FreeFlow

freeflow

Authors:
(1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611;
(2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803;
(3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005;
(4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Methodology Results Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770 Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Authors: Authors: (1) Yujie Hu, Department of Geography, University of Florida, Gainesville, FL 32611 and UF Informatics Institute, University of Florida, Gainesville, FL 32611; (2) Changzhen Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803; (3) Ruiyang Li, Children’s Environmental Health Initiative, Rice University, Houston, TX 77005; (4)Fahui Wang, Department of Geography & Anthropology, Louisiana State University, Baton Rouge, LA 70803. Table of Links Abstract and 1 Introduction. Abstract and 1 Introduction. Methodology Methodology Results Results Concluding comments, Acknowledgement and References Concluding comments, Acknowledgement and References Abstract Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers. Introduction Estimating a drive time matrix between locations is a critical task in spatial analysis, commonly encountered by researchers in geography, urban planning, transportation engineering, business management, and operational research, etc. To list a few, analytical models such as spatial interaction modeling (Simini et al., 2012), travel demand estimation (McFadden, 1974), location-allocation problems (ReVelle and Swain, 1970; Hu et al., 2019), spatial accessibility measures (Luo and Wang, 2003; Dony et al., 2015; Balomenos et al., 2019; Zhu et al., 2020), and delineation of health care market areas (Wang et al. 2020), rely on attainment of reliable drive time estimation from a set of origin locations to a set of destination locations. Such a task for a small-size matrix has become routine in many GIS and transportation packages, such as ESRI® ArcGIS and Caliper® TransCAD. However, it can be a challenging task for a large matrix. Most studies in this scope focus on small areas like individual cities or counties. Estimating the time matrix at larger geographic scales, such as a national scope, is of increasingly great importance to researchers and policy makers. Take the U.S. health care as an example, a national travel time matrix is the most critical component to study the average geographic access to health care (Onega et al., 2008; Boscoe et al., 2012), measure the variations in geographic access across regions (Onega et al., 2017), identify the areas where the health care geographic access is significantly lower than average, suggest the most appropriate sites for new care facilities, and facilitate the implementation of other strategies for reducing health care disparities such as remote health care, health care on wheels, and transit to care. The challenges for calibrating a large drive time matrix include availability of reliable road network (including traffic) data, programming expertise, and computational power. Many studies assume a free flow condition on roads to eliminate the data requirement on traffic, and are often limited to estimation of drive times from areas (e.g., ZIP code area, census tract) to the nearest locations (Onega et al., 2008; 2017; Boscoe et al., 2012; Ikram et al., 2015) or between locations within a short range (Shi et al., 2012) with a significantly reduced number of OD (origin-destination) pairs. Most recently, Saxon and Snow (2019) estimated a drive time matrix from each census tract to each primary care location within 62 miles (100 km) in the U.S. by tapping into advanced computing resources such as distributed computing and sophisticated algorithms, which may not be accessible by most researchers. They did not consider traffic conditions or node impedance, and the result tended to underestimate drive times especially for short trips. One way to account for traffic effect in drive time estimation relies on the utilization of traffic sensors or auxiliary data sources. These sensors include loop detectors (Kwon et al., 2000; Coifman, 2002) and automatic vehicle identification systems—such as toll collection system (El Faouzi et al., 2009), license plate recognition system (van Hinsbergen et al., 2009), and Bluetooth-based system (Bhaskar and Chung, 2013)—that are installed at certain locations along the road. They can accurately capture travel speeds and times. Other sensors are rather flexible, such as the floating or probe vehicles that consist of a sample of vehicles equipped with GPS units running with traffic (De Fabritiis et al., 2008). Based on collected information on a vehicle’s location, direction, and speed in a short time interval, drive times between any two locations in a network can be readily attained (Semanjski, 2015). A few recent studies attempted to estimate drive times using big data. Toole et al. (2015) used call detail records (CDRs) to obtain drive times for all road segments in five selected cities worldwide. Woodard et al. (2017) derived drive times on all roadways in the Seattle metropolitan region based on collected mobile phone GPS data. Although these methods can provide highly accurate estimates on actual drive times in traffic, their reliance on installation of physical equipment or big crowdsourced data restricts their usage to only small areas ranging from major corridors to metro regions. An alternative approach is to use third-party web mapping services such as Google Maps and MapQuest. For example, Google Maps Distance Matrix API uses the Google data such as its road network and collected traffic information to estimate drive times between a set of origins and a set of destinations. Similarly, MapQuest’s Route Matrix API uses open-source mapping data from the OpenStreetMap project to achieve this goal. Another benefit of using these commercial web services is to relieve analysts of the burden of preparing street network data and accessing GIS/transportation software (Boscoe et al., 2012). However, the free usage of these services comes with request limits. For instance, Google Maps offers free usage up to 40,000 OD records per month (Hu and Downs, 2019). A similar restriction applies to MapQuest. As a result, researchers usually use this approach to derive drive times for a limited number of OD pairs (Wang and Xu, 2011). Another issue related to drive time estimation between areas is the so-called aggregation error (Hu and Wang, 2016). The centroid-to-centroid approach assumes that all people in an area live at the centroid of an area (Hewko et al., 2002), and inevitably overlooks intrazonal travels (Kordi et al., 2012; Bhatta and Larsen, 2011). For example, the average commuting time within a traffic analysis zone (TAZ) is 11.3 minutes for auto drivers in Cleveland, Ohio (Wang, 2003). Given the average area of 82.25 square miles for the ZIP code areas in the U.S., intrazonal drive times at the ZIP code area level can be significant, especially in low-density suburban or rural areas. Its omission accounts for a high percentage in error for short-range trips. A common approach approximates intrazonal travel distance as the radius of an area-equivalent circle (Frost et al., 1998, Horner and Murray, 2002; Hu and Wang, 2015). Some recent studies use Monte Carlo simulation techniques to improve the estimation of trip lengths between area units (Hu and Wang, 2016; Hu et al. 2017), and offers a viable solution to intrazonal drive time estimation. This study seeks to estimate a very large drive time matrix between ZIP code areas in the U.S. ZIP code area is a popular geographic unit used in many nationwide datasets. For instance, ZIP code area is often the finest geographic scale at which health information is compiled and distributed in the U.S. (Berke and Shi, 2009). Our method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. Both the program and results will be available for free download, and provide a valuable resource for researchers. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [1T] his is a preprint of: Hu, Y., Wang, C., Li, R., & Wang, F. (2020). Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach. Journal of Transport Geography, 86, 102770. https://doi.org/10.1016/j.jtrangeo.2020.102770

A Nationwide Drive Time Matrix Between U.S. ZIP Code Areas: Abstract and Introduction

You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 18 of 38. Line of Effort 1: Support the Trusted Flow of Data and Advocate for Multistakeholder, Risk-Based Approaches to Digital and Data Governance Digital solidarity is further built and reinforced through the joint development, harmonization, and mutual recognition of rights-respecting approaches to data governance and digital trade. This work is currently ongoing through mechanisms such as Indo-Pacific Economic Framework for Prosperity (IPEF), Digital Transformation with Africa initiative (DTA), the Americas Partnership for Economic Prosperity (APEP), the G7, OECD, TTC, and the Quad. The United States supports the trusted free flow of data and an open Internet with strong and effective protections for individuals’ human rights and privacy and measures to preserve governments’ abilities to enforce laws and advance policies in the public interest. Legitimate concerns about data privacy can be addressed through protective mechanisms that follow the data while at the same time facilitate cross-border data flows and strengthen global cooperation among enforcement authorities. The United States will continue championing trusted cross-border data flows by promoting data transfer mechanisms that improve interoperability between different data privacy regimes. Working alongside our interagency partners, the Department of State supported the negotiation and implementation of the EU- U.S. Data Privacy Framework; the development of the OECD Declaration on Trusted Government Access to Data Held by the Private Sector, which identifies commonalities in the privacy safeguards democratic governments follow when accessing data for legitimate law enforcement and national security purposes; as well as initiatives on Data Free Flow with Trust at both the G7 and the OECD. The Department of State works with the Department of Justice to clarify application of the U.S. Clarifying Lawful Overseas Use of Data (CLOUD) Act and to negotiate bilateral agreements under the act. Along with Australia, Canada, Japan, the Republic of Korea, Mexico, the Philippines, Singapore, and Taiwan, the United States launched the Global Cross-Border Privacy Rules (CBPR) Forum in April 2022, building on the previously established Asia Pacific Economic Cooperation (APEC) CBPR system. The CBPR provides a data privacy certification backed by relevant authorities that facilitates data flows by promoting interoperable, enforceable data protection standards. Officials from the Departments of State and Commerce will continue efforts to bring new countries into the agreement, building on efforts such as workshops held in Kenya, Mexico, Chile, Brazil, UK, Israel, Jordan, Panama, Colombia, Fiji, and Barbados as well as ASEAN countries. While the United States and its likeminded trade partners share many of the same values, we often have differing approaches to how to regulate the digital economy. The U.S. government advocates for multistakeholder, risk-based approaches that target the challenges we face while providing the flexibility to realize the benefits of new and emerging technologies. Unilateral approaches in digital taxation and the imposition of network usage fees often do not address the core issues of accessibility and fairness expressed by their proponents. Additionally, the rise of a growing digital sovereignty narrative that has been embraced by some of our close partners and allies has the potential to undermine key digital economy and cybersecurity objectives. The Department of State, working with other agencies, will continue to argue against data localization, network usage fees, digital services taxes as well as other market access barriers that contribute to the perception of increased control, but in reality often can undermine growth and security objectives. Continue Reading Here. This post was originally published on May 6, 2024, by the U.S Department of State You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here. This part is 18 of 38. You can jump to any part of the United States International Cyberspace & Digital Policy Strategy here . This part is 18 of 38. here Line of Effort 1: Support the Trusted Flow of Data and Advocate for Multistakeholder, Risk-Based Approaches to Digital and Data Governance Digital solidarity is further built and reinforced through the joint development, harmonization, and mutual recognition of rights-respecting approaches to data governance and digital trade. This work is currently ongoing through mechanisms such as Indo-Pacific Economic Framework for Prosperity (IPEF), Digital Transformation with Africa initiative (DTA), the Americas Partnership for Economic Prosperity (APEP), the G7, OECD, TTC, and the Quad. The United States supports the trusted free flow of data and an open Internet with strong and effective protections for individuals’ human rights and privacy and measures to preserve governments’ abilities to enforce laws and advance policies in the public interest. Legitimate concerns about data privacy can be addressed through protective mechanisms that follow the data while at the same time facilitate cross-border data flows and strengthen global cooperation among enforcement authorities. The United States will continue championing trusted cross-border data flows by promoting data transfer mechanisms that improve interoperability between different data privacy regimes. Working alongside our interagency partners, the Department of State supported the negotiation and implementation of the EU- U.S. Data Privacy Framework; the development of the OECD Declaration on Trusted Government Access to Data Held by the Private Sector, which identifies commonalities in the privacy safeguards democratic governments follow when accessing data for legitimate law enforcement and national security purposes; as well as initiatives on Data Free Flow with Trust at both the G7 and the OECD. The Department of State works with the Department of Justice to clarify application of the U.S. Clarifying Lawful Overseas Use of Data (CLOUD) Act and to negotiate bilateral agreements under the act. Along with Australia, Canada, Japan, the Republic of Korea, Mexico, the Philippines, Singapore, and Taiwan, the United States launched the Global Cross-Border Privacy Rules (CBPR) Forum in April 2022, building on the previously established Asia Pacific Economic Cooperation (APEC) CBPR system. The CBPR provides a data privacy certification backed by relevant authorities that facilitates data flows by promoting interoperable, enforceable data protection standards. Officials from the Departments of State and Commerce will continue efforts to bring new countries into the agreement, building on efforts such as workshops held in Kenya, Mexico, Chile, Brazil, UK, Israel, Jordan, Panama, Colombia, Fiji, and Barbados as well as ASEAN countries. While the United States and its likeminded trade partners share many of the same values, we often have differing approaches to how to regulate the digital economy. The U.S. government advocates for multistakeholder, risk-based approaches that target the challenges we face while providing the flexibility to realize the benefits of new and emerging technologies. Unilateral approaches in digital taxation and the imposition of network usage fees often do not address the core issues of accessibility and fairness expressed by their proponents. Additionally, the rise of a growing digital sovereignty narrative that has been embraced by some of our close partners and allies has the potential to undermine key digital economy and cybersecurity objectives. The Department of State, working with other agencies, will continue to argue against data localization, network usage fees, digital services taxes as well as other market access barriers that contribute to the perception of increased control, but in reality often can undermine growth and security objectives. Continue Reading Here. Continue Reading Here . Here This post was originally published on May 6, 2024, by the U.S Department of State This post was originally published on May 6, 2024, by the U.S Department of State by the U.S Department of State

The U.S. Advocates for Trusted Data Flows and Multistakeholder Digital Governance

As the professional sphere rapidly evolves, the inflexible 9-to-5 routine has been supplanted by more dynamic and adaptive modes of operation. Remote and hybrid forms of collaboration have become the new standard for a vast number of enterprises, providing increased freedom and access to a wider talent base. While this shift holds numerous advantages, it has also created the difficult task of ensuring productivity, , and effective communication when personnel are not in the same physical space. This article provides several practical suggestions to promote successful collaboration amongst modern agency teams, drawing on the continual transformations in the labor market. The embrace of digital collaboration tools is essential for successful remote and hybrid team collaboration. A plethora of project management platforms and video conferencing software exist, all created with the intent of streamlining communication and boosting productivity. It is imperative to choose the ideal solutions corresponding to the prerequisites of your department. Tools such as Slack, Trello, and Asana can assist teams in staying organized, managing tasks effectively, and creating clear communication. Video conferencing platforms like Zoom or Microsoft Teams satisfy the need for something more personal than emails when it comes to virtual gatherings. Taking advantage of these technologies ensures that teams keep in contact, no matter their physical seclusion. Fostering an effective communication culture is essential for the achievement of distant and hybrid teams. Adequate and obvious communication helps in averting misapprehensions, promoting cooperation, and cultivating an awareness of solidarity among team members. Calling attention to routine check-ins, , and updates confirms that all are on the same page. Fostering pathways of free-flow discourse, where employees feel secure enough to express their thoughts and worries, is essential. This can be accomplished through regular , digital coffee pauses, or carefree conduits through joining platforms. A conversational atmosphere not only bolsters collaboration but also reinforces the all-embracing team synergy. Generating firm expectations and aims is key to successful remote and hybrid collaboration. Constructing realistic and attainable goals enables all members of a team to synchronize their energy to accomplish a joint goal. Plain rhetoric regarding duties, responsibilities, and project periods diminishes ambiguity and encourages accountability. In a , it is essential to specify performance metrics and essential performance indices (KPIs) unmistakably. Recurringly revisiting and evaluating these objectives assists teams in adjusting to varying situations and ensures that everyone is contributing to collective goals. efficient teamwork team meetings team-building endeavors virtual work atmosphere Ensuring Flexibility and Work-Life Harmony The great benefit of working remotely or in a hybrid set-up is the flexibility that is provided to colleagues. At the same time, an attempt should be made to ensure there is a sound . Organizations ought to cultivate a culture that gives importance to the welfare of their personnel and acknowledges the importance of leisure time. work-life equilibrium Supporting amenable working hours, supplying psychological well-being guidance, and promoting regular pauses are all initiatives that can aid in the cultivation of a healthier work atmosphere. A team that is rejuvenated and content will be better positioned to be enthusiastic and productive in their roles. Investing in Professional Advancement In the current climate of remote and hybrid work, it is essential to adopt incessant study and modify to new technologies and systems. Agencies should devote capital to the personal evolution of their teams, guaranteeing they stay current with up-to-date industry trends and innovations. This could include granting access to digital classes, conferences, or workshops. Moreover, developing a culture of tutelage and expertise exchange within the team aids in harnessing the distinct competencies and backgrounds of remote and hybrid teams. This allocation to professional progress not only strengthens individual capacities but also adds to the common development of the organization. Utilizing Data to Make Knowledgeable Decisions Data-informed decision-making is paramount in virtual and hybridized job settings. Establishments ought to capitalize on analytics and data tracking systems to obtain discernment into staff productivity, assignment progress, and all-encompassing synergy proficiency. Examining these data points aids in spotting areas for advancement and making prudent choices to refine procedures. Adopting (KPIs) to evaluate virtual collaboration, trace venture timetables, and monitor individual and group efficiency figures can offer invaluable data. This analytical system makes certain that bureaus can modify rapidly to any impediments and ceaselessly strengthen their far-off and combined collaboration approaches. key performance indicators Increasing Cybersecurity Procedures As remote work increases, the need for stringent cybersecurity cannot be downplayed. Organizations should not underestimate the significance of robust cybersecurity protection to defend confidential data and uphold the dependability of their operations. This needs undertaking secure talked ways, employing encrypted file-sharing systems, and training personnel on the possible hazards of cybersecurity. Regular exercises on cybersecurity guidelines, strong password administration, and consciousness regarding phishing assaults add to a secure remote working environment. By concentrating on cybersecurity, organizations can secure their intellectual property and preserve the trust of customers and partners. Conclusion The future of work is developing, and remote and hybrid collaboration is not going away. To properly manage these developments necessitates a proactive attitude towards communicating, technology, and team dynamics. By procuring the correct tools, sustaining a milieu of communication, and emphasizing versatility and work-life balance, organizations can ready themselves for achievements in this revitalized age of collaboration. Establishing explicit expectations, engaging in professional growth, utilizing data to make educated decisions, and increasing cybersecurity precautions further contribute to establishing a robust and bendable remote and hybrid work configuration. As agencies tackle the difficulties and advantages presented by the ever-changing workplace landscape, these methods will be paramount in guaranteeing perpetual triumph and productivity.

Strategies to Enhance Remote and Hybrid Collaboration in 2024

Click, order, and doorstep delivery—online shopping is now a daily thing. In the digital shopping wave, dropshipping stands out, changing how businesses tackle e-commerce. This shift from the traditional means of obtaining products and services is becoming more and more widespread and hence, businesses must act following consumers’ preferences. Let's take a critical look at how we can stay trendy in , exploring the ways dropshipping thrives in today's market and providing a fresh take on retail's future. online shopping Key Concepts of Dropshipping At its core, dropshipping is a retail fulfillment technique where a store doesn't keep the products it sells in stock. Instead, when a sale needs to be made, the seller proceeds to purchase the item from a third party and have it shipped directly to the customer. In essence, you act as a middleman orchestrating a seamless transaction. Unlike traditional retail, dropshipping liberates businesses from the burden of managing inventory. There's no need to sink capital into stockpiles of products. Instead, you focus on the front end — marketing and selling — while leaving the nitty-gritty logistics to others. Like a regular marketplace, dropshipping is aided by key market players. These players function as the lifeblood of the industry. They include: These entrepreneurs set up digital storefronts, curate product offerings, and craft compelling narratives that entice customers. Essentially, they are conductors of an online symphony, orchestrating a seamless shopping experience. Retailers: They are the backstage crew, holding the inventory and managing the logistics. These can range from manufacturers to wholesalers. The beauty? Retailers don’t need to buy in bulk or handle stock. The supplier handles warehousing and shipping, allowing retailers to concentrate on what they do best — connecting with customers. Suppliers: The end-users, the reason for the entire show. Customers place orders, blissfully unaware of the intricate behind-the-scenes ballet. They enjoy a vast array of products without the retailer having to navigate the complexities of inventory management. Customers: How Is It Beneficial? Many benefits come with online businesses. For dropshipping, this is even more crucial as it ensures the survival of the business. Some of these merits stand as the core foundation on which dropshipping is built. These include: Low Initial Investment Traditionally, launching a business involved hefty upfront costs—warehousing, inventory, and the perpetual fear of unsold stock gathering dust. Dropshipping flips this script. With minimal initial investment, entrepreneurs can dive into the world of commerce without the financial hurdles. No need to buy in bulk or rent storage space; dropshipping allows a free-flow entrepreneurship activity without a heavy price tag. Reduced Risk and Inventory Management Risk mitigation is the heartbeat of dropshipping. The burden of unsold inventory morphs into a relic of the past. Why? Because you only purchase products when you make a sale. No need to play the guessing game on which products will fly off the virtual shelves. This hands-off inventory management reduces financial risk and frees up mental bandwidth to focus on . growing your business Flexibility and Scalability Dropshipping is a business model that grows with you. Its inherent flexibility means you can operate from anywhere with an internet connection. The business adjusts to your pace, accommodating side hustles or full-time entrepreneurship. As your ambitions soar, so can your business. Scaling up is seamless; the more you sell, the more your dropshipping empire expands. It's an interplay of growth and adaptability. Global Reach and Market Expansion The world is your marketplace. Dropshipping catapults your products into the global scene without the logistical nightmares. No need to stress over international shipping or setting up shop in every corner of the world. With a few clicks, your products can reach customers on different continents. This global reach ensures you broaden your customer base and fortifies your brand's presence in the vast expanse of the digital marketplace. , the global dropshipping market size was valued at USD 225.99 billion in 2022 and is expected to register a compound annual growth rate (CAGR) of 23.4% from 2023 to 2030. According to Grand View Research The market is anticipated to witness significant growth owing to an increase in preference for online shopping, coupled with the rising trend of cross-border e-commerce trade. Challenges Involved In a landscape where you don't physically handle products, ensuring quality can be tricky. across the diverse products offered is akin to a tightrope walk. Maintaining a standardized level of quality Product information discrepancies can lead to unhappy customers, eroding trust in your brand. A meticulous approach to vetting suppliers and providing accurate product details becomes paramount. Involving synchronized steps with suppliers and third-party partners, if one partner misses a beat it ripples through the entire operation. Relying on external entities for inventory and shipping means relinquishing some control. master the art of forging robust partnerships, minimizing the impact of complications that may arise externally. Successful dropshippers The allure of dropshipping has attracted a multitude of entrepreneurs into the arena, intensifying competition. Standing out amidst the crowd demands strategic differentiation. From niche selection to unique value propositions, navigating the crowded marketplace requires a keen understanding of what makes your brand distinct and appealing. The distance between customer and product in dropshipping magnifies the importance of stellar customer service. Resolving customer queries promptly, managing expectations, and handling returns efficiently become pivotal. The ease with which customers can switch to another seller places a premium on creating a positive customer experience to foster loyalty. Implementing Successful Strategies Finding Your Niche and Products Imagine this: You're not just selling stuff but crafting an experience. It all begins by choosing a niche that lights up your passion. Explore areas where your excitement matches what people want. This goes beyond just selling products; it's about being a champion. Your golden ticket lies in products that vibe with you and attract the market. It is akin to riding trends and sticking to your true north. Building a Trustworthy Supplier Network In the world of dropshipping, suppliers are your allies. Forge connections like a blacksmith-shaped iron. Look for suppliers who share your commitment to quality and timeliness. Transparency is the glue of a strong partnership, so pick suppliers who talk openly. This network isn’t just about obtaining products; it ensures dependability. A reliable supplier is not just a source; it's the foundation of your brand’s trustworthiness. suggests that 84% of e-commerce retailers say that the biggest obstacle to starting a dropshipping business is finding a good supplier. This only confirms the importance of suppliers to the industry as a whole. Research by Printful Effective Marketing and Branding Your products might be the stars, but you create the stage. Design a brand that narrates a story – your story. Effective marketing isn't a loudspeaker; it's a connection. Harness social media, ride the waves of influencers, and let your brand's voice resonate. Consistency is your guide that ensures your brand shines like a lighthouse in the digital storm. Effective marketing doesn't shout; it whispers, creating echoes. Utilizing Technology and Automation In the e-commerce dance, timing is key. Welcome technology as your choreographer. Automate the routine, so you can direct the extraordinary. From handling inventory to , let algorithms be your backstage team. The more you automate, the more you free up time to innovate. Efficiency isn't just a measure; it’s your quiet partner for success. engaging with customers Marketing Strategies as Major Factors Social media is a treasure trove for dropshippers. Think Instagram, Facebook, and TikTok—they're fancy stages to flaunt what you've got. , tell a tale, and get people moving. Use Instagram's cool visuals, tap into Facebook's big crowd, and catch the TikTok trend. Your next buyer might just be a scroll away. Create ads that grab attention Team up with influencers who match your energy. Whether it's a beauty expert, fitness buff, or tech guru, use their influence to push your product. Genuine nods from influencers build trust and put your brand in the limelight. Remember, in dropshipping, the right influencer can make a big difference. In the vast online world, being seen is a must. That's where search engine optimization (SEO) comes in. Sprinkle your product listings with smart keywords so they pop up when folks hit that search button. Get how search engines work, and tweak your product descriptions and titles. Being SEO-smart ensures your products don't just exist; they thrive online. Wrapping Up As online shopping continues to evolve, it has become evident that dropshipping has come to stay. It is a thriving strategy in today’s market due to its flexibility, low initial investment, and reduced risk. These factors and other market strategies discussed ensure business occurs easily and with a higher chance of success. However, this does not come without a fair share of challenges. To fully harness this power, these challenges must be understood to ensure business runs efficiently and effectively. Feature image source

Keeping Up With Online Shopping: How Dropshipping Thrives in Today's Market

Six years ago, became a promising trend that helped companies automate numerous manual processes. Yet, the technology itself cannot guarantee efficiency enhancement. robotic process automation (RPA) It’s also crucial to establish that will help achieve desired business results. an autonomous and scalable innovation team Building one goes beyond just hiring experienced individuals; it demands strategic vision, a systematic approach, resilience, a clear understanding of limitations, a focus on business value, and a culture of innovation. In this article, I’ll share best practices for successful innovation onboarding, candidate selection, and more aspects to consider to build a high-performing team. Why Do Enterprises Build Innovation Teams? Innovation often comes with built-in risks, including different errors, and time and money investments. Establishing an innovative culture becomes a perfect solution to maintain cost efficiency and reduce the uncertainties related to the innovation process. Forward-looking enterprises often choose to with proven experience in onboarding innovations, establishing , forming functional teams, and delivering consistent results. assign innovative initiatives to vendors centers of excellence (CoE) They look for partners capable of handling responsibilities and risks across the entire project scope, from assessing feasibility to building a scalable innovation ecosystem and delivering the final project. Additionally, an experienced technology partner can evaluate the company's current situation and business goals and create a process definition document to find areas that need improvement. How to Choose the Right Innovation Candidate In my experience, it is important to outline ideal innovation scenarios, assess and rank potential risks, including dependencies that might affect the process, and deliver a proof of concept (PoC) to show the expected benefits and outcomes. Selecting an innovation candidate is the next objective which usually consists of 2 stages. First, you should pick a candidate or a group of candidates to understand the strengths and limitations of each. The top-priority candidates are those capable of providing the greatest and productivity gains while requiring minimal complexity in execution, aligned with the company's existing operational framework, and promoting a reliable and resilient process. for PoC/PoV ROI The second stage involves selecting one candidate that offers a balance The ideal initial candidate should deliver maximal value while demanding moderate effort and resources from the enterprise. between complexity, ROI, and impact. A Plan to Build an Innovation Team You can start building the innovation CoE team with 6-10 people and scale along the growing needs. But defining the team members’ persona should be your top priority from the start. Describe the Innovation Team Member’s Persona Remember that soft skills are just as vital as technological expertise; you not only need a candidate with a rare skillset but also someone with specific traits. I usually look for specialists with diverse backgrounds who are passionate about technology and unafraid of making mistakes. I also appreciate individuals with reasonable doubts who can evaluate their ideas before implementation to avoid unnecessary reworks. There’s also a challenge in finding team members with rare skills. For example, when I was establishing an RPA innovation center 6 years ago, there were few people familiar with this technology in the market. That’s why I prefer rather than deep expertise in a specific technology now. candidates with a broad skillset The more skills a person has, the more likely they are to acquire relevant knowledge for your needs. They know how to research and explore new approaches to achieving project goals and can learn from core members along the way. Form a Team While the team may expand significantly over time, it's crucial to first fill key roles in the core. The main role of an architect is to validate innovation. They need to choose the best solution, find the best way to integrate it into the corporate ecosystem, and ensure adherence to workflow standards. Technical leader (architect). This person oversees project outcomes and the value delivered to the business. They assess the value of onboarding technology and ensure that the innovation is relevant to the business, easy to integrate, and user-friendly. Analyst/product owner. Their core responsibility is to manage communication between business needs and the innovation team. Success managers should align business expectations with the actual capabilities of a particular technology. Success manager. Engineers write features that meet the needs and expectations of end users. They choose the most effective approach to implementing a solution, design user interactions with the PoC, and create intuitive and user-friendly interfaces. Engineers. Initially, their focus is on the quality of the PoC. They identify any defects or errors that may compromise functionality or performance, offer corrective measures to ensure the PoC operates smoothly, create improvement strategies, and envision ways to enhance the PoC's capabilities. Quality assurance engineers. The team can grow with the innovation center's workload and include additional roles. For example, a more complex innovation team structure may look like this: Establish Effective Leadership A team leader plays a crucial role in guiding the direction, streamlining processes, addressing problem areas, and driving progress. However, this task is not straightforward. Innovation is a Moreover, a team leader actively participates in hiring, interviewing, selecting, and onboarding candidates. dynamic process. So, it’s important to take some time and thoroughly evaluate potential leaders through comprehensive observations, hackathons, and continuous feedback from employees and team members to find the best fit. Build a Community Each member of the has valuable and relevant knowledge that can benefit the entire project. Allow the free flow of information; let everyone share their insights during regular meetings and contribute to a innovation team shared knowledge base. With knowledge-sharing practices in place, you will be able to expand your team easily. With a core of up to 10 people, adding new members to the innovation team becomes more manageable. And even if they don't precisely match your technical expectations, they will quickly learn all the tricks and become valuable assets for your innovation team. What Results Can You Achieve With an Innovation Team? An innovation team or a dedicated center of excellence can help businesses achieve a higher gross profit margin over time. By consolidating innovative expertise and knowledge within the CoE, an enterprise can streamline processes, scale up, and establish lasting best practices. This results in more efficient and cost-effective project delivery, ultimately leading to increased ROI. Moreover, this unity fosters collaboration among team members and helps companies smoothly integrate technology initiatives into the overall business strategies, boosting cost-efficiency and helping them to make the most of innovation.

A Winning Strategy to Building Innovation Dream Teams

উদ্ভাবন স্বপ্ন দল তৈরির একটি বিজয়ী কৌশল

Eine erfolgreiche Strategie zum Aufbau von Innovations-Dreamteams

Una estrategia ganadora para crear equipos de ensueño de innovación

Une stratégie gagnante pour créer des équipes de rêve en matière d’innovation

नवोन्मेषी स्वप्न टीमों के निर्माण के लिए एक विजयी रणनीति

イノベーションのドリームチームを構築するための必勝戦略

혁신 드림팀 구축을 위한 성공적인 전략

Uma estratégia vencedora para construir equipes de sonho de inovação

Выигрышная стратегия создания инновационных команд мечты

İnovasyon Rüya Takımları Oluşturmak İçin Kazandıran Bir Strateji

Chiến lược thành công để xây dựng đội ngũ trong mơ đổi mới

打造创新梦之队的制胜策略

is to startups as fuel is to an engine. Innovation There is no question that innovation is at the heart of this type of business and is the entire point of venturing into an entrepreneurial arena. However, driving innovation in a business is no easy task. It's not just about brainstorming and coming up with bold ideas; you need marketing strategies. True innovation is possible when it is embedded into the DNA of a startup. In practical terms, this means building a startup culture where innovation is a part of one’s work life. And not just at the level of management but for every employee, no matter what their role is. So, the big question is: how do you create a culture of innovation in a startup? And that's what we answer in this post. Here are some of the key insights that lead to true and make game-changing new products and services possible. cultural change Optimize your location There’s no question that your environment influences your beliefs, personality, lifestyle, work ethic, and behavior. It stands to reason that where you situated your startup will play a role in how innovative your company and your people are. If you can, set up your business in a location that is geared for innovation and empowering small business growth. Tech parks or hubs are good examples of locations that support Innovation and new business growth. Here, there is a free flow of information between businesses, governmental organizations, and individuals to support creativity and technological development. Even traditional and large businesses in such places play an active role in garnering new ideas. Take, for example, in Finland or Silicon Valley in California. These are places that have ecosystems dedicated to tech companies. They encourage entrepreneurship through accelerators, networking, and funding opportunities. Helsinki And while not everyone can simply pack up and move to these places, you should think seriously about your location. Even working in a coworking space can make a difference to your culture and help support innovation, so open your mind to ways to leverage our location. Embed Innovation into Your Company Culture From day one, strive to create a workplace that encourages out-of-the-box thinking and problem-solving. Do this by: Involving all team members in the decision-making process Encouraging open communication Providing a safe space for ideas to flourish. Add innovation goals to your mission, vision, and values Set up a decision tree that includes innovation as a framework that guides our decision-making process Consider Google as an example. Its mission "to organize the world's information and make it universally accessible and useful" encourages innovation. This mission, along with its value of "Focus on the user and all else will follow," fosters a culture where employees constantly innovate to provide the best user experience. Google also emphasizes '10x thinking'—where employees are encouraged to strive for solutions that are ten times better than what exists. These examples show how innovation is a core part of Google even when it is a giant in the tech industry and should be stumbling the bigger it grows. There is transformative power in adding innovation to your culture via the mission, vision, and values. So, don’t knock down the impact of these company elements. Reward Experiments, Mistakes, and Risks By its very nature, innovation = risks. And it’s painful how few leaders seem to get that. If you want to drive innovation in your company, you have to risks, mistakes, and experiments at the very least. tolerate But if you want to take creativity to the next level, reward people for thinking outside the box, even if the outcomes are negative. The minute you start punishing people for coloring outside the lines is the minute you burn creativity to a crisp. Make sure you celebrate small wins and embrace losses. And as importantly, be the leader who exemplifies risk-taking and innovation to your team. Be open, do different things, and share your losses and pains. When you embrace this mindset, you’ll create a safe space for innovation in your business. Promote Diversity and Inclusivity Diversity and inclusivity are crucial components of an innovative culture. A diverse team brings fresh perspectives, experiences, and ideas to the table as opposed to a team where too much similarity leads to biases and tunnel vision. It’s worth noting that more and more studies are able to quantify the - and this isn’t something to be ignored. link between diversity and growth Also, inclusivity is important. Being actively inclusive means ensuring that all team members feel valued and heard. By creating an environment where everyone feels comfortable sharing their thoughts and contributing to the company's growth, you are more likely to hear interesting new ideas. And also, you’ll hear it when you might be heading the wrong way. If you want to create diversity and inclusivity, you should create a welcoming and inclusive hiring process. Also, it provides diversity training for employees and fosters an open-minded environment that embraces differences. This not only leads to a more innovative culture but also attracts top talent from different backgrounds. In increase your company's potential for growth. Promote Learning Creativity and innovation seldom come from nothing. More often than not, they are the results of taking a different spin on what already exists. Innovation also comes about from combining different knowledge, techniques, ideas, or subjects. The takeaway is that if you want to promote innovation, promote learning. Encourage a ‘growth mindset’ in your people, where you share how we can all grow to change ourselves and our ways of thinking. And to reinforce this message, offering learning options and training. This could look like subscriptions to platforms like Udemy and Coursera. Or your own in-house online courses, training modules, and . certifications And why not invest in having trainers, coaches, and experts come into the office to teach and support your staff? Learning builds knowledge in your business overall, but what’s even more powerful is that it drives innovation. Support Flexible Work With Tools No matter how much you promote innovation in your business, it won’t matter unless you back it up with practical and supporting work experiences. I mean that your tools, workflows, processes, language, company culture, and everything else MUST enable innovation. Here’s how: Subscribe to collaboration and tools. We use Slack, Asana, and other platforms that allow communication, task tracking, and more on mobile and desktops. communication Allow asynchronous work. Working at specific times can be very limiting for creative people. When you allow people to work asynchronously, you create freedom, flexibility, and the experience of going at one’s own pace. This is powerful for people to find the best time and way to work for them. These are a few ways to support innovation practically and allow everyone to move and work in a flexible, creative way. Final Thoughts: Lead Your Team In the end, be the leader your company and team need. If you want to be innovative, embody this trait in your actions, words, and mindset. Asking your company why they aren’t being creative is meaningless if you’re micromanaging and stifling people as they do their regular work. Instead, be open with your goals and respond positively to your employees and team members when they share ideas. Also, celebrate mistakes. They’re the stepping stone to creativity. I hope you found these tips on driving innovation useful. Go ahead and apply these ideas to your business and see real changes take place.

How to Create a Culture of Innovation in Startups

A Comprehensive Dive into Bridges

Meta recently conceded that in-app behavioral advertising in the EU can no longer be opt-in by default, marking the end of an era. The regulators won, at least for now. Every few decades, major shifts happen that change how an industry fundamentally operates and who the new winners and losers are. We are in the midst of one such shift in the digital advertising industry. This has been driven by three critical changes in the ecosystem, all privacy-related: Apple announced the in 2021 App Tracking Transparency framework Google is in 2024 deprecating third-party cookies A slew of privacy legislation is either already in place (like the , ) or is about to be in place (like the ) California Privacy Rights Act EU General Data Protection Regulation EU Digital Services Act While the effects of these changes have been felt by the advertising industry globally, the for a few years now. . In the past three weeks, there have been significant announcements from , , and about upcoming changes to their products in the EU. It’s probably fair to say these changes mark the end of an era for behavioral advertising in the EU, and the platforms have made peace with that. EU, in particular, has been at war with behavioral advertising And winning Meta Snap TikTok In this piece, we’ll dive into: How the advertising ecosystem runs (or used to run) Privacy interventions and impact on behavioral advertising EU’s war on behavioral advertising and why platforms are conceding Possible future paths How the advertising ecosystem runs (or used to run) Let’s start with some simple concepts. An ad shown to you can be contextual or behavioral (sometimes both). A is something that is shown alongside organic results (non-ads) in the context of whatever you are doing. For example, if you are on a food delivery app like DoorDash and you see an ad for a restaurant alongside organic results, that is a contextual ad. In that case, your identity doesn’t really matter. DoorDash knows your location and what you’re looking for and therefore shows you a relevant ad. Knowing your identity and interests does help improve the relevance of these ads, but it’s secondary to the context itself. contextual ad A is something that’s shown to you because of your past behavior. For example, you might have visited an office chair brand’s page on Instagram, and therefore you get served ads for another office chair brand; that’s exactly what happened to me with the ad on the right. This can either be based on direct behavior (like the office chair example) or inferred behavior — people like you who looked for chairs also looked for monitors. One step further, people who looked at chairs are also likely to work from home more and therefore be open to lunch service. All of those are potential ads that can be shown to you. behavioral ad A subset of products, most often search products, are perfect for contextual ads — Google Maps, Google Search, DoorDash, Yelp, Thumbtack, TripAdvisor, and Zocdoc, to name a few. The user has high intent to do something specific. However, most high-intent products are low-medium frequency. You are not looking for plumbers every day on Thumbtack, and you are not looking to order food delivery every hour of the day on DoorDash; therefore, the amount of contextual ad inventory is lower — highly effective but lower in volume. On the other hand, social media platforms where users spend hours doomscrolling have a large volume of non-contextual inventory. Random ads are annoying and ineffective, and therefore the most effective way to monetize this inventory is through behavioral ads. Unsurprisingly, . Before ~2018 (when EU’s GDPR kicked in), there was essentially a about you, collected through a long list of highly effective AdTech mechanisms. Some notable examples: behavioral ads require a lot of data, specifically data about you free flow of data Every iOS or Android phone had a unique “ ” assigned to it (and by association, to you). This ID was accessible to all apps on your device, i.e. your Uber app and your Google Maps app both knew you are mobile advertising ID 123 and could easily cross-identify you across apps. mobile advertising ID Most browsers (except Safari and Firefox) support “ ”, which are essentially a piece of data that one site could place on your browser and another site could access. There would be a “cookie ID” associated with you, and you could be easily cross-identified across websites. third party cookies Note that . You could go deep inside your settings and reset these IDs, but most people never do that. This resulted in a couple of second-order effects. these identifiers, for all practical purposes, were permanent First, companies that are “ ” started purchasing data from several different data providers and ; for example, app 1 could tell the broker that you bought an expensive piece of furniture, website 2 could tell them you have an account with a high-end bank, and the data broker could put that together and categorize you as “high propensity of spend” person; note that when I say “you,” that refers to your identifiers (mobile advertising ID or third party cookie ID) data brokers building “profiles” about you As more and more data became available, the profiles had more fidelity to them, and identification became more sophisticated; for example, if app 1 had your mobile advertising ID and website 2 had your Chrome third-party cookie ID, a data broker or an ad platform could compare your IP addresses and know it is the same person. Take this one step further. A data broker could also use a third data source and find an email associated with your IDs. One more step further, they could buy your address from a utility or telecom company and find your address. This might sound far-fetched, but it’s not — are a very commonly used data product in the AdTech stack today, and telecom companies are . If you need a more entertaining take, check out . device graphs notorious for selling your data John Oliver’s episode on data brokers Second, ; through a combination of technical mechanisms, an advertiser could say you first saw an ad on Facebook, then an ad on Google Search, then a display ad on NYTimes, and eventually bought an item from their website, so each of those three advertising platforms get shared credit. Attribution/measurement of advertising campaigns became more and more precise While this sounds privacy invasive (and it is), this resulted in a highly efficient advertising ecosystem. Advertisers knew exactly which users they were targeting, and since they had all these extra behavioral signals to know how likely a user was to engage, they were willing to pay higher cost per impression (CPM) for ad inventory, thereby generating more revenue for a media publisher. Precise attribution/measurement turbocharged this further. However, you can see how this was . This rightfully raised concerns about data consolidation in the hands of both data brokers and large technology companies, and an intervention was inevitable. It was less a matter of if and more a matter of when. becoming the wild west — highly effective advertising no doubt, but also an uncontrolled orgy of data acquired with non-existent or questionable user consent Privacy interventions and impact on behavioral advertising Let’s dive into each of the three privacy interventions that arose in an attempt to tame the Wild West. First, Apple introduced the . It sounds jargon-y, but the change is relatively simple. Prior to ATT, every app by default had access to your advertising ID, i.e. it was opt-in by default. This meant you could easily be tracked across apps and therefore shown effective behavioral ads. For example, you installed the Strava app to track your runs, you are now on Facebook, and you are shown an ad for Strava Premium. After ATT, the access to this , i.e. an app had to show you a pretty aggressive prompt to get access to your identifier, and you explicitly needed to say yes. The average opt-in rate ended up close to (with a lot of caveats).' App Tracking Transparency (ATT) framework identifier became opt-out by default ~34% We won’t go into much detail here, but this prompt was launched by Apple in the guise of embracing privacy — a smart chess move. The consensus opinion today is that this was an opportunistic move from Apple, which no doubt improves privacy but also heavily hurts Apple’s competitors as they . The impact was that user identity was available much less often. prop up their own ads business Note here that for behavioral advertising to happen in the Strava-Facebook example, there is not one but two apps that need to have received opt-in from you, i.e. the addressable market does not drop to 34%, it drops to 34% * 34% = ~12%. Therefore, . cross-app behavioral advertising on iOS is no longer effective at scale Second, Google announced that it will . The consensus opinion is that the change helps Google achieve a dual purpose: appease regulators who are breathing down their neck for , while taking control back from what’s now a . Google’s post third-party cookies will still allow cross-site retargeting, but in a more private way where all information is stored on-device within the browser, i.e. there are no more cross-site “cookie IDs” assigned to you. While the new mechanism preserves some of the status quo, . deprecate third-party cookies in 2024 potential anti-trust behavior in AdTech fairly bloated advertising tech stack new mechanisms cross-site behavioral advertising is going to have much less fidelity and, therefore, effectiveness Which brings us to the third intervention — . The most aggressive of these is the EU’s GDPR, which went into effect in 2018. The California Consumer Privacy Act (CCPA) went into effect in 2020. While the progression has been gradual, the reason these laws matter for advertising companies today more than ever is because the laws take aim at the (i.e. you do a bunch of different things inside the Facebook app and Facebook gets to use that data to show you behavioral ads within the app). privacy regulations only remaining and mission-critical advertising mechanism — behavioral advertising within companies’ own apps EU’s war on behavioral advertising and why platforms are conceding A primary feature that makes the California laws (arguably the strictest privacy law in the US) less aggressive than the EU’s GDPR is that it . For example, the 2020 California Consumer Privacy Act (CCPA) requires companies that are considered “data sellers” under the law to provide explicit opt-outs on web pages, but the . does not require explicit opt-ins and only requires platforms to provide opt-outs default is still opt-in EU’s GDPR takes this up another notch and requires for behavioral advertising. This consent needs to be . For example, Meta cannot gate content behind a behavioral advertising consent prompt. explicit opt-in/consent freely given, specific, informed, and unambiguous So, the simplistic inference from this is that Meta needs to get explicit consent for all behavioral understanding, including the last remaining mechanism — showing ads within their own platform. If Meta is forced to do this, the opt-in will likely be small (the Apple opt-in rates were ~34%), and this majorly shrinks Meta’s addressable advertising market in the EU. To not meet that fate, Meta made a creative legal argument: GDPR requires any company to have one of ** **if the company needs to process personal data. Paraphrasing, these are — consent, a necessity to fulfill a contract, a necessity for legal compliance, a necessity to protect the vital interests of the user, a necessity to perform a public interest task, and a necessity for legitimate interests pursued by the company. six legal bases Of these six, Meta held the stance that they were processing personal data using “ legitimate interests” (the last of the six) as the legal basis and therefore did not need to show an explicit opt-in prompt. Last month, the Norwegian Data Protection Authority that . Paraphrasing the : provided their enforcement decision Meta’s use of “legitimate interests” as the legal basis is not valid enforcement decision Based on several past court rulings, Meta must prove that legitimate interest “cannot reasonably be achieved just as effectively by other means less restrictive of the fundamental rights and freedoms of data subjects.” Based on another past court ruling, despite the fact that Meta provides a free service, the user of that network cannot reasonably expect that the operator of the social network will process that user’s personal data, without his or her consent, for the purposes of personalized advertising. Four days after the Norwegian Data Protection Authority’s ruling, Meta that they would be changing their legal basis from “legitimate interests” to “consent.” In practice, what this means is that . While Apple’s changes significantly cut down the ability to advertise cross-app, Meta was holding on to hope that it would be able to preserve all in-app behavioral advertising (including in the EU), and the writing is now on the wall. shortly after with their own announcements that non-personalized versions of their products will soon be available to users. A total of are likely to follow suit. announced Meta is conceding that behavioral advertising within their own app in the EU can no longer be opt-in by default and followed suit Snap TikTok 19 platforms that are in scope Possible future paths It is still to be seen what percent of users opt into behavioral advertising. If the Apple opt-in rates are any indication, it may get to the ~34% rate seen there, but it may also go higher if platforms are allowed to be creative about opt-in language and user interfaces. It is hard to say what exactly the long-term effects of ending the opt-in-by-default regime for behavioral advertising in the EU will be, but here are some educated guesses: — Meta has made towards letting users provide inputs to the feed algorithm. I would expect the creation of more explicit preferences/interests that users explicitly select. While it has worked to a smaller extent for some products like , it is likely that the quality of content provided by just preferences will be nowhere close to the relevance of a system that is constantly ingesting data and inferring interests. Explicit preferences instead of inferred interests some progress Reddit : TikTok has announced that they will create a that is based on what’s popular in your region. Most social feed-based platforms will also (re)introduce simplistic ranking options like chronological. I am not bullish that any of these will create an engaging user experience. Non-personalized feeds non-personalized feed : My (potentially contentious) take is that opted-out users will see a significantly degraded experience and eventually decide to provide platforms consent to personalize. The platforms will likely use this mechanism to receive content for all personalization (including behavioral advertising) — It’s unclear if there are any regulatory constraints that force them not to bundle consent for user experience personalization and behavioral advertising. I don’t expect the consent rate to go anywhere close to the status quo, but I think it will be a meaningful bump to what’s seen with Apple’s opt-in rates. Degraded user experience, leading to more opt-in : Twitter launched Twitter Verified a few months back, and Meta is with a similar concept. I am not bullish that a majority (or even a minority) of users will be willing to pay for social media products after years of being conditioned to get these products for free. Experiments with subscriptions experimenting If platforms are not able to effectively monetize the EU market, it is likely that they will start de-prioritizing investments for the EU market, which is a real risk of an aggressive regulatory regime. I don’t expect them to stop supporting EU markets, but every new feature built is an investment, and companies will start heavily questioning if they are willing to do any extra work for this market. For example, if there’s additional work to make a feature comply with GDPR, that might not be worth it anymore. We have seen other equivalent situations of this — several apps have a much better experience on iOS than Android because iOS apps monetize better. De-prioritization of EU markets: Conclusion The advertising market exploded over the last several decades due to the availability of essentially infinite user data, which brought both large efficiencies in advertising and major privacy risks. Regulation was necessary. Great regulation is about finding the balance between promoting innovation/letting new businesses emerge that move society forward and having guardrails so people are protected. Did the EU go too far? I personally think they did, and if that is true, the effects will start showing in a 5–10 year horizon, and course correction will follow. Until then, this is the new reality of behavioral advertising in the EU. The regulators and legislators came all guns blazing, and they won. 🚀 If you liked this piece, consider subscribing to Every week, I publish one in the form of a 10-minute read. Best, Viggy. . my weekly newsletter deep-dive analysis on a current tech topic/product strategy Also published . here

The New Reality of Behavioral Advertising in the EU

TL:DR Opside's NCRC (Native Cross Rollup Communication) protocol offers a trustless solution for Rollup interoperability. The NCRC protocol doesn't involve adding an additional third-party bridge to each Rollup; Instead, it transforms the native bridge of ZK-Rollup at the system level, allowing direct utilization of the native bridges of different ZK-Rollups for cross-Rollup communication. This approach is more streamlined and comprehensive, inheriting the absolute security of native bridges while avoiding the system complexity and trust costs associated with third-party bridges. has successfully implemented NCRC on the testnet. Anyone can now experience it on the official website at Opside https://pre-alpha-assetshub.opside.network/. Why Do We Need Trustless Cross Rollup Communication? Rollups have gained significant attention and adoption for their ability to enhance blockchain scalability, reduce transaction costs, and improve overall efficiency. Opside provides ZK-RaaS services for Web3 applications, enabling developers to create their own Rollups through the Opside Rollup Launchbase. In this era of multiple Rollups, we anticipate a growing coexistence of various Rollups, making seamless interoperability between different Layer 2 solutions crucial. Currently, interactions between Rollups remain relatively isolated, lacking real-time cross-chain communication and asset interoperability. This isolation has led to a fragmented landscape, where assets are confined within specific Rollups, restricting their free flow and utilization across different networks. The absence of efficient cross Rollup communication not only limits the potential of individual Rollups but also impacts the overall user experience. Users attempting to transfer assets or execute cross-chain transactions between Rollups face cumbersome and time-consuming processes. This suboptimal experience weakens the appeal of Rollups and to some extent hinders the widespread adoption of Layer 2 scaling solutions. Existing cross Rollup bridging solutions often involve deploying new sets of interchain contracts on Rollup chains and utilizing multi-chain liquidity incentives to achieve asset cross-chain functionality. However, these solutions are not universally applicable for message-based cross-chain interactions and come with risks of centralization and trust. To fully unlock the potential of the multi Rollup era, there is an urgent need for a trustless and universal cross Rollup communication protocol. Opside's solution: Native Cross Rollup Communication In fact, each ZK-Rollup comes with an L1<>L2 bridge inherently, which we refer to as the Native bridge. Unlike third-party bridges that utilize liquidity-based schemes, the Native bridge operates as the unique "mint-burn" cross-chain mechanism. It ensures security through zero-knowledge proofs while maintaining trustlessness. All assets on a Rollup originate from deposit transactions through the native bridge and receive ultimate security endorsement from it. We firmly believe in the principle of Occam's Razor – "Entities are not to be multiplied beyond necessity." Third-party bridges may offer cheaper and faster cross-chain experiences, but they introduce additional trust costs and security risks. The recent Multichain incident is a case in point. Therefore, from the outset, Opside's inspiration for cross-Rollup communication was straightforward: leverage the native bridge directly to achieve multi-Rollup interoperability, rather than introducing an additional third-party bridge. This concept gave birth to the NCRC (Native Cross Rollup Communication) protocol. Prerequisites for NCRC To enable NCRC among multiple Rollups, the following two prerequisites need to be met: These Rollups must belong to the ZK-Rollup type. These Rollups must reside on the same L1. Rollups satisfying these two conditions theoretically possess the same level of security as the underlying L1. Similarly, the security level of the native bridge among these Rollups is identical and requires no trust between them. All NCRC transactions are verified by validity proofs, serving as the fundamental source of security assurance for NCRC. Rollup Recognition Contract（RRC） As of August 2023, several ZK-Rollups have gone live on the Mainnet, including Polygon zkEVM, zkSync era, Linea, and more. However, these ZK-Rollups are independent and unrelated, leading to the fragmentation of user assets. The fundamental reason for this issue lies in the fact that their contracts on L1 (Ethereum Mainnet) are unrelated. They remain unaware of each other's existence and are unable to directly communicate through native Rollup bridges. Therefore, the first step we need to take is deploying a specialized contract on L1 to enable Rollups to discover and recognize each other. This is referred to as the RRC (Rollup Recognition Contract). The RRC is responsible for managing all participating ZK-Rollups in the NCRC, including additions, pauses, and exits of Rollups. Each Rollup within the RRC is assigned a dedicated Rollup ID, while the ID for L1 remains fixed at 0. When initiating cross-Rollup transactions through the native bridge on a Rollup, addresses can specify the target Rollup ID: If the Rollup ID is 0, it signifies crossing the message to L1, such as withdrawal. If the Rollup ID is not 0, it indicates sending the message to another Rollup. Opside will deploy an RRC contract on every L1 layer and allow corresponding ZK-Rollups to join or exit without permission. This RRC contract will be used to maintain information for each Rollup ID, including the bridge contract address on L1. It's important to note that the RRC contract solely provides data retrieval services and does not directly interact with cross-chain assets. Compatibility with Native Bridge Smart Contracts and Services Generally, Rollup's native bridge is divided into three components: the bridge contract on L1, the bridge contract on L2, and a bridge service responsible for message relay. The NCRC protocol leverages these components at the underlying level and adds higher-level encapsulation. The main modifications are as follows: Bridge contract on L2: While preserving the original methods, a new method named bridgeAsset is added. This method allows users to specify the target Rollup's ID in the destinationNetwork parameter. Bridge contract on L1: A new method is encapsulated to handle the cross-chain messages of the new bridgeAsset method. The bridge contract, based on the Rollup ID found in the RRC contract, locates the information of the target Rollup and transfers the cross-chain assets to the bridge contract of the target Rollup. The cross-chain assets are deposited into the target Rollup there. Bridge service: Responsible for message relay and charges users fees for cross-Rollup transactions. Once a Rollup completes the NCRC-related compatibility adaptation mentioned above, it can register with the RRC to join the native cross-Rollup communication network. Process of Native Cross-Rollup Transactions For users, the operation of NCRC is entirely consistent with that of Rollup's native bridge. Initiating a cross-Rollup transaction from Rollup1 to Rollup2 is an automated process, including the following steps: The initiator, User1, on Rollup1, invokes the bridgeAsset method of the native bridge to initiate the cross-chain transaction. The destinationNetwork parameter in this transaction is set to the Rollup ID of Rollup2. This Rollup ID will be used to retrieve the corresponding L1 bridge contract address. If the Rollup ID is 0, it signifies the target network as L1. Subsequently, this transaction is packaged by Sequencer1 of Rollup1. The initiator, User1, bears the cost of the cross-Rollup transaction, paying it to Sequencer1 on Rollup1. Rollup1's Bridge service then transfers the cross-chain asset to the Rollup1 bridge contract on L1. At this point, both Rollup1 and L1 complete the burn and release operations of the asset. To complete the cross-Rollup asset transfer, Rollup1's Bridge service queries the RRC contract to retrieve information about the target Rollup2 corresponding to the destinationNetwork parameter. This information provides the L1 bridge contract address of Rollup2. Then, the bridge contract of Rollup2 takes control of these assets and maps them to Rollup2 through the bridgeAsset method. Finally, once the transaction is successfully packaged and the proof is generated, Rollup2's Bridge service executes the claimAsset operation. Consequently, the cross-chain assets initiated by Rollup1 safely arrive at the designated address on Rollup2. It's worth mentioning that throughout the cross-chain process, the user's assets flow through the following path: Rollup1 -> Rollup1's L1 bridge contract -> Rollup2's L1 bridge contract -> Rollup2. In other words, the user's assets do not go through any third-party protocol; they leverage Rollup's native bridge. The entire process is secure and trustless. When users execute cross-chain operations on Rollup1, selecting Rollup2 as the destination, the technical process actually involves three entities: Rollup1, L1, and Rollup2. However, users do not need to be aware of the existence of L1 in this process; their experience is simply a direct cross from Rollup1 to Rollup2. The underlying reality is that cross-chain assets undergo two bridging operations on L1, creating a seamless connection from Rollup1 to Rollup2 in the user's perception. During this process, operations on L1 are handled automatically, and users do not need to perform any additional actions. From the user's perspective, their current Rollup can perform cross-chain operations to both L1 and any other Rollup. This design enhances user experience fluidity while concealing underlying complexities. NCRC is now live on the Opside testnet! Opside has successfully implemented native cross-rollup communication on the Testnet. Anyone can now experience it on the official website at .We also welcome users and developers to help us identify potential bugs and security risks and provide any valuable suggestions. https://pre-alpha-assetshub.opside.network/ We believe that trustless native cross-rollup communication will not only securely share liquidity across all Rollups but also provide robust multi-Rollup interoperability, opening up new possibilities for decentralized applications and . DeFi protocols

Opside’s NCRC Trustless Native Protocol in Focus

El protocolo nativo sin confianza NCRC de Opside en foco

Focus sur le protocole natif sans confiance NCRC d'Opside

ओपसाइड का एनसीआरसी ट्रस्टलेस नेटिव प्रोटोकॉल फोकस में है

Opside の NCRC Trustless ネイティブ プロトコルに注目

Protocolo NCRC Trustless Native da Opside em foco

Trọng tâm là Giao thức gốc không tin cậy NCRC của Opside

Opside 的 NCRC 无信任原生协议成为焦点

OpenAI launches a default opt-in crawler to scrape the Internet, while FTC pursues an obscure consumer deception investigation Last week, Open AI (maker of ChatGPT) officially announced their — this is a piece of software that scrapes content from all websites across the internet, which is then used for AI model training. web crawler The existence of the crawler is not surprising and several legitimate web crawlers exist today, including Google’s crawler that indexes the entire internet. However, this is the first time OpenAI explicitly announced its existence and also provided a mechanism for websites to opt out of being scraped. Note that the crawler is , i.e., you need to explicitly change a piece of code on your website to ask the crawler not to scrape your data. Opt-in /out defaults are sticky and often determine what the majority behavior is because most people don’t take the effort to change defaults. opt-in by default It is the same reason why have had a major impact on the digital advertising industry. Apple’s iOS14 privacy changes So, why even provide the opt-out? This is likely a preemptive move from OpenAI in response to against the company alleging that content owners’ copyright was infringed (deeper article on if you want to poke more). recent lawsuits data scraping ChatGPT competitor Google Bard faces a similar challenge but Google has not yet announced an equivalent solution — they did put out a request for comment on how to upgrade to address this issue (written with some ). robots.txt neat PR penmanship In this article, we’ll dive into: Implications of OpenAI’s crawler for content owners FTC’s current investigation into OpenAI Today’s legal landscape that we operate in Why the FTC’s approach of going after OpenAI is (yet another) misstep Implications of OpenAI’s Crawler for Content Owners While the announcement provides an option for advertisers to block OpenAI’s crawler from scraping their data, a couple of things are not great: It’s opt-in by default, which means OpenAI can keep scraping till sites explicitly tell them not to There (which would essentially be the case with anyone who is forced into a default opt-in) hasn’t been a clear legal ruling one way or another about content owners’ rights when their data is scraped for model training without consent Today, there are two legal constructs that determine whether it’s okay or not for language models to take all this data without consent — . Copyright and Fair Use Copyright provides protection to specific types of content but also has carve-outs /exceptions: Copyright protection subsists, in accordance with this title, in original works of authorship fixed in any tangible medium of expression, now known or later developed, from which they can be perceived, reproduced, or otherwise communicated, either directly or with the aid of a machine or device. Works of authorship include the following categories: (1) literary works; (2) musical works, including any accompanying words; (3) dramatic works, including any accompanying music; (4) pantomimes and choreographic works; (5) pictorial, graphic, and sculptural works; (6) motion pictures and other audiovisual works; (7) sound recordings; and (8) architectural works. (b) In no case does copyright protection for an original work of authorship , regardless of the form in which it is described, explained, illustrated, or embodied in such work extend to any idea, procedure, process, system, method of operation, concept, principle, or discovery For example, copyright protects most original work (e.g., if you wrote an original blog article or book on a topic) but (e.g., you cannot claim that you were the first person to write about how AI impacts data rights, and therefore the idea belongs to you). does not protect broad ideas Another carve-out/exception from Copyright protection is Fair Use: The fair use of a copyrighted work, including such use by reproduction in copies or phonorecords or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright. In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include (1) the purpose and character of the use, including or is for nonprofit educational purposes; (2) the nature of the copyrighted work; (3) the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and (4) the effect of the use upon the potential market for or value of the copyrighted work. whether such use is of a commercial nature For example, if you picked up content from a research paper and wrote a critique about it, that’s okay, and you are not infringing on the content owner’s copyright. It’s the same situation when I link another article from this page and add quoted text from that article. Both of these concepts were created to protect content owners’ rights while also allowing the free flow of information, especially in the context of education, research, and critique. I am not a legal expert but based on my research/understanding of the language above, is: where this gets fuzzy with AI models scraping training content AI companies typically scrape full text from a content owner’s website (this is protected by Copyright), train the models to learn the “idea”/“concept”/“principle” (this is not protected by Copyright), and then the models eventually spit out different text. In this case, does the content owner receive Copyright protection or not? Since the trained language models are now eventually used for commercial purposes (e.g., ChatGPT Plus is a paid product), is that a violation of the content owner’s Copyright (because the Fair Use exception no longer applies)? There have been no court rulings around this yet, so it’s hard to predict where this lands. My not-a-lawyer take is that the second one is probably easier to land: OpenAI scraped data and used it to create a commercial product, and therefore, they do not get an exception under Fair Use. I would imagine the first one (did the model train on an “idea” or just original text) is anyone’s guess. Note that both those bullets need to be in content owners’ favor for them to win, i.e., content owners only win if both the above exceptions (“idea” exception or Fair Use exception) don’t apply to OpenAI. I bring up this nuance because in the spectrum of AI risks (non-exhaustive) — from content owners’ rights to amplifying fraud to jobs being automated to AGI / destruction of humanity — the , as evidenced by the flurry of lawsuits and the impact on content platforms (e.g., ). most pressing near-term issue is content owners’ rights the StackOverflow story While regulators like the FTC can ponder about the really long-term problems and come up with hypothetical/creative ways to address these risks, their real short-term potential lies in being able to tackle risks that will impact us in the 5–10 year horizon. Like copyright infringement. Which brings us to what the FTC is doing about it. FTC’s Current Investigation Into OpenAI In mid-July, FTC announced that it is investigating OpenAI. What makes it interesting (and frustrating) is . the reason FTC is investigating them for The maker of ChatGPT is being investigated to evaluate whether the company broke any by . consumer protection laws putting personal reputation and data at risk Doesn’t make sense? You’re not alone. Let’s lay out some more background on how this came to be. The FTC’s on AI regulation came out in April: “There is no AI exemption to the laws on the books, and the FTC will vigorously enforce the law to combat unfair or deceptive practices or unfair methods of competition.” most vocal stance Then came a couple of defamation-related issues: Radio host after ChatGPT accused him of defrauding a non-profit, and a law professor was . Mark Walters sued OpenAI falsely accused by ChatGPT of sexual harassment Both these scenarios suck for the people involved, and I empathize with that. However, it’s a known fact that language models (like GPT) and products built on top of them (like ChatGPT) “hallucinate” and are often incorrect. The first half of FTC’s premise for the investigation is that — ChatGPT hallucinates and therefore creates reputational harm. In a heated Congressional hearing, one representative (rightfully) why they are going after defamation and libel, which are typically handled by state laws. FTC Chairperson Lina Khan gives a : asks the FTC convoluted argument Khan responded that libel and defamation aren’t a focus of FTC enforcement, but that misuse of people’s private information in AI training could be a form of fraud or deception under the FTC Act. “We’re focused on, ‘Is there substantial injury to people?’ Injury can look like all sorts of things,” Khan said. To tie up the full argument — FTC is saying that . ChatGPT’s hallucination produces incorrect information (including defamation), which then could be a form of consumer deception Additionally, sensitive user private information could have been used/leaked (based on that OpenAI quickly fixed). one bug As part of the investigation, the FTC has asked for a long list of things from OpenAI —  from details about how their model is trained to what data sources they use to how they position their product to customers to situations where model releases have been paused because of identified risks. The question is — Is the best approach for the FTC to regulate what is arguably going to be one of the largest AI companies, especially given the current legal landscape? Today’s Legal Landscape That We Operate in To critique FTC’s strategy with OpenAI, it’s useful to understand the legal landscape we operate in today. We won’t go into too much detail, but let’s do this briefly with the as an example: history of anti-trust In the 1900s, massive conglomerates (“trusts”) came into existence, and the balance of public-private power shifted to these companies. In response, the Sherman Act of 1890 was passed to add checks on private power and preserve competition; this law was used to litigate and break down “trusts” that were engaged in anti-competitive practices (predatory pricing, cartel deals, distribution monopoly). Around the 1960s, judges faced a lot of backlash for judging based on the spirit of the law instead of the letter of the law; for example, interpreting the Sherman law to determine if a set of companies “unreasonably restrain trade” involved subjectivity, and judges were accused of engaging in judicial activism. To introduce objectivity, the Chicago School pioneered the consumer welfare standard — “courts should be guided exclusively by consumer welfare” (e.g., a monopoly increasing prices in a blatant manner is wrong but, for other activities, the burden of proof is on regulators to prove consumer harm.) This continues to be the standard today and is one of the reasons the FTC and DOJ have a difficult job taking down big tech — for example, the FTC cannot make the argument that Google is increasing prices since most of their products are free, even if Google is engaged in other anti-competitive practices. The takeaway from this is — we continue to operate today in a landscape where cases are litigated heavily on the “letter of the law” and not the “spirit of the law.” This, along with the composition of the US Supreme Court today, has resulted in fairly conservative interpretations of the law. What this means for the FTC is to embrace the reality of this landscape and . The operating model of the FTC and DOJ (rightfully so) is to go after a handful of big cases and lay down harsh enforcement so that the long tail of companies think twice before breaking laws. figure out a way to win cases To make that happen, and it needs a . FTC needs to win big on a few issues, winning strategy within the constraints of the current legal landscape Why the FTC’s Approach of Going After OpenAI Is (Yet Another) Misstep The FTC has had a streak of losses against Big Tech, and I would argue that the losses can all be attributed to a failed “we hate everything big tech”, hammer-not-scalpel strategy of taking on these companies. For example, FTC took a brute-force approach to stop the $69B Microsoft-Activision acquisition and (pretty badly, I’d say). FTC argued that Microsoft acquiring Activision would kill competition in the gaming market. lost The judge wrote a fairly throwing out all of FTC’s arguments; here’s one of the judge’s comments: blunt ruling There are no internal documents, emails, or chats contradicting Microsoft’s stated intent not to make Call of Duty exclusive to Xbox consoles. Despite the completion of extensive discovery in the FTC administrative proceeding, including production of nearly 1 million documents and 30 depositions, the FTC has not identified a single document which contradicts Microsoft’s publicly-stated commitment to make Call of Duty available on PlayStation (and Nintendo Switch). Another brute force case was the FTC’s attempt to block Meta’s acquisition of a VR company Within, and . Why did they pursue this? They wanted to test out the waters to see if there was an appetite to block acquisitions before a particular market becomes large, and given the current legal landscape, it was unsurprisingly thrown out. they lost The problem with FTC’s investigation of OpenAI is similar: They are going after (what in my opinion) is a pretty trivial issue and a known limitation of language models — hallucinations; they should instead be focusing on actual AI issues that matter in the 5–10 year horizon, like Copyright. Despite multiple “creative” legal approaches being thrown out in the current legal landscape, they are attempting another creative argument: hallucination → defamation → consumer deception. The generous interpretation of their actions is that they want to set a precedent for their “AI is not exempt from existing laws” stance and that this wild goose chase gets them a large amount of self-reported data from OpenAI (FTC issues ). 20 pages of asks However, given their track record of repeatedly pursuing brute force/anything big tech is uncompetitive approach + combining those with creative arguments which are getting repeatedly dismissed in courts, I believe that the FTC has not earned the benefit of the doubt in this case. Conclusion I absolutely think OpenAI should be regulated. Not because their LLMs hallucinate (of course, they do) but because they are blatantly using creators’ content without permission. Not because it will change the past but because it will help set up content owners for a healthy future where their copyrights cannot be blatantly infringed upon. But the FTC is repeating its missteps with the hammer-not-scalpel approach. There is a clear precedent for successes against big tech with a scalpel approach, the most notable one being UK’s Competition and Markets Authority. The two big cases they won against Google have focused on specific anti-competitive mechanisms: to its own product in the AdTech stack and allowing for in-app payments. stopping Google from providing preferential treatment other payment providers If FTC continues on its current path, its streak of losses is going to embolden tech companies to continue doing whatever they want because they know they can win in court. It’s time the FTC reflected on its failures, learned from other regulators’ successes, and course corrected. 🚀 If you liked this piece, consider subscribing to Every week, I publish one in the form of a 10-minute read. . my weekly newsletter deep-dive analysis on a current tech topic/product strategy Best, Viggy. Also published here

A Look Inside OpenAI's Web Crawler and the Continuous Missteps of the FTC

OpenAI-এর ওয়েব ক্রলার এবং FTC-এর ক্রমাগত ভুল-ভ্রান্তির ভিতরের দিকে নজর দিন

Ein Blick in den Webcrawler von OpenAI und die ständigen Fehltritte der FTC

Una mirada al interior del rastreador web de OpenAI y los continuos errores de la FTC

Un regard sur le Web Crawler d'OpenAI et les faux pas continus de la FTC

ओपनएआई के वेब क्रॉलर और एफटीसी की निरंतर गलतियों के अंदर एक नज़र

OpenAI の Web クローラーの内部と FTC の連続する失敗を振り返る

OpenAI의 웹 크롤러와 FTC의 지속적인 실수 살펴보기

Uma olhada no rastreador da Web da OpenAI e os erros contínuos da FTC

OpenAI'nin Web Tarayıcısına ve FTC'nin Sürekli Yanlış Adımlarına Bir Bakış

Nhìn vào bên trong Trình thu thập dữ liệu web của OpenAI và những sai lầm liên tục của FTC

深入了解 OpenAI 的网络爬虫以及 FTC 的持续失误

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