A heartwarming video of a monkey gracefully swimming in a water body has taken the internet by storm.

Viral video shows monkey swimming like a pro, internet is stunned

Analysts expect the deal to boost Reliance’s position in the country’s fiercely competitive video streaming sector.

Reliance’s video streaming ambitions to get a boost with Hotstar-JioCinema combine

YouTube Create is an application that lets its users edit videos with easy-to-use production tools. , Technology &amp; Science News, Times Now

YouTube Create App Will Help You To Edit Long And Short Videos For Free | Technology & Science News, Times Now

rideOS

rideos

Six trends to watch, from artificial intelligence and mobility to digital diplomacy and blockchain. So far, the month of January has seen many write ups and analyses on technology and what to expect in 2019. For instance, I was pleasantly surprised to see digital diplomacy as one of mentioned by Brad Smith, President and Chief Legal Officer at Microsoft, in a recent article. Indeed, digital diplomacy has been a key focus for the Seattle-based company — not as a way to communicate with audiences, but rather as a tech for good tool, at the intersection between foreign policy, technology, and cybersecurity. The Top 10 Tech Issues for 2019 “The tech sector continues to prioritize cybersecurity innovation and investments,” writes Smith in a LinkedIn article co-authored with Carol Ann Browne, Director of Communications at Microsoft. “The year’s biggest step came in November,” they say. “ , led by French President , launched an important appeal to deter indiscriminate cyberattacks and protect electoral processes. It’s based on multi-stakeholder action, garnering more than 450 signatories from more than 50 governments and roughly 400 companies and civil society groups.” The Paris Call for Trust and Security in Cyberspace Emmanuel Macron The New Year brings a new opportunity to bring everyone together. 1. DIGITAL DIPLOMACY This is why digital diplomacy will represent, I think, an important topic of discussion in tech in 2019. Most think of digital diplomacy as one of the many ramifications of public diplomacy — social media being the most visible part of this equation. However, digital diplomacy has been evolving beyond social media. Some call it “techplomacy;” some others call it “cyberdiplomacy.” I look at it in terms of actors. In the past few years, tech companies like Microsoft, Facebook, and Google have become important foreign policy players. The 2017 launch of the Global Internet Forum to Counter Terrorism (GIFCT) to tackle extremism and terrorism online and work together with governments and civil society groups was a key step forward — including Big tech presence at the and at the G7. United Nations Another important step was last year’s at the in Paris, where 60+ technology leaders and chief executive officers gathered together to talk about how technology can contribute to the common good and how it can be leveraged around three topics — education, labor, and diversity. Rwandan President Paul Kagame was among the guests, as were UNESCO Director Audrey Azoulay, Mitchell Baker, President of the Foundation, and , Founder of the Foundation. Also in attendance CEO , IBM CEO Ginni Rometty, Facebook founder and CEO Mark Zuckerberg, of Microsoft, Alexandre Dayon of , of Slack, and , co-founder and CEO of . Tech for Good Summit Élysée Mozilla Jimmy Wales Wikimedia Uber Dara Khosrowshahi Satya Nadella Salesforce Stewart Butterfield John Collison Stripe On the government side, Denmark’s “techplomacy” platform has been another big step forward in how diplomacy and technology interact. Following the appointment in 2017 of Casper Klynge as the first-ever tech ambassador to Silicon Valley and the global tech industry, the Danish Government has now launched a new . Foreign and Security Policy Strategy 2019–2020 2. AI FOR GOOD AND ETHICS IN AI Artificial intelligence and machine learning represent one of the most exciting trends in technology: virtual assistants, autonomous cars, self-learning algorithms. These are challenges many tech companies and startups at looking at to push innovation forward. But the number of AI critics is multiplying as these technologies have also a dark side. 2018 has been a key year for artificial intelligence as concerns about the repercussions of AI on society and human activity are mounting, even within Big Tech. Trust is also a big issue as some believe that the recent rush towards AI may suggest that we are turning over the keys of reason to machines. However, companies like Google, Microsoft, and Amazon are now exploring ways to tap into AI for social good and humanitarian projects and aid. Google was the latest to enter this space of what some refers to as AI for good. The company announced in November its to grant about $25 million globally in 2019 to humanitarian and environmental projects seeking to use artificial intelligence to speed up and grow their efforts. AI Impact Challenge Reuters pointed out that “Focusing on humanitarian projects could aid Google in recruiting and soothe critics by demonstrating that its interests in machine learning extend beyond its core business and other lucrative areas, such as military work.” Earlier this year, following a harsh and public employee backlash, Google announced that it would not renew a deal to analyze US military drone footage in a AI-based program. Google AI Chief Operating Officer Irina Kofman told Reuters the new AI for good program was not a reaction to what happened earlier this year, but noted that thousands of employees are eager to work on “social good” projects despite the fact that those programs often do not directly generate revenue. Microsoft, on the other hand, just silently announced that it would sell the US military and intelligence agencies whatever advanced technologies they needed “to build a strong defense,” including its machine learning and AI tools. “We want the people of this country and especially the people who serve this country to know that we at Microsoft have their backs,” wrote Brad Smith, Microsoft President, in a blog post. “They will have access to the best technology that we create. At the same time, we appreciate that technology is creating new ethical and policy issues that the country needs to address in a thoughtful and wise manner. That’s why it’s important that we engage as a company in the public dialogue on these issues.” The counter the mounting criticism, Microsoft has recently launched a series of new AI programs, totaling $115 million, including , its new project to put AI to work for the future of our planet, and , a new $40 million, five-year program to harness the power of AI to focus on four priorities — helping the world recover from disasters, addressing the needs of children, protecting refugees and displaced people, and promoting respect for human rights. AI for Earth AI for Humanitarian Action 3. BLOCKCHAIN The Internet “has been spectacular in so many ways,” , founder of startup and co-founder of , said . “It has transformed global society, but it’s broken.” Joseph Lubin ConsenSys ethereum in his keynote address during the Ethereal Summit in Brooklyn in May In his keynote presentation, Lubin pointed out how the technology behind the Internet and the World Wide Web “is getting stretched to its limits” and security is one of the main issues. “Its [the Internet] foundations were formed decades ago, in naive times,” Lubin highlighted in one of his slides. “Security evolved as a patchwork later.” Blockchain is going to be a revolution in IT security because every transaction against your infrastructure is a strongly and cryptographically authenticated and granularly authorized. Joseph Lubin on stage at ConsenSys’ Ethereal Summit 2018. “You have a force for universal disintermediation” enabling content creators, resource providers, service providers to directly access consumers with little to none intermediary extracting value, without adding any commensurate value. ConsenSys has been exploring these approaches already with platforms like for music, for news, and Cellarius for collaborative, fan-crowdsourced, and fan-curated stories on many formats. But also in the area of services and resources, linking them directly to consumers, with projects like , , , , , , and many others. And recently even the cloud has been part of the evolution of blockchain, from the partnership between and Amazon Web Services (AWS) to the launch of W3BCLOUD, a blockchain-based cloud computing infrastructure, developed in partnership with semiconductor producer AMD. Ujo Civil: Self-Sustaining Journalism Golem Project SONM Kauri Team Swarm Storj Labs Pangea Kaleido This constitute Web 3.0 for ConsenSys and its mesh of projects, with trusted transactions, automated agreements, and smart software objects on Ethereum, a single world computer, a single execution space; as well as other protocols like decentralized storage, decentralized bandwidth, and heavy compute. All of this is going to enable us as people and corporation to interoperate much more fluidly. In a way, Web 3.0 brings the Internet back to its beginning as a decentralized architecture. “But efficiencies and drive for wealth led to siloed, walled gardens,” Lubin highlighted in a slide. “This was due to a lack of mechanism for shared ownership of open platforms.” Web 3.0 is what we’re just of the cusp of. And Web 4.0 is going to be very interesting. Lubin mentioned how Web 4.0 is “what we’re just starting to think about.” It is a system where artificial intelligent agents are empowered by value through blockchain and tokens. “The Internet of the machines economy is going to be very interesting and definitely coming to a blockchain near you,” he said. Essentially, in 1–2–3 years I think it’s going to feel like blockchain is everywhere. It’s still not true right now. 4. MICRO MOBILITY AND ELECTRIC SCOOTERS Even , co-founder of AOL and now Chairman & CEO of , mentioned the electric scooters in his write up of 2018 here n , which he titled Steve Case Revolution Team Medium What We Can Learn from the Biggest Tech Policy Mistakes of 2018 . “Scooters were all the rage this year, popping up in cities and on sidewalks all across the country,” he wrote about 2018. “Without coordination or notification to city officials, some felt blindsided and reacted by impounding scooters. Issues around transportation and smart cities will continue to be important as public and private entities look for ways to create innovative solutions for challenges like traffic, pollution, walkability, etc.” It seems it’s a very polarizing business… Either you love the scooters, or you hate them. “We have this deeply ingrained car culture in the US that we have to challenge in multiple ways to really get at the heart of it,” said bike, scooter, and pedestrian policy to . Lyft Caroline Samponaro Fast Company Lyft is one of the many companies exploring the electric scooter business, including ; Lime, formerly ; Bird; , recently bought by ; Skip; and many others outside of the US, like dott in the Netherlands, co-founded by , Ofo’s former head of Europe, the Middle East, and Africa; Mexico City-based Grin; Berlin-based Tier; and São Paulo-based Yellow. Uber LimeBike Spin Ford Motor Company Maxim ROMAIN All are investing in the future of micro mobility — or, as dott’s VC partner of calls it: (ShutaaS). Lars Jörnow EQT Ventures Short-haul-urban-transportation-as-a-Service And this is another area in which companies in the space can become important tech-for-good players. Startups and cities, for example, should work with think tanks in the areas of urban planning and greening our cities. Organizations like , , and are all more than willing to work with cities and companies to mobilize and to support ambitious, measurable, planned climate and energy action. A better and greener mobility strategy is a good think for us all! C40 Cities Climate Mayors The Global Covenant of Mayors for Climate & Energy 5. SELF-DRIVING CARS AND AUTONOMOUS VEHICLES In a recent blog post on Medium, , CEO and co-founder of rideOS, mentioned , including fully self-driving ride hailing services, and consolidation of self-driving companies. Justin Ho five trends to watch in 2019 Ho also predicts “hundreds, then thousands of live, fully autonomous vehicles all over China, while the rest of the world continues to grapple with regulatory hurdles.” “Self-driving tech in the US will be slower to scale because of unfavorable policies and higher technological barriers due to outdated infrastructure,” he explains. “In order to minimize losses, other governments all over the world need to move faster to adopt policies like China’s around the development of regulations and infrastructure for self-driving vehicles.” 6. ELECTIONS, FAKE NEWS, AND TECHNOLOGY After much of the attention on the role of technology and social media on the US presidential election in 2016 and the US midterms in 2018, this year the focus is on key elections in Europe, in India, and in Canada. It is a debate that not only includes transparency and data privacy, but also how social media companies like Facebook, Google, and Twitter will counter misinformation — or fake news as most refer to the issue. Lots will happen in 2019 at the intersection between elections, technology, and fake news. No need to say more…

The state of tech in 2019

Damn Near Every Digital Service is Ripping You Off to Float Free-riders

Damn Near Every Digital Service is You Ripping off to Float Free-riders

Fast jeder digitale Dienst betrügt Sie, um Trittbrettfahrer zu gewinnen

Casi todos los servicios digitales te están estafando para convertirte en usuarios libres

Presque tous les services numériques vous arnaquent pour faire flotter les free-riders

लगभग हर डिजिटल सेवा आपको मुफ्त में लाभ कमाने के लिए लूट रही है

ほぼすべてのデジタルサービスが、ただ乗りする人をだますためにあなたを騙している

빌어먹을 거의 모든 디지털 서비스가 당신을 떠다니는 무임승차로 끌어들이고 있습니다.

Quase todos os serviços digitais estão enganando você para flutuar como free-riders

Черт возьми, почти каждый цифровой сервис обманывает вас, чтобы пускать в ход безбилетников

Lanet olsun, Neredeyse Her Dijital Hizmet Sizi Serbest Sürüşçülere Yöneltiyor

Chết tiệt, gần như mọi dịch vụ kỹ thuật số đều đang lừa bạn để thả nổi những người đi xe miễn phí

几乎每一项数字服务都在欺骗你，让你搭便车

The rideshare and delivery markets are undergoing an upheaval driven by rapid technological advancements and shifting consumer behaviors. Companies like Wave are surfing this change with bespoke artificial intelligence (AI) technology, carving out a competitive edge in a burgeoning industry. As the market expands, opportunities abound for those willing to adapt. Wave’s own personal brand of AI-driven safety and community involvement has positioned it quite advantageously, taking the market by storm in U.S. cities like Atlanta and Chicago. The Expanding U.S. Sector The global rideshare market is projected to reach $335 billion by 2025, growing at a compound annual growth rate (CAGR) of 18.2% from 2020 to 2025. This growth is fueled by increasing urbanization, the rising preference for shared mobility, and the convenience of on-demand services. Similarly, the online food delivery market is expected to surpass $213 billion by 2030. This explosive expansion is primarily driven by the proliferation of platform-to-consumer services and the lasting impact of the COVID-19 pandemic. In the United States, DoorDash leads the food delivery market with a 67% market share, followed by Uber Eats with 23%. The intense competition among these giants has focused on efficiency and customer experience, with companies investing heavily in AI and automation to streamline operations and reduce costs. "AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well," says Diondre Lewis, CEO of Wave. "At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support." Room for Growth in the Markets The rideshare and delivery markets present numerous opportunities for savvy businesses looking to capitalize on recent technological developments. One among these trends is the increasing efficiency of last-mile delivery, which now represents 53% of the total cost of transportation. Companies are exploring various technologies, including AI, autonomous vehicles, and crowdsourcing, to reduce these costs and improve delivery times. Autonomous vehicles, including drones, are also becoming a viable solution for last-mile deliveries, offering the potential to cut costs and enhance efficiency significantly. This trend is expected to gain momentum as technology advances and regulatory frameworks evolve, addressing labor shortages and improving service efficiency. Another opportunity lies in the growing demand for personalized and efficient delivery services. AI-powered platforms can analyze vast amounts of data to provide personalized recommendations, optimize delivery routes, and predict peak demand times, much like Wave itself does. "The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport," Lewis explains. The Role of AI in Shaping the Future AI technology is credited with most of the transformation in the rideshare and delivery markets. Companies increasingly rely on AI to optimize their operations, improve safety, and provide personalized services. Wave's AI-powered platform is the very epitome of the potential AI presents. The company's proprietary AI algorithms, such as dynamic micro-zone demand prediction and real-time optimal route adjustment, aid drivers with picking the most efficient paths, reducing fuel consumption and wait times. These artificial intelligence algorithms are also utilized in Wave’s steadfast focus on safety. The algorithm analyzes vehicular operation to suggest the best time to perform routine maintenance and through automated disturbance or vehicular accident detection. Wave's Technological Potential Wave has carefully positioned itself to capitalize on these market opportunities. Since its inception, the company has focused on underserved markets, such as non-emergency medical transport and exclusive event transit. This allows it to tap into niche segments that larger competitors often overlook. "Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place," Lewis says. As consumer preferences shift toward shared mobility and on-demand services, Wave readies itself for further expansion. The rideshare and delivery markets are ripe with opportunities for companies willing to evolve. Wave's drive to further build upon its bespoke AI platform positions it as a formidable contender in the market. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. The rideshare and delivery markets are undergoing an upheaval driven by rapid technological advancements and shifting consumer behaviors. Companies like Wave are surfing this change with bespoke artificial intelligence (AI) technology, carving out a competitive edge in a burgeoning industry. Wave Wave As the market expands, opportunities abound for those willing to adapt. Wave’s own personal brand of AI-driven safety and community involvement has positioned it quite advantageously, taking the market by storm in U.S. cities like Atlanta and Chicago. The Expanding U.S. Sector The Expanding U.S. Sector The global rideshare market is projected to reach $335 billion by 2025, growing at a compound annual growth rate (CAGR) of 18.2% from 2020 to 2025. This growth is fueled by increasing urbanization, the rising preference for shared mobility, and the convenience of on-demand services. Similarly, the online food delivery market is expected to surpass $213 billion by 2030. This explosive expansion is primarily driven by the proliferation of platform-to-consumer services and the lasting impact of the COVID-19 pandemic. In the United States, DoorDash leads the food delivery market with a 67% market share, followed by Uber Eats with 23%. The intense competition among these giants has focused on efficiency and customer experience, with companies investing heavily in AI and automation to streamline operations and reduce costs. " AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well, " says Diondre Lewis, CEO of Wave. " At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support. " AI has changed the rules of the game. Not only in transportation or food delivery but every other aspect of the industry as well, At Wave, AI has enabled us to optimize routes, predict demand, enhance safety, and provide better customer support. Room for Growth in the Markets Room for Growth in the Markets The rideshare and delivery markets present numerous opportunities for savvy businesses looking to capitalize on recent technological developments. One among these trends is the increasing efficiency of last-mile delivery, which now represents 53% of the total cost of transportation. Companies are exploring various technologies, including AI, autonomous vehicles, and crowdsourcing, to reduce these costs and improve delivery times. Autonomous vehicles, including drones, are also becoming a viable solution for last-mile deliveries, offering the potential to cut costs and enhance efficiency significantly. This trend is expected to gain momentum as technology advances and regulatory frameworks evolve, addressing labor shortages and improving service efficiency. Another opportunity lies in the growing demand for personalized and efficient delivery services. AI-powered platforms can analyze vast amounts of data to provide personalized recommendations, optimize delivery routes, and predict peak demand times, much like Wave itself does. " The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport ," Lewis explains. The name of the game is customer satisfaction. There is no better way to meet the varied demands of our clients than with machine learning. We offer services from food delivery to corporate rideshares and non-emergency medical transport The Role of AI in Shaping the Future The Role of AI in Shaping the Future AI technology is credited with most of the transformation in the rideshare and delivery markets. Companies increasingly rely on AI to optimize their operations, improve safety, and provide personalized services. Wave's AI-powered platform is the very epitome of the potential AI presents. The company's proprietary AI algorithms, such as dynamic micro-zone demand prediction and real-time optimal route adjustment, aid drivers with picking the most efficient paths, reducing fuel consumption and wait times. These artificial intelligence algorithms are also utilized in Wave’s steadfast focus on safety. The algorithm analyzes vehicular operation to suggest the best time to perform routine maintenance and through automated disturbance or vehicular accident detection. Wave's Technological Potential Wave's Technological Potential Wave has carefully positioned itself to capitalize on these market opportunities. Since its inception, the company has focused on underserved markets, such as non-emergency medical transport and exclusive event transit. This allows it to tap into niche segments that larger competitors often overlook. " Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place ," Lewis says. Beyond profit, Wave stands for serving those groups often forgotten by bigger companies. We even offer services for transportation of young children, with the requisite safety measures and monitoring in place As consumer preferences shift toward shared mobility and on-demand services, Wave readies itself for further expansion. The rideshare and delivery markets are ripe with opportunities for companies willing to evolve. Wave's drive to further build upon its bespoke AI platform positions it as a formidable contender in the market. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here here

Wave's AI-Based Innovations: The Key to Dominating the Rideshare and Delivery Market

ওয়েভের এআই-ভিত্তিক উদ্ভাবন: রাইডশেয়ার এবং ডেলিভারি বাজারে আধিপত্য বিস্তারের চাবিকাঠি

KI-basierte Innovationen von Wave: Der Schlüssel zur Beherrschung des Rideshare- und Liefermarkts

Innovaciones basadas en inteligencia artificial de Wave: la clave para dominar el mercado de viajes compartidos y entregas

Les innovations basées sur l'IA de Wave : la clé pour dominer le marché du covoiturage et de la livraison

वेव के एआई-आधारित नवाचार: राइडशेयर और डिलीवरी बाजार पर हावी होने की कुंजी

Wave の AI ベースのイノベーション: ライドシェアと配送市場を支配する鍵

Wave의 AI 기반 혁신: 차량 공유 및 배달 시장을 지배하는 열쇠

Inovações baseadas em IA da Wave: a chave para dominar o mercado de transporte compartilhado e entrega

Инновации Wave на основе искусственного интеллекта: ключ к доминированию на рынке совместных поездок и доставки

Wave'in Yapay Zeka Tabanlı Yenilikleri: Araç Paylaşımı ve Teslimat Pazarına Hakim Olmanın Anahtarı

Những đổi mới dựa trên AI của Wave: Chìa khóa để thống trị thị trường giao hàng và chia sẻ xe

Wave 基于人工智能的创新：主宰拼车和配送市场的关键

The United States v Meta Platforms Court Filing October 24, 2023 is part of . You can jump to any part in this filing . This is part 47 of 98. HackerNoon’s Legal PDF Series here COUNT II: VIOLATIONS OF THE ARIZONA CONSUMER FRAUD ACT, ARIZ. REV. STAT. §§ 44-1521 to -1534 860. The State of Arizona incorporates and realleges each of the paragraphs 1 through 850 as if fully set forth herein. 861. The conduct described in the preceding paragraphs of this Complaint constitutes deception, deceptive or unfair acts or practices, fraud, false pretenses, false promises, misrepresentations, or concealment, suppression or omission of material facts with intent that others rely on such concealment, suppression or omission, in connection with the sale or advertisement of merchandise in violation of Ariz. Rev. Stat. §§ 44-1521 to -1534. 862. While engaging in the acts and practices alleged in this Complaint, Meta knew or should have known that its conduct was of the nature prohibited by Ariz. Rev. Stat. § 44-1522, subjecting it to enforcement and penalties as provided in Ariz. Rev. Stat. § 44-1531(A). 863. With respect to the concealments, suppressions, or omissions of material fact described above, Meta did so with intent that others rely on such concealments, suppressions, or omissions. 864. With respect to the unfair acts and practices described above, these acts and practices caused or were likely to cause substantial injuries to consumers that were not reasonably avoidable by consumers and were not outweighed by countervailing benefits to consumers or to competition. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:23-cv-05448 retrieved on October 25, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Washingtonpost.com

Meta's Wild West: Arizona Rides In with Consumer Fraud Allegations

New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City is ramping up its efforts to support electric vehicle (EV) adoption as it plans to install 600 new curbside EV chargers. Mayor Eric Adams recently announced that the initiative is part of a $15 million federal grant being awarded to the city to bolster its EV charging infrastructure — with a focus on underserved and low-income communities. “We’re building a cleaner, greener, and more affordable city for all New Yorkers, and through initiatives like this, we’re making sure that no New Yorkers are left behind,” wrote Mayor Adams in a blog post. “This $15 million grant will let us build the nation’s largest EV charging program, focused on low-income and disadvantaged areas so people living in all five boroughs can afford to drive and charge electric vehicles.” By 2050, New York City aims to get 20% of all new vehicles registered as electric. Thanks to incentives like the Inflation Reduction Act tax credits and Drive Clean rebates, a growing number of NYC drivers are switching to EVs. Despite these efforts, the city's grid is already struggling to keep up with the power and infrastructural requirements of current EV owners. Can NYC’s Electrical Grid Keep Up with the Growing Demand for EV Charging? NYC is currently grappling with two key challenges to scale its charging infrastructure — space and available power. For commercial transportation, charging hubs require significant space for infrastructure and larger electric trucks. In dense urban areas, especially NYC, real estate is limited and expensive, making it difficult to find suitable locations to accommodate charging stations and vehicles without disrupting city traffic. “Most of the federal EV infrastructure grants are for chargers deployed along highways (not buildings), limiting monetary incentives available to building developers deploying EV chargers,” Akshay Singh, Industrial and Automotive Industries Principal at PwC Strategy&, told me. “City planners need to work closely with utilities to make sure upgrades are being planned & performed timely, especially to supply dense urban areas with the highest EV adoption.” New York’s grid is already strained as the city attempts to electrify not only its transportation but also its buildings. Singh asserts that developers often do not understand the level of grid upgrades required due to a lack of available information. Moreover, these challenges are expected to become more prevalent with charging sites located in high-rise buildings that have not had capacity upgrades in years. “Major challenges with EV charging today are around how to make the economics of operating EV chargers feasible. DC fast chargers that operate at 100kW+ power output (to charge EVs within 30-45 mins) can cost upwards of $50k per unit which makes upfront costs of a multi-unit charging site very expensive,” added Singh. Electric Vehicles as Untapped Grid Assets? As New York pushes toward its ambitious goal of having 400,000 EVs on the road by 2030 as part of the "Green Rides" initiative, it’s clear that the city needs more fast-charging sites. Conglomerates like Revel are already making strides with its "superhub" in Brooklyn, boasting 25 high-speed chargers. But even with their success, the company faces a significant challenge: finding enough grid power to keep these large fast-charging stations running smoothly. While the city grapples with its grid limitations, some startups are stepping in to offer innovative solutions through A.I. and edge computing. Brooklyn-based Synop, for instance, aims to help stabilize the grid by integrating data from grid operators, EV charging stations, and EV fleet operators. “We focus on edge computing to enhance charging management,” Gagan Dhillon, CEO of Synop, told me. By bringing cloud-based functionality closer to the charging site, the company says it can increase reliability and reduce latency, “which is critical in areas with constrained grids or complex microgrid setups.” Dhillon added that one overlooked aspect of EV adoption is that electric vehicles are essentially batteries on wheels, and with the right infrastructure, they can serve as valuable assets rather than just power consumers. “EVs can act as backup power sources during times of high grid demand, providing energy back to the grid or even powering homes when needed,” he said. “With the integration of smart charging technology, EVs can be programmed to charge during off-peak times when energy is more abundant. This flexibility makes them less of a strain on the grid than often imagined.” With more people driving EVs and a grid under a lot of strain, NYC's EV goal success will hinge on how open it is to new technologies, speeding up infrastructure projects, and investing in sustainable energy solutions. “There will be a need for higher powered EV chargers that will further increase the grid upgrade requirements. Developers need to work together with local utilities from the start to successfully deploy EV projects,” Singh told me. “Along with that, additional [capital expenditure] needed to sustain the grid for more demand needs to be laid out and discussions on who will bear the cost burden.” New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City is ramping up its efforts to support electric vehicle (EV) adoption as it plans to install 600 new curbside EV chargers. Mayor Eric Adams recently announced that the initiative is part of a $15 million federal grant being awarded to the city to bolster its EV charging infrastructure — with a focus on underserved and low-income communities. “We’re building a cleaner, greener, and more affordable city for all New Yorkers, and through initiatives like this, we’re making sure that no New Yorkers are left behind,” wrote Mayor Adams in a blog post . “This $15 million grant will let us build the nation’s largest EV charging program, focused on low-income and disadvantaged areas so people living in all five boroughs can afford to drive and charge electric vehicles.” By 2050, New York City aims to get 20% of all new vehicles registered as electric. Thanks to incentives like the Inflation Reduction Act tax credits and Drive Clean rebates, a growing number of NYC drivers are switching to EVs. Despite these efforts, the city's grid is already struggling to keep up with the power and infrastructural requirements of current EV owners. blog post blog post Inflation Reduction Act Inflation Reduction Act switching switching Can NYC’s Electrical Grid Keep Up with the Growing Demand for EV Charging? NYC is currently grappling with two key challenges to scale its charging infrastructure — space and available power. For commercial transportation, charging hubs require significant space for infrastructure and larger electric trucks. In dense urban areas, especially NYC, real estate is limited and expensive, making it difficult to find suitable locations to accommodate charging stations and vehicles without disrupting city traffic. “Most of the federal EV infrastructure grants are for chargers deployed along highways (not buildings), limiting monetary incentives available to building developers deploying EV chargers,” Akshay Singh , Industrial and Automotive Industries Principal at PwC Strategy&, told me. “City planners need to work closely with utilities to make sure upgrades are being planned & performed timely, especially to supply dense urban areas with the highest EV adoption.” Akshay Singh Akshay Singh New York’s grid is already strained as the city attempts to electrify not only its transportation but also its buildings. Singh asserts that developers often do not understand the level of grid upgrades required due to a lack of available information. Moreover, these challenges are expected to become more prevalent with charging sites located in high-rise buildings that have not had capacity upgrades in years. “Major challenges with EV charging today are around how to make the economics of operating EV chargers feasible. DC fast chargers that operate at 100kW+ power output (to charge EVs within 30-45 mins) can cost upwards of $50k per unit which makes upfront costs of a multi-unit charging site very expensive,” added Singh. Electric Vehicles as Untapped Grid Assets? As New York pushes toward its ambitious goal of having 400,000 EVs on the road by 2030 as part of the " Green Rides " initiative, it’s clear that the city needs more fast-charging sites. Conglomerates like Revel are already making strides with its " superhub " in Brooklyn, boasting 25 high-speed chargers. But even with their success, the company faces a significant challenge: finding enough grid power to keep these large fast-charging stations running smoothly. Green Rides Green Rides superhub superhub While the city grapples with its grid limitations, some startups are stepping in to offer innovative solutions through A.I. and edge computing. Brooklyn-based Synop, for instance, aims to help stabilize the grid by integrating data from grid operators, EV charging stations, and EV fleet operators. “We focus on edge computing to enhance charging management,” Gagan Dhillon , CEO of Synop, told me. By bringing cloud-based functionality closer to the charging site, the company says it can increase reliability and reduce latency, “which is critical in areas with constrained grids or complex microgrid setups.” Gagan Dhillon Gagan Dhillon Dhillon added that one overlooked aspect of EV adoption is that electric vehicles are essentially batteries on wheels, and with the right infrastructure, they can serve as valuable assets rather than just power consumers. “EVs can act as backup power sources during times of high grid demand, providing energy back to the grid or even powering homes when needed,” he said. “With the integration of smart charging technology, EVs can be programmed to charge during off-peak times when energy is more abundant. This flexibility makes them less of a strain on the grid than often imagined.” With more people driving EVs and a grid under a lot of strain, NYC's EV goal success will hinge on how open it is to new technologies, speeding up infrastructure projects, and investing in sustainable energy solutions. “There will be a need for higher powered EV chargers that will further increase the grid upgrade requirements. Developers need to work together with local utilities from the start to successfully deploy EV projects,” Singh told me. “Along with that, additional [capital expenditure] needed to sustain the grid for more demand needs to be laid out and discussions on who will bear the cost burden.”

NYC's $15 Million EV Charger Initiative: Can the City Grid Handle Rising Energy Demands?

I recently Googled And IBM.com gave the top result. As it explains, smart contracts automate coded actions on a cryptocurrency platform (blockchain). So I’m thinking trader bots and escrow agreements. The result also says they can automate a workflow. So hourly, weekly, or monthly (cryptocurrency) payments to a payroll. OK. I guess there is something smart about smart contracts. Coz people usually handle the aforementioned tasks. Then I got to thinking, can smart contracts be as smart as artificial intelligence algorithms? Yes, I Googled. It’s not a thing. Every site talks of a smart contract combined with AI. I want it to become AI. I know, we all want things. If wishes were horses. Nonetheless, I have a few quirky similarities to share. Starting with the cover image. Notice how the Rootstock logo looks a lot like an Artificial Neural Network? I did. Bitcoin’s supply mechanics and the Boltzmann distribution Rootstock is a Bitcoin side chain which means at its heart, it is Bitcoin’s supply mechanics that govern the halving and the difficulty adjustment and more. The true engine of all its smart contracting. While Rootstock was chugging along its Bitcoin rails, on the other side of town, the Nobel Prize in Physics was being awarded to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”- https://www.nobelprize.org/. Their work in machine learning led to GPTs (Generative Pre-trained Transformers), a now famous architecture in major AI systems today. What’s interesting is that foundational to all Artificial Neural Networks (ANNs), as mentioned in the Advanced information section of NobelPrize.org, is something called the Boltzmann Distribution. Below is its mathematical equation to capture the energy E of an ANN; Notice the similarity to the Bitcoin supply equation below (with 3 parameters too! Replace W_ij above with 210,000, S_j with 50, and S_i with 1/2^i) ; What can I say, there is something Bitcoin-like to AI. Both the Boltzmann distribution and the Bitcoin supply equation model an exponential decay/reduction. The former is generalized for the energy of a closed system with n particles, while the latter models the number of bitcoins available to be mined. This too is a closed system with n = number of halvings. The particles can be as many as they want to be (so whether Bitcoin is a planet-sized computational network or a modest network of a dozen PCs, doesn’t matter). But they will all decay in 32 halvings to mine all the bitcoins that can possibly be mined. Approximately 21 million. Now bitcoins are mined in a system that unlike standard computer systems, where linguistic logic coded by a programmer dictates the state of 0s and 1s, this machine changes when it is fed energy... - Sydney Bright, BitcoinMagazine. How smart are Rootstock smart contracts? We have seen that both Rootstock smart contracts and AI algorithms consume energy. So the question is, how well do they consume the energy? Well, both consume it pretty well to make some good money. They are both smart money algorithms in that regard. To see an instance of this, let’s put Rootstock next to ChatGPT in a certain way. Look at the below statistics. Of course, ChatGPT has more users, more money flowing in, and more employees worldwide. It is smart. We all know this. But pound for computational pound, Rootstock does hold its own. Let’s make some assumptions like a Physicist. For example, we can see that Rootstock.io has 261,406 monthly transactions. Assume they are from 261,406 different people. Meanwhile, ChatGPT has 800 million monthly users, generating billions of prompts (similar to transactions) every month. Let’s take their number of monthly users as equivalent to their monthly transactions. Now let’s divide market cap by these monthly figures (this is a made-up metric, but a good one): Rootstock’s “Market cap per monthly user” = $380,000,000 / 261,406 = $1453 ChatGPT’s “Market cap per monthly user” = $157,000,000,000 / 800,000,000 = $196 As you can see, ChatGPT is not doing better than Rootstock in this calculation! And if those 2740 RBTC pegged to BTC soon climb to $100k+ per RBTC (courtesy of the Trump Administration), you bet, ChatGPT will learn all about it. And OpenAI just might consider allying with a newly-unicorn Rootstock to provide paid ChatGPT services in RBTC. Bottom line similarity: Both Rootstock smart contracts and ChatGPT are smart money algorithms with good performance metrics. The former caters to a smaller clientele (Bitcoiners) with deep pockets, while the latter caters to a larger clientele (the world’s literate population) with relatively modest pockets. Speedsters In the Rootstock white paper, while the average confirmation time of BTC transactions is 10 minutes, Rootstock’s RBTC, which holds a 1:1 on-chain peg to BTC, takes only 30 seconds in average confirmation time. This implies that RBTC transactions are almost as fast as Bitcoin Lightning Network transactions, which are the fastest transactions in the world. Just as well, ChatGPT prompts are so fast that users can get replies in a few seconds for straightforward queries. So while superhuman digital intelligence zips across the world in seconds to make our minds smarter, digital capital zips across the world in seconds to quench wealth asymmetries and provide opportunity to us all. Fast money and Fast intelligence. My kind of speed. *** Miscellaneous: Two whales to inspire smarter smart contracts Given what is happening in the AI and Cryptocurrency/Bitcoin space, future Rootstock smart contracts would do well to peg to a fast-growing Bitcoin whale like MicroStrategy, or a technology whale like Nvidia. Why? Here’s Nvidia’s stock graph in October. And here’s MicroStrategy’s stock graph. MSTR did better than NVDA last month, but they are both going to the moon in the long run. Just like the Bitcoin and AI markets whose CAGR is looking at double digits all the way to 2030 (>20% for both as per Statista.com). For their sake, Rootstock smart contracts will be on these rocket rides. Then they just might become really smart smart contracts. I recently Googled And IBM.com gave the top result. As it explains, smart contracts automate coded actions on a cryptocurrency platform (blockchain). So I’m thinking trader bots and escrow agreements. The result also says they can automate a workflow. So hourly, weekly, or monthly (cryptocurrency) payments to a payroll. OK. I guess there is something smart about smart contracts. Coz people usually handle the aforementioned tasks. Then I got to thinking, can smart contracts be as smart as artificial intelligence algorithms? Yes, I Googled. It’s not a thing. Every site talks of a smart contract combined with AI. I want it to become AI. combined become I know, we all want things. If wishes were horses. Nonetheless, I have a few quirky similarities to share. Starting with the cover image. Notice how the Rootstock logo looks a lot like an Artificial Neural Network? I did. Bitcoin’s supply mechanics and the Boltzmann distribution Rootstock is a Bitcoin side chain which means at its heart, it is Bitcoin’s supply mechanics that govern the halving and the difficulty adjustment and more. The true engine of all its smart contracting. While Rootstock was chugging along its Bitcoin rails, on the other side of town, the Nobel Prize in Physics was being awarded to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”- https://www.nobelprize.org/ . “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”- https://www.nobelprize.org/ Their work in machine learning led to GPTs (Generative Pre-trained Transformers), a now famous architecture in major AI systems today. What’s interesting is that foundational to all Artificial Neural Networks (ANNs), as mentioned in the Advanced information section of NobelPrize.org, is something called the Boltzmann Distribution. Advanced information Below is its mathematical equation to capture the energy E of an ANN; energy Notice the similarity to the Bitcoin supply equation below (with 3 parameters too! Replace W_ij above with 210,000, S_j with 50, and S_i with 1/2^i) ; What can I say, there is something Bitcoin-like to AI. Both the Boltzmann distribution and the Bitcoin supply equation model an exponential decay/reduction. The former is generalized for the energy of a closed system with n particles, while the latter models the number of bitcoins available to be mined. This too is a closed system with n = number of halvings. The particles can be as many as they want to be (so whether Bitcoin is a planet-sized computational network or a modest network of a dozen PCs, doesn’t matter). But they will all decay in 32 halvings to mine all the bitcoins that can possibly be mined. Approximately 21 million. Now bitcoins are mined in a system that unlike standard computer systems, where linguistic logic coded by a programmer dictates the state of 0s and 1s, this machine changes when it is fed energy... - Sydney Bright, BitcoinMagazine. unlike standard computer systems, where linguistic logic coded by a programmer dictates the state of 0s and 1s, this machine changes when it is fed energy ... - Sydney Bright, BitcoinMagazine . energy BitcoinMagazine How smart are Rootstock smart contracts? We have seen that both Rootstock smart contracts and AI algorithms consume energy. So the question is, how well do they consume the energy? Well, both consume it pretty well to make some good money. They are both smart money algorithms in that regard. To see an instance of this, let’s put Rootstock next to ChatGPT in a certain way. Look at the below statistics. Of course, ChatGPT has more users, more money flowing in, and more employees worldwide. It is smart. We all know this. But pound for computational pound, Rootstock does hold its own. Let’s make some assumptions like a Physicist. For example, we can see that Rootstock.io has 261,406 monthly transactions. Assume they are from 261,406 different people. Meanwhile, ChatGPT has 800 million monthly users, generating billions of prompts (similar to transactions) every month. Let’s take their number of monthly users as equivalent to their monthly transactions. Now let’s divide market cap by these monthly figures (this is a made-up metric, but a good one): Rootstock’s “ Market cap per monthly user ” = $380,000,000 / 261,406 = $1453 Market cap per monthly user ChatGPT’s “ Market cap per monthly user ” = $157,000,000,000 / 800,000,000 = $196 Market cap per monthly user As you can see, ChatGPT is not doing better than Rootstock in this calculation! And if those 2740 RBTC pegged to BTC soon climb to $100k+ per RBTC (courtesy of the Trump Administration), you bet, ChatGPT will learn all about it. And OpenAI just might consider allying with a newly-unicorn Rootstock to provide paid ChatGPT services in RBTC. Bottom line similarity: Both Rootstock smart contracts and ChatGPT are smart money algorithms with good performance metrics. The former caters to a smaller clientele (Bitcoiners) with deep pockets, while the latter caters to a larger clientele (the world’s literate population) with relatively modest pockets. Speedsters In the Rootstock white paper , while the average confirmation time of BTC transactions is 10 minutes, Rootstock’s RBTC, which holds a 1:1 on-chain peg to BTC, takes only 30 seconds in average confirmation time. Rootstock white paper This implies that RBTC transactions are almost as fast as Bitcoin Lightning Network transactions, which are the fastest transactions in the world . fastest transactions in the world Just as well, ChatGPT prompts are so fast that users can get replies in a few seconds for straightforward queries . users can get replies in a few seconds for straightforward queries So while superhuman digital intelligence zips across the world in seconds to make our minds smarter, digital capital zips across the world in seconds to quench wealth asymmetries and provide opportunity to us all. Fast money and Fast intelligence. My kind of speed. *** Miscellaneous: Two whales to inspire smarter smart contracts Given what is happening in the AI and Cryptocurrency/Bitcoin space, future Rootstock smart contracts would do well to peg to a fast-growing Bitcoin whale like MicroStrategy, or a technology whale like Nvidia. Why? Here’s Nvidia’s stock graph in October. And here’s MicroStrategy’s stock graph. MSTR did better than NVDA last month, but they are both going to the moon in the long run. Just like the Bitcoin and AI markets whose CAGR is looking at double digits all the way to 2030 (>20% for both as per Statista.com). For their sake, Rootstock smart contracts will be on these rocket rides. Then they just might become really smart smart contracts. really smart

What's With the Similarities Between Rootstock Smart Contracts and Artificial Intelligence

ما هي أوجه التشابه بين عقود Rootstock الذكية والذكاء الاصطناعي

Какво има в приликите между интелигентните договори на Rootstock и изкуствения интелект

রুটস্টক স্মার্ট চুক্তি এবং কৃত্রিম বুদ্ধিমত্তার মধ্যে মিলের সাথে কী আছে

Was ist mit den Ähnlichkeiten zwischen Rootstock Smart Contracts und künstlicher Intelligenz?

Τι υπάρχει με τις ομοιότητες μεταξύ των έξυπνων συμβάσεων Rootstock και της τεχνητής νοημοσύνης

¿Qué tienen en común los contratos inteligentes de Rootstock y la inteligencia artificial?

מה הקשר בין חוזים חכמים Rootstock לבין בינה מלאכותית

Ի՞նչ նմանություններ կան Rootstock խելացի պայմանագրերի և արհեստական ինտելեկտի միջև

Apa Persamaan Antara Kontrak Cerdas Rootstock dan Kecerdasan Buatan?

Rootstock スマート コントラクトと人工知能の類似点とは

Seo se lego ka go swana Magareng ga Dikonteraka tša Rootstock Smart le Bohlale bja Maitirelo

“Rootstock” akylly şertnamalary we emeli intellektiň arasyndaky meňzeşlikler näme

As a Product Manager, you have great ideas! Ideas that could change the way people do things, ideas that could solve the world’s most pressing problems. And all you need to do now, is go ahead and build it! Let’s build an MVP (minimum viable product), sell the hell out of it (because the idea is super-disruptive in your mind) and then iterate. Soon to realize 6–8 months down the line that your product no longer has that level of adoption that you had expected, or that you now have to pivot to cater to the needs of your ‘revised’ audience because the initial market definition was too narrow. Product Managers are nothing but entrepreneurs, trying to launch their startup, pitching a business plan to internal stakeholders (your investors), gathering resources (creating a startup team), launching and selling to customers (marketing and launching your product). Thus, as we all know the fact that more than 80% of startups fail, the path to building a successful product should mimic the approach to launching a successful company. The Lean-product approach The Lean Startup approach discoursed by Eric Ries, has popularized the ‘fail fast, iterate often’ notion. This involves evaluating the problem the customer has with a prototype and then iterating with measured data from the feedback loop, using minimum resources. A core component of lean-startup methodology is the build-measure-learn feedback loop. I take this methodology and apply it to defining, validating and iterating to achieve the right product-market fit and create a rock-solid product strategy. Problem definition Innovative ideas lead to great solutions, however, without a deep understanding of the problem space, you are at a risk of getting ahead of yourself and building capabilities which do not receive customer traction. Defining your problem statement involves understanding of the problem space, the sense of urgency to solve the problem, the existing solutions in the market that have attempted to solve the problem all in the context of your target audience. “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said. One example of the many ways of framing a problem statement is by defining: What is the problem?
Who is facing this problem?
How often?
Why do you need to solve it? In your initial level surveys or focus group interviews, these are a few statements that could be derived: “I am a salesperson, constantly on the road and am always struggling with managing and submitting tiny paper receipts for reimbursement as I keep losing them.” Using our above framework we have our problem statement e.g. “Business travelers or salespersons (who) need a way to capture and manage their travel expenses (what) because they are always on the road (how often). We need a solution to make this process less-time consuming and less-cumbersome (why).” This, by the way, is the problem as identified by DePasquale, founder of Outtask that made an online tool, Cliqbook, that allowed workers to book and manage their online travel and which in 2006, was acquired by Concur. Target Audience In the problem definition we have identified who is facing the problem you are trying to solve. This is where you identify the core audience who will be using your product. Your user as well as buyer personas. It is important to be honest about sizing your market here. This is not your $2B TAM pitch to investors. Be realistic and as specific as possible. Core Value Proposition A value-proposition is not simply a list of your benefits. It explains what benefit you provide for who and how you do it uniquely well. It should be a single statement articulating what is the value promised to the customer? There are tons of frameworks to define and state your value prop in a concise way. The simplest, perhaps is the template from Steve Blank, “We help [X] to [Y] by [Z]” Some great examples are: Dollar Shave Club*: “A Great Shave for a Few Bucks a Month”*
Stripe*: “Scale faster by building your business on Stripe’s payment processing platform.”*
Uber*: “Everyone’s private Driver”* Once you have your value prop defined, it is extremely important to continuously evaluate and test it with potential customers, even before you start building your MVP. Why is your product offering unique? This is probably the most important question you need to ask while determining the customer’s ‘willingness to pay’ for your product. Why should the customer go for your product vs the competitors? This becomes even more important when you are trying to displace a largely incumbent or crowded space such as enterprise productivity tools or online food delivery. Also, remember, you do not need to differentiate on a fancy feature or capability. Sometimes the simplest of things such as a clean design and user experience is what customers may need the most. Figuring out what matters to your customers the most and then choosing to differentiate on that aspect should be the approach. There is a reason why Zoom has completely disrupted the video conferencing market with their focus on creating virtual experiences when it comes to digitizing the workspace. The current space consisted of a stack of products, some wider and some narrower and some part of a larger ecosystem (Skype and Cisco Webex). Zoom clearly understood the need to converge video, web and mobile. Zoom’s offering that integrated audio, web conferencing and mobile integration was a simple, straightforward solution to what customers needed. Also, their willingness to work with most platforms and devices coupled with their all-inclusive fremium model was different from how all the other players went to market. Product quality, technology, user experience, price, go-to-market model and customer-service are just a handful of factors that you can think of and choose to base your differentiation upon. Metrics Ries’s lean framework follows a build, measure, learn loop. However, defining what to measure and how to evaluate the success for your product needs to be done as early as possible. You should be able to answer the question “How is the product doing?” at any point in time. And defining the response in clear KPIs (Key Performance Indicators) helps you evaluate the progress in a very objective way. The AARM method defined by Lewis Lin is a good framework to pick your metrics in each of the areas of acquisition, activation, retention and monetization. However, you need to go beyond frameworks, pick the appropriate metrics and customize them for your product and growth stage. Metrics creation and measurement is a highly iterative process and hence you constantly need to set KPI goals, evaluate and track these goals, and create new ones as your product grows. Apart from coming up with metrics for areas of acquisition, engagement and monetization, what needs to be agreed upon from an early stage is the ‘North-Star Metric’ that is going to drive most product decisions. This topic needs a separate article so watch out for another blog from me, deep diving into defining and evaluating metrics. Example Uber is a classic example where there was instant product market fit. Uber quickly realized their problem statement was “Limited, inconvenient access to taxis and transportation options in urban areas”. To solve this problem, Uber used GPS-enabled mobile apps to match drivers with riders, providing a reliable, real-time way to find and book a ride. Uber built their technical architecture  focused on real-time data processing and location-based services, which supported their rapid request-response model. Geolocation Services: Built using Google Maps API and Mapbox for precise location tracking and route optimization.
Dispatch System: Microservices handling real-time dispatching of drivers based on location and demand, with APIs for matching, pricing, and tracking.
Data Layer: Uses Cassandra for high-availability, fault-tolerant storage and MySQL for relational data.
Queueing: RabbitMQ or Kafka for asynchronous communication between services, handling surge pricing, availability, and ride requests. Uber’s use of microservices and a modular data architecture helps ensure that their systems remain responsive and scalable in real-time. Conclusion In conclusion, a successful product identifies and tests the product-market fit hypothesis from the get go and throughout the process of building the MVP. The Lean Startup message sums it up well. “What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly”. As a Product Manager, you have great ideas! Ideas that could change the way people do things, ideas that could solve the world’s most pressing problems. And all you need to do now, is go ahead and build it! Let’s build an MVP (minimum viable product), sell the hell out of it (because the idea is super-disruptive in your mind) and then iterate. Soon to realize 6–8 months down the line that your product no longer has that level of adoption that you had expected, or that you now have to pivot to cater to the needs of your ‘revised’ audience because the initial market definition was too narrow. Product Managers are nothing but entrepreneurs, trying to launch their startup, pitching a business plan to internal stakeholders (your investors), gathering resources (creating a startup team), launching and selling to customers (marketing and launching your product). Thus, as we all know the fact that more than 80% of startups fail, the path to building a successful product should mimic the approach to launching a successful company. The Lean-product approach The Lean Startup approach discoursed by Eric Ries, has popularized the ‘fail fast, iterate often’ notion. This involves evaluating the problem the customer has with a prototype and then iterating with measured data from the feedback loop, using minimum resources. A core component of lean-startup methodology is the build-measure-learn feedback loop. Lean Startup I take this methodology and apply it to defining, validating and iterating to achieve the right product-market fit and create a rock-solid product strategy. Problem definition Innovative ideas lead to great solutions, however, without a deep understanding of the problem space, you are at a risk of getting ahead of yourself and building capabilities which do not receive customer traction. Defining your problem statement involves understanding of the problem space, the sense of urgency to solve the problem, the existing solutions in the market that have attempted to solve the problem all in the context of your target audience. “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said. “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said. “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said. One example of the many ways of framing a problem statement is by defining: What is the problem? Who is facing this problem? How often? Why do you need to solve it? What is the problem? Who is facing this problem? How often? Why do you need to solve it? In your initial level surveys or focus group interviews, these are a few statements that could be derived: “I am a salesperson, constantly on the road and am always struggling with managing and submitting tiny paper receipts for reimbursement as I keep losing them.” “I am a salesperson, constantly on the road and am always struggling with managing and submitting tiny paper receipts for reimbursement as I keep losing them.” Using our above framework we have our problem statement e.g. “Business travelers or salespersons (who) need a way to capture and manage their travel expenses (what) because they are always on the road (how often). We need a solution to make this process less-time consuming and less-cumbersome (why).” “Business travelers or salespersons (who) need a way to capture and manage their travel expenses (what) because they are always on the road (how often). We need a solution to make this process less-time consuming and less-cumbersome (why).” This, by the way, is the problem as identified by DePasquale, founder of Outtask that made an online tool, Cliqbook, that allowed workers to book and manage their online travel and which in 2006, was acquired by Concur. Target Audience In the problem definition we have identified who is facing the problem you are trying to solve. This is where you identify the core audience who will be using your product. Your user as well as buyer personas. It is important to be honest about sizing your market here. This is not your $2B TAM pitch to investors. Be realistic and as specific as possible. Core Value Proposition A value-proposition is not simply a list of your benefits. It explains what benefit you provide for who and how you do it uniquely well. It should be a single statement articulating what is the value promised to the customer? There are tons of frameworks to define and state your value prop in a concise way. The simplest, perhaps is the template from Steve Blank , “ We help [X] to [Y] by [Z]” Steve Blank We help [X] to [Y] by [Z]” Some great examples are: Dollar Shave Club*: “A Great Shave for a Few Bucks a Month”* Stripe*: “Scale faster by building your business on Stripe’s payment processing platform.”* Uber*: “Everyone’s private Driver”* Dollar Shave Club *: “A Great Shave for a Few Bucks a Month”* Dollar Shave Club Dollar Shave Club Stripe *: “Scale faster by building your business on Stripe’s payment processing platform.”* Stripe Stripe Uber *: “Everyone’s private Driver”* Uber Uber Once you have your value prop defined, it is extremely important to continuously evaluate and test it with potential customers, even before you start building your MVP. Why is your product offering unique? This is probably the most important question you need to ask while determining the customer’s ‘willingness to pay’ for your product. Why should the customer go for your product vs the competitors? This becomes even more important when you are trying to displace a largely incumbent or crowded space such as enterprise productivity tools or online food delivery. Also, remember, you do not need to differentiate on a fancy feature or capability. Sometimes the simplest of things such as a clean design and user experience is what customers may need the most. Figuring out what matters to your customers the most and then choosing to differentiate on that aspect should be the approach. There is a reason why Zoom has completely disrupted the video conferencing market with their focus on creating virtual experiences when it comes to digitizing the workspace. The current space consisted of a stack of products, some wider and some narrower and some part of a larger ecosystem (Skype and Cisco Webex). Zoom clearly understood the need to converge video, web and mobile. Zoom’s offering that integrated audio, web conferencing and mobile integration was a simple, straightforward solution to what customers needed. Also, their willingness to work with most platforms and devices coupled with their all-inclusive fremium model was different from how all the other players went to market. Product quality, technology, user experience, price, go-to-market model and customer-service are just a handful of factors that you can think of and choose to base your differentiation upon. Metrics Ries’s lean framework follows a build, measure, learn loop. However, defining what to measure and how to evaluate the success for your product needs to be done as early as possible. You should be able to answer the question “How is the product doing?” at any point in time. And defining the response in clear KPIs (Key Performance Indicators) helps you evaluate the progress in a very objective way. The AARM method defined by Lewis Lin is a good framework to pick your metrics in each of the areas of acquisition, activation, retention and monetization. However, you need to go beyond frameworks, pick the appropriate metrics and customize them for your product and growth stage. Metrics creation and measurement is a highly iterative process and hence you constantly need to set KPI goals, evaluate and track these goals, and create new ones as your product grows. Apart from coming up with metrics for areas of acquisition, engagement and monetization, what needs to be agreed upon from an early stage is the ‘North-Star Metric’ that is going to drive most product decisions. This topic needs a separate article so watch out for another blog from me, deep diving into defining and evaluating metrics. AARM method Example Uber is a classic example where there was instant product market fit. Uber quickly realized their problem statement was “Limited, inconvenient access to taxis and transportation options in urban areas”. To solve this problem, Uber used GPS-enabled mobile apps to match drivers with riders, providing a reliable, real-time way to find and book a ride. Uber built their technical architecture  focused on real-time data processing and location-based services, which supported their rapid request-response model. Geolocation Services: Built using Google Maps API and Mapbox for precise location tracking and route optimization. Dispatch System: Microservices handling real-time dispatching of drivers based on location and demand, with APIs for matching, pricing, and tracking. Data Layer: Uses Cassandra for high-availability, fault-tolerant storage and MySQL for relational data. Queueing: RabbitMQ or Kafka for asynchronous communication between services, handling surge pricing, availability, and ride requests. Geolocation Services : Built using Google Maps API and Mapbox for precise location tracking and route optimization. Geolocation Services Dispatch System : Microservices handling real-time dispatching of drivers based on location and demand, with APIs for matching, pricing, and tracking. Dispatch System Data Layer : Uses Cassandra for high-availability, fault-tolerant storage and MySQL for relational data. Data Layer Queueing : RabbitMQ or Kafka for asynchronous communication between services, handling surge pricing, availability, and ride requests. Queueing Uber’s use of microservices and a modular data architecture helps ensure that their systems remain responsive and scalable in real-time. Conclusion In conclusion, a successful product identifies and tests the product-market fit hypothesis from the get go and throughout the process of building the MVP. The Lean Startup message sums it up well. “What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly”. “What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly”. “What differentiates the success stories from the failures is that the successful entrepreneurs had the foresight, the ability, and the tools to discover which parts of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly”.

Achieving Product-Market Fit: How the Startups Did It

In Vietnam, Porn Is Better Served and More Accessible Than Starbucks

Why is Tesla's coming Robotaxi such a big deal? Uber was partially invented right in front of me in a Paris snowstorm by Garrett Camp and Travis Kalanick. That set me on a path of understanding Transportation as a Service, which really is what Uber is. You order a ride on a computer, usually your phone, and one arrives. If I want to take one to San Francisco Airport it usually costs around $70 for an hour drive from my home in San Jose. Uber grew from that Paris snowstorm to being in many countries within three years. Uber is, so far, human driven. Humans have some advantages, they come with their own cars, so Uber didn't need to take on any inventory risk. But they come with many disadvantages too. Some talk too much. Some smell. Even of weed. Some arrive inebriated. Some abuse their riders, or worse. Yes, most of the time a great human shows up with a new model car that's well maintained and clean, but use Uber all around the world, like I have, and you'll hit these problems. Inconsistencies. And notice we haven't talked about how much the human costs, either in dollars (minimum wage is about $20 in California) or in safety costs (human tragedy in cars is pretty frequent, with around 40,000 deaths in USA every year alone). While a new Tesla costs about $3 to sit in your driveway every hour, add a human and the cost runs much more. Go back to Elon's first sales pitch (he gave it to me personally at the beginning) and he talked about beating legacy auto on price and experience. He's been planning Robotaxi since before the Model 3 shipped in 2017. How do I know that? Because only his cars have electric vents, something that still gets him derided. Mercedes engineers told me personally ts customers hated electric vents, and that they wanted knobs instead. But it is the electric vents that will give you a better experience, along with a number of other things, including a better audio system than the others have. But compared to the others it will have a better brand due to this focus on experience and consistency. What makes a great brand? Not, necessarily having the best product. Starbucks, for instance, doesn't, and I can find you better things to drink than Coke, but those two are great brands because they are consistent and ubiquitous. So, why does Tesla matter here? What about Waymo, Zoox, Cruise, or a handful of other potential Robotaxi competitors? What is its moat? Years ago I did consumer research, going around America asking people "are you willing to get into a vehicle that has no steering wheel?" Most answered "f**k no." I have studied several technological paradigm shifts and heard protests every time, but this was the most adamant of my career. I was talking about this with Peter Norvig, who used to play a key role at Google R&D and claims he is the guy who got the funding to start its autonomous vehicle project, about this. He told me Google already had the data to know that after three rides in a Waymo they will deeply trust autonomous vehicles to the point that they will want to use them for every ride. It is this key point "use them for every ride" that gets execs and investors interested in autonomous vehicles. I've heard this too from many San Francisco residents who love Waymo's service of being able to hop into an autonomous vehicle. But why does Tesla matter? Tesla's AI started out behind Waymo/Google's but has been learning faster. I believe that it is already better than Waymo at generalization (Waymo is only available in a few cities while my Tesla drove me 900 miles to Sun Valley from my home in San Jose). Yaman had an excellent show with Omar of Whole Mars Catalog that everyone should watch that compares Waymo and Tesla. Already Tesla's AI is smoother in many places. Back to the point. If you arrive in Las Vegas for a conference or on vacation which one do you choose to use? Tesla? Waymo? Zoox? Cruise? Uber? Lyft? Or some other brand or taxi? My prediction is that Tesla will win most of this business in 2030. Why? Experience. Being in a Tesla vehicle is safer in a crash than the vehicles that the other companies use.


Being in a Tesla will have a better experience, from the vents to the sound system.


Using a Tesla will be more consistent. Everyone knows what ordering a Cybertruck will do. Taxis and Uber always shows up with different vehicles so won't be as consistent. Starbucks shows that people will choose a consistent product over one that's better quality, but isn't as consistent. But it is the fleet size that really matters. When I interviewed Travis Kalanick, Uber's founder, in front of Stanford Business School, he told me that he had one big goal: to have a vehicle near you so that when you ordered one, it only took a few minutes to show up. If three vehicles were waiting nearby then the drivers weren't being used efficiently, pissing them off for not getting paid. If none are nearby then it takes too long for a vehicle to get to you. So who has the biggest fleet? Of autonomous vehicles? None of them other than Tesla is on my street and on my street there are 13 Teslas already. So many that I can walk to another Tesla in seconds. Faster than I could get one in San Francisco. It wouldn't even need to drive to me! The fleet size matters in many other ways, particularly experience. Why? Well, let's say there are a bunch of Teslas on the road in front of you. They can "see" all sorts of things, from fires, to protests, to police actions, and can report those to the control system that runs the network. So it could route around problems faster than any other network. That makes the experience better. If you can see that it has real-time info, you will learn to trust the one that has the real-time info on the screen more than one that doesn't have as much info. Which gets us to another thing. All us Tesla owners have its app running on our phones. That could feed a lot of new data into the network, like about how busy a nightclub or a restaurant is. Or details about a news event. Literally none of us have a Waymo app on our phones. Most humans haven't even heard of, or seen, a Waymo. Uber? Sure! But it comes with a human which brings the above problems and a higher price. Speaking of price. A Waymo has quite a few expensive LiDARs and more compute on board than Tesla does to handle both camera feeds and LiDAR feeds. Tesla has gone with a camera only approach, which is not only cheaper per vehicle, but looks a lot nicer too and can be hidden inside a standard-looking vehicle. Waymo's have gotten vandalized several times because they are the symbols of a new, dystopian, age, as one of my friends put it. Tesla's just are everywhere in many cities, especially anyone near San Francisco so won't attract the same kind of attention. So, my predictions: By 2030: Tesla robotaxi will be cheaper than other networks. Tesla will have a better screen and audio system for you to use. Movies, education, music, video conference calls, etc, will be better in a Tesla and more consistent. Tesla will be safer, and go more places. Tesla's route picker will be way better. Especially around massive events like concerts, sporting events, and more. Tesla's vents, seats, mirrors, audio level, are automatically set to personalize to you. Tesla's app and vehicles gives you more details, which leads to a better experience "there's a 14-minute line at the McDonalds." Let's put it bluntly: Tesla's customer acquisition cost will be nearly zero, while Waymo or Zoox will need to spend a lot more to create both awareness and behavior. I don't think they will be able to convince a Tesla user to switch to their platforms given the better experience and lower price I'm expecting Tesla to have. To wrap it up, for now, let's say you want to take your lover to a downtown for a date night. In an Uber there is a driver taking up one of the two best seats. In a Tesla? Nope. So date night is way better. On Thursday night we'll see just how much better. Why is Tesla's coming Robotaxi such a big deal? Uber was partially invented right in front of me in a Paris snowstorm by Garrett Camp and Travis Kalanick. That set me on a path of understanding Transportation as a Service, which really is what Uber is. You order a ride on a computer, usually your phone, and one arrives. If I want to take one to San Francisco Airport it usually costs around $70 for an hour drive from my home in San Jose. Uber grew from that Paris snowstorm to being in many countries within three years. Uber is, so far, human driven. Humans have some advantages, they come with their own cars, so Uber didn't need to take on any inventory risk. But they come with many disadvantages too. Some talk too much. Some smell. Even of weed. Some arrive inebriated. Some abuse their riders, or worse. Yes, most of the time a great human shows up with a new model car that's well maintained and clean, but use Uber all around the world, like I have, and you'll hit these problems. Inconsistencies. And notice we haven't talked about how much the human costs, either in dollars (minimum wage is about $20 in California) or in safety costs (human tragedy in cars is pretty frequent, with around 40,000 deaths in USA every year alone). While a new Tesla costs about $3 to sit in your driveway every hour, add a human and the cost runs much more. Go back to Elon's first sales pitch (he gave it to me personally at the beginning) and he talked about beating legacy auto on price and experience. He's been planning Robotaxi since before the Model 3 shipped in 2017. How do I know that? Because only his cars have electric vents, something that still gets him derided. Mercedes engineers told me personally ts customers hated electric vents, and that they wanted knobs instead. But it is the electric vents that will give you a better experience, along with a number of other things, including a better audio system than the others have. But compared to the others it will have a better brand due to this focus on experience and consistency. What makes a great brand? Not, necessarily having the best product. Starbucks, for instance, doesn't, and I can find you better things to drink than Coke, but those two are great brands because they are consistent and ubiquitous. So, why does Tesla matter here? What about Waymo, Zoox, Cruise, or a handful of other potential Robotaxi competitors? What is its moat? Years ago I did consumer research, going around America asking people "are you willing to get into a vehicle that has no steering wheel?" Most answered "f**k no." I have studied several technological paradigm shifts and heard protests every time, but this was the most adamant of my career. I was talking about this with Peter Norvig, who used to play a key role at Google R&D and claims he is the guy who got the funding to start its autonomous vehicle project, about this. He told me Google already had the data to know that after three rides in a Waymo they will deeply trust autonomous vehicles to the point that they will want to use them for every ride. It is this key point "use them for every ride" that gets execs and investors interested in autonomous vehicles. I've heard this too from many San Francisco residents who love Waymo's service of being able to hop into an autonomous vehicle. But why does Tesla matter? Tesla's AI started out behind Waymo/Google's but has been learning faster. I believe that it is already better than Waymo at generalization (Waymo is only available in a few cities while my Tesla drove me 900 miles to Sun Valley from my home in San Jose). Yaman had an excellent show with Omar of Whole Mars Catalog that everyone should watch that compares Waymo and Tesla. Already Tesla's AI is smoother in many places . smoother in many places Back to the point. If you arrive in Las Vegas for a conference or on vacation which one do you choose to use? Tesla? Waymo? Zoox? Cruise? Uber? Lyft? Or some other brand or taxi? My prediction is that Tesla will win most of this business in 2030. Why? Experience. Being in a Tesla vehicle is safer in a crash than the vehicles that the other companies use. Being in a Tesla will have a better experience, from the vents to the sound system. Using a Tesla will be more consistent. Everyone knows what ordering a Cybertruck will do. Taxis and Uber always shows up with different vehicles so won't be as consistent. Starbucks shows that people will choose a consistent product over one that's better quality, but isn't as consistent. Being in a Tesla vehicle is safer in a crash than the vehicles that the other companies use. Being in a Tesla vehicle is safer in a crash than the vehicles that the other companies use. Being in a Tesla will have a better experience, from the vents to the sound system. Being in a Tesla will have a better experience, from the vents to the sound system. Using a Tesla will be more consistent. Everyone knows what ordering a Cybertruck will do. Taxis and Uber always shows up with different vehicles so won't be as consistent. Starbucks shows that people will choose a consistent product over one that's better quality, but isn't as consistent. Using a Tesla will be more consistent. Everyone knows what ordering a Cybertruck will do. Taxis and Uber always shows up with different vehicles so won't be as consistent. Starbucks shows that people will choose a consistent product over one that's better quality, but isn't as consistent. But it is the fleet size that really matters. When I interviewed Travis Kalanick, Uber's founder, in front of Stanford Business School, he told me that he had one big goal: to have a vehicle near you so that when you ordered one, it only took a few minutes to show up. If three vehicles were waiting nearby then the drivers weren't being used efficiently, pissing them off for not getting paid. If none are nearby then it takes too long for a vehicle to get to you. So who has the biggest fleet? Of autonomous vehicles? None of them other than Tesla is on my street and on my street there are 13 Teslas already. So many that I can walk to another Tesla in seconds. Faster than I could get one in San Francisco. It wouldn't even need to drive to me! The fleet size matters in many other ways, particularly experience. Why? Well, let's say there are a bunch of Teslas on the road in front of you. They can "see" all sorts of things, from fires, to protests, to police actions, and can report those to the control system that runs the network. So it could route around problems faster than any other network. That makes the experience better. If you can see that it has real-time info, you will learn to trust the one that has the real-time info on the screen more than one that doesn't have as much info. Which gets us to another thing. All us Tesla owners have its app running on our phones. That could feed a lot of new data into the network, like about how busy a nightclub or a restaurant is. Or details about a news event. Literally none of us have a Waymo app on our phones. Most humans haven't even heard of, or seen, a Waymo. Uber? Sure! But it comes with a human which brings the above problems and a higher price. Speaking of price. A Waymo has quite a few expensive LiDARs and more compute on board than Tesla does to handle both camera feeds and LiDAR feeds. Tesla has gone with a camera only approach, which is not only cheaper per vehicle, but looks a lot nicer too and can be hidden inside a standard-looking vehicle. Waymo's have gotten vandalized several times because they are the symbols of a new, dystopian, age, as one of my friends put it. Tesla's just are everywhere in many cities, especially anyone near San Francisco so won't attract the same kind of attention. So, my predictions: By 2030: Tesla robotaxi will be cheaper than other networks. Tesla will have a better screen and audio system for you to use. Movies, education, music, video conference calls, etc, will be better in a Tesla and more consistent. Tesla will be safer, and go more places. Tesla's route picker will be way better. Especially around massive events like concerts, sporting events, and more. Tesla's vents, seats, mirrors, audio level, are automatically set to personalize to you. Tesla's app and vehicles gives you more details, which leads to a better experience "there's a 14-minute line at the McDonalds." Let's put it bluntly: Tesla's customer acquisition cost will be nearly zero, while Waymo or Zoox will need to spend a lot more to create both awareness and behavior. I don't think they will be able to convince a Tesla user to switch to their platforms given the better experience and lower price I'm expecting Tesla to have. To wrap it up, for now, let's say you want to take your lover to a downtown for a date night. In an Uber there is a driver taking up one of the two best seats. In a Tesla? Nope. So date night is way better. On Thursday night we'll see just how much better.

How Tesla's Positioned to Win the Robotaxi Market By 2030 Over Waymo, Zoox, Cruise, Uber, and Lyft

كيف يمكن لشركة Tesla الفوز بسوق سيارات الأجرة الآلية بحلول عام 2030 على حساب Waymo وZoox وCruise وUber وLyft

So positioniert sich Tesla, um bis 2030 den Robotaxi-Markt vor Waymo, Zoox, Cruise, Uber und Lyft zu erobern

Cómo se posiciona Tesla para ganar el mercado de robotaxi en 2030 frente a Waymo, Zoox, Cruise, Uber y Lyft

Como se posiciona Tesla para gañar o mercado de Robotaxi para 2030 sobre Waymo, Zoox, Cruise, Uber e Lyft

Bagaimana Tesla Diposisikan untuk Memenangkan Pasar Robotaxi pada Tahun 2030 Mengalahkan Waymo, Zoox, Cruise, Uber, dan Lyft

テスラは2030年までにウェイモ、ズークス、クルーズ、ウーバー、リフトを抑えてロボタクシー市場を制覇する立場にある

Тесла 2030-жылга чейин Роботакси рыногун Waymo, Zoox, Cruise, Uber жана Lyft аркылуу кантип утуп алды

Bagaimana Tesla Kedudukan untuk Memenangi Pasaran Robotaxi Menjelang 2030 Berbanding Waymo, Zoox, Cruise, Uber dan Lyft

Hoe Tesla zich positioneert om de markt voor robotaxi's in 2030 te veroveren ten koste van Waymo, Zoox, Cruise, Uber en Lyft

Ukuntu Tesla ihagaze kugirango itsinde isoko rya Robotaxi Muri 2030 hejuru ya Waymo, Zoox, Cruise, Uber, na Lyft

Paano Nakaposisyon ang Tesla na Manalo sa Robotaxi Market Pagsapit ng 2030 Sa Waymo, Zoox, Cruise, Uber, at Lyft

Tesla 2030 yilga kelib Robotaxi bozorini Waymo, Zoox, Cruise, Uber va Lyft orqali qanday qilib yutib oladi

Tesla's back on the menu, baby! The world's most valuable car company became even more valuable this past week after it posted its quarterly results, and boy, they were something to marvel at. The company's profits were up — by 17% from last year — while sales grew 8% when compared to the same period in 2023. More importantly, not only did Elon Musk's company say that it was producing cars at their cheapest levels yet, but that it was producing them at record volumes. On top of that, Musk said it expected Tesla's vehicle sales to grow 20% to 30% next year as it introduces more affordable models in the first half of 2025. Naturally, investors went crazy, and the company's stock recorded their biggest single-day gain in over a decade on Thursday — a day after the results were released — adding nearly $150 billion to the company's market value. This couldn't have come at a better time for Musk as Wall Street was becoming increasingly concerned that Tesla was focusing a little too much on new projects like the robotaxi, away from its core business of selling electric cars. In fact, when Tesla announced the robotaxi and the so-called Cybercab at its "We, Robot" event just a few weeks prior, its stock dropped around 9% — indicating investors' dislike of Musk's plan to transform Tesla into an artificial intelligence and robotics company. Never the one to shy away from proving his naysayers wrong, Musk isn't giving up on the promise of offering autonomous transport at a "cost per mile below rideshare, personal car ownership and even public transit." Some would argue that is a bold goal, while others would point to the long list of Musk's broken promises. I'm just wondering if Musk is trying to reinvent public transportation 😂 I'm not alone, since Tesla investors are also wondering how Musk plans to pull off his grand vision, with Reuters reporting that Musk's optimism on having driverless ride-hailing services in California and Texas next year is likely going to face an uphill battle due to regulatory and technical challenges. One element that is not being discussed at all is of course Musk's relationship with Donald Trump. It's still too early to call who will win the elections, since both Trump and Kamala Harris are neck to neck, but if Trump were to become the next US president, Musk will benefit from it one way or the other. Trump has, in recent days, praised Musk's SpaceX on at least two occasions: once at an event in Chicago hosted by Bloomberg, and the second during his appearance on the Joe Rogan podcast. Musk has also been a part of Trump's campaign trail and has made numerous appearances during the Republican candidate's rallies, including the most recent one at Madison Square Garden. In fact, Trump has gone as far as to indicate he will appoint Musk as the head of a new government efficiency commission if he gets elected to office. But there might be more at play here. Trump has been very vocal about imposing tariffs on goods coming from foreign countries, including cars, due to a belief that it would generate jobs in America and boost the economy. Such a move would directly benefit Musk since Tesla can't compete against Chinese electric car makers, meaning any policy that makes them more expensive in the US would be a net positive for one of the world's richest men. For now, Musk can bask in the glory of his company's quarterly win. As things heat up, I'll be around to report on it. Tesla ranked #9 on HackerNoon's Tech Company Rankings this week. GET PUBLISHED ON HACKERNOON.
Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. In Other News.. 📰 Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk
Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch
X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN
Microsoft's AI demand under scrutiny as investors seek payday — via Reuters
Meta strikes multi-year AI deal with Reuters — via Axios
How I turned ChatGPT into my tour guide in Italy — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tesla 's back on the menu, baby! Tesla Tesla The world's most valuable car company became even more valuable this past week after it posted its quarterly results, and boy, they were something to marvel at. The company's profits were up — by 17% from last year — while sales grew 8% when compared to the same period in 2023. More importantly, not only did Elon Musk's company say that it was producing cars at their cheapest levels yet, but that it was producing them at record volumes. On top of that, Musk said it expected Tesla's vehicle sales to grow 20% to 30% next year as it introduces more affordable models in the first half of 2025. producing producing Naturally, investors went crazy, and the company's stock recorded their biggest single-day gain in over a decade on Thursday — a day after the results were released — adding nearly $150 billion to the company's market value. recorded recorded This couldn't have come at a better time for Musk as Wall Street was becoming increasingly concerned that Tesla was focusing a little too much on new projects like the robotaxi, away from its core business of selling electric cars. In fact, when Tesla announced the robotaxi and the so-called Cybercab at its "We, Robot" event just a few weeks prior, its stock dropped around 9% — indicating investors' dislike of Musk's plan to transform Tesla into an artificial intelligence and robotics company. dropped dropped Never the one to shy away from proving his naysayers wrong, Musk isn't giving up on the promise of offering autonomous transport at a "cost per mile below rideshare, personal car ownership and even public transit." Some would argue that is a bold goal, while others would point to the long list of Musk's broken promises. I'm just wondering if Musk is trying to reinvent public transportation 😂 I'm not alone, since Tesla investors are also wondering how Musk plans to pull off his grand vision, with Reuters reporting that Musk's optimism on having driverless ride-hailing services in California and Texas next year is likely going to face an uphill battle due to regulatory and technical challenges. how reporting reporting One element that is not being discussed at all is of course Musk's relationship with Donald Trump. It's still too early to call who will win the elections, since both Trump and Kamala Harris are neck to neck, but if Trump were to become the next US president, Musk will benefit from it one way or the other. Trump has, in recent days, praised Musk's SpaceX on at least two occasions: once at an event in Chicago hosted by Bloomberg, and the second during his appearance on the Joe Rogan podcast. hosted hosted appearance appearance Musk has also been a part of Trump's campaign trail and has made numerous appearances during the Republican candidate's rallies, including the most recent one at Madison Square Garden. In fact, Trump has gone as far as to indicate he will appoint Musk as the head of a new government efficiency commission if he gets elected to office. recent recent head head But there might be more at play here. Trump has been very vocal about imposing tariffs on goods coming from foreign countries, including cars, due to a belief that it would generate jobs in America and boost the economy. Such a move would directly benefit Musk since Tesla can't compete against Chinese electric car makers, meaning any policy that makes them more expensive in the US would be a net positive for one of the world's richest men. For now, Musk can bask in the glory of his company's quarterly win. As things heat up, I'll be around to report on it. Tesla ranked #9 on HackerNoon's Tech Company Rankings this week. GET PUBLISHED ON HACKERNOON. Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON . GET PUBLISHED ON HACKERNOON Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. In Other News.. 📰 In Other News.. 📰 Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN Microsoft's AI demand under scrutiny as investors seek payday — via Reuters Meta strikes multi-year AI deal with Reuters — via Axios How I turned ChatGPT into my tour guide in Italy — via CNBC Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk CoinDesk CoinDesk Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch TechCrunch TechCrunch X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN CNN CNN Microsoft's AI demand under scrutiny as investors seek payday — via Reuters Reuters Reuters Meta strikes multi-year AI deal with Reuters — via Axios Axios Axios How I turned ChatGPT into my tour guide in Italy — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

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