AiTrillion

aitrillion

Michael Saylor Wants To Turn MicroStrategy Into a Trillion-Dollar Bitcoin Bank

মাইকেল স্যালর মাইক্রোস্ট্র্যাটেজিকে ট্রিলিয়ন-ডলার বিটকয়েন ব্যাংকে পরিণত করতে চান

Michael Saylor will MicroStrategy in eine Billionen-Dollar-Bitcoin-Bank verwandeln

Michael Saylor quiere convertir a MicroStrategy en un banco de Bitcoin de un billón de dólares

Michael Saylor veut transformer MicroStrategy en une banque Bitcoin d'un milliard de dollars

माइकल सैलर माइक्रोस्ट्रेटजी को एक ट्रिलियन डॉलर के बिटकॉइन बैंक में बदलना चाहते हैं

マイケル・セイラーはマイクロストラテジーを1兆ドルのビットコイン銀行にしたいと考えている

Michael Saylor, MicroStrategy를 1조 달러 규모의 비트코인 은행으로 만들고 싶어

Michael Saylor quer transformar a MicroStrategy em um banco de Bitcoin de um trilhão de dólares

Майкл Сэйлор хочет превратить MicroStrategy в биткоин-банк стоимостью триллион долларов

Michael Saylor, MicroStrategy'yi Trilyon Dolarlık Bir Bitcoin Bankasına Dönüştürmek İstiyor

Michael Saylor muốn biến MicroStrategy thành một ngân hàng Bitcoin trị giá nghìn tỷ đô la

Michael Saylor 希望将 MicroStrategy 打造成一家价值万亿美元的比特币银行

The latest edition of One More Thing in AI Newsletter. Technology is best when it brings people together.” – Matt Mullenweg, founder of Auttomatic and developer of WordPress. Hey AI AI-curious reader, Thank you for subscribing to the One More Thing in AI Newsletter. This is specially curated for Startup founders and Business Leaders like you who want to get smarter about AI in less than 5 minutes. It is a snapshot of what I read and learned about AI in the last two weeks. I hope you enjoy reading this edition. Keep learning and applying AI. Best, Renjit Philip Goldman Sachs Experts Weigh In Goldman Sachs has released a report on the massive spending on artificial intelligence (AI). They explore whether this huge investment will pay off. Tech companies are set to spend over $1 trillion on AI in the next few years. The report includes views from experts who are both hopeful and doubtful about AI's economic potential. Key Insights and Arguments Doubts About AI’s Economic Impact
Daron Acemoglu (MIT) thinks the benefits from AI will be smaller than expected. He predicts only a 0.5% increase in productivity and a 1% increase in GDP over the next ten years. He believes AI won't bring significant changes quickly and doubts it will be cost-effective in automating many tasks soon.
Jim Covello (Goldman Sachs) says AI needs to solve very complex problems to justify its high costs. He is skeptical that AI will ever be cheap enough to replace many tasks affordably.


Hopes for AI’s Potential
Joseph Briggs (Goldman Sachs) is more optimistic. He predicts AI could automate 25% of all work tasks, leading to a 9% increase in US productivity and a 6.1% GDP growth over the next decade. He believes the cost savings and the trend of technology becoming cheaper support a positive long-term impact of AI.
Kash Rangan and Eric Sheridan (Goldman Sachs) are excited about AI’s potential, even though there hasn't been a clear "killer application" yet. They believe current spending on AI infrastructure is reasonable and expect long-term benefits as AI becomes more integrated into different industries.


Challenges and Constraints
Chip Shortages: Analysts warn that chip shortages, especially in High-Bandwidth Memory technology, will limit AI growth in the near future.
Power Supply Issues: Analysts highlight that the rise in power demand from AI and data centers could strain the US power grid, potentially limiting AI’s growth. Market Implications
AI Bubble Concerns: Despite doubts about AI’s fundamental value, some experts caution that the AI bubble could last for a while, benefiting infrastructure providers. Utilities might be the next big winners. Long-Term Equity Returns: Only the most favorable AI scenarios, where AI boosts growth and profitability without increasing inflation, will result in above-average long-term returns for the S&P 500. Takeaway The report presents a balanced view, acknowledging both AI investments' potential and challenges. While there is hope for AI’s transformative power and long-term benefits, there are concerns about immediate economic impacts, technological constraints, and market sustainability. This analysis shows the complexity and uncertainty of AI’s future economic contributions. It's worth a read for business leaders! The AI $600B Gold Rush: Where to Next? AI is making waves, and everyone's wondering how it will shape the future. Let’s dive into the latest insights from Sequoia Capital. But the real question is, where's the money going? For every dollar spent on a GPU, another dollar is needed for energy to run it in a data center. If Nvidia makes $50B from GPUs by the end of the year, data center costs will be about $100B. Companies using these GPUs, like Starbucks or Tesla, must make a 50% profit margin (assume that margin, which is a reasonable average). So, for every $50BN worth of chips sold, the companies need to create $200BN worth of revenues to make the purchase viable. The actual gap is $600BN. (Image from Sequoia’s blog) Takeaways from the post: Supply shortage has subsided: Late 2023 was the peak of the GPU supply shortage. Startups were desperate for GPUs, but now, it's much easier to get them with reasonable lead times. GPU stockpiles are growing: In Q4, Nvidia reported that about half of its data center revenue came from large cloud providers, with Microsoft alone likely contributing 22%. Hyperscale CapEx is at historic levels, and Big Tech is heavily investing in GPUs. Stockpiling hardware continues, and a reset will occur when stockpiles are large enough to reduce demand. OpenAI still leads AI revenue: OpenAI’s revenue reached $3.4B, up from $1.6B in late 2023. Most startups are still far behind, with revenues under $100M. AI companies need to deliver significant value to consumers to justify ongoing expenses. Lack of Pricing Power: Unlike physical infrastructure like railroads, which can charge more because there can only be so many tracks, GPU data centers can’t do that. GPU computing is becoming a cheap, hourly service. More companies keep building AI clouds, stopping any single company from controlling prices, so prices drop, like in the airline industry. Investment Losses: Investing in new technologies often leads to big losses. History shows that during technology booms, many people lose money, like with railroads. It’s easier to see which investments will lose money than which will win. Depreciation: Technology like GPUs gets better quickly. Nvidia will keep making better chips, like the B100, which will make current chips lose value fast. People overestimate how long current chips will stay valuable, unlike railroads, which don’t get better as quickly. Last word: Winners vs. Losers: There are always winners, even when there’s too much “building” fuelled by VC money. AI is expected to be the next big thing. Lower GPU costs are suitable for new ideas and startups. While investors might lose money, AI company founders will likely succeed due to lower costs and the lessons learned during this time. Forget about the current costs of AI training and GPUs and start building solutions that add value to end customers. It is still early days of AI. This is an excerpt from my newsletter published on the web at www.onemorethinginai.com The latest edition of One More Thing in AI Newsletter. The latest edition of One More Thing in AI Newsletter. Technology is best when it brings people together.” – Matt Mullenweg, founder of Auttomatic and developer of WordPress. Technology is best when it brings people together.” – Matt Mullenweg, founder of Auttomatic and developer of WordPress. Hey AI AI-curious reader, Thank you for subscribing to the One More Thing in AI Newsletter. This is specially curated for Startup founders and Business Leaders like you who want to get smarter about AI in less than 5 minutes. It is a snapshot of what I read and learned about AI in the last two weeks. I hope you enjoy reading this edition. Keep learning and applying AI. Best, Renjit Philip Goldman Sachs Experts Weigh In Goldman Sachs has released a report on the massive spending on artificial intelligence (AI). They explore whether this huge investment will pay off. Tech companies are set to spend over $1 trillion on AI in the next few years. The report includes views from experts who are both hopeful and doubtful about AI's economic potential. Key Insights and Arguments Key Insights and Arguments Doubts About AI’s Economic Impact
Daron Acemoglu (MIT) thinks the benefits from AI will be smaller than expected. He predicts only a 0.5% increase in productivity and a 1% increase in GDP over the next ten years. He believes AI won't bring significant changes quickly and doubts it will be cost-effective in automating many tasks soon.
Jim Covello (Goldman Sachs) says AI needs to solve very complex problems to justify its high costs. He is skeptical that AI will ever be cheap enough to replace many tasks affordably. Hopes for AI’s Potential
Joseph Briggs (Goldman Sachs) is more optimistic. He predicts AI could automate 25% of all work tasks, leading to a 9% increase in US productivity and a 6.1% GDP growth over the next decade. He believes the cost savings and the trend of technology becoming cheaper support a positive long-term impact of AI.
Kash Rangan and Eric Sheridan (Goldman Sachs) are excited about AI’s potential, even though there hasn't been a clear "killer application" yet. They believe current spending on AI infrastructure is reasonable and expect long-term benefits as AI becomes more integrated into different industries. Challenges and Constraints
Chip Shortages: Analysts warn that chip shortages, especially in High-Bandwidth Memory technology, will limit AI growth in the near future.
Power Supply Issues: Analysts highlight that the rise in power demand from AI and data centers could strain the US power grid, potentially limiting AI’s growth. Doubts About AI’s Economic Impact
Daron Acemoglu (MIT) thinks the benefits from AI will be smaller than expected. He predicts only a 0.5% increase in productivity and a 1% increase in GDP over the next ten years. He believes AI won't bring significant changes quickly and doubts it will be cost-effective in automating many tasks soon. Jim Covello (Goldman Sachs) says AI needs to solve very complex problems to justify its high costs. He is skeptical that AI will ever be cheap enough to replace many tasks affordably. Doubts About AI’s Economic Impact Daron Acemoglu (MIT) thinks the benefits from AI will be smaller than expected. He predicts only a 0.5% increase in productivity and a 1% increase in GDP over the next ten years. He believes AI won't bring significant changes quickly and doubts it will be cost-effective in automating many tasks soon. Doubts About AI’s Economic Impact Jim Covello (Goldman Sachs) says AI needs to solve very complex problems to justify its high costs. He is skeptical that AI will ever be cheap enough to replace many tasks affordably. Hopes for AI’s Potential
Joseph Briggs (Goldman Sachs) is more optimistic. He predicts AI could automate 25% of all work tasks, leading to a 9% increase in US productivity and a 6.1% GDP growth over the next decade. He believes the cost savings and the trend of technology becoming cheaper support a positive long-term impact of AI. Kash Rangan and Eric Sheridan (Goldman Sachs) are excited about AI’s potential, even though there hasn't been a clear "killer application" yet. They believe current spending on AI infrastructure is reasonable and expect long-term benefits as AI becomes more integrated into different industries. Hopes for AI’s Potential Joseph Briggs (Goldman Sachs) is more optimistic. He predicts AI could automate 25% of all work tasks, leading to a 9% increase in US productivity and a 6.1% GDP growth over the next decade. He believes the cost savings and the trend of technology becoming cheaper support a positive long-term impact of AI. Hopes for AI’s Potential Kash Rangan and Eric Sheridan (Goldman Sachs) are excited about AI’s potential, even though there hasn't been a clear "killer application" yet. They believe current spending on AI infrastructure is reasonable and expect long-term benefits as AI becomes more integrated into different industries. Challenges and Constraints
Chip Shortages: Analysts warn that chip shortages, especially in High-Bandwidth Memory technology, will limit AI growth in the near future. Power Supply Issues: Analysts highlight that the rise in power demand from AI and data centers could strain the US power grid, potentially limiting AI’s growth. Challenges and Constraints Chip Shortages: Analysts warn that chip shortages, especially in High-Bandwidth Memory technology, will limit AI growth in the near future. Challenges and Constraints Power Supply Issues: Analysts highlight that the rise in power demand from AI and data centers could strain the US power grid, potentially limiting AI’s growth. Market Implications AI Bubble Concerns: Despite doubts about AI’s fundamental value, some experts caution that the AI bubble could last for a while, benefiting infrastructure providers. Utilities might be the next big winners. Market Implications Long-Term Equity Returns: Only the most favorable AI scenarios, where AI boosts growth and profitability without increasing inflation, will result in above-average long-term returns for the S&P 500. Takeaway The report presents a balanced view, acknowledging both AI investments' potential and challenges. While there is hope for AI’s transformative power and long-term benefits, there are concerns about immediate economic impacts, technological constraints, and market sustainability. This analysis shows the complexity and uncertainty of AI’s future economic contributions. It's worth a read for business leaders! Takeaway The AI $600B Gold Rush: Where to Next? The AI $600B Gold Rush: Where to Next? AI is making waves, and everyone's wondering how it will shape the future. Let’s dive into the latest insights from Sequoia Capital. But the real question is, where's the money going? For every dollar spent on a GPU, another dollar is needed for energy to run it in a data center. If Nvidia makes $50B from GPUs by the end of the year, data center costs will be about $100B. Companies using these GPUs, like Starbucks or Tesla, must make a 50% profit margin (assume that margin, which is a reasonable average). So, for every $50BN worth of chips sold, the companies need to create $200BN worth of revenues to make the purchase viable. The actual gap is $600BN. (Image from Sequoia’s blog) Takeaways from the post: Supply shortage has subsided: Late 2023 was the peak of the GPU supply shortage. Startups were desperate for GPUs, but now, it's much easier to get them with reasonable lead times. GPU stockpiles are growing: In Q4, Nvidia reported that about half of its data center revenue came from large cloud providers, with Microsoft alone likely contributing 22%. Hyperscale CapEx is at historic levels, and Big Tech is heavily investing in GPUs. Stockpiling hardware continues, and a reset will occur when stockpiles are large enough to reduce demand. Supply shortage has subsided: OpenAI still leads AI revenue: OpenAI’s revenue reached $3.4B, up from $1.6B in late 2023. Most startups are still far behind, with revenues under $100M. AI companies need to deliver significant value to consumers to justify ongoing expenses. OpenAI still leads AI revenue: Lack of Pricing Power: Unlike physical infrastructure like railroads, which can charge more because there can only be so many tracks, GPU data centers can’t do that. GPU computing is becoming a cheap, hourly service. More companies keep building AI clouds, stopping any single company from controlling prices, so prices drop, like in the airline industry. Lack of Pricing Power: Investment Losses: Investing in new technologies often leads to big losses. History shows that during technology booms, many people lose money, like with railroads. It’s easier to see which investments will lose money than which will win. Depreciation: Technology like GPUs gets better quickly. Nvidia will keep making better chips, like the B100, which will make current chips lose value fast. People overestimate how long current chips will stay valuable, unlike railroads, which don’t get better as quickly. Investment Losses: Last word: Winners vs. Losers: There are always winners, even when there’s too much “building” fuelled by VC money. AI is expected to be the next big thing. Lower GPU costs are suitable for new ideas and startups. Last word: Winners vs. Losers: While investors might lose money, AI company founders will likely succeed due to lower costs and the lessons learned during this time. Forget about the current costs of AI training and GPUs and start building solutions that add value to end customers. It is still early days of AI. This is an excerpt from my newsletter published on the web at www.onemorethinginai.com

Where's the AI Revenue? Plus, Will the Trillion-Dollar AI Gamble Pay Off?

From improbable to inevitable. Nvidia’s emergence as the frontrunner to become the world’s first $4 trillion stock has been sustained by the generative AI boom. With no signs of slowing, NVDA’s potential appears endless. The third quarter of 2024 began with the same level of momentum on display for Nvidia as KeyBanc analyst John Vinh raised his target price on the stock to $180. This boost of 33% from current levels would see the stock surpass the $4 trillion market capitalization mark and become the first company to do so. These lofty expectations encapsulate the meteoric rise of the semiconductor firm. Just two years ago, NVDA was battling a sustained Wall Street decline amid tech stock sell-offs. The prospect of becoming a $1 trillion stock seemed like a pipedream, let alone mounting a challenge to become the world’s most valuable stock. The generative AI boom changed everything. Bloomberg estimates suggest that the generative AI market alone will grow at a CAGR of 42% over the next 10 years into a $1.3 trillion market by 2032. Nvidia’s status as a market leader in providing AI chipsets ensures that the company will be a driving force in this growth. Strength in Fundamentals Although the long-term sustainability of the generative AI frenzy has been called into question, there’s little doubt that Nvidia has showcased tangible fundamentals to support its impressive Wall Street performance in recent months. In May, the semiconductor giant reported revenues that tripled to $26.04 billion year over year, weighing in well above analyst forecasts. Additionally, Nvidia saw net income rise to $14.88 billion, comfortably up from the $2.04 billion reported a year prior. Nvidia’s buoyant outlook is further supported by forecasts for Q2 2024 revenue reaching $28 billion, plus or minus two percent. This would also fall way above analyst expectations. Driving Force in Generative AI The brilliance of Nvidia’s position as a market leader in providing the computer chips that will power the generative AI boom has drawn some impressive comparisons. According to Jim Keller, a former AMD and Tesla engineer and current CEO of AI firm Tenstorrent, Nvidia is becoming the IBM of the AI era. Given the sheer scale of the ongoing AI boom, Nvidia could surpass IBM in terms of performance and make an unprecedented claim to become the cornerstone of generative AI. This claim could be sustained by the launch of Nvidia’s Blackwell computing processors, which were unveiled in spring 2024. The new products will replace the company’s popular H100 chips and facilitate further revenue generation. The arrival of Blackwell processors comes at a time when an increasing number of firms are seeking ways to incorporate generative AI into their operations. This could help drive sales even higher for Nvidia. However, Nvidia isn’t simply driving the future of AI hardware for enterprises. The firm announced in its June 2024 keynote address plans to integrate its AI services into the world of video games through its ACE-powered Convert Protocol to create fresh on-demand interactions between games and gamers. Given that Microsoft opted to acquire Activision Blizzard for $68.7 billion on the belief that gaming is the future of entertainment and the metaverse, Nvidia’s strides in the landscape could drive its growth further into the future. Is the GenAI Boom Sustainable? Although it appears clear that generative AI will continue its emergence as a disruptive force throughout a vast array of industries, Nvidia’s position as the king of GenAI isn’t necessarily assured. Competition is growing, and more budget-focused firms like Taiwan Semiconductor Manufacturing Company and Advanced Micro Devices are names that could position themselves as cheaper alternatives as the technology continues its emergence. Likewise, many of the tech giants that are currently among Nvidia’s list of customers are intent on taking the production of AI chips in-house. These factors could all eat into NVDA’s dominance. “Investors should exercise caution and conduct thorough research before investing in any field, especially one as dynamic as GenAI,” warns Maxim Manturov, head of investment research at Freedom24. “Success in GenAI depends not only on cutting-edge technology but also on a robust data infrastructure, a skilled workforce, and strategic planning.” While Nvidia’s position as a generative AI market leader appears solid today, there will certainly be challenges to its dominance in the years to come. Should a rival begin generating cheaper AI chipsets, the company’s stock could tumble. Thinking Two Moves Ahead The most important factor in determining Nvidia’s path to $4 trillion is the fact that the company is never content with resting on its laurels. With Nvidia investing billions of dollars into developing leading generative AI computer chips, we’re looking at a company that’s seeking to stay two moves ahead of its rivals. As the market grows, new competitors are likely to emerge, but as one of the world’s largest companies, it’s unlikely that they would have the firepower to go toe-to-toe with Nvidia. With this in mind, Nvidia’s future is in the firm’s hands. If $4 trillion is inevitable, then the likelihood is that CEO Jensen Huang already has $5 trillion and beyond in his sights. From improbable to inevitable. Nvidia’s emergence as the frontrunner to become the world’s first $4 trillion stock has been sustained by the generative AI boom. With no signs of slowing, NVDA’s potential appears endless. The third quarter of 2024 began with the same level of momentum on display for Nvidia as KeyBanc analyst John Vinh raised his target price on the stock to $180 . This boost of 33% from current levels would see the stock surpass the $4 trillion market capitalization mark and become the first company to do so. target price on the stock to $180 target price on the stock to $180 These lofty expectations encapsulate the meteoric rise of the semiconductor firm. Just two years ago, NVDA was battling a sustained Wall Street decline amid tech stock sell-offs. The prospect of becoming a $1 trillion stock seemed like a pipedream, let alone mounting a challenge to become the world’s most valuable stock. The generative AI boom changed everything. Bloomberg estimates suggest that the generative AI market alone will grow at a CAGR of 42% over the next 10 years into a $1.3 trillion market by 2032. Nvidia’s status as a market leader in providing AI chipsets ensures that the company will be a driving force in this growth. CAGR of 42% over the next 10 years CAGR of 42% over the next 10 years Strength in Fundamentals Although the long-term sustainability of the generative AI frenzy has been called into question, there’s little doubt that Nvidia has showcased tangible fundamentals to support its impressive Wall Street performance in recent months. In May, the semiconductor giant reported revenues that tripled to $26.04 billion year over year , weighing in well above analyst forecasts. tripled to $26.04 billion year over year tripled to $26.04 billion year over year Additionally, Nvidia saw net income rise to $14.88 billion, comfortably up from the $2.04 billion reported a year prior. Nvidia’s buoyant outlook is further supported by forecasts for Q2 2024 revenue reaching $28 billion, plus or minus two percent. This would also fall way above analyst expectations. Driving Force in Generative AI The brilliance of Nvidia’s position as a market leader in providing the computer chips that will power the generative AI boom has drawn some impressive comparisons. According to Jim Keller, a former AMD and Tesla engineer and current CEO of AI firm Tenstorrent, Nvidia is becoming the IBM of the AI era . becoming the IBM of the AI era becoming the IBM of the AI era Given the sheer scale of the ongoing AI boom, Nvidia could surpass IBM in terms of performance and make an unprecedented claim to become the cornerstone of generative AI. This claim could be sustained by the launch of Nvidia’s Blackwell computing processors , which were unveiled in spring 2024. The new products will replace the company’s popular H100 chips and facilitate further revenue generation. launch of Nvidia’s Blackwell computing processors launch of Nvidia’s Blackwell computing processors The arrival of Blackwell processors comes at a time when an increasing number of firms are seeking ways to incorporate generative AI into their operations. This could help drive sales even higher for Nvidia. However, Nvidia isn’t simply driving the future of AI hardware for enterprises. The firm announced in its June 2024 keynote address plans to integrate its AI services into the world of video games through its ACE-powered Convert Protocol to create fresh on-demand interactions between games and gamers. fresh on-demand interactions fresh on-demand interactions Given that Microsoft opted to acquire Activision Blizzard for $68.7 billion on the belief that gaming is the future of entertainment and the metaverse, Nvidia’s strides in the landscape could drive its growth further into the future. future of entertainment future of entertainment Is the GenAI Boom Sustainable? Although it appears clear that generative AI will continue its emergence as a disruptive force throughout a vast array of industries, Nvidia’s position as the king of GenAI isn’t necessarily assured. Competition is growing, and more budget-focused firms like Taiwan Semiconductor Manufacturing Company and Advanced Micro Devices are names that could position themselves as cheaper alternatives as the technology continues its emergence. Likewise, many of the tech giants that are currently among Nvidia’s list of customers are intent on taking the production of AI chips in-house . These factors could all eat into NVDA’s dominance. taking the production of AI chips in-house taking the production of AI chips in-house “Investors should exercise caution and conduct thorough research before investing in any field, especially one as dynamic as GenAI,” warns Maxim Manturov, head of investment research at Freedom24. “Success in GenAI depends not only on cutting-edge technology but also on a robust data infrastructure, a skilled workforce, and strategic planning.” While Nvidia’s position as a generative AI market leader appears solid today, there will certainly be challenges to its dominance in the years to come. Should a rival begin generating cheaper AI chipsets, the company’s stock could tumble. Thinking Two Moves Ahead The most important factor in determining Nvidia’s path to $4 trillion is the fact that the company is never content with resting on its laurels. With Nvidia investing billions of dollars into developing leading generative AI computer chips, we’re looking at a company that’s seeking to stay two moves ahead of its rivals. As the market grows, new competitors are likely to emerge, but as one of the world’s largest companies, it’s unlikely that they would have the firepower to go toe-to-toe with Nvidia. With this in mind, Nvidia’s future is in the firm’s hands. If $4 trillion is inevitable, then the likelihood is that CEO Jensen Huang already has $5 trillion and beyond in his sights.

$4 Trillion Appears Inevitable as Nvidia Remains the Star of the Generative AI Boom

ARK Investment Management LLC predicts that AI agents will influence up to $9 trillion of global online sales of consumer goods and services by the end of the decade. This number is almost negligible today. In their recent research, "Generative AI: A New Consumer Operating System" they shared valuable insights into the future of AI in consumer technology. One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." However, the blog also suggests that, in the long run, "AI agents at the operating system level will aim to maximize consumer utility, encouraging most marketplaces to offer their inventory through these agents." Yes, it's no secret that a significant portion of our consumption decisions will be influenced by AI agents in the future. But, how will these agents shape the way we are influenced and make purchasing decisions? Personalization is no longer just a buzzword, nor is it limited to changing the color of a landing page based on predictive user anaysis. Now, let's break down the mechanisms of AI agents in advertising, as illustrated by the authors: Traditional Online Shopping Experience: Awareness Phase: The user initiates the process with a search query, typically typed into a search engine. Discovery + Research Phase: The user explores the search results, often clicking on links to marketplaces or merchant websites. This phase can involve multiple iterations, with the user navigating back and forth between the search engine and various merchant sites. Ad Opportunities: During this process, the user encounters numerous ads, both on search engines and merchant websites, providing abundant opportunities for digital ad impressions. Checkout: Once the user finds their desired product, they proceed to checkout. AI-Agent Powered Online Shopping Experience: Awareness Phase: The user initiates the process with a text or voice command to their personalized AI agent, integrated into their device or operating system. Autonomous Discovery + Research Phase: The AI agent takes charge, querying multiple search engines and marketplaces simultaneously. Curated Results: The AI agent compiles and presents refined results, reducing the user's active involvement in discovery and research. Checkout: The user reviews the curated results and proceeds to checkout, similar to the traditional process. So, what are the key differences and implications? First, efficiency: The AI agent-powered process offloads the discovery and research burden from the user to the agent, enhancing efficiency and saving time. Ad Opportunities in the Traditional Model: The user is exposed to multiple ads throughout their journey across search engines and merchant sites. Ad Opportunities in the AI Model: More limited and concentrated within the AI agent's curated results, reducing ad impressions. In the traditional model, the user actively engages in each step, requiring more effort and time. The AI-powered model offers a streamlined process where the user delegates most tasks to the AI agent, potentially improving the overall user experience. The digital advertising ecosystem is indeed poised for substantial disruption due to the increasing role of AI assistants. This will result in a reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. To maintain user trust, AI agents will need to curate advertising content that is natural, unobtrusive, and respectful of user privacy. Two anticipated forms of AI agent advertising are: Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads.
Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. With these developments, the influence of AI agents in consumer spending will grow, and AI-facilitated ad spend is projected to reach an astonishing $9 trillion globally by the year 2030. AI agents are estimated to extract more than $800 billion in intermediation revenue from this massive ad spend. Now, let's turn our attention to another valuable insight from the blog: Voice and Natural User Interfaces (NUIs). Voice-centric NUIs, coupled with real-time data from AI wearables, will take personalization to the next level, transforming hardware into predictive assistants. Voice is becoming the new medium obviously. However, voice and AI wearables are expected to augment rather than replace smartphones, as privacy concerns and the need for audiovisual entertainment will likely maintain the demand for screens. Lastly, let's consider the potential category winners and platform strategies brought to us by ARK researchers. The shifts in market share between PC and mobile operating systems suggest that winning consumer hardware platforms will employ varied strategies. Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Established tech giants like Apple, Google, Microsoft, and Meta possess significant advantages, including their vast ecosystems, data and AI capabilities, and extensive distribution networks. "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." However, the paper also highlights the potential for new entrants, such as Humane Ai Pin and Rabbit R1, to disrupt the market by focusing on consumer-centric innovations that extend beyond the ecosystems of the current leaders, offering user-friendly designs that cater to unmet needs. It's super exciting to consider the role of AI agents in our purchasing decisions and how this might evolve in the near future. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. So, what do you think? Are you open to the idea of AI agent-powered spending, or do you prefer to maintain control over your purchases? Have you had any positive or negative experiences with AI-assisted shopping thus far? ARK Investment Management LLC predicts that AI agents will influence up to $9 trillion of global online sales of consumer goods and services by the end of the decade. ARK Investment Management LLC This number is almost negligible today. In their recent research, " Generative AI: A New Consumer Operating System " they shared valuable insights into the future of AI in consumer technology. Generative AI: A New Consumer Operating System Generative AI: A New Consumer Operating System One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." However, the blog also suggests that, in the long run, "AI agents at the operating system level will aim to maximize consumer utility, encouraging most marketplaces to offer their inventory through these agents." Yes, it's no secret that a significant portion of our consumption decisions will be influenced by AI agents in the future. consumption decisions will be influenced by AI agents in the future. But, how will these agents shape the way we are influenced and make purchasing decisions? Personalization is no longer just a buzzword, nor is it limited to changing the color of a landing page based on predictive user anaysis. Now, let's break down the mechanisms of AI agents in advertising, as illustrated by the authors: Traditional Online Shopping Experience: Awareness Phase: Awareness Phase: The user initiates the process with a search query, typically typed into a search engine. Discovery + Research Phase: Discovery + Research Phase: The user explores the search results, often clicking on links to marketplaces or merchant websites. This phase can involve multiple iterations, with the user navigating back and forth between the search engine and various merchant sites. Ad Opportunities: Ad Opportunities: During this process, the user encounters numerous ads, both on search engines and merchant websites, providing abundant opportunities for digital ad impressions. Checkout: Checkout: Once the user finds their desired product, they proceed to checkout. AI-Agent Powered Online Shopping Experience: Awareness Phase: Awareness Phase: The user initiates the process with a text or voice command to their personalized AI agent, integrated into their device or operating system. Autonomous Discovery + Research Phase: Autonomous Discovery + Research Phase: The AI agent takes charge, querying multiple search engines and marketplaces simultaneously. Curated Results: Curated Results: The AI agent compiles and presents refined results, reducing the user's active involvement in discovery and research. Checkout: Checkout: The user reviews the curated results and proceeds to checkout, similar to the traditional process. So, what are the key differences and implications? First, efficiency: The AI agent-powered process offloads the discovery and research burden from the user to the agent, enhancing efficiency and saving time. agent, enhancing efficiency and saving time. Ad Opportunities in the Traditional Model: The user is exposed to multiple ads throughout their journey across search engines and merchant sites. Ad Opportunities in the AI Model: More limited and concentrated within the AI agent's curated results , reducing ad impressions. More limited and concentrated within the AI agent's curated results In the traditional model, the user actively engages in each step, requiring more effort and time. The AI-powered model offers a streamlined process where the user delegates most tasks to the AI agent, potentially improving the overall user experience. The digital advertising ecosystem is indeed poised for substantial disruption due to the increasing role of AI assistants. This will result in a reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. To maintain user trust, AI agents will need to curate advertising content that is natural, unobtrusive, and respectful of user privacy. Two anticipated forms of AI agent advertising are: Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads. Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads. Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. With these developments, the influence of AI agents in consumer spending will grow, and AI-facilitated ad spend is projected to reach an astonishing $9 trillion globally by the year 2030. AI agents are estimated to extract more than $800 billion in intermediation revenue from this massive ad spend. Now, let's turn our attention to another valuable insight from the blog: Voice and Natural User Interfaces (NUIs). Voice and Natural User Interfaces (NUIs). Voice-centric NUIs, coupled with real-time data from AI wearables, will take personalization to the next level, transforming hardware into predictive assistants. Voice is becoming the new medium obviously. However, voice and AI wearables are expected to augment rather than replace smartphones, as privacy concerns and the need for audiovisual entertainment will likely maintain the demand for screens. voice and AI wearables are expected to augment rather than replace smartphones, Lastly, let's consider the potential category winners and platform strategies brought to us by ARK researchers. The shifts in market share between PC and mobile operating systems suggest that winning consumer hardware platforms will employ varied strategies. Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Established tech giants like Apple, Google, Microsoft, and Meta possess significant advantages, including their vast ecosystems, data and AI capabilities, and extensive distribution networks. "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." However, the paper also highlights the potential for new entrants, such as Humane Ai Pin and Rabbit R1, to disrupt the market by focusing on consumer-centric innovations that extend beyond the ecosystems of the current leaders, offering user-friendly designs that cater to unmet needs. It's super exciting to consider the role of AI agents in our purchasing decisions and how this might evolve in the near future. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. So, what do you think? Are you open to the idea of AI agent-powered spending, or do you prefer to maintain control over your purchases? Have you had any positive or negative experiences with AI-assisted shopping thus far?

AI Facilitated Online Sales Forecasted to Reach $9 Trillion by 2030 

AI সহজলভ্য অনলাইন বিক্রয় 2030 সালের মধ্যে $9 ট্রিলিয়ন পৌঁছানোর পূর্বাভাস 

Prognose: KI-gestützte Online-Verkäufe werden bis 2030 9 Billionen US-Dollar erreichen

Se prevé que las ventas en línea facilitadas por IA alcancen los 9 billones de dólares en 2030 

Les ventes en ligne facilitées par l’IA devraient atteindre 9 000 milliards de dollars d’ici 2030 

2030 तक AI समर्थित ऑनलाइन बिक्री 9 ट्रिलियन डॉलर तक पहुंचने का अनुमान

AI を活用したオンライン販売は 2030 年までに 9 兆ドルに達すると予測

AI를 활용한 온라인 매출은 2030년까지 9조 달러에 이를 것으로 예상됩니다. 

Vendas on-line facilitadas por IA devem atingir US$ 9 trilhões até 2030 

Прогнозируется, что к 2030 году объем онлайн-продаж, содействующих искусственному интеллекту, достигнет 9 триллионов долларов 

Yapay Zeka Kolaylaştırılan Çevrimiçi Satışların 2030'a Kadar 9 Trilyon Dolara Ulaşacağı Tahmin Ediliyor 

Doanh số bán hàng trực tuyến được hỗ trợ bởi AI được dự đoán sẽ đạt 9 nghìn tỷ USD vào năm 2030 

预计到 2030 年，人工智能推动的在线销售将达到 9 万亿美元

Riding the Wave: A Closer Look at How Real Estate is Playing a Big Part in the Tokenization Revolution In the dynamic finance world, a transformation is unfolding with the tokenization of real-world assets. The potential growth for tokenized assets is significant. Analysts at , which includes real estate and other financial assets, to reach at least USD 10 trillion by 2030, a marked increase from the current valuation of around USD 300 billion. Roland Berger project the market for asset tokenization Real estate stands at the vanguard of this revolution, ripe for innovation amidst current market challenges including complex international transactions, prohibitive third-party costs, complex legal frameworks, and limited market liquidity. A recent study titled by Laurens Swinkels examined the tokenization of 58 residential properties in the U.S., primarily in Detroit. This study offers several key insights: Empirical evidence on the ownership and liquidity of real estate tokens, [Swinkels, L. Empirical evidence on the ownership and liquidity of real estate tokens. Financ Innov 9, 45 (2023)] : On average, each property had 254 owners, showcasing high fragmented ownership and enabling diversified investment, reducing risk. Fragmented Ownership and Diversification : Investors with substantial holdings in real estate tokens diversified across properties and cities, indicating sophisticated investment strategies. Investment Patterns : The frequency of ownership changes, particularly on decentralized exchanges, suggests higher liquidity in tokenized real estate compared to traditional markets. Ownership Dynamics and Liquidity : Real estate token prices align with the house price index, offering economic exposure to residential house prices. Price Movement and Economic Exposure This focus on Detroit, affected by the 2007–2008 financial crisis, suggests these findings may not fully represent other markets. Despite covering a small market segment, the study underscores tokenization's potential in the expansive real estate market. The study also notes legal and regulatory requirements, such as investor whitelisting, might limit liquidity. Nonetheless, a vibrant secondary market for these tokens was observed. This real-world application of blockchain technology adds empirical depth to the blockchain literature, demonstrating its potential to disrupt traditional market structures in real estate. Tokenization in Global Finance: The IMF Perspective Insights from an delve deeper into tokenization's potential in international real estate transactions. The IMF sees tokenization as a transformative tool, representing property rights on electronic ledgers that are synchronized, secure, and easily updatable. This addresses currency exchange complexities and regulatory compliance in international transactions. Technologies like encryption and smart contracts enhance security and simplify legal processes, respectively. IMF report by Tobias Adrian and Tommaso Mancini-Griffoli The report also highlights the role of the public sector, particularly central banks, in leveraging these technologies to improve payment infrastructures. are crucial here, serving as both monetary instruments and infrastructure for transaction clearing and settlement. The IMF proposes a multilateral platform for cross-border payments, enabling financial institutions to trade digital representations of central bank reserves internationally. Central Bank Digital Currencies ( ) CBDCs The integration of these technologies could make international real estate transactions more streamlined, efficient, and compliant with global standards. The use of a single ledger and smart contracts minimizes risks and ensures consistency and automatic execution. Ironically, the technologies that drove the crypto revolution might be instrumental in public sector initiatives to enhance international financial infrastructures, particularly in the real estate sector, bringing efficiency, safety, and interoperability to international transactions. Revolutionizing Real Estate: The Emergence of NFTs and Tokenization Exploring the insights from the IMF and the Detroit real estate study further illuminates how NFTs and tokenization are reshaping the real estate landscape. These technological advancements are not just trends; they represent a significant shift in property investment and ownership dynamics: Reducing Costs and Complexity of Real Estate Transactions Real estate transactions, especially international ones, are often burdened by high costs and complex legalities. Tokenization simplifies this by allowing direct transactions using blockchain technology. For example, in 2019, the real estate firm completed the first-ever blockchain-based real estate sale in the EU, demonstrating how tokenization can streamline cross-border transactions. Propy Improving Market Liquidity Tokenization can fractionalize property ownership, inviting broader investor participation and enhancing market liquidity. A notable example is the which enabled investors to trade fractional ownership tokens. St. Regis Aspen Resort's tokenization, Enhancing Transparency and Security Blockchain's transparency and immutability can significantly mitigate fraud and misrepresentation risks in international real estate transactions. Countries like Sweden and Georgia are testing blockchain technology for land registries, enhancing security and transparency. Overcoming Regulatory Hurdles Different countries' varying regulations can complicate international real estate transactions. Tokenization, through smart contracts, can standardize processes and ensure compliance. Global Accessibility Tokenization democratizes access to the real estate market, enabling international investors to engage in markets previously out of reach. Predictions for the Future Experts foresee continued exponential growth in the tokenization market, with technological advancements opening new sectors for tokenization. The future of tokenization is expected to be diverse, encompassing various assets like fine art and infrastructure projects. Tokenization of Real Estate: A Necessary Evolution in Global Finance Tokenization in real estate, represents not just a technological advancement but a crucial evolution in global finance. This innovation is set to streamline international transactions, reduce costs, and democratize investment opportunities, ushering in a new era of asset management and investment. As we progress towards a more interconnected and efficient global real estate market, tokenization's implications are profound and far-reaching. There are still a few hurdles in the realm of real estate tokenization, but that's where the excitement lies.

Riding the Tokenization Wave: Transforming Assets into Trillions by 2030

2023 has seen leading chipmaker Nvidia [NASDAQ: NVDA] embark on an extraordinary journey into an exclusive club of $1 trillion companies on Wall Street. But with fresh challenges looming on the horizon, is the stock ready to build on its recent performance? Thanks to a whirlwind price rally throughout the first half of 2023, Nvidia became to reach a market capitalization of $1 trillion and is currently one of six firms alongside Apple (AAPL), Microsoft (MSFT), Alphabet (GOOG), Amazon (AMZN), and Saudi Aramco to hold a 13-figure value. just the ninth company \Nvidia’s growth, buoyed by the , has been nothing short of exceptional. Peaking at 244% growth in Q3, the stock has since undergone a correction before recovering much of its lost value in time for Q4 2023 in a strong show of resilience. generative AI boom Such extraordinary growth came as the result of a conscious shift towards anticipating the artificial intelligence boom, which was sparked by the launch of OpenAI’s ChatGPT large language model (LLM) in late 2022. “We had the good wisdom to go put the whole company behind it,” , CEO of Nvidia, in a CNBC interview. explained Jensen Huang “We saw early on, about a decade or so ago, that this way of doing software could change everything. And we changed the company from the bottom all the way to the top and sideways. Every chip that we made was focused on artificial intelligence.” Such industry foresight will be music to the ears of Nvidia’s growing mass of investors. The stock’s sustained performance is helping to draw comparisons between Nvidia and Tesla [NASDAQ: TSLA] due to their respective performance built on their commitment to innovation. However, such comparisons aren’t always productive for stocks and can cause bubbles that swell due to investor complacency. "Nvidia is the stock market's new Tesla, where the market blindly assigns a ridiculously high and unrealistic valuation," , corporate finance expert at New Constructs. "We're not denying that Nvidia is a great company, but we are pointing out that its valuation is beyond lofty and unjustifiable." warned David Trainer So, could Nvidia’s newfound Tesla status see the company push towards a $2 trillion valuation in the future? Or is a market correction inevitable? Evidence suggests that the path to $2 trillion is likely to be full of fresh challenges for the star of the generative AI boom. Dismantling Nvidia’s Monopoly The reason behind Nvidia’s exceptional market performance is clear. With an estimated 80% global market dominance for the chips most suitable to run generative AI applications, Nvidia’s position within the industry is largely uncontested. Much of Nvidia’s success has been down to the timely launch of its H100 chip, which has been widely regarded as the best of its kind when powering and LLM applications. generative AI The H100 chip has been positively received throughout the industry. “During the quarter, major cloud service providers announced massive NVIDIA H100 AI infrastructures,” in a Q2 earnings statement. Huang claimed “ ” Leading enterprise IT system[s] and software providers announced partnerships to bring NVIDIA AI to every industry. The race is on to adopt generative AI. However, there appear to be multiple threats to Nvidia’s generative AI market monopoly that are gathering momentum. One of Nvidia’s closest competitors, AMD, recently , an open-source artificial intelligence software startup, in a bid to compete more directly in AI markets. announced the acquisition of Nod.AI Nvidia also appears set to face competition from some of the AI landscape’s leading players. The growth of ChatGPT has directly aided the stock market rally experienced by Nvidia, but with news that ChatGPT’s parent company OpenAI may be considering the to cut costs and lower its dependence on Nvidia as well as major investor Microsoft, the days of Nvidia’s AI market dominance may be numbered. development of its own AI chips Generative AI’s “Cold Shower” Nvidia’s rally to a $2 trillion market cap will also be challenged by questions over sustainability as well as the emergence of rivals. According to a for the future of tech, analysts expect that the generative AI market “gets a cold shower in 2024” owing to entry costs, risk, and sustainability concerns, which ultimately “replaces the hype” swelling around the transformative technology. CCS Insight report The cost of running ChatGPT is estimated to weigh in at around $700k per day, , and these high barriers to entry are likely to cause more problems across industries as the hype for generative AI subsides and enterprises are faced with a hefty bill for embracing the technology early. suggests SemiAnalysis analyst Dylan Patel Costs associated with developing AI models can range and require the hiring of expensive researchers, buying into market leader Nvidia’s servers, and hiring AI developers who still have to buy the text, image, and knowledge base data required to train models. far higher than traditional software In addition to this, growing of this extensive chip usage to power generative AI models have called into question the compliance of generative AI adoption for ESG-focused firms. concerns surrounding the carbon footprint Could Nvidia Become The Next FAANG Sensation? Even with considerable challenges on the horizon, Nvidia’s market head start means that the company is well-positioned to continue innovating beyond its rivals and to establish itself in an industry that appears set for sustained long-term growth. “ ” claims Maxim Manturov, head of investment research at . “ ” Factors such as generative artificial intelligence and the metaverse may provide NVIDIA with a FAANG level of popularity, Freedom Finance Europe Generative artificial intelligence is a rapidly growing field that has the potential to revolutionize many industries. Although short-term volatility may come into play as reality bites for the generative AI boom, Bloomberg Intelligence’s forecast that the industry valuation by 2032, representing a CAGR of 42% over a decade, could become a reality when considering the vast transformative implications of AI for countless global industries. will attain a $1.3 trillion As for the path towards a $2 trillion market cap, Nvidia would need to double its annual revenue to $54 billion and adjusted net income to $17 billion at between 2023 and 2030 to double its current output. respective CAGRs of 10% Whether this seemingly achievable level of performance can be reflected in a $2tr capitalization will likely be down to the state of investor appetite for generative AI stocks by the end of the decade. What we can be sure of is that Nvidia has proven time and again that it’s ready to out-innovate its rivals and capitalize on opportunities long before they materialize. With such a pioneering focus, it seems like anything can be possible for the star of generative AI.

The Path from $1 Trillion to a $2 Trillion Market Cap Will be Harder for Generative AI Star Nvidia

জেনারেটিভ এআই স্টার এনভিডিয়ার জন্য $1 ট্রিলিয়ন থেকে $2 ট্রিলিয়ন মার্কেট ক্যাপে যাওয়ার পথ কঠিন হবে

Der Weg von einer Billion US-Dollar zu einer Marktkapitalisierung von 2 Billionen US-Dollar wird für den generativen KI-Star Nvidia schwieriger sein

El camino de 1 billón de dólares a una capitalización de mercado de 2 billones de dólares será más difícil para la estrella de la IA generativa Nvidia

Le chemin d’une capitalisation boursière de 1 000 milliards de dollars à 2 000 milliards de dollars sera plus difficile pour la star de l’IA générative Nvidia

जेनरेटिव एआई स्टार एनवीडिया के लिए $1 ट्रिलियन से $2 ट्रिलियन मार्केट कैप तक का रास्ता कठिन होगा

生成 AI のスターである Nvidia にとって、時価総額 1 兆ドルから 2 兆ドルへの道はより困難になる

생성 AI 스타 Nvidia에게는 1조 달러에서 2조 달러의 시가총액으로 가는 길은 더 어려울 것입니다

O caminho de US$ 1 trilhão para um valor de mercado de US$ 2 trilhões será mais difícil para a Generative AI Star Nvidia

Путь от 1 триллиона долларов до рыночной капитализации в 2 триллиона долларов будет труднее для звезды генеративного искусственного интеллекта Nvidia

Üretken Yapay Zeka Yıldızı Nvidia için 1 Trilyon Dolardan 2 Trilyon Dolara Piyasa Değerine Giden Yol Daha Zor Olacak

Con đường từ 1 nghìn tỷ USD đến vốn hóa thị trường 2 nghìn tỷ USD sẽ khó khăn hơn đối với Ngôi sao AI sáng tạo Nvidia

对于生成型 AI 明星 Nvidia 来说，从 1 万亿美元市值到 2 万亿美元市值的道路将更加艰难

A look at the RWA’s promise of bringing trillions of dollars worth of value to the crypto space. A new acronym has entered the crypto vocabulary and appears to be firmly establishing itself. , short for Real World Assets, is gaining recognition as one of the most promising use cases in the crypto space now. RWA The idea is not new: . Stablecoins, for instance, can be considered a form of RWA as they introduce US dollars (or other fiat currencies) to the blockchain. Synthetic assets, like those issued by Synthetix, achieve a similar goal by tokenizing popular stocks. at their core, RWAs are good old tokenized assets, such as tokenized shares of real estate, commodities, art, and even debt, like the US T-bills However, the RWA acronym has been recently used mostly to describe lending protocols built around these assets. They generate yield by investing users’ coins in off-chain assets. In return, users receive not only passive revenue but also a sort of IOU token, which can then be used across a variety of . DeFi protocols The benefits of bringing RWA on the blockchain are plenty: , enhancing asset liquidity, enabling more efficient asset management, unlocking various new use cases within DeFi protocols… etc. democratizing investment With the potential to bring – literally – trillions of dollars worth of , this new trend is capturing the attention of an increasing number of crypto investors. value to the blockchain However, while some protocols look serious and promising, others raise suspicion. Let’s see why the RWA is so hyped now and what protocols stand out. A trillion-dollar potential The potential impact of RWA on crypto finance is enormous. Global real estate is estimated at some $326 trillion, US debt now makes a staggering $33 trillion, and gold’s market cap is $13 trillion. Such assets make up a huge chunk of traditional finance but are still very modestly represented in crypto. This simple rationale prompted Citi to declare . In their released in March, the bank’s analysts predicted that $1.9 trillion of non-financial debt, $1.5 trillion of real estate funds, $0.7 trillion of private equity, $1 trillion of securities financing, and $1 trillion of trade finance volumes would be tokenized by 2030. RWA tokenization the foremost catalyst for crypto adoption report This estimation means . Considering that the current market cap of the entire crypto space is slightly over $1 trillion, this prospect appears nothing short of ground-breaking. bringing over $6 trillion of value to the blockchain RWA in crypto now , reaching a TVL of over $2.3 billion, according to . However, this figure is certainly much higher in reality, as some already established DeFi protocols are increasingly investing in RWA. The weight of RWA protocols is constantly growing DeFiLlama MakerDAO One of the most prominent examples of such “conversion” is MakerDAO, a veteran DeFi lending protocol and the issuer of $DAI stablecoin. The DAO is actively remaking itself, and a growing RWA allocation is likely to be a part of the plan. As of now, (not included in DeFiLlama’s calculations), including over $1.1 billion of treasury bonds and $500 million of Coinbase Prime yield . That accounts for almost 50% of its funds. MakerDAO boasts a $2.3 billion RWA portfolio (source: @steakhouse on ) Dune Analytics Ondo Finance One of the (relatively) early entrants into the , has been gaining significant and consistent traction since its launch in January, amassing some . RWA-specific space Ondo Finance $164 million of TVL According to the company website, Ondo Finance invests in “multi-billion dollar, highly liquid exchange-traded funds, managed by the world’s preeminent bond managers”, mentioning notably BlackRock and Pimco. The firm also discloses the third-party custodians and auditors responsible for overseeing the funds. Built on Ethereum and recently extended to Polygon, Ondo Finance invests users’ stablecoins in different funds, issuing them a corresponding token in return. OUSG for BlackRock’s short-term US treasuries (only accredited investors), OMMF for US money market funds (only accredited investors), and USDY for Short-term US Treasuries and bank deposits, plus equity subordination (only non-US citizens). Depending on risk level, Ondo Finance offers between 4.5% and 7.7% APY. Most importantly, however, the tokens that users receive as proof of their investment in real world assets (US dollars and US debt in this case) can be then used in any DeFi protocol that would list them. This is the main difference from traditional funds, making investments liquid, borderless, and accessible to all. stUSDT Launched in July, , of which almost half was added just last week. stUSDT has become the biggest RWA protocol by far, securing a stunning $1.8 billion of TVL That looked somewhat irregular to us, and indeed, digging into the protocol revealed a number of red flags. The protocol is built on and TRON, the brainchild of the controversial crypto personality Justin Sun, who is also sitting on the board of Huobi, the crypto exchange. Ethereum , which he hopes someday will become the “web3 alternative to Alipay’s Yu’e Bao”. Users can stake their $USDT on the platform (current yield is around 4% APY), which invests them into “high-grade short-term government bonds”, as outlined on its website. In return, users receive $stUSDT, which they can then use in other financial protocols. Mr. Sun touted stUSDT as a money market fund Launched by RWA DAO, stUSDT is operated by JustLend DAO under the custody agreement between the two. Other than this information, little is known about its governance, and the protocol cruelly lacks transparency. Furthermore, it became known that Justin Sun was behind the sudden increase of the protocols’ TVL. Last Friday, he made a series of operations that puzzled the crypto space: he minted $815 million of the small-cap stablecoin TUSD, which he then transferred to another address and burned. Coincidentally, almost the same amount of stUSDT was minted, raising brows in the community. Overall, this project is not deprived of interest, but some would say the red flags are too many, and t does not stand opacity. he DeFi space is steadily morphing into CeFi, which in turn acts as a gateway to DeFi. It is a fascinating process to watch, and while the current public opinion still dissociates the crypto and the fiat worlds, in reality, they are merging. Traditional finance Slowly, then suddenly. Also published . here

Real World Assets: The Crypto Trend with Trillions in Potential

The acceleration of the digital transition from 2020 onwards emphasized one thing: there is only one global market, and in this global market, talent is more valuable than ever. According to a report by Advantis, , while consulting firm Korn Ferry estimates that by 2030, there will be a . 68% of middle-market firms reported struggles to attract top talent skilled labor shortage of 85 million people However, while many see this as a risk, I see it as an opportunity. We already know enough about freelancing and how it provides businesses and employees with a win-win arbitrage value proposition, which is why there is a lot of hype surrounding the HR Tech marketplace. To understand it, we need to look at the statistics related to freelancers. According to , 1.57 billion people in the world are freelancers. That equates to almost 47% of the workforce. There are reports, like this one , which estimate the value of global freelancing at $1.5 trillion, and predict that it will grow at a rate of 15% annually. World Bank data shared by the University of Cincinnati However, there are challenges to make it work. First, freelancing involves an increasing amount of cross-border collaboration. If freelancing is slated to grow at 15%, cross-border freelancing is poised to grow over 30% annually, according to . The same report also mentions how a popular freelancing platform had over 70% of its revenues coming from outside the USA. Finextra Hiring international freelancers brings the challenge of payroll. How do we ensure that all these contractors get paid on time? Furthermore, how do we minimize the cost at which they get paid? Dealing with freelancers involves, on one side, moving money across countries, which becomes expensive when dealing with conventional banking institutions. And if this doesn’t flow well, it is likely that, with plenty of options, freelancers will take their talents elsewhere. shared that 40% of freelancers would work for a company that pays them faster, and 59% of freelancers work for more than one company. In this crowded marketplace, talent has the upper hand, especially when it comes to high-level specialists who are always sought-after. A deep dive by Pymnts Furthermore, there is the legal aspect. Hiring contractors can be tricky when nations have different laws and regulations, and the wrong expectations can lead to costly problems down the line. Having said this, the pressing need for a service that helps companies scale globally by working with freelancers and providing freelancers with the necessary legal and compliance tools has not gone unnoticed. Human resources tech, or HR tech, is often labeled as the next big thing. Alex Wilhelm, from TechCrunch, mentioned that some of the hottest IPOs (Initial Public Offerings) in the second half of 2023 . could be companies in the HR tech field While technological capabilities play an essential role in the scalability of the solutions offered, there is another factor at play that can make a company stand out in this increasingly-crowded market, as more companies are vying to attain a piece of the market that, just for payroll, Sacra . estimated to be $1.4 trillion This key factor in serving this increasingly-global demand is having a thorough understanding of the nature of each of the markets where contractors are and where business is being conducted. It involves earning the loyalty of customers as people and making human connections instead of transactional ones. To succeed, HR tech companies must be culturally adaptable and recognize that every market posits a different set of challenges, and as such, providing companies and freelancers with the necessary tools to make this cross-border collaboration seamless. With this in mind, let’s keep one statistic in mind. The same World Bank report that said that there are 1.57 billion freelancers in the world also said that there are 73.3 million freelancers in the United States. This means that there are over 1 billion freelancers that are in different parts of the world. And they will be hired by companies that could be anywhere: in Dubai, Beijing, or Tel Aviv. For HR tech companies, and especially for those focused on payroll, the opportunity this opens up is unparalleled. A report from Hello Bonsai disclosed that , and the reason why that happens is because of the complexity involved with international transactions. In addition to tolerating delays, freelancers need to put up with hefty commissions, especially those charged by platforms like PayPal. 29% of freelancers are paid late Hence, there is a pressing need for an efficient, seamless service with human-centered design in mind that is able to satisfy companies and freelancers alike. With cross-border collaboration likely to keep increasing, and millions of people joining the freelancing ranks on a yearly basis, the company that can capitalize on this will have a foot ahead in the race for the $1.5 trillion pie.

Payroll Solutions for Cross-Border Freelancers: A $1.4 Trillion Opportunity

Apple became the first company to cross the $1 trillion mark in August 2018. And hit the $2 trillion mark on the 20th of August, 2020, despite the negative impact of the pandemic. On January 3, 2022, it briefly became the first $3 trillion company in the world. Steve Jobs, Steve Wozniak, and Ronald Wayne founded the company in a garage when they produced their first personal computers in 1976, but ever since, it's been topsy-turvy. However, since Tim Cook took over from Steve Jobs as Chief Executive in 2011, its shares have quadrupled, making it a trillion-dollar company. But things could have been different today from Steve Jobs' resignation and return, NeXT, and Cook’s takeover. To understand the success of Apple, we must understand its history. . But how did the trillion-dollar company become so successful? Apple, today, is worth $2.74 trillion, with its relentless rally putting $ 3 trillion in view The Inspiration Behind Apple The story all began when two long-time friends, one named Steve Wozniak, built a blue box that could make long-distance calls, and the other, Steve Jobs, encouraged him to sell it. They ended up making $6,000. Wozniak made other inventions, like a game with a circuit box design, but it was after the pair attended a Computer club in 1975 that Apple became clear in sight. In 1976, Wozniak made a computer in a garage, and Jobs encouraged him to sell it. They named it Apple I, and that's how the company started. Early Stage of Apple Apple I, the desktop computer Steve Wozniak built, was with wooden casing and sold for $666.66 with over 200 computers made. They negotiated with Byte shop owner Paul Terrell, who offered to buy 50 units for $500 with cash on delivery if they came fully assembled. Before now, buyers would have to solder necessary chips onto the circuit board themselves and integrate their own keyboard, display, and power supply. Jobs and Wozniak began making products that were easily useable by everyone due to this. It also gave insight into what Apple II should look like. Apple II wholly sold out after being presented at Westcoast Computer Fair in April, after which Apple went public. Sales went from $ 7.8 million in 1978 to $ 117 million that year. But Apple III was a failure. Steve Wozniak didn't build this computer, but Wendell Sanders did. Jobs had strange expectations for this computer. He wanted a computer with no cooling fan to sustain his desired design. The motherboard got hot too quickly and warped, and chips popped out of their sockets due to the overpopulated board, resulting in severe problems with the entire system. Apple branched out and worked on Lisa and Macintosh to cover up for its Apple III mess by giving both graphical user interfaces. Lisa emerged in 1983 for $10,000, and the nine-inch screen Macintosh sold for $2,500. The former failed due to its price tag, while the latter had mass market success and ushered in the modern age of personal computing. The Run Of CEO's and Jobs’ Dismissal Michael Scott became Apple’s first CEO in 1977 before Mike Markkula took over from 1981 to 1983. After Markkula's departure, Jobs pitched the then Pepsi CEO John Scully to becoming Apple CEO in 1983 by asking him if he wanted to sell sugared water for the rest of his life or wanted an opportunity to change the world. Scully took the chance immediately. But things changed quickly for the company in 1985 when Jobs attempted to oust Scully. They had several disagreements due to their management styles and the price value of the Macintosh. The board thought to resolve this by moving Jobs into the role of Chairman, a ceremonial role, which only meant he could no longer make significant decisions. But Jobs, unimpressed, left the company. Wozniak also left because he was unhappy not playing the Engineering role. Jobs founded the NeXT computer company, while Sculley increased Apple’s Annual Revenue from $569 million to $8.3 billion. But Scully got fired in 1993 over the failed launch of Newton PDA and moving the company’s computer to Power PC chips instead of Intel Processors, which cost the company a fortune and kept Mac prices high. The company was declining, and Scully had to step down for Michael Spindler to take over in 1993. Michael tried but decided the best move for Apple was to sell the company. The board decided against it and replaced him with Gil in 1996. Gil Amelio became the CEO in 1996 and saved the company by reducing its $250 million expenses and laying off over 3,000 workers. He purchased Jobs’ NeXT for $427 million to use the company’s operating system. But the company’s losses continued to mount, which got Amelio fired, with Jobs becoming the company's interim CEO in February 1997. Jobs Reason For Apple’s Decline Jobs tried to revive the company by analyzing its failure to loss of its iconic identity and product prices. Prices went up, and Mac stopped selling due to its cost, and the product line was proliferated, so it was hard to distinguish between models. There were products of all kinds, including printers, to help boost sales. The cost was too much for the company. Jobs Steps to Revival Jobs made daring efforts to restore Apple’s fortune. First, the company struck a $150 million partnership with its fierce rival, Microsoft. This alliance with Microsoft led to the creation of a Mac version of its popular office software. Second, Apple announced a transition to Intel’s x86 processors for its Mac computers at Apple's Worldwide Developers Conference in 2005. Aside from the slow delivery of IBM’s chip, the shift from IBM-built PowerPC processors was necessary because its processors couldn’t match what Intel was doing. PowerPC processors were fast, but they ran hot and required a lot of power, which is terrible for laptops. Another considerable advantage of this transition was that, for the first time, Mac users could run Windows natively on Mac hardware. Third, Apple’s introduction of an all-in-one colorful iMac computer in 1998, Mac OS X, iPod, and iTunes products was another step to the Mac maker’s recovery. The company branched out to iPods in 2001 because music players at that time were substandard. They could barely contain 10-20 songs. Apple improved the product by releasing its iPods that could hold 1,000 songs. In 2003, Musicians could now sell their songs through the iTunes store. Apple changed music from cassettes to bytes. This way, the company became relevant in the music industry. Apple products began creating many possibilities, touching every sector and granting access to various audiences. The company opened its first physical retail stores on May 19, 2001, for the presentation of the company’s products after successfully trying out online stores in 1997. In 2007, The Mac company introduced revolutionary products like the iPhone. Followed by the App Store in 2008, the iPad, and Macbook Air. The iPad sold out 1 million in 28 days compared to iPhone’s 74 days. Jobs eliminated everything, causing them to lose profits, and steered the company on the right track by producing revolutionary products that met consumers' needs. Tim Cook After Jobs passed away, Cook continued the culture of Apple’s creation of revolutionary products with versions of iPhone 8, 8 Plus, and X, which contain retina, high definition display, all bionic chip, and wireless charging. In 2016, Apple introduced smartwatches. Wireless earbuds and subscription-based services like Apple Tv+, Apple Music+, and Apple Arcade followed suit. The Secret of Apple’s Success Creativity and Innovation The company places so much value on creativity and innovation. One can observe that in their creation of revolutionary and disruptive products. Apple became the first to produce a phone, music player, photo editor, and game console. Though they were not the first to create a smartphone or mp3 player, when they produced theirs, it was revolutionary and far better in design and performance, which made their competitors play the game of catch-up. Staying Ahead of Competitors They are always far ahead of their competitors by producing future products in the present. Its creativity and innovation gave birth to products the world had not seen before and made them two years ahead of its competitors. High-Quality Products As Apple designer Jonathan Ive said, “Our goals are very simple — to design and make better products. If we can’t make something better, we won’t do it.” Apple ensures they dish out high-quality products that are flawless in performance and perfect in design. Apple, initially a personal computer, has switched to other sectors and dominated it because of its high-quality products. Consumers Integration They try to see how the world affects them to understand their customer’s needs. They create a lovely customer experience by giving the audience direct access to various products and ensuring their products are simple and easy to use. The choices were not many, and so were the complications. Their products give their audience a sense of fulfillment and belonging. Apple has a brand legacy of trust. If it's Apple, it’d be better to have made their customers willing to pay for anything, no matter the price. Outsiders' View On Why Apple Is Thriving that consumers are drawn to purchasing Apple products by its clever advertising. University of New South Wales marketing senior lecturer Gary Gregory told The New Daily “Apple has integrated into people’s lives with its advertising by showing people how its products can fit into their lifestyles, unlike its competitors that advertise its product features,” Mr. Gregory said. He said Apple had won the tech market with its ability to control and channel distribution. “The company’s control over its distribution is incredible because it’s tough to be an authorized seller, and its pricing is consistent worldwide.” said Apple had also won consumers with its strong data policies. The Swinburne University of Technology senior lecturer in media, Dr. Belinda Barnet, “Privacy is the core of Apple’s business model, and they charge a lot for data storage, which shows how seriously they take consumer privacy compared to other companies,” Dr. Barnet told The New Daily. “Ironically, when Apple started, they were an upstart company, but they have now dominated the market with others trying to keep up.” said Apple’s products were distinctively “sexier” than its competitors. Melbourne University digital and mobile journalism lecturer Corinne Podger “They look and feel good, and the user experience of Apple products has always attracted consumers,” Ms Podger said. Bottom Line The company’s Marketing, Revolutionary products, Consumer priority, Branding, and Leadership style sets it apart from its competitors. Apple had maintained its core value no matter what, and when it tried to deviate from it, it almost paid the total cost. Today, the initial computer company earns 52% of its revenue from its iPhone and 19% from its services. Macs, wearables, and iPads make up the remaining 19%. Apple is reaping the fruit of taking that bold step to branch out while maintaining its core value. It won’t be long till we see the company hit the $3 trillion mark again. Investors keep tabs.

 How Apple Became A Trillion Dollar Company

Since well before the ICO craze, the market has been looking for narratives with the potential to 10x crypto’s market cap - decentralized finance, NFTs, hedging US dollar inflation, Layer 2s, Elon Musk, Microstrategy. The list goes on. Each narrative spurs an injection of new capital, developers, financial engineers, investors, influencers - creating new use cases and market cycles from which further narratives are born. Being early to these narratives is key — and with that being said, let’s talk about Real World Assets (RWA). What are real world assets (RWA)? In short RWAs refer to the tokenization of physical world assets. In this context, “asset” refers to capital assets - significant pieces of property such as homes, cars, commercial real estate, loan portfolios, stocks, and bonds. Even physical art like collectibles can be capital assets. By tokenizing these assets on-chain, RWAs essentially bridge the gap between decentralized finance ($47 billion TVL) and traditional finance (the market cap of Nasdaq stocks alone is $18 trillion). How will it bring value into crypto and what does it mean for the ecosystem? The crypto market as it stands today is largely circular. DeFi is akin to a closed financial system and most of its revenue is derived from endogenous sources i.e. swap fees, borrowing fees. By introducing real world assets onto the blockchain, RWAs promise to open up new avenues for capital, collateral, and revenue to flow into the crypto ecosystem. In turn, blockchain and DeFi building blocks have the potential to bring liquidity to historically illiquid assets, lower the cost of credit, widen the reach of access for credit and collateral, and bring transparency to what is currently an opaque black box of middlemen, wall street horse trading, and compounding inefficiencies. The cross-border, impartial nature of blockchain and smart contracts help put capital assets to use by people or organizations who would traditionally never have had the opportunity to do so, maximizing economic surplus (total welfare/productivity). It’s also an attractive proposition for institutions who tend to seek liquidity wherever they can and are constantly looking for ways to reduce inefficiencies. On-chain management of assets can dramatically reduce middle and back-office operations across firms and intermediaries. To put it in perspective, shaving just 0.5% of the cost from $5bn of transaction settlements would save $25m, a meaningful difference on the balance sheet. To top it all off, successful onboarding of RWAs can serve as a framework for tokenizing other physical assets, like inventory (supply chain management), identity, and taxes – all key building blocks and potential growth opportunities for crypto. What does it look like in its current form? And where is it most likely to start? The biggest use of RWAs to date has been stablecoins— which are essentially a tokenization of US treasury bonds into US dollar units on-chain. The introduction and rapid adoption of stablecoins have proven that there is a real and growing appetite for tokenized RWAs. However, stablecoins serve one basic function - to be a store of value used for earning and spending (making gainz 🚀 or getting rekt 🤡, depending on your perspective). RWAs like gold and stocks have also been tokenized on-chain, either as a different form of (pegged to the price of gold) or as a mirrored asset to be fractionalized and traded using blockchain rather than traditional settlement. stablecoin Projects that explicitly flaunt the “RWA” label, however, have mostly been a tokenization of credit. By “credit” we mean traditional interest bearing assets, like packaged real estate or automobile loans, which are collateralized and brought on-chain as: a)  interest bearing assets for the lender b)  a pool of liquidity, usually stablecoins, for the borrower By essentially securitizing these loans on-chain, they can be easily bought and sold, and come with built-in interest distribution mechanisms via smart contract logic. Securitization allows for the efficient transfer of risk (lenders), and allows people (borrowers) to raise capital and create businesses that provide goods and services— which ultimately allows society to grow and thrive. Real world yields, especially in today’s bearish regime, are often higher than yields that one can find on-chain, increasing the value proposition of RWAs that much more. What are some current RWA projects? MakerDAO ( ) is one of the leaders when it comes to RWAs. RWAs currently account for and recently onboarded a , a Pennsylvania chartered bank. Essentially MakerDAO will allow for HVB to access up to $100m in liquid DAI —which can be converted into fiat— in return for the equivalent in collateralized loans on commercial and residential real estate, e-commerce, industrial and consumer loans. This allows for another $100m in stablecoin to be put on-chain, while MakerDAO receives interest for continuing MakerDAO and DAI operations. $MKR 12.3% (about $907m) of DAI’s collateral $100m loan portfolio from Huntingdon Valley Bank (HVB) Centrifuge ( ) is another leader when it comes to bringing credit on-chain, with over , some of which account for the backing of DAI via MakerDAO. Centrifuge is an open financing platform that allows businesses and borrowers to create fully collateralized asset pools from which investors can provide liquidity in return for shares of the pool that return interest. Centrifuge aims to lower the cost of borrowing for businesses around the world and gives liquidity providers a source of yield while ensuring that they have legal recourse in the case of a default. The credit is also revolving, meaning investment and redemption (the withdrawal of invested capital) orders can come in at any time, and assets can be financed and repaid continuously. $CFG $206m in active loans GoldFinch ( ) aims to build credit markets for emerging markets, providing credit for businesses in Africa, Southeast Asia, and Latin America to the tune of . GoldFinch helps decentralize the auditing process by requiring borrowers to receive approval votes from auditors who stake GFI tokens and are either rewarded tokens or have their stake slashed depending on the outcome of the loan’s repayment. This ensures an alignment of incentives and has allowed funding of startups and impact investments all over the world. $GFI $103m active loans as of today Maple Finance ( ) is similar to GoldFinch and Centrifuge, with over $135m in active loans. Maple uses pool delegates to manage lending pools and enable these delegates to attract global capital and provide funding to a network of “premium” (i.e. vetted) borrowers, which are judged on their investment strategies, credit quality, reputation, and industry. Borrowers submit a loan proposal which is reviewed by pool delegates to calibrate interest rates and collateral ratios. $MPL Institutional Players that are using blockchain for RWA A very promising signal for RWAs has been rapidly growing interest from traditional institutional players. Some examples of recent RWA developments from the world of traditional finance: Feb 14 - Siemens issues its , worth $60m, on the first digital bond Polygon blockchain Feb 16 - Hong Kong issues it’s first tokenized green bond of $101m Apr 3 - Credit Agricole CIB and Sweden’s SEB begin to develop a blockchain-based platform for digital bonds Apr 14 - Bank of America like commodities, currencies, and equities as a key driver of digital asset adoption details tokenization of RWAs Apr 20 - Societe Generale introduces a , CoinVertable on Ethereum - the first institutional stablecoin deployed on a public blockchain Euro stablecoin Apr 26 - Mitsubishi UFG Trust and Banking Corporation (cosmos) as the communication protocol and the Corda blockchain infrastructure develops a digital securities platform using IBC What does the future look like for RWA? Given that tokenized real-world assets depend on the existence of parallel traditional financial institutions, their trust properties will likely never be the same as a DeFi ecosystem which solely deals in crypto-native assets. However, it remains a very compelling case as the market finds relative stability and more advanced data, onboarding, and custody solutions over time. RWAs will bring more holders and users to the ecosystem. While Web3 natives who already hold tokens are the clear initial product-market-fit of DeFi products, bringing DeFi to the traditional and institutional consumers will require assets and financial products that traditional users are familiar with and already own in one shape or another. The TVL in DeFi is a drop in the bucket compared to the hundreds of trillions available in traditional capital markets, and is similarly dwarfed by physical non-capital and even intangible assets mentioned earlier. Think about the concept of the , where most of the world’s transactions are done via some form of abstracted digital representation (your mobile app banking account, your social media identity, text messaging). If you continue along that trend and take it to its logical end within the context of crypto, it is pretty easy to envision a world where all assets are tokenized, mapped to the blockchain, and traded or transacted through the RWA process. With such a large opportunity and gap in the market - bringing another $1 trillion dollars of market cap to the market via RWAs seems like an easy reach. digital twin Author: Toby Fan, Web3 Strategist @ LunarCrush | Twitter LinkedIn

Why RWAs (Real World Assets) will onboard the next $1 Trillion into crypto

Stories, News & Mentions about AITRILLION

Aitrillion’s Stories on HackerNoon