where market participants have a significant say in the platform's evolution.

Cryptographic protocols and consensus mechanisms play a crucial role in determining the supply of the RNDR token, aligning with the Austrian School's emphasis on market-driven mechanisms. The value of RNDR is subject to market perception and individual actors' decisions, reflecting the Austrian School's belief in the natural order of free market mechanisms.

Render Network promotes autonomy in transactions without central control, further emphasizing its commitment to decentralized principles. The platform relies on natural market mechanisms for stability, allowing supply and demand forces to shape its dynamics. This decentralized and market-driven approach positions Render Network within the framework of the Austrian School of Economics.

Contrastingly, Render Network stands in contrast to the Keynesian Economics paradigm. Keynesian principles involve central authorities regulating and controlling the money system, a concept that differs from Render Network's decentralized model. While Keynesian economics supports government intervention through fiscal and monetary policy to stimulate demand, Render Network prioritizes a more hands-off approach, enabling market forces to dictate outcomes.

Central banks, a key element in Keynesian economics, regulate the money supply. In Render Network, cryptographic protocols and decentralized consensus mechanisms determine the token supply, diverging from the centralized control emphasized by Keynesian theories. The value of RNDR on Render Network is driven by market dynamics rather than government policy and monetary interventions, a departure from the Keynesian belief in the direct influence of government actions.

In conclusion, Render Network aligns more closely with the Austrian School of Economics, emphasizing decentralization, minimizing government intervention, and relying on market-driven mechanisms. Its structure and principles promote a decentralized and community-centric platform, fostering an environment where market participants actively shape the network's trajectory.

For more information about the relation between crypto and the Austrian school, see my newsletter: The Economic Paradox: Crypto's Rebellion Against Fiat's Rule.

Competition

In the rapidly evolving landscape of blockchain and cryptocurrency, Render Network faces competition from various projects that share a focus on decentralized computing, distributed GPU rendering, and innovative solutions for the creative and artificial intelligence industries. Some potential competitors for Render Network include:

Golem (GLM): Golem positions itself as a decentralized network that enables users to share and monetize their computing power. With a focus on rendering capabilities, Golem competes in the space of distributed computing resources.

iExec (RLC): Specializing in decentralized cloud computing, iExec aims to deliver scalable and secure off-chain computing services. Its platform allows users to harness computing power without relying on traditional centralized cloud services.

Elastic (XEL): Elastic ventures into the realm of decentralized supercomputing, allowing individuals to rent out their computing resources. This project aligns with Render Network in the broader scope of providing decentralized computational power.

SONM (SNM): SONM positions itself as a decentralized fog computing platform, providing users with the ability to rent or lease computing resources. It competes in the broader domain of decentralized computing solutions.

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Ethereum continues to evolve, and one of the key elements at the center of recent discussions is the gas limit. As Ethereum co-founder Vitalik Buterin calls for a 33% increase in the gas limit, it has reignited debates among developers, node operators, and users. Let's delve into the intricacies of gas on Ethereum, exploring what it is, where it matters, and why finding the right balance is crucial.

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Understanding Gas on Ethereum

In the Ethereum ecosystem, gas serves as the essential fuel propelling transactions and smart contract executions. Analogous to the fuel needed to power a car, gas represents the fee users must pay to execute any operation on the blockchain. Users, when sending transactions or engaging with smart contracts, pay for the computational resources consumed during these processes, and the payment is made in the form of gas.

The significance of gas in the Ethereum network cannot be overstated. It functions as a crucial measure of computational effort, playing a pivotal role in preventing abuse and inefficiency. Beyond its fundamental role as a transaction fee, gas is intricately tied to the concept of the gas limit. This limit sets the maximum amount of gas allowable for executing transactions or smart contracts within a single block. Striking the right balance is paramount, as it directly influences the network's overall performance and synchronization. Gas thus becomes a cornerstone in maintaining the seamless operation of the Ethereum blockchain.

As users engage with the Ethereum platform, understanding the role of gas becomes essential. It goes beyond being a mere transaction fee; gas embodies the intricate mechanism that ensures the integrity and efficiency of the Ethereum network, making it a fundamental concept for participants navigating this decentralized ecosystem.

Vitalik Buterin's Call for Change

In a recent Reddit Ask-Me-Anything session, Ethereum co-founder Vitalik Buterin suggested a "modest" 33% increase in the gas limit to potentially improve network throughput. Currently set at 30 million, Buterin's proposal to raise it to 40 million aims to accommodate the growing network usage and adoption. However, this suggestion has sparked concerns among developers.

The Concerns Raised by Developers

Blockchain State Size: One prominent concern, voiced by Ethereum developer Marius van der Wijden, revolves around the potential increase in the size of the blockchain state. As the gas limit rises, so does the size of data containing account balances and smart contract information. With the total space needed for the state already at approximately 267 GB, further growth could lead to challenges in data access and modification speed.

Sync Times and Bandwidth: Developers, including Gnosis co-founder Martin Köppelmann, have expressed concerns about increased synchronization times and potential bandwidth challenges associated with a higher gas limit. Ethereum team lead Péter Szilágyi echoed these sentiments, emphasizing that increasing the gas limit could have downsides, including faster-growing state and heightened denial-of-service potential.

The relationship between gas limits and the size of the blockchain state is a pivotal aspect of Ethereum's functionality. The gas limit directly influences the capacity for processing transactions and smart contracts within a block. As the gas limit increases, allowing for more computational operations, the corresponding blockchain state expands to accommodate the additional data generated from account balances and smart contract information. This growth in data is a direct consequence of the increased computational activities permitted by a higher gas limit.

Currently standing at around 267 GB, the total space needed for the Ethereum blockchain state reflects the cumulative data of account balances and smart contracts. The potential for further growth poses challenges in terms of data access and modification speed. Accessing and modifying large sets of data becomes a more resource-intensive task, potentially leading to slower transaction processing and synchronization times. Therefore, the delicate balance between gas limits and blockchain state size becomes crucial for maintaining efficient network performance, ensuring that the Ethereum blockchain remains a scalable and responsive platform for its users. Striking this balance is essential to accommodate the evolving demands on the network while mitigating the potential drawbacks associated with an expanding blockchain state.

Proposed Solutions and Future Outlook

In response to these concerns, developers are exploring solutions such as upgrades like EIP-4444, addressing chain history expiration, and EIP-4844, tackling rollup data availability using "blobs." These measures aim to manage long-term growth trends and maintain efficiency.

As Ethereum navigates through this critical phase, the community faces the challenge of finding the right balance. While Vitalik Buterin's proposal seeks to enhance network throughput, developers emphasize the need for cautious considerations to avoid compromising the network's scalability and efficiency.

In conclusion, the ongoing discussions about Ethereum's gas limit underscore the delicate balance required for blockchain protocols to scale effectively while maintaining security and decentralization. As the Ethereum community weighs the pros and cons, the decisions made will shape the future landscape of this pioneering blockchain platform.

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In January 2024, the cryptocurrency market experienced dynamic shifts, marked by a $21 billion contraction and notable developments in Bitcoin's market dominance. Both Bitcoin and Ether exhibited upward price trends since November 9, 2022, introducing potential gains but also highlighting the transitory nature of market dynamics. Projections for price and annual return on investment underscore the complexities in forecasting, with the interconnected influences of technical indicators urging continuous vigilance and adaptability. The following analysis delves into the intricate landscape of cryptocurrency trends, emphasizing the need for nuanced interpretation and strategic decision-making in the face of evolving market conditions.

Crypto cap

In January 2024, the overall cryptocurrency market experienced a decline, witnessing a reduction of $21 billion, equivalent to a 1.3% contraction in the total crypto cap. Breaking down this decrease across various segments, the predominant driver of growth during this period was Bitcoin, which exhibited a noteworthy increase of 0.8%. Consequently, Bitcoin's market capitalization dominance surged by 2.2%, reaching a new level of 52.7%. This rise in dominance for Bitcoin was a notable shift from 51.6% to 52.7%.

The observed trend, where cryptocurrencies with higher market capitalization outperform those with smaller market caps, may indicate a risk-averse sentiment in the market. This dynamic is particularly noteworthy when considering the concurrent increase in the dominance of stablecoins. The intricate interplay of these factors reflects the nuanced landscape of the cryptocurrency market in January 2024.

Bitcoin

Price trend

Since November 9, 2022, Bitcoin has been following an upward trajectory in its price trend. It is crucial to acknowledge that such trends are not perpetual. While the current trend persists, there is a 50% likelihood that Bitcoin's price will either exceed or fall below $44.2k by February 28, 2024. Additionally, there is a high probability of 98% that the price will remain within the range of $36.5k and $52.0k during the same period.

It's essential to note that these projections are contingent on the ongoing momentum. If Bitcoin experiences shifts in price momentum, there is the potential for it to surpass or fall below the aforementioned figures. The dynamic nature of the cryptocurrency market underscores the need for continual monitoring and adaptability to changing market conditions.

Annual return on investment trend

One method to assess price momentum is through the examination of the annual return on investment (annual ROI), which gauges the ROI if one had purchased Bitcoin precisely a year ago. Similar to the price trend, the annual ROI for Bitcoin has been on an upward trajectory since November 9, 2022. However, it is imperative to recognize that trends in annual ROI, like price trends, are not everlasting.

As of last month, it became evident that the annual ROI trend deviated from the 98% probability channel established earlier. We had anticipated this outcome, projecting that at least one of the two trends—price and annual ROI—would exit the 98% probability channel.

It is essential to note that these projections are contingent on the prevailing momentum. Should Bitcoin's price gain or lose momentum, it has the potential to surpass or fall below the specified figures, as observed at this moment. The interconnected nature of both the price trend and annual ROI underscores the complexity and fluidity of the cryptocurrency market.

Monthly Chart Analysis

Analyzing the monthly chart of Bitcoin reveals a mixed but nuanced picture from various technical indicators. The 20-period Moving Average (MA) is currently positioned below the 50-period MA, though there is a noticeable convergence, suggesting a potential shift. Despite this, the price remains above both moving averages and the upper Bollinger Bands (BB) line, signaling a prevailing bullish trend. The Relative Strength Index (RSI) stands at 62.7, indicating an ongoing upward trend, while increasing volume, coupled with long-wicked monthly candlesticks, suggests a positive correlation between rising prices and heightened market activity. The Moving Average Convergence Divergence (MACD) further supports the bullish sentiment, remaining positive and above its signal line.

Weekly Chart Analysis

Switching to the weekly chart, a more optimistic picture emerges. The golden cross, characterized by the 50-day MA being above the 200-day MA, is a robust bullish signal. Additionally, the price is trading above both moving averages, reinforcing the potential for a sustained bullish trend. The RSI at 68.4% on the weekly chart indicates a balanced market, with neither overbought nor oversold conditions.

However, the MACD on the weekly chart, while still positive, exhibits a narrowing gap between itself and the signal line. This suggests a potential weakening of bearish momentum, introducing an element of caution. In summary, the overall technical analysis of the Bitcoin chart presents a mixed outlook. The golden cross and the position of the price above both moving averages on the weekly chart suggest a probable shift toward a bullish trend. Nevertheless, the negative MACD on the monthly chart raises concerns about short-term weakness in the market. Traders and investors should remain vigilant, closely monitoring these indicators for a comprehensive understanding of the evolving market dynamics.

Ethereum

Price trend

Since November 9, 2022, Ether has been riding an upward price trend, marking a period of consistent growth in its market value. It is crucial to recognize, however, that such trends in the cryptocurrency market are not perpetual.

As the current upward price trend persists, there is a 50% probability that the price of Ether will either exceed or fall below the $2.3k mark by February 28, 2024. Additionally, there is a high probability of 98% that the price will remain within the range of $1.8k and $2.8k during the same period, offering a more specific window for potential price movements.

It's worth noting that these projections are contingent on the ongoing momentum and are subject to potential shifts. The interconnected nature of cryptocurrency markets is highlighted by the reference to Bitcoin; should BTC experience notable gains or losses in momentum, it could influence Ether's price trend, potentially pushing it above or below the specified figures.

Annual return on investment trend

Examining price momentum through the lens of annual return on investment (annual ROI), we focus on the ROI corresponding to Ether's purchase exactly one year ago. Mirroring the upward trajectory of the price, Ether's annual ROI has been on the rise since November 9, 2022. However, it's essential to acknowledge that just like price trends, annual ROI trends are not perpetual.

As the current annual ROI trend endures, there exists a 50% probability that it will either exceed or fall below $3.4k by February 28, 2024. Additionally, with a high probability of 98%, the annual ROI is expected to remain within the range of $2.5k and $4.2k during the same period, providing a more specific projection. This establishes a probability channel that outlines the anticipated scope of annual ROI movements.

It is noteworthy that, considering the dynamic nature of cryptocurrency markets, these projections are contingent on the continued momentum and are subject to potential shifts. Furthermore, our initial expectation was that one of the trends, either price or annual ROI, would fall out of the 98% probability channel. However, as of the current moment, this anticipated deviation has not occurred.

In summary, while both Ether's price and annual ROI exhibit positive trends since November 9, 2022, the inherent volatility of the cryptocurrency market underscores the importance of continual monitoring. The absence of the expected deviation from the 98% probability channel at this moment emphasizes the complex and unpredictable nature of market dynamics. Investors and analysts should remain vigilant, considering potential influences, particularly those related to Bitcoin, that could impact Ether's price and annual ROI trends.

Monthly Chart Analysis

In examining the monthly Ether chart, several key indicators shed light on the current market dynamics. The upper Bollinger Bands (BB) line holds a position above the price, while the 20-period Moving Average (MA) resides below, indicating a bullish trend. This alignment suggests a positive market sentiment, with potential for continued upward movement.

The Relative Strength Index (RSI) registers at 58.15, positioning itself in neutral territory. This reading implies a balanced market, steering clear of both overbought and oversold conditions. Additionally, the rising volume accompanying the upward price movement further bolsters the bullish outlook. The Moving Average Convergence Divergence (MACD) reinforces this sentiment by remaining positive and residing above its signal line. The collective analysis suggests a strong likelihood of the uptrend persisting in the monthly timeframe.

Weekly Chart Analysis

Shifting focus to the weekly Ether chart, the analysis incorporates moving averages, RSI, and MACD to provide a comprehensive view of the market.

The chart features two moving averages, the 20-period MA and the 50-period MA. The price is currently trading above the 20-period MA, signaling a bullish short-term trend. Meanwhile, the 50-period MA resides below both the price and the 20-period MA, adding further support to the overall bullish sentiment.

The RSI is noted at 60.10, indicating a moderately positive market sentiment without reaching extreme levels. Turning attention to the MACD, its positive status and position above the signal line align with the overall bullish outlook. However, the narrowing gap between the MACD and its signal line warrants attention, suggesting a potential decrease in bullish momentum.

In summary, the technical analysis of the monthly and weekly Ether charts collectively points towards a predominantly bullish market sentiment. The convergence of multiple indicators indicates a positive trajectory, but traders should remain vigilant, especially considering the diminishing gap in the MACD on the weekly chart, which could signal a potential weakening of bullish momentum.

Summary

In January 2024, the cryptocurrency market faced a $21 billion decrease, marking a 1.3% contraction, yet Bitcoin displayed notable growth, elevating its market capitalization dominance from 51.6% to 52.7%. Bitcoin's upward price trend since November 9, 2022, introduces the potential for gains, but projections for its price and annual return on investment (annual ROI) trends highlight the transitory nature of market dynamics. While Bitcoin's monthly chart portrays a prevailing bullish trend, the weekly chart suggests caution with a narrowing gap in the Moving Average Convergence Divergence (MACD), indicating possible weakening momentum. Ether, following an upward price trend since November 9, 2022, presents a similar narrative, with projections emphasizing the complex and unpredictable nature of market dynamics. The interconnected influences of price trends, annual ROI, and technical indicators underscore the need for continual monitoring and adaptability in navigating the cryptocurrency landscape in early 2024.

In my first newsletter, "The Economic Paradox: Crypto's Rebellion Against Fiat Rule," we explored contrasting economic theories like the Austrian School and Keynesian economics. Beyond these perspectives, there are additional theories that seek to explain economic phenomena. One of these is behavioral economics, a field that studies how psychological, cognitive, and emotional factors influence economic decisions and behavior. It challenges the traditional assumption of purely rational decision-making, highlighting human biases, heuristics, and social influences in shaping choices. In this newsletter, we'll delve into a specific branch of behavioral economics called narrative economics. This branch focuses on the impact of stories, anecdotes, and popular narratives on economic decision-making, shedding light on how these narratives shape markets, investments, and financial outcomes.

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A short history

The concept of narrative economics gained significant attention through the work of Nobel laureate Robert Shiller, particularly in his book "Narrative Economics: How Stories Go Viral and Drive Major Economic Events," published in 2019. However, the roots of narrative economics trace back to earlier ideas and research.

Shiller, known for his work in behavioral finance and his insights into the role of narratives in financial markets, highlighted the impact of stories on economic behavior. He argued that narratives have long been a powerful force in shaping economic decisions, going beyond traditional economic models that primarily focus on numbers and rationality. The idea that stories and collective beliefs influence economic behavior can be traced back to behavioral economics pioneers like Daniel Kahneman and Amos Tversky, who explored the cognitive biases and heuristics that affect decision-making. They laid the groundwork for understanding how psychological factors impact economic choices.

However, Shiller's book "Irrational Exuberance," published in 2000, was one of the early works where he delved into the influence of narratives on financial markets. He highlighted how speculative bubbles are often driven by captivating stories rather than solely by rational analysis of market fundamentals. In "Narrative Economics," Shiller expanded on this concept, examining historical events and showing how various narratives, whether related to economic downturns, technological advancements, or societal changes, have shaped economic behavior throughout history. He drew attention to the contagious nature of stories, their ability to spread rapidly through media, social networks, and collective conversations, ultimately impacting economic decisions and outcomes.

Since the publication of Shiller's book, the field of narrative economics has gained traction among economists and behavioral scientists. Researchers have been exploring how narratives form, evolve, and influence economic behavior in various contexts, shedding light on the complex interplay between stories and economic events. The field continues to evolve, with ongoing studies and analyses focusing on understanding the mechanisms behind the influence of narratives on economic behavior, from the dynamics of information dissemination to the psychological factors driving individuals and markets.

Narrative economics

Narrative economics plays a fascinating role in the world of cryptocurrencies like Bitcoin. The story behind Bitcoin's creation by the mysterious Satoshi Nakamoto and the narrative surrounding its potential to revolutionize finance has had a profound impact on its adoption and market behavior. When Bitcoin emerged in 2009, it came with a powerful narrative. It was touted as a decentralized digital currency, free from government control and immune to traditional financial crises. This narrative of "digital gold" or a hedge against inflation gained traction, attracting both tech enthusiasts and investors seeking alternative assets. The stories surrounding Bitcoin's early adoption, tales of people becoming millionaires by investing early, and its association with cutting-edge technology further fueled its narrative. These narratives, disseminated through social media, news outlets, and online forums, contributed significantly to Bitcoin's popularity and price surges.

However, narratives aren't always unidirectional in their effects. For example, when regulatory concerns or negative narratives about Bitcoin's environmental impact gained prominence, they influenced market sentiment, leading to price corrections or increased volatility. Elon Musk's tweets, for instance, have been known to sway Bitcoin's value, showcasing the power of influential figures in shaping narratives and subsequently impacting markets.

Narrative economics helps us understand that the value of Bitcoin isn't solely based on traditional financial metrics, but is also deeply intertwined with the stories, beliefs, and sentiments surrounding it. The narratives - whether positive or negative - influence investor behavior, market dynamics, and the overall trajectory of cryptocurrencies like Bitcoin. This interplay between narratives and economic behavior showcases the profound impact of stories on the financial world, underscoring the significance of narrative economics in understanding the complexities of these markets.

How can you use it yourself

Understanding narratives is a vital skill for investors, as these stories often wield significant influence over market sentiments and asset prices. To navigate this terrain effectively, investors should pay close attention to the prevailing stories surrounding specific assets or markets. Analyzing how these narratives are portrayed across various platforms such as financial news, social media, and investment forums provides insight into the general sentiment.

It’s equally crucial to identify key influencers shaping these narratives. Whether it’s a well-known investor, a prominent CEO, or influential figures on social media, understanding who holds sway over these stories can be instrumental. Additionally, observing how narratives evolve around significant events like bitcoin halvings, regulatory changes, or product launches can offer valuable short-term insights into market movements.

Investors should look for recurring narrative patterns and assess how these stories impact market behavior. This includes understanding how certain types of narratives might lead to overreactions or corrections. It’s also essential to explore conflicting or counter-narratives, as they can sometimes present unique investment opportunities that go against popular beliefs.

While short-term narratives can drive market fluctuations, assessing the sustainability and potential long-term impact of these stories is equally crucial. Combining narrative analysis with traditional fundamental analysis can provide a more comprehensive view, aiding investors in making informed decisions that align with prevailing sentiments while considering underlying fundamentals.

Staying adaptable and continuously monitoring changes in narratives is key. Narratives can shift rapidly, influencing market sentiments and dynamics. By diversifying information sources and staying informed about evolving narratives, investors can better anticipate trends and make more informed investment decisions.

Conclusion

Understanding narratives in investing is crucial, as stories significantly impact market sentiments and asset prices. Investors should analyze prevailing narratives surrounding assets, observe key influencers shaping these stories, and assess how events drive short-term market movements. Recognizing recurring narrative patterns, exploring counter-narratives, and aligning narrative analysis with fundamental research can aid in making informed decisions. Sustainability and long-term impacts of narratives should be considered, alongside adaptability to evolving stories. In future newsletters, we'll delve deeper into examples of narratives within the crypto sphere, showcasing their influence on market dynamics and investment decisions.

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The BRC-20 token standard emerged as a pioneering development on the Bitcoin network, aiming to introduce native tokenization capabilities akin to Ethereum's ERC-20 tokens. The concept of BRC-20 tokens originated in early 2023, driven by the goal of enabling token creation and management directly on the Bitcoin blockchain. Its genesis can be traced back to the need for Bitcoin to diversify its utility beyond being solely a digital currency. This aspiration prompted developers to explore innovative ways to embed data onto the Bitcoin network, paving the way for the creation of tokens with specific functionalities.

The BRC-20 standard took inspiration from Ethereum's ERC-20 standard, which had already established a robust ecosystem of tokens on the Ethereum blockchain. Developers recognized the potential of replicating this functionality on Bitcoin, leading to the formulation of the BRC-20 token standard.

Introduced by an anonymous developer going by the pseudonym "domo" on March 9, 2023, the BRC-20 standard utilized the concept of ordinals and inscriptions. Ordinals allowed for the logical ordering and unique numbering of individual satoshis, while inscriptions enabled the embedding of data onto these satoshis, facilitating the creation of tokens natively on the Bitcoin network. The release of the BRC-20 standard sparked immense interest and experimentation within the cryptocurrency community. Developers began exploring the possibilities of creating various tokens, including meme coins, digital collectibles, and utility tokens, leveraging the unique features offered by BRC-20. An example of one BRC-20 tokens is ORDI with a market cap of $1.6 billion at end December 2023.

Since its inception, the BRC-20 token standard has seen a rapid surge in adoption and token creation. With thousands of tokens minted using this standard by mid-2023, the BRC-20 ecosystem showcased its potential to diversify and expand the use cases of the Bitcoin network. The development of BRC-20 tokens represented a significant milestone in the evolution of Bitcoin, demonstrating the network's adaptability and potential for innovation beyond its original use as a peer-to-peer digital currency. As an experimental standard, the journey of BRC-20 tokens continues to unfold, marking an exciting chapter in the exploration of tokenization on the Bitcoin blockchain.

The BRC-20 token standard represents an experimental leap in creating and managing fungible tokens within the Bitcoin blockchain. Contrary to other established token standards like ERC-20 on Ethereum, BRC-20 tokens leverage Bitcoin's Ordinals protocol, enabling users to mint, deploy, and transfer fungible tokens. They do not rely on smart contracts, distinguishing them from ERC-20 tokens.

At its core, BRC-20 tokens operate on the Ordinals system, which incorporates ordinal inscriptions, allowing the embedding of JavaScript Objection Notation (JSON) data into individual satoshi tokens. These inscriptions accommodate various forms of data, from text to images and code. While this initially gives the tokens a non-fungible aspect akin to Bitcoin NFTs, BRC-20 tokens introduce fungibility by enabling interchangeability among tokens of the same value. Each BRC-20 token can hold up to 4 MB of unique data, even after being transferred, further distinguishing them from their non-fungible counterparts.

The implementation of BRC-20 tokens has already impacted the Bitcoin blockchain, adding significant data to each transaction involving these tokens. This increase in data has led to transaction backlogs and increased fees, with users experiencing delays and higher costs due to the additional load on the network. However, BRC-20 tokens offer several advantages, including compatibility with the Bitcoin blockchain, enhanced security due to Bitcoin's robustness, and ease of use without the complexities of smart contracts.

Despite their advantages, BRC-20 tokens also face challenges, including higher transaction costs and limitations in functionality due to the absence of smart contracts, limiting their use in more complex scenarios.

The future trajectory of BRC-20 tokens remains uncertain. While they've garnered attention in meme coin creation and speculative trading, concerns persist regarding their impact on the Bitcoin blockchain's scalability and potential risks associated with these experimental tokens. However, the inherent utility in storing data on the Bitcoin network opens possibilities for tokenizing real-world assets and expanding into decentralized finance (DeFi) applications, fostering potential growth beyond their current speculative usage.

Welcome to our series exploring various use cases, technologies, and solutions within the realm of cryptocurrencies. Throughout this series, we delve into different applications, user cases, and the underlying technologies behind crypto innovations. It's essential to note that this series aims to provide informative insights and analysis, remaining neutral without endorsing any specific solution or technology. Our goal is to present diverse perspectives within the crypto space, showcasing the potential and possibilities while offering a comprehensive understanding of the evolving landscape.

What is Tellor Tributes

Tellor (TRB) is a decentralized oracle network built on Ethereum. Oracles serve as bridges between blockchain systems and real-world data by fetching external information and delivering it on-chain for smart contracts to utilize. Tellor specializes in providing a secure and decentralized way to retrieve off-chain data for decentralized applications (dApps) or DeFi (Decentralized Finance) protocols.

Tellor's mechanism involves a group of miners competing to provide accurate data by staking the native TRB tokens and submitting data queries. The network reaches consensus through a process of mining and voting, ensuring the accuracy and reliability of the information provided. This design aims to prevent oracle manipulation or data tampering.

TRB token holders can participate in governance by voting on proposed protocol changes or upgrades, contributing to the decentralized nature of the network. Tellor aims to offer a trustworthy and reliable solution for decentralized applications that require real-world data, enhancing the capabilities and use cases of blockchain technology.

Unique selling point

Tellor addresses the issue of obtaining reliable and accurate real-world data for smart contracts and decentralized applications (dApps) on the blockchain. In decentralized systems, accessing off-chain information, such as market prices, weather data, sports scores, or any external information necessary for executing smart contracts accurately, poses a significant challenge.

The problem lies in trust and reliability. Smart contracts are self-executing and deterministic, meaning they need accurate data inputs to function properly. However, obtaining this data in a trustless, decentralized manner is difficult. Centralized oracles can introduce a single point of failure or susceptibility to manipulation, compromising the integrity of smart contracts.

Tellor aims to solve these issues by providing a decentralized oracle solution. Through a network of miners staking tokens and competing to provide accurate data, Tellor creates a consensus mechanism to ensure the reliability of the off-chain information fed into smart contracts. This process helps in preventing data manipulation, ensuring that decentralized applications can rely on trustworthy external data sources.

Essentially, Tellor's goal is to bridge the gap between blockchain-based smart contracts and the real world by offering a secure, decentralized, and reliable way to fetch and verify off-chain data.

The Token

TRB (Tellor Tributes), the native token, plays a multifaceted and pivotal role in ensuring the reliability, security, and functionality of the Tellor platform:

* Staking Mechanism: TRB serves as the staking asset for miners participating in data retrieval. Staking TRB aligns incentives, ensuring miners are committed to providing accurate and truthful information to the network.

* Governance Token: TRB holders have voting rights, enabling them to participate in decision-making processes regarding protocol upgrades and changes. This democratic governance structure empowers the community to shape the platform's direction.

* Incentivization: Miners are rewarded with TRB tokens for validating and submitting accurate data. This incentive mechanism motivates miners to maintain data integrity and ensures a reliable flow of off-chain information for smart contracts and dApps.

* Economic Model: TRB's economic model is designed to align incentives among participants, ensuring the network's security, reliability, and sustainability. It fosters a robust ecosystem by incentivizing honest participation and contribution.

* Lifeblood of Tellor: TRB serves as the fundamental element of the Tellor network, underpinning its decentralized oracle solution. It incentivizes participation, facilitates governance, and reinforces the integrity of off-chain data utilized by decentralized applications.

Technical explanation how Tellor works

The Tellor oracle system operates as a decentralized community focusing on securely placing data on-chain. It functions as a protocol for answering any on-chain question in any format, surpassing mere price data. At its core, Tellor comprises a group of "reporters" who provide on-chain answers to queries, incentivized through TRB token rewards and inflationary incentives. To participate, reporters must deposit a specified minimum bond amount and wait for one week before withdrawing. They can then submit values for various queries, such as ETH/USD or Bitcoin block header information, earning rewards in TRB and tips. Tellor's security lies in the deposited TRB, acting as a bond that reporters risk losing if their submitted data is successfully disputed.

Reporters have a waiting period after submitting a value, aiming to encourage more participants and allow time for disputes. Rewards include tips and time-based inflationary rewards, with 2% distributed among all bonded reporters. Users seeking data more frequently than the time-based rewards can cover can expedite the process by adding tips, enabling Tellor to operate at the required speed, albeit at a cost.

The Tellor system assigns each data request an ID on-chain, while specifications for the data remain off-chain. Data is submitted in bytes, enabling various data types or multiple variables in a single query. Reporters select the values they submit and can propose new queries, adhering to Tellor's data specifications and incentivizing support through higher tips.

Disputes arise when any party challenges a data submission. Tellor's governance contract votes on the validity of reported values, with multiple rounds of disputes allowed. Dispute fees increase with each dispute round to deter potential censorship attempts. Disputed data can be replaced by re-requesting it, and votes can result in true, false, or invalid outcomes, with corresponding actions taken.

Tellor's governance aims for equitable voting power among TRB holders, reporters, users, and the team. Various stakeholders hold weight, enhancing their voting share through system participation. The system deploys a fixed minting policy, changeable only through a fork, to incentivize reporters and ensure protocol improvements. Security in Tellor relies on the governance contract, community diligence, and economic incentives, making it resistant to censorship and requiring substantial resources for malicious attacks.

Legal structure

Tellor operates as a decentralized oracle network with a governance structure embedded in smart contracts, embodying decentralized principles through its model. While it doesn't fit a strict definition of a traditional decentralized autonomous organization (DAO), its governance involves various stakeholders—TRB token holders, reporters, users, and the development team. Token holders exert influence based on holdings, reporters gain voting power through data submissions, users contribute based on tips, and the team participates in governance, providing checks and balances. This decentralized model reflects DAO-like aspects, as decisions are made through consensus mechanisms and smart contracts, although it might not precisely align with the traditional concept of a DAO due to its distributed influence across multiple participant categories. Ultimately, Tellor's structure revolves around community-driven decision-making and decentralized control, similar in essence to a DAO while incorporating a broader spectrum of participant involvement.

Principles of the Austrian school

Tellor demonstrates a robust alignment with key aspects of the Austrian School of Economics. First, its decentralized nature is a fundamental pillar, scoring high due to the absence of a central authority in its oracle network. Through community-driven governance and consensus mechanisms, Tellor achieves a decentralized structure, ensuring autonomy in transactions and scoring exceptionally well in this aspect. Additionally, Tellor minimizes government intervention, allowing a free market ethos to thrive. This dedication to market freedom, coupled with cryptographic protocols influencing supply, illustrates Tellor's commitment to minimizing external interference and fostering a free market, marking another strong alignment with Austrian School principles.

The platform's emphasis on the natural order of free market mechanisms is evident through its reliance on market perception and individual actors to determine value. Tellor's token value is subject to market dynamics and user interactions, showcasing a decentralized system where value is shaped organically. Moreover, Tellor's reliance on cryptographic protocols and consensus mechanisms to determine token supply furthers its Austrian School alignment, emphasizing self-regulation and market-driven mechanisms.

However, while Tellor excels in decentralization, autonomy, and minimizing intervention, its reliance solely on market mechanisms for stability might have limitations. Market dynamics can be volatile and might not always reflect true stability, potentially presenting challenges for complete reliance on natural market mechanisms.

In conclusion, Tellor showcases a strong adherence to several Austrian School aspects, particularly decentralization, minimization of government intervention, reliance on market perception, and autonomy in transactions. Its reliance on cryptographic protocols and community-driven governance furthers these alignments. Yet, while Tellor's structure embodies many Austrian School principles, the reliance on natural market mechanisms for stability might pose some considerations in terms of achieving complete stability and predictability.

For more information about the relation between crypto and the Austrian school, see my newsletter: The Economic Paradox: Crypto's Rebellion Against Fiat's Rule.

Competition

In comparison to Tellor, each oracle solution has its unique approach and challenges, offering varying degrees of decentralization, focusing on different industries or data types, and facing hurdles in adoption and security within the decentralized ecosystem.

Chainlink (LINK)

Chainlink has achieved widespread adoption across various industries, offering a highly decentralized oracle network known for its reliability and security. However, compared to Tellor, Chainlink might face criticisms regarding potential centralization due to a reliance on a limited number of node operators, whereas Tellor emphasizes a more distributed approach to data retrieval, potentially offering a different level of decentralization.

Band Protocol (BAND)

Band Protocol emphasizes cross-chain compatibility and scalability in its oracle design. In contrast, Tellor's differentiation might lie in its approach to incentivizing miners through its staking mechanism, which differs from Band's methodology. Security and achieving comparable decentralization levels are challenges shared between both Tellor and Band Protocol.

DIA (Decentralized Information Asset)

DIA specializes in providing verified financial market data, mainly for DeFi applications, focusing on transparency in data provision and governance. Compared to Tellor, DIA's niche focus might limit its use cases, potentially impacting broader adoption. Tellor, on the other hand, aims to provide a more generalized solution for decentralized data retrieval across various industries.

API3

API3's governance model is controlled by a decentralized autonomous organization (DAO), enhancing community involvement, differing from Tellor's governance structure. API3 prioritizes decentralized APIs for developer accessibility. Both projects face challenges in gaining recognition amidst established competitors, but their approaches to governance and developer focus set them apart.

Umbrella Network

Umbrella Network emphasizes scalability and cost-effectiveness for off-chain data and cross-chain integration. Compared to Tellor, Umbrella Network's potential limited adoption and security concerns regarding its measures might pose similar challenges in the competitive landscape.

Warmest greetings and a happy New Year to all our readers in 2024! In a year filled with remarkable growth and shifting trends, the crypto market of 2023 witnessed phenomenal surges and intriguing dynamics. Bitcoin dominated the scene, propelling a significant market cap increase, yet quarterly and monthly insights unveiled smaller crypto assets outperforming, signaling risk-on sentiments amid evolving market forces. Bitcoin and Ether's price and ROI trends, hinted at potential trajectories, influenced by shifting momentum and market indicators. Join us as we delve into the nuanced market behaviors and evolving trends shaping the crypto landscape this year.

Crypto cap

In 2023, the crypto market experienced an exceptional surge, witnessing a staggering $849 billion increase, marking an impressive 112.3% growth. Notably, the bulk of this growth was fueled by Bitcoin, showcasing a remarkable 160.4% surge. Consequently, Bitcoin's dominance surged significantly by 22.6% from 42.1% to 51.6%. This shift, where larger market cap cryptos outperform smaller ones, often signifies a risk-off move in the market.

Zooming into the quarterly performance during Q4 of 2023, the crypto market saw a substantial $348 billion increase, indicating a robust 27.7% growth. Surprisingly, this surge was primarily attributed to 'Other crypto,' showcasing a dominant 56.6% increase. Consequently, Bitcoin's market cap dominance experienced a decline of 4.2% to 51.6%, while 'Other crypto' witnessed a significant rise in dominance from 10.8% to 13.3% by 22.7%. Such a scenario, where smaller market cap cryptos outshine larger ones, often signals a risk-on sentiment, especially amid declining stablecoin dominance.

Furthermore, December 2023 revealed a $214 billion surge in the crypto market, denoting a 15.4% growth. Similarly, the surge was driven mainly by 'Other crypto,' marking a notable 32.3% increase. Consequently, Bitcoin's market cap dominance experienced a dip of 2.8% to 51.6%, while 'Other crypto' witnessed a substantial rise in dominance from 11.6% to 13.3% by 14.7%. Once again, this trend of smaller market cap cryptos outpacing larger ones often signifies a risk-on move, particularly when stablecoin dominance diminishes.

In conclusion, the yearly, quarterly, and monthly trends in 2023 showcased varying dynamics. While the yearly performance highlighted a dominance shift towards larger market cap cryptos, quarterly and monthly movements emphasized the trend of smaller market cap cryptos outshining larger ones, reflecting alternating risk-on and risk-off sentiments in the market. These fluctuations underscore the nuanced nature of market behavior, influenced by the performance of different crypto categories and the evolving landscape of stablecoin dominance.

Bitcoin

Price trend

Since November 9th, 2022, Bitcoin has steadily followed an upward price trajectory. It's crucial to acknowledge that such trends aren't perpetual. As of January 31, 2024, there's a 50% likelihood that Bitcoin's price could hover either above or below $41.2k. Moreover, there's a 98% probability that the price will fall within the range of $33.4k to $49.0k by the same date. However, alterations in BTC's momentum could potentially push the price beyond these projected figures, leading to either surpassing or falling short of these estimated values.

Annual return on investment trend

The Bitcoin annual Return on Investment (ROI) has mirrored the price's upward trajectory since November 9th, 2022. However, just as with price trends, these annual ROI trends aren't indefinite. By January 31, 2024, there's a 50% chance that the annual ROI might rest above or below 159.6% (equivalent to $60.1k). Additionally, there's a 98% probability that the annual ROI will fall within the range of 117.3% ($50.3k) to 201.8% ($69.8k) by the same date.

However, the fluctuations in Bitcoin's momentum could potentially surpass or fall below these projected figures, much like the current scenario. Considering both the annual ROI and price trends, it's likely that at least one of these trends might deviate from the anticipated 98% probability outcome, indicating the possibility of a divergence between these two trend indicators.

Monthly chart

Bitcoin's recent monthly candlestick closed above the upper line of the Bollinger Bands, signaling a positive momentum for its price. The monthly volume has surged, surpassing both the previous month and the levels observed in September 2023, which adds to the positive outlook, especially when paired with an ascending price trajectory. Moreover, the Relative Strength Index (RSI) is on the rise, indicating increased buying strength, while the Moving Average Convergence Divergence (MACD) continues its upward trend, underscoring sustained positive momentum in the market.

Weekly chart

Bitcoin's recent three weekly candlesticks dipped below the upper line of the Bollinger Bands, indicating a shift in momentum. Over the past four weeks, the price action has formed a pendant pattern, suggesting a phase of consolidation. This consolidation might be attributed to traders seeking balance amid the holiday season, contributing to the market's stabilized movement.

While the Relative Strength Index (RSI) has been decreasing, it remains within the overbought area. This situation might persist if the ongoing uptrend continues, potentially allowing for a sustained period of stability. However, the Moving Average Convergence Divergence (MACD) shows signs of losing momentum, evident in the convergence of the MACD and signal lines as they descend and draw closer together. This indicates a potential slowing down of the recent bullish momentum.

Ethereum

Price trend

Since November 9th, 2022, Ether has been following an upward trajectory in its price. However, it's essential to note that such price trends aren't perpetual. By January 31, 2024, if this trend persists, there's a 50% likelihood that Ether's price could either exceed or fall below $2,177. Moreover, there's a 98% probability that the price will fluctuate within the range of $1,727 to $2,627 by the same date. Yet, alterations in ETH's momentum could potentially push the price beyond these projected figures.

Annual return on investment trend

The Ether annual Return on Investment (ROI) has been on an upward trajectory since November 9th, 2022. However, akin to price trends, these annual ROI trends aren't perpetual. By January 31, 2024, if this trend persists, there's a 50% chance that Ether's annual ROI could either surpass or fall below 102.0% (equivalent to $3,205). Additionally, there's a 98% probability that the annual ROI will fluctuate within the range of 48.7% ($2,360) to 155.3% ($4,050) by the same date.

Nevertheless, alterations in ETH's momentum could potentially push the annual ROI beyond these projected figures, similar to the ongoing scenario. As observed in Bitcoin trends, it's likely, but not necessary, that at least one of these two trend indicators might diverge from the anticipated 98% probability outcomes, signaling a potential deviation from the expected trend behavior.

Monthly chart

The monthly candlestick for Ether recently closed, albeit below the upper line of the Bollinger Bands, signaling a cautiously positive outlook for its price. Over the past four months, Ether has exhibited a consistent uptrend with increasing volume, adding to the positive sentiment surrounding its performance. Moreover, both the monthly Relative Strength Index (RSI) and the momentum of the Moving Average Convergence Divergence (MACD) are on the rise.

However, there's a notable observation regarding the Moving Averages (MA) - the MA50 is approaching the MA20, hinting at the possibility of a potential 'death cross.' Whether this cross occurs or is avoided remains uncertain at this juncture, creating a pivotal moment that may influence Ether's price trajectory in the near future. This convergence of moving averages suggests a critical point in the market, where the direction could pivot depending on the interplay of these indicators.

Weekly chart

The previous week's closure for the weekly candlestick positioned itself beneath the upper line of the Bollinger Bands, showcasing a substantial wick that tested this upper boundary. Notably, the chart lacks a definitive volume trend, making it challenging to ascertain a clear direction based on this parameter alone. Both the Relative Strength Index (RSI) and the MACD-cross are currently signaling a loss in momentum, suggesting a potential slowdown in Ether's recent trajectory.

Meanwhile, a significant observation lies in the Moving Averages (MA) - the MA20 is approaching proximity with the MA50, hinting at the possibility of a potential 'golden cross.' If this alignment occurs, it could potentially signal a bullish market trend. However, achieving this golden cross hinges on favorable market conditions and the sustained momentum of Ether. This convergence of moving averages represents a pivotal moment that traders are keenly watching, as it could influence Ether's future price movement.

Summary

The crypto market experienced monumental growth in 2023, with a staggering $849 billion surge, largely propelled by Bitcoin's exceptional 160.4% increase. This surge significantly elevated Bitcoin's dominance by 22.6%, indicating a shift toward larger market cap cryptos and signaling a risk-off move. However, in the quarterly and monthly scopes, smaller market cap cryptos outshone larger ones, signaling risk-on sentiments as they surpassed Bitcoin's dominance amid declining stablecoin influence. These varied trends showcase the market's dynamic nature, influenced by different crypto categories and changing stablecoin dominance.

Bitcoin's price, since November 9th, 2022, has shown an upward trend, with projections indicating potential price ranges. The annual ROI trends align with price trends but may diverge from expected outcomes due to fluctuating momentum. Weekly and monthly charts highlight shifting momentum and potential consolidations, with key indicators like RSI, MACD, and moving averages influencing future price directions.

Ether, following an upward price trajectory since November 9th, 2022, presents a similar trend, though the outcomes are subject to momentum fluctuations. Annual ROI trends follow price movements but may deviate, mirroring Bitcoin's dynamics. Ether's charts exhibit cautious positivity, with indicators like RSI, MACD, and moving averages indicating potential pivotal moments and future price actions. The market's intricate patterns suggest potential shifts in trend directions, highlighting the need for close monitoring amid varying sentiments and market dynamics.

A Brief history of AI

AI has evolved significantly since its early days, witnessing remarkable growth and transformation over the decades. In the late 20th century, particularly the 1990s and early 2000s, AI faced a phase known as the "AI Winter," marked by limited recognition and skepticism despite notable technological strides. Many groundbreaking AI innovations were assimilated into general applications, shedding the explicit "AI" label as they became integral tools in various fields like informatics, knowledge-based systems, and computational intelligence. The commercial realm, scarred by unmet promises from previous AI endeavors, saw reluctance in embracing the term "artificial intelligence," fearing skepticism and being perceived as unrealistic dreamers. However, this era laid the groundwork for future progress, establishing a robust foundation for AI's eventual resurgence.

The 21st century heralded a renaissance for AI, bolstered by unprecedented access to vast datasets (big data), faster computing capabilities, and advancements in machine learning techniques. Around 2010, these elements converged, leading to a surge in applying AI to multifaceted challenges across industries. Deep learning, a subset of machine learning utilizing intricate neural network architectures, gained prominence for its capacity to abstract high-level data representations. Deep learning's prowess was evident in domains like computer vision, natural language processing, and speech recognition, achieving levels of accuracy that rivaled human capabilities. Additionally, the era saw the proliferation of big data applications, catalyzing innovation, competition, and productivity across sectors, propelling AI-related markets to multibillion-dollar scales by the mid-2010s.

In recent years, language models like ChatGPT have emerged as pivotal innovations in the AI landscape. Models such as GPT-3, released in 2020 by OpenAI, marked a watershed moment, showcasing the potential of large-scale language models trained on extensive datasets. These models, built on transformer architecture, have demonstrated remarkable proficiency in natural language understanding and generation, enabling diverse applications in text completion, language translation, and content generation. Moreover, the continual evolution of these models has shown promising steps towards broader contextual comprehension and nuanced conversational abilities, fostering more engaging and human-like interactions. This progress underscores the ongoing trajectory of AI, emphasizing not just the sophistication of algorithms but also their integration into everyday applications, transforming the ways humans interact with technology.

A Brief History of AI use in Crypto

In the early 2010s, as cryptocurrencies gained traction, AI made initial forays into the crypto sphere. AI-driven algorithms began analyzing burgeoning volumes of market data, providing traders with insights for decision-making. These algorithms decoded patterns and trends, setting the stage for AI's role in market analysis within the crypto space.

Around 2015, the emergence of AI-powered trading bots revolutionized crypto trading. These bots, armed with advanced algorithms, autonomously executed trades based on predefined strategies, offering speed and efficiency. Simultaneously, AI bolstered security measures in crypto exchanges, using machine learning to detect and prevent fraudulent activities, enhancing cybersecurity within the ecosystem.

By the late 2010s and early 2020s, predictive analytics became a cornerstone of AI in crypto. Machine learning models leveraged historical data for forecasting price movements, assessing market volatility, and conducting sentiment analysis. This predictive capability empowered market participants to navigate the volatile crypto landscape more strategically.

Recent advancements, particularly in DeFi around 2022, saw AI integration into smart contracts. These intelligent contracts automated various financial protocols, such as lending, borrowing, and yield farming. Challenges persist, including regulatory uncertainties and ethical considerations. However, ongoing advancements in reinforcement learning and natural language processing promise a transformative future for cryptocurrencies, facilitating more sophisticated financial instruments and robust decentralized ecosystems.

User cases in Crypto

AI's integration into blockchain and smart contracts has unveiled a spectrum of compelling use cases, showcasing the synergy between these technologies. One prominent application lies in fraud detection and security enhancement within blockchain networks. AI-powered algorithms can scrutinize transactions, detect anomalies, and preemptively identify potential threats, fortifying the security of decentralized systems. This fusion bolsters trust and reliability, crucial in ensuring the integrity of transactions and mitigating risks inherent in digital currencies and smart contracts.

Furthermore, AI-driven predictive analytics finds a niche in optimizing smart contract functionality. By analyzing historical data and market trends, AI algorithms can forecast market behaviors and aid in the creation of dynamic, self-executing smart contracts. These contracts adapt to changing conditions, triggering predefined actions based on real-time data inputs. This evolution augments the flexibility and efficiency of smart contracts, streamlining processes in various sectors, including finance, supply chain management, and insurance, by automating complex decision-making and reducing reliance on intermediaries. This convergence opens avenues for innovative contract design, improving accuracy, and responsiveness while reducing manual intervention.

The integration of AI for security in blockchain

The integration of AI for security measures within blockchain networks stands as a pioneering approach in fortifying decentralized systems against evolving threats. By leveraging artificial intelligence, blockchain platforms enhance their security protocols, offering predictive analytics to identify potential vulnerabilities and mitigate risks effectively. Projects like SingularityNET have delved into this realm, exploring AI solutions that reinforce blockchain security. SingularityNET's decentralized AI network integrates machine learning algorithms to enhance security across various applications, including blockchain, by leveraging predictive analysis and anomaly detection techniques.

AI plays a pivotal role in the proactive identification of potential threats within blockchain networks. Utilizing machine learning algorithms, these systems can analyze transaction patterns, user behaviors, and network activities in real-time, detecting irregularities that could signal unauthorized or fraudulent actions. Additionally, Ocean Protocol, through its data marketplace, incorporates AI-powered security solutions to ensure the integrity of data shared on the blockchain. These initiatives involve smart contract auditing tools and robust encryption methods, bolstering the reliability and trustworthiness of blockchain-based data transactions.

Furthermore, AI continuously learns from new data patterns, enabling adaptive responses to emerging threats. Fetch.ai, another project in this domain, harnesses machine learning and autonomous agents to secure blockchain ecosystems. These agents autonomously monitor network activities, enhancing security by swiftly adapting to counteract potential threats. The fusion of AI and blockchain security not only fortifies the networks against malicious activities such as hacking attempts and data breaches but also ensures the integrity of smart contracts and data transactions, fostering a more resilient and trustworthy decentralized ecosystem.

The role of AI in consensus mechanisms

The integration of AI within consensus mechanisms represents a groundbreaking evolution in the functionality and efficiency of blockchain networks. AI's role in consensus mechanisms is to optimize decision-making processes, enhance scalability, and mitigate energy consumption. Projects such as Fetch.ai are exploring AI-powered consensus mechanisms to revolutionize how nodes agree on the state of the network. Through its autonomous economic agents, Fetch.ai aims to create a more adaptive and efficient consensus model by utilizing machine learning algorithms that adapt to network conditions and participant behaviors.

AI-driven consensus mechanisms seek to address scalability challenges by dynamically adjusting network parameters based on real-time data. By leveraging predictive analytics, these mechanisms optimize block creation and validation, improving throughput while maintaining network security. SingularityNET, known for its decentralized AI marketplace, is exploring AI-based consensus mechanisms that leverage collective intelligence to enhance decision-making among nodes. This approach aims to achieve greater consensus efficiency by harnessing the collective knowledge and reasoning of participating nodes.

Moreover, AI-integrated consensus mechanisms strive to enhance network security and resilience against various attacks. By continuously analyzing network behavior and patterns, these mechanisms can detect and respond to potential threats more effectively. Ocean Protocol, focusing on decentralized data exchange, integrates AI for consensus mechanisms to safeguard data integrity and ensure secure data transactions. AI algorithms contribute to verifying and validating data integrity, bolstering the trustworthiness of the network consensus.

The amalgamation of AI and consensus mechanisms holds the promise of significantly improving blockchain networks' scalability, security, and decision-making processes. As these technologies advance, they pave the way for more adaptable, efficient, and secure decentralized ecosystems, fostering innovation across various industries and applications.

How AI is shaping decentralized finance (DeFi) protocols

AI is playing a pivotal role in reshaping decentralized finance (DeFi) protocols, enhancing efficiency, security, and decision-making within these systems. Crypto projects like Fetch.ai are spearheading the integration of AI in DeFi protocols to optimize various functionalities, including lending, borrowing, and automated market making. By employing AI-driven algorithms, these protocols can analyze vast datasets in real-time, enabling better risk assessment, asset allocation, and yield optimization.

The utilization of AI in DeFi protocols is particularly impactful in automating processes and reducing human intervention. SingularityNET, known for its AI marketplace, envisions AI-driven DeFi solutions that enhance liquidity provision and optimize yield farming strategies. By utilizing AI models to analyze market conditions and user behavior, these protocols aim to automate trading strategies and provide more effective yield-generating opportunities.

Moreover, AI is revolutionizing decentralized exchanges (DEXs) by enabling intelligent algorithms to optimize liquidity provision and pricing mechanisms. Projects like Ocean Protocol are exploring AI-powered DEXs to enhance liquidity management and ensure fair pricing by analyzing market dynamics and user preferences. These AI-driven DEXs aim to improve trading experiences and minimize slippage for users engaging in decentralized trading.

AI's integration into DeFi protocols is reshaping the landscape by improving efficiency, reducing operational costs, and enhancing user experiences. As these technologies continue to evolve, they hold the potential to foster a new era of decentralized finance that is more robust, adaptable, and user-centric, revolutionizing the way individuals access and interact with financial services in the digital realm.

Risks of using AI in crypto

The integration of AI into blockchain and smart contracts presents a compelling frontier but isn't without its risks and downsides. Security concerns emerge as a primary challenge. While AI fortifies the security of blockchain networks, it also introduces vulnerabilities. Sophisticated AI algorithms may potentially be manipulated or targeted by adversarial attacks, compromising the integrity of decentralized systems. Moreover, the reliance on predictive analytics in smart contract optimization raises concerns about data accuracy and bias. AI models, if trained on flawed or biased data, might perpetuate existing biases or make erroneous predictions, impacting the functionality and fairness of smart contracts.

Another significant risk revolves around regulatory compliance and legal implications. The automation facilitated by AI-driven smart contracts can lead to complexities in legal interpretation and enforcement. The lack of legal frameworks governing smart contracts might pose challenges in dispute resolution or contractual breaches, necessitating new regulatory approaches to accommodate these technological advancements. Additionally, as AI algorithms self-optimize based on real-time data, ensuring transparency and accountability in decision-making becomes intricate, potentially leading to regulatory scrutiny and ethical dilemmas regarding accountability for automated actions.

Furthermore, the complexity of AI-driven smart contracts raises concerns about their comprehensibility and auditability. The intricate nature of these contracts, coupled with the autonomy of AI decision-making, could hinder human understanding and auditing processes, amplifying the potential for errors or unintended consequences that are challenging to rectify or predict. This complexity may also hinder scalability and adoption, as the intricate nature of these systems demands significant expertise and resources for development, implementation, and maintenance.

Projects

SingularityNET, Fetch.ai, and Ocean Protocol are prominent crypto projects that incorporate artificial intelligence (AI) into their blockchain or smart contract frameworks. SingularityNET operates as a decentralized AI marketplace, facilitating the interaction and collaboration of various AI agents within the blockchain ecosystem. Its platform allows users to access diverse AI services, including prediction markets and automated trading strategies, all within the secure and transparent environment of blockchain technology.

Fetch.ai focuses on constructing an open-access network where autonomous economic agents (AEAs) execute intricate tasks and transactions using AI algorithms. This network supports various industries, particularly supply chain management and decentralized finance (DeFi), aiming to optimize resource allocation and enhance operational efficiency through AI-powered agents.

Ocean Protocol, on the other hand, provides a decentralized data exchange protocol that enables the sharing and monetization of data. While not exclusively an AI-focused project, Ocean Protocol integrates AI and machine learning tools into its framework, allowing data providers to leverage AI algorithms for data analysis and processing within secure, blockchain-based environments. This facilitates the creation of data marketplaces where AI capabilities can be harnessed to derive valuable insights from shared data assets.

These projects signify innovative approaches to merging AI technologies with blockchain and smart contracts, creating ecosystems where AI services can be accessed, shared, and utilized efficiently while ensuring data security and transparency.

A glimpse into the future

The marriage of artificial intelligence (AI) with the crypto realm is poised to usher in transformative advancements. Foremost among these is the reinforcement of security measures and fraud detection. By integrating predictive analytics, AI fortifies blockchain networks, proactively identifying potential security threats and fortifying transactions, ensuring a robust shield for crypto assets. This synergy is poised to redefine the paradigm of security within the crypto sphere, setting higher standards for safety and reliability.

Moreover, the evolution of AI-powered smart contracts is on the horizon. These contracts, enriched with machine learning capabilities, promise dynamic adaptability to changing conditions or unforeseen scenarios. By autonomously adjusting terms based on real-time data, AI-infused smart contracts offer a more agile framework for agreements within the crypto domain. This fusion is expected to drive efficiency and responsiveness, setting a new precedent for automated, intelligent contracts in the decentralized landscape.