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Do you think tokenizing real world assets $RWA is the future of DeFi? 🤔

The 18-year real estate cycle

Dubai has launched a pilot program to tokenize its real estate assets, marking a significant move to integrate blockchain technology into the property sector. The Dubai Land Department (DLD), in collaboration with the Virtual Assets Regulatory Authority (VARA) and the Dubai Future Foundation (DFF), has kicked off the project to convert property title deeds into digital tokens recorded on the blockchain. The DLD anticipates that by 2033, tokenized real estate could constitute approximately 7% of Dubai’s total property transactions, translating to a market value of around 60 billion dirhams, which is approximately $16 billion. Using blockchain tech could easily improve the processes of buying, selling, and investing in real estate in Dubai. Tokenization lets you own just a fraction of a property, which means it could lower the bar for new investors and make the market more active. Related: Crypto.com Cleared for Derivatives Trading in Dubai: Eyes Major Expansion It also offers a more defined ownership structure than crowdfunding, where investor money is combined for property acquisition. That said, there might be some obstacles with real estate. According to a McKinsey report on tokenization from last year, real estate may see slower tokenization growth due to operational challenges. This move to tokenize real estate is actually the second big step Dubai has taken recently to become a leader in adopting blockchain solutions. Just two months ago, in January, Dubai-based developer DAMAC Group partnered with blockchain platform MANTRA to tokenize assets worth at least $1 billion in the Middle East. DAMAC is a major Dubai developer with holdings in real estate and data centers. Furthermore, it has been investing in data centers globally, so this is bound to have an impact. Related: Dubai Government-Owned Bank Emirates NBD Activates Digital Currency Trading Services As for MANTRA, they had also teamed up with MAG Property Development last year to tokenize $500 million in real estate assets, starting with a residential project right in Dubai, a main center for Gulf tourism and business. All things considered, it seems obvious that the UAE and Dubai want to establish themselves as a worldwide digital asset center, which includes the crypto industry as well. Disclaimer: The information presented in this article is for informational and educational purposes only. The article does not constitute financial advice or advice of any kind. Coin Edition is not responsible for any losses incurred as a result of the utilization of content, products, or services mentioned. Readers are advised to exercise caution before taking any action related to the company.

While Bitcoin aims for $200,000 by the end of 2025, XRP is emerging as a strong contender for institutional investment. Both cryptocurrencies are vying for dominance, each with unique strengths. Analysis from Scott Melker’s firm suggests Bitcoin’s growing financial sector role gives it an edge. However, XRP’s focus on real-world payment solutions presents a compelling alternative for institutions. Despite sustainability debates, XRP’s recent price surge shows market interest, and its unique capabilities could attract institutional investment, challenging Bitcoin’s current lead. Bitcoin’s acceptance as a strategic reserve asset grows globally. Yet, XRP focuses on efficient, low-cost cross-border payments, a limited area for Bitcoin. While XRP’s valuation faces skepticism, its practical financial application is significant for institutions. Bitcoin’s role as a store of value contrasts with XRP’s focus on swift, low-cost transactions via the Ripple network, offering a compelling option beyond just holding digital assets. Related: Bitcoin Speculative Trading Loses Steam: Is Trump’s Crypto Push to Blame? Blockchain is primarily a settlement ledger, highlighting Bitcoin’s institutional appeal. XRP uses the XRP Ledger for rapid, inexpensive transactions. This difference could sway institutions prioritizing payment processing over secure settlement. XRP’s real-world payment utility offers a tangible advantage over Bitcoin’s store-of-value narrative. Bitcoin’s 15-year history without downtime provides a strong foundation of credibility, a factor highly valued by financial professionals. However, XRP, backed by Ripple, has been actively forging partnerships with financial institutions worldwide, demonstrating its potential to disrupt traditional payment systems. While Bitcoin’s reliability is a plus, XRP’s proactive approach to real-world integration presents a significant competitive advantage. Related: Ripple CTO David Schwartz Explains Why Bitcoin Lost Its Transactional Edge Industry leaders like Michael Saylor have been influential in driving institutional adoption of Bitcoin. Conversely, Ripple has focused on building direct relationships with financial institutions, showcasing XRP’s capabilities for improving payment infrastructure. While Bitcoin benefits from vocal advocates, XRP’s tangible partnerships offer a concrete path to institutional integration and potential challenge to Bitcoin’s dominance. Bitcoin’s value proposition as a trustless settlement system is compelling, but XRP’s strength lies in its ability to operate as a fast and cost-effective payment network, minimizing reliance on traditional banking intermediaries for transactions. As central banks consider digital currencies, XRP’s established payment infrastructure could position it as a strong contender against Bitcoin’s store-of-value focus. Bitcoin’s current market trends show bullish potential, but XRP has also demonstrated resilience and maintained its position as a significant player. While Bitcoin’s market dominance is clear, XRP’s focus on specific use cases and its potential for adoption by financial institutions could lead to significant growth and a stronger competitive stance against Bitcoin . Bitcoin’s price action correlates with NASDAQ, indicating its growing integration with traditional financial markets. However, XRP’s value proposition is less tied to traditional market sentiment and more focused on its utility in facilitating global payments. This real-world application could make XRP a more attractive option for institutions looking for practical solutions rather than just speculative assets. Disclaimer: The information presented in this article is for informational and educational purposes only. The article does not constitute financial advice or advice of any kind. Coin Edition is not responsible for any losses incurred as a result of the utilization of content, products, or services mentioned. Readers are advised to exercise caution before taking any action related to the company.

Artificial intelligence has taken a decisive step forward with the meteoric rise of ChatGPT, which has revolutionized both the general public and businesses. Yet, faced with the limitations of giant models, a new approach is emerging: intelligent agents. Capable of acting and interacting with their digital environment, they redefine the future of AI by moving from simple text generation to executing concrete and autonomous tasks. Just a few years ago, interacting with an artificial intelligence seemed like science fiction to the general public. But when ChatGPT appeared at the end of 2022, a radical evolution took place. Based on the GPT-3.5 model and freely accessible online, ChatGPT experienced a meteoric rise, reaching 100 million monthly users in just two months, a historic record for a consumer application. In comparison, services like TikTok took nearly 9 months to reach such an audience. While democratizing text generation by AI, ChatGPT has enabled non-specialists to experience the power of large language models, also known as LLMs. From schoolchildren to professional engineers, everyone could ask questions, get summaries, create code, and generate content ideas through a natural language computing conversation. The impact in the professional world has been just as significant. Several companies quickly integrated these models into their products and workflows. OpenAI generated nearly 1 billion dollars in revenue in 2023, potentially reaching 3.7 billion in 2024. This ascent was supported by the development of AI APIs and commercial licenses. The formation of major partnerships, such as with Microsoft, allowed ChatGPT to be included in users’ daily routines (search engines, office suites), further amplifying its impact. GPT-3.5 was a true turning point. AI could now compose coherent text on demand. GPT-4, created at the beginning of 2023, affirmed the revolutionary aspect of the software by notably improving its reasoning capabilities and image comprehension. In record time, text-generative AI has transitioned from a laboratory curiosity to an essential consumer tool, both for less experienced users and for companies seeking automation. However, this meteoric rise has been called into question by the evolution of giant models. Indeed, major players in the web, such as Open AI and its competitors (Anthropic, Google, Meta, Grok in the United States, Mistral in France, Deepseek and Qwen in China) have worked to increase the power of their LLMs since 2024. Thus, new records of performance and intelligence have been established at the cost of significant efforts and massive expenses. Nevertheless, gains tend to plateau compared to the initial spectacular jumps. Indeed, according to “scaling laws”, each new advancement now requires an exponential increase in resources (model size, data used, computing power), which progressively limits the real progress margin of artificial intelligences. In fact, doubling the intelligence of a model would not merely double the initial cost but multiply it by ten or a hundred: it would require both more computing power and more training data. Where the transition from GPT-3 to GPT-4 brought significant improvements (with GPT-4 performing approximately 40% better than GPT-3.5 on certain standardized academic exams), OpenAI’s next model (codenamed Orion) is said to offer only minimal improvements over GPT-4, according to some sources. This dynamics of diminishing returns affects the entire sector: Google reportedly found that its Gemini 2.0 model does not meet expected goals, and Anthropic even temporarily paused the development of its main LLM to reassess its strategy. In short, the exhaustion of large high-quality training data corpora, as well as the unsustainable costs in computing power and energy needed to improve models, lead to a sort of technical ceiling, at least temporarily. The numbers confirm this on benchmarks. The multitask understanding scores (MMLU) of the best models converge: since 2023, almost all LLMs achieve similar performances on these tests, indicating we are approaching a plateau. Even much smaller open-source models are beginning to compete with the giants trained by billions of dollars in investments. The race for enormity of models is therefore showing its limits, and the giants of AI are changing strategies: Sam Altman (OpenAI) stated that the path to truly intelligent AI will likely no longer come from simply scaling LLMs, but rather from a creative use of existing models. In clear terms, it involves finding new approaches to gain intelligence without simply multiplying the size of neural networks. Certain techniques, such as Chain-of-Thought (or Tree-of-Thought), allow the model to generate a “reasoning” (often referred to as “thinking” models) before providing its answer, within which it can explore possibilities and realize its mistakes… This is the hallmark of models o1, o3 from OpenAI , R1 from Deepseek , and the „Think“ mode of Grok… This method offers remarkable intelligence gains, particularly in mathematical problems. However, it still comes at a cost: one of the major benchmarks for testing model intelligence is the ARC-AGI (“Abstract and Reasoning Corpus for Artificial General Intelligence”), published by François Chollet in 2019, which tests the intelligence of models on generalization tasks like the one below : This benchmark remained a challenge too difficult for the entirety of general models for a long time, taking 4 years to progress from 0 % completion with GPT-3 to 5 % with GPT-4o. But last December, OpenAI published the results of its range of o3 models, with a specialized model on ARC-AGI achieving 88 % completion : However, each problem incurs a cost of over $3,000 to execute (not counting training expenses), and takes over ten minutes. The limit of giant LLMs is now evident. Instead of accumulating billions of parameters for ever-smaller returns in intelligence, the AI industry now prefers to equip it with “arms and legs” to transition from simple text generation to concrete action. Now, AI no longer merely answers questions or generates content passively, but connects itself to databases, triggers APIs, and executes actions: conducting internet searches, writing code and executing it, booking a flight, making a call… It is clear that this new approach radically transforms our relationship with technology. This paradigm shift allows companies to rethink their workflows and use the power of LLMs to automate tedious and repetitive tasks. This modular approach focuses on interaction intelligence rather than brute parametric force. The real challenge now is to enable AI to collaborate with other systems to achieve tangible results. Several intelligent agents already illustrate the disruptive potential of this approach: Anthropic, creator of Claude, recently published a new standard, the Model Context Protocol (or MCP), which should ultimately allow connection between a compatible LLM and “servers” of tools chosen by the user. This approach has already garnered much attention in the community. Some, like Siddharth Ahuja (@sidahuj) on X (formerly Twitter), use it to connect Claude to Blender, the 3D modeling software, generating scenes just with queries : The arrival of these agents marks a decisive turning point in our interaction with AI. By allowing an artificial intelligence to take action, we witness a transformation of work methods. Companies integrating agents into their systems can automate complex processes, reduce delays, and improve operational accuracy, whether it’s about synthesizing vast volumes of information or driving complete applications. For professionals, the impact is immediate. An analyst can now delegate the research and compilation of information to Deep Research, freeing up time for strategic analysis. A developer, aided by v0, can turn an idea into reality in just a few minutes, while GitHub Copilot speeds up code production and reduces errors. The possibilities are already immense and continue to grow as new agents are created. Beyond the professional realm, these agents will also transform our daily lives, sliding into our personal tools and making services once reserved for experts accessible: it is now much easier to “photoshop” an image, generate code for a complex algorithm, or obtain a detailed report on a topic… Thus, the era of giant LLMs may be coming to an end, while the arrival of AI agents opens a new era of innovation. These agents – Deep Research, Manus, v0 by Vercel, GitHub Copilot, Cursor, Perplexity AI, and many others – seem to demonstrate that the true value of AI lies in its ability to orchestrate multiple tools to accomplish complex tasks, save time, and transform our workflows. But beyond these concrete successes, one question remains: what does the future of AI hold for us? What innovations can we expect? Perhaps an even deeper integration with edge computing, or agents capable of learning in real time, or modular ecosystems allowing everyone to customize their digital assistant? What is certain is that we are still only at the beginning of this revolution, which may be the largest humanity will ever experience. And you, are you eager to discover Orion (GPT5), Claude 4, Llama 4, DeepHeek R2, and other disruptive innovations? Which tool from this future excites you the most?