Introduction In today’s digital age, influencer marketing is a cornerstone of brand strategy, driving millions in revenue and creating instant connections with target audiences. But a new trend is reshaping the influencer landscape—AI-generated influencers. These virtual personas are taking social media by storm, offering brands innovative ways to engage consumers. With their growing influence and […]
The post AI-Generated Influencers: The Future of Social Media Marketing appeared first on CHESA.]]>In today’s digital age, influencer marketing is a cornerstone of brand strategy, driving millions in revenue and creating instant connections with target audiences. But a new trend is reshaping the influencer landscape—AI-generated influencers. These virtual personas are taking social media by storm, offering brands innovative ways to engage consumers. With their growing influence and the promise of seamless branding, AI-generated influencers like Lil Miquela, Aitana Lopez, and Lu do Magalu are more than a passing trend. They represent the future of social media marketing.
This article delves into the rise of AI-generated influencers, their benefits, challenges, and the ethical considerations surrounding this new marketing phenomenon.
AI-generated influencers are virtual characters created through artificial intelligence, computer graphics, and machine learning. These influencers engage with audiences on platforms like Instagram, TikTok, and YouTube, much like human influencers do. But while they interact with followers, post branded content, and even collaborate with major companies, AI-generated influencers don’t exist in the physical world. Instead, they are meticulously designed by creative agencies and powered by AI to reflect human-like behaviors, preferences, and aesthetics.
Lil Miquela, for example, has amassed over 2.6 million followers on Instagram and has partnered with high-end brands like Prada and Calvin Klein. Similarly, Aitana Lopez, a virtual influencer created by a Spanish modeling agency, boasts over 300,000 followers and represents gaming, fitness, and cosplay culture, earning up to $1,000 per advert she’s featured in. In Brazil, Lu do Magalu, created by retail giant Magazine Luiza, is the most followed virtual influencer in the world and has seamlessly integrated product reviews and lifestyle content into her persona.
The first known “virtual influencer” was actually a mannequin named Cynthia, created in the 1930s. Photographed at major social events, she caused a media sensation, appearing to engage in real social activities. Cynthia became the first non-human to promote brands like Tiffany & Co. and Cartier by showcasing their jewelry at high-profile gatherings. While primitive by today’s standards, Cynthia laid the groundwork for fictional characters influencing media and marketing.
In 1958, the Chipmunks (Alvin, Simon, and Theodore) made their debut in the hit song “The Chipmunk Song.” Created by Ross Bagdasarian, Sr., the animated characters became cultural icons, winning Grammy Awards and spawning cartoons, movies, and merchandise. Although presented as “real” performers, these fictional characters helped blur the lines between reality and virtuality in music.
The first computer-generated virtual influencer to make a splash in popular culture was Max Headroom. Introduced in 1985 as a fictional AI TV host, Max became a pop culture sensation, appearing in commercials (notably for Coca-Cola), music videos, and talk shows. While Max was largely driven by human actors and computer graphics, he represented the future potential of virtual characters to engage with media in lifelike ways.
In 2007, Hatsune Miku—a virtual singer created using Vocaloid voice-synthesizing software—became a global sensation. The computer-generated character, with long turquoise hair and a futuristic aesthetic, performed in holographic concerts worldwide. Miku became the world’s first virtual pop star, showcasing how far virtual personas could go in influencing audiences and building a loyal fan base.
The breakthrough of AI-generated influencers as we know them today came with Lil Miquela in 2016. Created by the LA-based company Brud, Miquela is a CGI character with a highly realistic appearance, who posts lifestyle, fashion, and social commentary content. Her collaborations with major brands like Calvin Klein, Dior, and Prada cemented her place as a pioneering AI influencer in the social media world. Miquela marked the beginning of a new era of virtual influencers designed specifically for social media.
Creating AI influencers involves advanced technology, combining AI, CGI, and machine learning. AI algorithms learn from vast amounts of data, allowing these influencers to mimic human expressions, body movements, and speech with remarkable accuracy. Some influencers even have AI-powered voices, giving them the ability to “speak” during live streams or in promotional videos.
These virtual influencers operate 24/7, do not age, and never encounter scheduling conflicts. Brands can program them to act and respond exactly as desired, ensuring a consistent image and tone. This level of control is one reason why brands find them so attractive. But the story of AI-generated influencers is about more than just technology—it’s about how they’re reshaping the marketing world.
One of the most significant advantages of AI-generated influencers is the complete control they offer to brands. Unlike human influencers, AI personas do not have personal opinions, need breaks, or run the risk of scandals. Brands can design their virtual influencers to embody the values and aesthetics they want to promote, ensuring consistent messaging across campaigns. This level of control makes them ideal for long-term partnerships or global campaigns that require consistency in different markets.
AI-generated influencers are also highly adaptable. For example, an AI influencer can seamlessly switch languages, connect with audiences from multiple regions, and “appear” in different virtual environments without ever needing to leave their platform. This adaptability makes them a powerful tool for global brands looking to target diverse audiences.
While there are upfront costs involved in developing AI influencers, in the long run, they can prove more cost-effective than human influencers. Virtual influencers do not require travel expenses, photo shoots, or ongoing payments for appearances. Once developed, they can generate content 24/7, offering brands a cost-efficient alternative to traditional influencer marketing.
AI-generated influencers like Lu do Magalu demonstrate the ability to transcend cultural and language barriers. They are always available, providing continuous engagement with audiences around the world, without any concerns about time zones or availability conflicts. This ability to reach global audiences without geographic or logistical constraints is a powerful advantage in today’s interconnected world.
One of the biggest challenges with AI-generated influencers is their lack of real-world experiences, which can make it difficult for them to build authentic connections with audiences. Human influencers are loved for their personal stories, experiences, and ability to connect emotionally with their followers. AI-generated influencers, by contrast, are entirely fabricated, and while they may look and act convincingly, they lack the genuine emotions and personal narratives that foster deeper connections with their audience.
Many consumers are still skeptical about engaging with virtual influencers. The “uncanny valley” effect—a sense of unease that can arise when human-like figures don’t quite appear real—can deter some users. Moreover, there’s the question of trust. Can an AI influencer’s endorsement of a product carry the same weight as that of a human influencer who has personally tested it? This issue of credibility can be a barrier for brands, especially when marketing products that rely on personal experience or authenticity.
AI influencers, designed with perfect proportions and flawless features, can contribute to unrealistic beauty standards. Their digitally enhanced appearances, often created to appeal to broad audiences, may set unattainable ideals that impact the self-esteem of real people. The perfect, algorithmically generated looks of these influencers can blur the lines between reality and fiction, raising concerns about body image and mental health in the social media age.
Another critical challenge for brands using AI influencers is transparency. As technology advances, it’s becoming harder for audiences to distinguish between real and AI-generated influencers. This raises ethical concerns about honesty in marketing. The FTC has already made it clear that AI influencers must disclose sponsored content just like human influencers, but the question of whether users are fully aware that they’re interacting with a virtual persona remains.
With the rapid development of AI, the future of AI-generated influencers looks promising. Advancements in augmented reality, virtual reality, and AI-powered voices are pushing the boundaries of what these virtual personas can do. The incorporation of real-time character scripting and AI-generated voices could soon allow AI influencers to interact more naturally with followers, providing more personalized and immersive experiences.
Platforms like Lil Miquela and Aitana Lopez are pioneering the future of this trend, and we may soon see AI-generated influencers blending seamlessly with their human counterparts. As AI becomes more sophisticated, it’s likely that these virtual personas will play an even larger role in the future of social media marketing.
AI-generated influencers represent a major shift in the world of social media marketing, offering brands new ways to engage with audiences, create consistent messaging, and reach global markets. While they come with challenges—particularly around authenticity, transparency, and ethical concerns—their advantages cannot be ignored. As AI technology continues to evolve, virtual influencers are likely to become an integral part of marketing strategies, reshaping the landscape of digital branding and influencer marketing.
The future of AI influencers is bright, and while they may never fully replace the authenticity of human connection, they will certainly shape the way we think about marketing in the digital age.
The post AI-Generated Influencers: The Future of Social Media Marketing appeared first on CHESA.]]>
Introduction AI is reshaping the future of film and TV production in unprecedented ways. One of its most fascinating developments is the rise of AI-generated actors—digital creations that mimic the appearance, voice, and mannerisms of real people, living or deceased. These virtual actors are taking on more roles in Hollywood, not just augmenting human performers […]
The post AI Virtual Actors: Revolutionizing Hollywood and Resurrecting Legends appeared first on CHESA.]]>AI is reshaping the future of film and TV production in unprecedented ways. One of its most fascinating developments is the rise of AI-generated actors—digital creations that mimic the appearance, voice, and mannerisms of real people, living or deceased. These virtual actors are taking on more roles in Hollywood, not just augmenting human performers but, in some cases, replacing them entirely. With AI now powerful enough to resurrect long-dead celebrities like James Dean for new films, it raises important questions about creativity, ethics, and the future of acting in a digital world.
AI virtual actors are digitally created entities that can perform in movies, television shows, and commercials. They are generated using advanced techniques like deep learning, CGI, and motion capture. While CGI characters have been part of Hollywood for decades, AI has taken these virtual actors to a whole new level. AI not only makes them more lifelike but also enables them to perform autonomously, using algorithms to learn and imitate human behavior, expressions, and voice patterns.
A major turning point came with James Dean’s digital resurrection. Nearly 70 years after his death, Dean is set to star in the upcoming sci-fi film Back to Eden, thanks to AI technology that uses old footage, audio, and photos to digitally clone the iconic actor. Dean’s AI-powered clone will interact with real actors on-screen, raising profound questions about what it means to perform in a world where the dead can “come back to life”.
This development echoes earlier breakthroughs in CGI. For instance, Carrie Fisher, Paul Walker, and Harold Ramis were all digitally resurrected for posthumous appearances in films like Star Wars: The Rise of Skywalker and Ghostbusters: Afterlife. But AI goes beyond merely pasting an old face onto a new body. The technology now allows for more seamless, believable performances where the virtual actor can speak, move, and respond in ways that blur the line between human and machine.
The concept of digital or virtual actors has a long history. As technology has evolved, so too has the ambition to create lifelike performers. Here’s a look at how virtual actors have developed over time:
While not digitally created, early forms of “virtual” performers date back to the 1930s with mechanical mannequins like Cynthia, a life-sized mannequin that became a celebrity in her own right. Cynthia was used in fashion and entertainment, becoming one of the earliest examples of non-human entities marketed as performers.
In 1958, Alvin and the Chipmunks entered pop culture, marketed as real performers despite being animated. Their music career and cartoon series became cultural phenomena, setting the stage for virtual characters to engage audiences as entertainers.
Max Headroom, introduced in 1985, was the first computer-generated TV personality. Though partially portrayed by a human actor, the character was a breakthrough in the integration of CGI and live-action, foreshadowing the future of virtual actors.
In 2001, the movie Final Fantasy: The Spirits Within became the first film to feature a fully CGI lead character, Dr. Aki Ross. This was a significant leap forward, demonstrating how digital characters could act as lifelike performers, paving the way for more sophisticated AI-driven actors in the future.
The 2010s saw the return of deceased actors through digital means. Peter Cushing was digitally resurrected to reprise his role as Grand Moff Tarkin in Rogue One: A Star Wars Story. Additionally, Carrie Fisher and Paul Walker were also digitally recreated for final film appearances after their deaths, marking a new era of posthumous digital performances.
Today, AI-generated actors like James Dean in Back to Eden will become increasingly common. These actors are no longer just CGI models controlled by human puppeteers but are powered by AI algorithms that allow them to perform autonomously, learning human behaviors and expressions.
The creation of AI actors involves combining several advanced technologies. CGI is used to recreate the physical appearance of the actor, while AI algorithms control their speech, facial expressions, and movements. Motion capture data from real actors can also be used to give AI characters a lifelike performance. This technology allows AI actors to “learn” how to mimic real humans, down to the smallest gestures or intonations in their voice.
One notable example of this is the Star Wars franchise, where both Carrie Fisher and Peter Cushing were digitally brought back to life. AI enabled filmmakers to create realistic performances from actors who had passed away or were unavailable. The result was virtual actors that not only looked like their real-life counterparts but also moved and spoke as convincingly as any living performer.
For filmmakers, AI virtual actors offer several advantages. First, they provide greater flexibility. AI actors don’t have schedules, they don’t age, and they can be “cast” in roles long after the real actor has passed away. This allows for the return of beloved characters or the casting of actors who otherwise wouldn’t be available. AI actors also present no risks when performing dangerous stunts, reducing the need for human stunt doubles.
Additionally, AI offers unparalleled creative control. Directors can manipulate every aspect of the actor’s performance, ensuring consistency and precision. This is particularly valuable in big-budget productions where time and cost efficiency are crucial. With AI, filmmakers can have their digital actors perform tirelessly, take direction without question, and deliver perfect performances on command.
Using AI actors can also lower production costs. Traditional actors require salaries, travel expenses, and accommodations, and they need time off for rest. AI actors, however, do not have these demands. Once the digital model is created, the actor can be used repeatedly across different scenes or even films without additional costs. In an industry where budgets are often tight, this level of efficiency can be game-changing.
The rise of AI in Hollywood has sparked debates about the balance between creativity and profitability. Actors’ unions, including the Screen Actors Guild, have raised concerns about the potential for AI to replace human actors, reducing job opportunities in an already competitive field. AI actors could monopolize certain roles, especially for voice-over or background characters, eliminating opportunities for real performers to showcase their talent.
Actors like Susan Sarandon have expressed concern about the creative limitations AI may impose. Sarandon warned of a future where AI could make her “say and do things I have no choice about”. This scenario could lead to actors losing control over their own image, with AI manipulating their likeness without their consent.
Another ethical dilemma arises with the digital resurrection of deceased actors. With AI capable of creating lifelike performances, actors who have long since passed away can now “star” in new films. But who owns the rights to their digital likeness? James Dean’s appearance in Back to Eden was only possible with permission from his estate. However, the broader question remains—what rights do actors, or their estates, have over their likeness once they’ve died?
There’s also the issue of creative integrity. Would James Dean have wanted to appear in a sci-fi film had he been alive? What if an actor’s AI likeness was used in a film or genre they would have never agreed to? These are questions that the film industry will need to address as AI continues to blur the lines between the living and the digital.
AI is poised to play an even bigger role in the future of Hollywood, especially as the technology continues to evolve. We may soon see fully AI-generated actors starring in their own films, without any connection to a real-life counterpart. These actors could take on any role, in any genre, and even adapt their performance based on audience feedback or input from directors in real time.
Some experts predict that AI-generated actors could dominate the industry, especially in genres like science fiction or animation where CGI already plays a major role. However, there is still likely to be a demand for human actors, particularly in roles that require emotional depth and genuine human connection.
AI virtual actors are transforming Hollywood, offering unprecedented flexibility, creative control, and cost efficiency. While the resurrection of legends like James Dean and Carrie Fisher has captured public attention, it also raises serious ethical questions about ownership, consent, and the future of human performers in an industry increasingly dominated by technology. As AI continues to advance, it will undoubtedly shape the future of filmmaking, blurring the line between reality and the digital world. However, the challenge will be ensuring that creativity and human expression remain at the heart of storytelling in cinema.
Introduction AI is fundamentally transforming the music industry, doing much more than helping musicians compose tracks or experiment with new sounds. AI is creating entire virtual musicians, some of whom never existed in the real world, and resurrecting long-deceased artists through sophisticated algorithms and deep learning techniques. This fascinating frontier raises questions about creativity, authenticity, […]
The post AI Musicians: Virtual Voices, Resurrected Legends, and the Future of Music appeared first on CHESA.]]>AI is fundamentally transforming the music industry, doing much more than helping musicians compose tracks or experiment with new sounds. AI is creating entire virtual musicians, some of whom never existed in the real world, and resurrecting long-deceased artists through sophisticated algorithms and deep learning techniques. This fascinating frontier raises questions about creativity, authenticity, and the future of music. How are fans embracing these virtual creations? And what does the rise of AI musicians mean for the future of the industry?
This article will explore the world of AI-generated musicians, the digital resurrection of legends, and the industry’s complex reaction to these technological advancements.
In the world of AI-generated music, the boundary between human artistry and machine-made creation is becoming increasingly indistinct. Today, AI is capable of generating entire musical personas that are indistinguishable from those created by humans. AI-generated musicians can compose and perform songs, appear in virtual concerts, and even interact with fans, offering new experiences that stretch the limits of creativity.
One remarkable example is the AI-generated band Aisis, a virtual homage to the iconic Britpop group Oasis. Using sophisticated machine learning models trained on Liam Gallagher’s voice and style, Aisis released songs that captured the essence of the original band. Fans were amazed by how accurately AI was able to recreate the sound, prompting widespread curiosity about the future of AI in music. This experiment demonstrated the potential of AI not only to mimic but to evolve existing musical styles.
Similarly, the pseudonymous producer Ghostwriter used AI to generate convincing “collaborations” between artists like Drake, The Weeknd, and Bad Bunny. While these tracks stirred controversy, sparking legal and ethical debates, they also showcased the growing interest in AI-generated music that mimics well-known artists without their involvement.
Japan has long embraced the concept of virtual idols—computer-generated personas who perform in concerts, release albums, and interact with fans online. Leading the charge is Hatsune Miku, a digital pop star who performs at sold-out holographic concerts worldwide. Created by Crypton Future Media, Miku is one of Japan’s most beloved virtual influencers, with a loyal fan base that continues to grow. Virtual idols like Miku not only dominate the music scene in Japan but are increasingly popular across the globe.
Alongside Miku, other virtual stars like Kizuna AI and Liam Nikuro are reshaping what it means to be a musical artist. These digital idols have thriving social media profiles, produce hit songs, and collaborate with major brands—all without human intervention. Their influence is so significant that they are often seen as a new class of musicians, one that merges music, technology, and digital culture seamlessly.
Perhaps the most controversial use of AI in music is the resurrection of deceased artists. AI has the potential to analyze recordings, performances, and even interviews of late musicians, recreating their voices and styles with stunning accuracy. This capability allows fans to hear “new” music from long-deceased legends, raising both excitement and ethical concerns.
In 2023, AI played a crucial role in the release of a new song by The Beatles, isolating John Lennon’s voice from an old demo tape and allowing it to be featured on a new track. This collaboration between AI and the remaining band members resulted in a pristine, posthumous performance from Lennon, creating both wonder and unease about the future of music.
Similarly, the estate of Steve Marriott, the late lead singer of Small Faces and Humble Pie, has discussed using AI to generate new recordings. By analyzing Marriott’s past performances and vocal style, AI could produce entirely new music that aligns with his original work. This kind of technological resurrection points toward a future where music legends could continue creating well after their deaths.
While some see AI as a valuable creative tool, many musicians view it as a significant threat to the authenticity and integrity of music. In April 2024, more than 200 prominent artists, including Billie Eilish, Katy Perry, Smokey Robinson, and Nicki Minaj, signed an open letter urging AI developers to stop using their voices and likenesses without permission. The letter, organized by the Artist Rights Alliance (ARA), warned that AI is “sabotaging creativity” and undermining artists’ rights by allowing anyone to replicate their voices without consent.
These concerns highlight the broader issue of intellectual property in the age of AI. As AI systems become more sophisticated, the lines between human and machine-made music blur, raising fears that AI could replace human musicians, lead to job losses, and diminish the authenticity of artistic expression. Steve Grantley, drummer for Stiff Little Fingers, expressed concern that AI could dehumanize music entirely, envisioning a future where fans may not even know if their favorite songs were composed by humans or machines.
Despite these fears, many artists believe that AI has the potential to enhance creativity rather than replace it. Platforms like Amper Music and BandLab enable musicians to generate chord progressions, melodies, and beats quickly, providing inspiration and allowing artists to focus on more complex aspects of music-making.
Tina Fagnani, drummer for Frightwig, acknowledges that while AI offers new ideas and perspectives, it cannot replace the emotional and spiritual depth of human-generated music. For many, AI represents a powerful tool for experimentation and collaboration, but it lacks the “soul” that defines great music).
AI’s role as an assistant to musicians may ultimately be its most effective application. By automating tedious tasks like mixing, mastering, and generating ideas for new tracks, AI frees up artists to focus on the more nuanced, emotional aspects of music creation. This AI-human collaboration could push the boundaries of musical experimentation, resulting in sounds and styles that would have been impossible to achieve with human creativity alone.
Interestingly, younger generations of fans are more likely to embrace AI-generated music. As digital culture becomes increasingly pervasive, AI musicians feel like a natural extension of online life. AI-generated songs and virtual artists have a growing presence on platforms like TikTok, where novel AI-human collaborations often go viral.
Virtual K-pop groups like Aespa have successfully combined real members with AI-generated avatars, appealing to fans who are as interested in the technology behind the performance as they are in the music itself. These groups showcase how the future of music could seamlessly blend human and virtual performers, creating immersive experiences that push the boundaries of live and recorded entertainment.
Virtual idols like Hatsune Miku and Kizuna AI are also gaining a foothold among international audiences. These idols perform in live concerts as holograms, release AI-generated music, and even engage with fans via social media. The appeal of these digital performers lies in their flawless, carefully curated personas, which are immune to scandals or personal issues that might affect human artists.
Despite the excitement surrounding AI music, it raises major ethical questions. Who owns the rights to AI-generated music that imitates deceased artists? How should the royalties from these creations be distributed? More fundamentally, can AI ever truly replicate the emotional depth of human-generated music?
Music has always been deeply personal, reflecting the artist’s experience of love, loss, joy, and pain. While AI can mimic human voices with technical precision, it lacks the life experience that gives music its emotional power. For now, AI excels at recreating sounds and styles but struggles to match the emotional authenticity of human composers.
These questions will only grow more urgent as AI continues to evolve, with more estates considering the use of AI to resurrect deceased artists for new releases. Balancing technological innovation with the preservation of human creativity will be one of the defining challenges for the future of the music industry.
The most likely future for AI in music may lie in collaboration rather than competition. AI offers immense potential for generating new sounds, experimenting with structures, and blending genres in ways humans may never have imagined. Musicians can use these AI-generated compositions as a foundation, adding their emotional depth, creativity, and personal touch to create something entirely unique.
However, the challenge will be to ensure that AI complements, rather than replaces, human artistry. The future of music will depend on how well artists, technologists, and policymakers can balance the creative possibilities of AI with the need to protect the authenticity and rights of human musicians.
AI-generated musicians are a fascinating glimpse into the future of music, offering both exciting opportunities and significant challenges. From creating virtual artists like Aisis to resurrecting deceased musicians, AI is reshaping the way music is made, performed, and consumed. However, while younger generations may embrace these digital creations, the music industry must carefully navigate the ethical and creative implications of AI-generated music.
As AI technology continues to evolve, the line between human and machine-made music will blur. But at its core, music remains an emotional, personal experience that AI alone cannot replicate. The future of music lies in collaboration—where AI serves as a tool for innovation, and human musicians provide the heart and soul that makes music truly resonate.
Overview of C2PA The Coalition for Content Provenance and Authenticity (C2PA) is a groundbreaking initiative aimed at combating digital misinformation by providing a framework for verifying the authenticity and provenance of digital content. Formed by a consortium of major technology companies, media organizations, and industry stakeholders, C2PA’s mission is to develop open standards for content […]
The post Understanding C2PA: Enhancing Digital Content Provenance and Authenticity appeared first on CHESA.]]>The Coalition for Content Provenance and Authenticity (C2PA) is a groundbreaking initiative aimed at combating digital misinformation by providing a framework for verifying the authenticity and provenance of digital content. Formed by a consortium of major technology companies, media organizations, and industry stakeholders, C2PA’s mission is to develop open standards for content provenance and authenticity. These standards enable content creators, publishers, and consumers to trace the origins and modifications of digital media, ensuring its reliability and trustworthiness.
C2PA’s framework is designed to be globally adopted and integrated across various digital platforms and media types. By offering a standardized approach to content verification, C2PA aims to build a more transparent and trustworthy digital ecosystem.
In today’s digital age, misinformation and manipulated media are pervasive challenges that undermine trust in digital content. The ability to verify the provenance and authenticity of media is crucial for combating these issues. Provenance refers to the history and origin of a digital asset, while authenticity ensures that the content has not been tampered with or altered in any unauthorized way.
C2PA addresses these challenges by providing a robust system for tracking and verifying the origins and modifications of digital content. This system allows consumers to make informed decisions about the media they consume, enhancing trust and accountability in digital communications. By establishing a reliable method for verifying content authenticity, C2PA helps to mitigate the spread of misinformation and fosters a healthier digital information environment.
CHESA fully embraces the tenets of the C2PA now officially as a Contributing Member, and is poised to assist in implementing these standards into our clients’ workflows. By integrating C2PA’s framework, CHESA ensures that our clients can maintain the highest levels of content integrity and trust.
CHESA offers customized solutions that align with C2PA’s principles, helping clients incorporate content provenance and authenticity into their digital asset management systems. Our expertise ensures a seamless adoption process, enhancing the credibility and reliability of our clients’ digital content.
The C2PA framework is built on a set of core components designed to ensure the secure and reliable verification of digital content. The architecture includes the following key elements:
These components work together to provide a comprehensive solution for content provenance and authenticity, facilitating the adoption of C2PA standards across various digital media platforms.
Central to the C2PA framework is the establishment of trust in digital content. The trust model involves the use of cryptographic signatures to verify the identity of content creators and the integrity of their contributions. When a piece of content is created or modified, a digital signature is generated using the creator’s unique cryptographic credentials. This signature is then included in the provenance data, providing a verifiable link between the content and its creator.
To ensure the credibility of these signatures, C2PA relies on Certification Authorities (CAs) that perform real-world due diligence to verify the identities of content creators. These CAs issue digital certificates that authenticate the identity of the creator, adding an additional layer of trust to the provenance data. This system enables consumers to confidently verify the authenticity of digital content and trust the information provided in the provenance data.
Claims and assertions are fundamental concepts in the C2PA framework. A claim is a statement about a piece of content, such as its origin, creator, or the modifications it has undergone. These claims are cryptographically signed by the entity making the claim, ensuring their integrity and authenticity. Assertions are collections of claims bound to a specific piece of content, forming the provenance data.
The process of creating and managing claims involves several steps:
Binding provenance data to content is a critical aspect of the C2PA framework. This binding ensures that any changes to the content are detectable, preserving the integrity of the provenance data. There are two main types of bindings used in C2PA: hard bindings and soft bindings.
Both binding types play a crucial role in maintaining the integrity and reliability of provenance data, ensuring that consumers can trust the content they encounter.
C2PA is designed with a strong emphasis on privacy and user control. The framework allows content creators and publishers to control what provenance data is included with their content, ensuring that sensitive information can be protected. Users have the option to include or redact certain assertions, providing flexibility in how provenance data is managed.
Key principles guiding privacy and control include:
These principles ensure that the C2PA framework respects user privacy while maintaining the integrity and reliability of the provenance data.
To prevent misuse and abuse of the C2PA framework, a comprehensive harms, misuse, and abuse assessment has been integrated into the design process. This assessment identifies potential risks and provides strategies to mitigate them, ensuring the ethical use of C2PA technology.
Key aspects of this assessment include:
By addressing potential misuse proactively, C2PA aims to create a safe and ethical environment for digital content verification.
Security is a paramount concern in the C2PA framework. The framework incorporates a range of security features to protect the integrity of provenance data and ensure the trustworthiness of digital content.
These features include:
These security features work together to create a robust system for verifying the authenticity and provenance of digital content, protecting both content creators and consumers from potential threats.
One of the most significant applications of C2PA is in journalism, where the integrity and authenticity of content are paramount. By using C2PA-enabled devices and software, journalists can ensure that their work is verifiable and tamper-proof. This enhances the credibility of journalistic content and helps combat the spread of misinformation.
Real-world examples include photojournalists using C2PA-enabled cameras to capture images and videos that are then cryptographically signed. These assets can be edited and published while retaining their provenance data, allowing consumers to verify their authenticity. This process increases transparency and trust in journalistic work.
C2PA provides numerous benefits for consumers by enabling them to verify the authenticity and provenance of the digital content they encounter. With C2PA-enabled applications, consumers can check the history of a piece of content, including its creator, modifications, and source. This empowers consumers to make informed decisions about the media they consume, reducing the risk of falling victim to misinformation.
Tools and applications developed for end-users can seamlessly integrate with C2PA standards, providing easy access to provenance data and verification features. This accessibility ensures that consumers can confidently trust the content they interact with daily.
Beyond journalism and consumer use, C2PA has significant applications in corporate and legal contexts. Corporations can use C2PA to protect their brand by ensuring that all published content is verifiable and tamper-proof. This is particularly important for marketing materials, official statements, and other critical communications.
In the legal realm, C2PA can enhance the evidentiary value of digital assets. For example, in cases where digital evidence is presented in court, the use of C2PA can help establish the authenticity and integrity of the evidence, making it more likely to be admissible. This application is vital for legal proceedings that rely heavily on digital media.
In the M&E industry, content integrity is crucial. C2PA’s standards ensure that digital media, including videos, images, and audio files, retain their authenticity and provenance data throughout their lifecycle. This is essential for maintaining audience trust and protecting intellectual property.
CHESA’s integration of C2PA into client workflows will help streamline the process of content creation, editing, and distribution. By automating provenance and authenticity checks, media companies can focus on creating high-quality content without worrying about the integrity of their digital assets.
For media companies, protecting intellectual property is a top priority. C2PA’s framework provides robust mechanisms for verifying content ownership and tracking modifications, ensuring that original creators receive proper credit and protection against unauthorized use.
C2PA aims to achieve global, opt-in adoption by fostering a supportive ecosystem for content provenance and authenticity. This involves collaboration with various stakeholders, including technology companies, media organizations, and governments, to promote the benefits and importance of adopting C2PA standards.
Strategies to encourage global adoption include:
By implementing these strategies, C2PA aims to create a robust and diverse ecosystem that supports the widespread use of content provenance and authenticity standards.
To ensure consistent and effective implementation, C2PA provides comprehensive guidance for developers and implementers. This guidance includes best practices for integrating C2PA standards into digital platforms, ensuring that provenance data is securely managed and verified.
Key recommendations for implementation include:
By following these recommendations, developers and implementers can create reliable and user-friendly applications that adhere to C2PA standards.
C2PA is committed to ongoing maintenance and updates to its framework to address emerging challenges and incorporate new technological advancements. Future developments will focus on enhancing the robustness and usability of the framework, expanding its applications, and fostering a diverse and inclusive ecosystem.
Key goals for future developments include:
By focusing on these goals, C2PA aims to maintain its relevance and effectiveness in promoting content provenance and authenticity in the digital age.
The Coalition for Content Provenance and Authenticity (C2PA) represents a significant step forward in the fight against digital misinformation and the promotion of trustworthy digital content. By providing a comprehensive framework for verifying the authenticity and provenance of digital media, C2PA enhances transparency and trust in digital communications.
Through its robust technical specifications, guiding principles, and practical applications, C2PA offers a reliable solution for content creators, publishers, and consumers. The framework’s emphasis on privacy, security, and ethical use ensures that it can be adopted globally, fostering a healthier digital information environment.
As C2PA continues to evolve and expand, its impact on the digital landscape will only grow, helping to build a more transparent, trustworthy, and informed digital world.
The post Understanding C2PA: Enhancing Digital Content Provenance and Authenticity appeared first on CHESA.]]>Discover essential strategies for effective data and content management, including indexing, storage solutions, toolsets, and cost optimization from an experienced media manager and Senior Solutions Architect. Introduction Data and content management is a critical concern for organizations of all sizes. Implementing effective strategies can significantly optimize storage capacities, reduce costs, and ensure seamless access to […]
The post Strategies for Effective Data and Content Management appeared first on CHESA.]]>Discover essential strategies for effective data and content management, including indexing, storage solutions, toolsets, and cost optimization from an experienced media manager and Senior Solutions Architect.
Data and content management is a critical concern for organizations of all sizes. Implementing effective strategies can significantly optimize storage capacities, reduce costs, and ensure seamless access to valuable media. Drawing from my experience as a media manager and a Senior Solutions Architect, this article will explore best practices for data and content management, offering insights and practical solutions to enhance your organization’s efficiency.
The first step in data or media management involves identifying the locations of your content and the appropriate tools for indexing and management. Utilizing an asset management system, which typically covers roughly 40% of your total data, whether structured or unstructured, is a common approach to managing the subset of media or content. To begin organizing your full data set, consider these questions:
Answering these questions will set you on the right path toward effective management and cost optimization. Additionally, implementing measures like checksums during content indexing can help media managers quickly identify duplicate content in the storage, enhancing efficiency.
Media management toolsets can vary significantly in their interfaces, ranging from Command Line Interfaces (CLI) to more visual interfaces like Finder or Asset Management UIs. Each interface offers a unique way to interact with and manage media effectively.
Most Media Asset Management (MAM), Production Asset Management (PAM), and Digital Asset Management (DAM) systems feature Web UIs that support saved searches. These saved searches enable consistent content management across different teams and facilitate the sharing of management strategies. Implementing routine searches—whether daily, weekly, or monthly—is considered best practice in media management. For instance, during my time at a news broadcasting company in NYC, we used the term “Kill Kill Kill” to tag content for rapid removal. This industry-specific term signaled to everyone in production that the content was no longer in use. Although the word “Kill” might appear in a news headline or tagging field, it was distinctive in this triple format, making it a straightforward target for search-based content removal. This method efficiently reclaimed production and editorial storage space.
Searches could also be organized by creation dates or hold dates to manage content systematically. Content older than three months was typically archived or deleted, and anything past its “hold” date by a week was also removed.
For content like auto-saves and auto-renders in editorial projects, specific searches through a “finder”-like application were vital. Having a well-organized storage system meant we knew exactly where to look for and find this content. If content remained on physical storage but was no longer on the MAM, aka- “Orphaned”, it could be identified by its modified date.
Using a CLI for content management is generally more complex and unforgiving, often reserved for content that was not deleted using other methods. This process should be handled solely by an administrator with the appropriate storage credentials. Preparing a list of CLI commands beforehand can significantly streamline the use of this interface.
Just as nearly everyone has a junk drawer at home, organizations typically have their equivalent where users casually store content and documents, often forgetting about them. This leads to the gradual accumulation of small files that consume significant storage capacity.
To address this, organizations can benefit from assigning storage volumes or shares for specific uses rather than allowing open access, which helps prevent wasted space. For example, ensuring that only editorial content resides on the “Editing Share” simplifies the identification and management of caching and temporary files.
Implementing a storage tiering policy for data at rest can also optimize production costs. By relocating less active projects to nearline storage, space is freed up for active projects. Many organizations differentiate between high-cost, high-performance Tier 1 storage and lower-cost Tier 3 storage, such as Production and Archive. Data that is not actively in use but should not yet be archived can remain costly if kept on Tier 1 storage due to its higher per-terabyte cost. For instance, if Tier 1 storage costs $30 per terabyte and Tier 2 costs $6 per terabyte, maintaining dormant data on Tier 1 can be unnecessarily expensive—$24 more per terabyte. This cost differential becomes especially significant in cloud storage, where monthly fees can quickly accumulate. Choosing a cloud provider with “free-gress” will also help control or enable costs to be predictable.
Additionally, configuring alerts to notify when storage capacities are nearing their limits can help media managers prioritize their processes more effectively. These notifications also aid in reducing or eliminating overage fees charged by cloud providers when limits are exceeded.
“Evergreen content” refers to materials that are frequently used and never become obsolete, thus exempt from archiving. This includes assets like lower thirds, wipes, banners, intros, outros, and animations—items that are continually in demand. Such content benefits from being stored on nearline for swift access or on Tier 1 production storage, where it can be effectively managed with an optimized codec and bitrate to reduce its storage footprint while maintaining quality. The choice of codec is crucial here; graphic content that is originally rendered as lossless and uncompressed can be compressed before distribution to enhance efficiency and speed up access.
Additionally, evergreen “beauty shots” such as videos of building exteriors or well-known landmarks should also be stored on nearline rather than archived. This placement allows for easy updating or replacement as soon as the content becomes dated, ensuring that it remains current and useful. Systems that allow for proxy editing should also use a strategy, where non-essential or evergreen content remains on the Tier 2 nearline. This ensures that content is housed at a cost effective and accessible space.
Cloud costs are a critical consideration in media management, especially with egress fees associated with restoring archived content, which can quickly accumulate if not carefully managed. Media managers can significantly reduce these costs with strategic planning. When content is anticipated to be frequently used by production teams, fully restoring a file is advisable. This will prevent multiple users from partially restoring similar content with mismatching timecodes. Additionally, carefully selecting a representative set of assets on a given topic and communicating this selection to production staff can streamline processes and reduce costs.
For example, in the context of news, when a story about a well-known celebrity emerges, a media manager might choose to restore a complete set of widely recognized assets related to that celebrity. This approach prevents multiple users from restoring parts of the same content with different timecodes. Providing a well-chosen, easily accessible set of assets on a specific topic can prevent production teams from unnecessarily restoring a large volume of content that ultimately goes unused.
Each organization has unique production and data management needs. By strategically planning, defining, and organizing content lifecycles, they can streamline access to frequently used assets and minimize unnecessary expenses. Effective data and content management are essential for optimizing storage capacities, reducing costs, and ensuring unrestricted access to valuable media. Implementing diverse media management toolsets and defined retention policies facilitates organized archiving and retrieval, enhancing team collaboration and storage space optimization. By adopting these approaches and strategies, organizations can maintain a well-organized, cost-effective, and highly accessible data storage system that supports both current and future needs, ensuring seamless content management and operational efficiency.
The post Strategies for Effective Data and Content Management appeared first on CHESA.]]>Introduction Compression has been crucial in managing the storage and transmission of large media files. However, as technological advancements continue, the role of compression is evolving. This article delves into the history of media compression, differentiates its role in post-production and broadcast consumption, and explores the future of lossless media. We also discuss the evolution […]
The post The Rise of Lossless Media: A Compression Tale appeared first on CHESA.]]>Compression has been crucial in managing the storage and transmission of large media files. However, as technological advancements continue, the role of compression is evolving. This article delves into the history of media compression, differentiates its role in post-production and broadcast consumption, and explores the future of lossless media. We also discuss the evolution of bandwidth, streaming platforms, and wireless technologies driving this transformation. As we move towards a future where terabytes per second of data transfer speeds and petabytes of storage become commonplace, lossy compression may become a relic of the past, giving way to a new era of lossless, high-fidelity media.
Fun Fact: Claude Shannon, known as the father of information theory, developed the first theoretical model of data compression in 1948. His groundbreaking work laid the foundation for all modern data compression techniques.
Compression techniques were developed to address the limitations of early digital storage and transmission technologies, enabling the efficient handling of large media files.
These early codecs and non-linear editing (NLE) systems, despite their limitations, were essential in the development of digital video technology. They enabled the first steps towards online video streaming, multimedia content distribution, and advanced video editing workflows. While many of these codecs and systems have since fallen out of use, they paved the way for the advanced compression technologies and editing capabilities we rely on today.
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The future of media compression can be divided into two distinct areas: post-production and broadcast consumption. Each has unique requirements and challenges as we move towards a world with less reliance on compression.
In the realm of post-production, the trend is unmistakably moving towards lossless and uncompressed media. This shift is driven by the pursuit of maintaining the highest possible quality throughout the editing process. Here’s why this evolution is taking place:
Quality Preservation: In post-production, maintaining the highest possible quality is paramount. Compression artifacts can interfere with editing, color grading, and special effects, ultimately compromising the final output. By working with uncompressed media, filmmakers and editors can ensure that the integrity of their footage is preserved from start to finish.
Storage Solutions: The rapid advancement in storage technology has made it feasible to handle vast amounts of lossless media. High-speed NVMe SSDs and large-capacity HDDs provide the necessary space and access speeds for handling these large files efficiently. Additionally, cloud storage solutions offer virtually unlimited space, further reducing the dependency on compression.
High-Resolution Content: The increasing demand for 4K, 8K, and even higher resolution content requires lossless files to preserve every detail and maintain dynamic range. As viewing standards continue to rise, the need for pristine, high-quality footage becomes even more critical.
These RAW and uncompressed formats are essential for professional video production, providing filmmakers with the flexibility and quality needed to achieve the best possible results in post-production. The move towards lossless workflows signifies a commitment to excellence and the pursuit of the highest visual standards in the industry.
Modern NLE systems have advanced to support the editing of RAW formats, providing filmmakers and editors with unparalleled flexibility and control over their footage. NLEs such as Adobe Premiere Pro, Final Cut Pro, DaVinci Resolve, and Avid Media Composer are equipped to handle various RAW formats like REDCODE RAW, Apple ProRes RAW, ARRIRAW, Blackmagic RAW, and more. These systems enable real-time editing and color grading of RAW footage, allowing editors to leverage the full dynamic range and color depth captured by high-end cameras. By preserving the original sensor data, NLEs offer extensive post-production capabilities, including non-destructive adjustments to exposure, white balance, and other critical image parameters, ensuring the highest quality output for professional film and video projects.
On the consumption side, the trend towards losslessly compressed media is gaining significant momentum, although the challenges here are different from those in post-production.
Bandwidth Expansion: The rollout of 5G and the expansion of fiber optic networks promise dramatically increased internet speeds. This advancement makes it feasible to stream high-quality, lossless media to end-users, reducing the need for traditional lossy compression techniques. With these higher speeds, consumers can enjoy pristine audio and video quality that was previously unattainable due to bandwidth limitations.
Streaming Platforms: Services like Apple Music, Amazon Music HD, and Tidal have been offering lossless audio streaming for some time, providing users with a higher quality listening experience. This trend is likely to extend to video streaming, with platforms like Netflix and Disney+ exploring ways to deliver losslessly compressed 4K and HDR content. As these services push the envelope, they will set new standards for media quality in the streaming industry.
Wireless Technologies: Advances in wireless technology, including Wi-Fi 6, Wi-Fi 7, and future iterations, will support higher data rates and more reliable connections. These improvements will facilitate the streaming of lossless media, making it more accessible to a broader audience. With these advancements, users can expect seamless streaming experiences with minimal buffering and superior quality, regardless of their location.
As the infrastructure for high-speed internet and advanced wireless technologies continues to grow, the consumption of losslessly compressed media will become more widespread. This shift not only enhances the user experience but also pushes the industry towards a new standard of quality, reflecting the full potential of modern digital media technologies.
Several modern video codecs and technologies are emerging that offer significant improvements in compression efficiency and quality, with some poised to support lossless video capabilities. Additionally, advancements in storage and transmission technologies will facilitate the handling of large lossless media files
Video Codecs
AI and Compression: AI is increasingly being used to develop smarter compression algorithms. For example, Google’s AI compression system, RAISR, uses machine learning to enhance images after compression, reducing file sizes while maintaining quality.
Storage and Transmission Technologies
These emerging formats and technologies are set to transform the landscape of media production, storage, and consumption, driving us towards a future where uncompressed and lossless media become the norm.
Just as Moore’s Law predicts the doubling of transistors on a chip every two years, Nielsen’s Law of Internet Bandwidth states that high-end user connection speeds grow by 50% per year. As bandwidth increases, so too does the demand for new technologies that consume it. This phenomenon is often referred to as the “bandwidth paradox.” Despite advancements that provide higher speeds and greater capacity, emerging technologies continually push the limits of available bandwidth.
Virtual Reality (VR) and Augmented Reality (AR)
Advanced Immersive Recording Devices
Cloud Gaming and Interactive Streaming
The Growing Demand for High-Quality Streaming
Contradiction: Chattanooga, TN, already boasts 25Gb home internet, yet the adoption rate of 1Gb speeds remains low, highlighting the ongoing challenges in achieving widespread high-speed internet saturation.
As we stand on the brink of a new era in digital media, the concept of compression as we know it is poised to become a relic of the past. The relentless march of technological advancement in storage and bandwidth promises a future where lossless or uncompressed, high-fidelity media becomes the norm. Imagine a world where terabytes per second of data transfer speeds and petabytes of storage are commonplace, even on devices as ubiquitous as smartphones. Just twenty years ago, in 2004, typical consumer hard drives had capacities ranging from 40 GB to 160 GB—considered impressive at the time. This impending reality will usher in unprecedented levels of quality and immediacy in media consumption and production. The shift towards uncompressed workflows in post-production, driven by the need for maximal quality, coupled with the exponential growth in streaming capabilities through 5G, fiber optics, and beyond, sets the stage for a future where the limitations of today are no more. As these technologies mature, the cumbersome processes of compression and decompression will fade into history, making way for a seamless digital experience that reflects the true potential of human creativity and technological innovation.
References
Google. (2010). Acquisition of VP8 and WebM Project.
The post The Rise of Lossless Media: A Compression Tale appeared first on CHESA.]]>Introduction Blockchain technology is revolutionizing various industries, with media production being among the most promising beneficiaries. Blockchain storage, in particular, offers a novel approach to managing vast amounts of data securely and efficiently. This comprehensive guide explores how blockchain storage works, its benefits, challenges, and specific applications within the M&E industry. We will also look […]
The post Blockchain Storage Demystified: Transforming Media Production appeared first on CHESA.]]>Blockchain technology is revolutionizing various industries, with media production being among the most promising beneficiaries. Blockchain storage, in particular, offers a novel approach to managing vast amounts of data securely and efficiently. This comprehensive guide explores how blockchain storage works, its benefits, challenges, and specific applications within the M&E industry. We will also look at current vendors, use cases, and future trends.
Blockchain storage refers to the use of blockchain technology to manage and store data across a decentralized network. Unlike traditional centralized storage systems where data is stored on a single server or a group of servers, blockchain storage distributes data across multiple nodes in a network. Each piece of data is encrypted, time-stamped, and linked to the previous and subsequent data entries, forming a secure chain.
Traditional cloud storage solutions offered by industry giants like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure are significant competitors to blockchain storage. These services provide highly scalable and efficient storage without the complexities of blockchain technology.
However, the big three are not resting on their laurels. They are actively exploring and integrating advanced technologies to enhance their offerings:
The Big Three’s Response to Blockchain Storage:
In summary, while traditional cloud storage remains a strong competitor to blockchain storage, the big three—AWS, Google Cloud, and Microsoft Azure—are not only maintaining their current offerings but also innovating and integrating blockchain technologies into their services. This proactive approach ensures they stay competitive in the evolving landscape of data storage solutions.
Blockchain storage holds significant promise for managing the large data sets used in M&E. Its security, transparency, and immutability can revolutionize how media assets are stored and managed. While challenges like scalability and regulatory uncertainty need to be addressed, ongoing innovations and advancements are paving the way for a more robust and sustainable future for blockchain storage. As the technology evolves, it is poised to become an integral part of media production, enhancing security, efficiency, and collaboration.
The 357 Model: A Strategic Framework for Technology Management No technology plan or model is bulletproof (and yes, pun intended), but embracing a 3-5-7 model for technology analysis, expansion, refresh, and retirement helps organizations stay at the cutting edge of innovation while keeping their systems fully supported. This model isn’t a universal fix for every […]
The post The 357 Model appeared first on CHESA.]]>No technology plan or model is bulletproof (and yes, pun intended), but embracing a 3-5-7 model for technology analysis, expansion, refresh, and retirement helps organizations stay at the cutting edge of innovation while keeping their systems fully supported. This model isn’t a universal fix for every type of technology lifecycle, but it proves quite effective for hardware, software, and infrastructure when applied independently.
Understanding the Technology Flywheel Concept
A technology flywheel is a metaphor for a self-reinforcing cycle that gains momentum and efficiency as it grows—imagine a heavy wheel that becomes easier to spin the faster it goes. In the world of technology and business, it’s akin to a process where advancements in one area lead to increased performance, reduced costs, or enhanced capabilities, thereby unlocking new avenues for further innovation. This creates a virtuous circle, where each success builds upon the last, spiraling up to drive exponential growth and a competitive edge. Having demystified the flywheel concept, let’s connect it to our proposed model for media supply chains and technology lifecycles.
Detailed Breakdown of the 3-5-7 Model:
Application of the 3-5-7 Model in Video Production Technology
Focusing on video production technology, let’s see how software fits into this 3-5-7 framework. Two years post-purchase (note: not implementation), it’s crucial to concentrate on minor version updates, feature enhancements, industry advancements, and how well the system integrates with existing platforms while assessing its alignment with your organization’s specific needs. This stage is ideal for a detailed cost-benefit analysis to determine the anticipated return on investment, setting the stage for decisions about immediate purchases versus what can wait until Year 5. Whether it’s adopting a new release, updating to a major version, or switching vendors for a better fit, the analysis conducted in Year 3 lays the groundwork. Year 5 restarts the purchasing and commissioning cycle, and Year 7 closes the chapter with a thorough legacy migration and decommissioning.
Hardware’s lifecycle, though distinct from software, also aligns well with the 3-5-7 framework. Inspired by Moore’s Law—which observes that the capacity of integrated circuits roughly doubles every two years, leading to significantly enhanced computing capabilities—this model is particularly apt. For example, the performance evolution of workstations and laptops, closely tied to processor speeds, reflects this trend and impacts their compatibility with operating systems and software. IT departments typically initiate hardware upgrades in the third year and aim to retire them by the fifth year, with a final act of securely erasing or destroying the hardware by the seventh year. Server replacements, though more gradual, follow this rhythm as well, with the third year reserved for planning and the fifth for upgrades, ensuring a robust, supported, and secure technology infrastructure. By the seventh year, clients are usually notified of the product’s end of sale or service, often with a six-month heads-up.
Storage systems, which utilize processors within their controllers, similarly adhere to Moore’s Law. The third year is an opportune time to assess storage performance and utilization, deciding whether additional capacity is needed or if integrating more cost-effective nearline storage for inactive data is advisable. This assessment is vital for budgeting enhancements in the fifth year, with many storage controllers needing upgrades by the seventh year due to EOSL.
Avoiding Pitfalls: The Risk of Bargain Bin Purchases
While cost optimization is generally beneficial, “Bargain Bin” shopping can disrupt the Flywheel’s momentum, as manufacturers often offer significant discounts for technology nearing EOSL. To achieve the best return on investment, value-engineered solutions should leverage the 3-5-7 model. A frequent pitfall for smaller organizations is acquiring technology close to EOSL, forcing them to rely on platforms like eBay for spare parts or face unexpected full product replacements.
Integrating New Technologies: Ensuring Maturity and Compatibility
The allure of “New Technology” every three years can be tempting, but its integration and API maturity must be assessed to avoid costly and continuous upgrades that disrupt the Flywheel. The increasing interdependence of different technological systems (e.g., IoT devices, cloud computing, AI-driven analytics) suggests that changes in one area can necessitate faster adaptations elsewhere, potentially requiring more frequent review intervals.
Challenges and Opportunities with Cloud Technology Under the 3-5-7 Model
The application of the 3-5-7 model to cloud technology mirrors its use in software lifecycle management. Often, cloud solutions project ROI beyond the five-year mark, meaning initial migration costs may not yield immediate returns. By the fifth year, hardware upgrades fall to the cloud provider, usually without disrupting the end-user. This shifts the end-user group’s focus from infrastructure analysis to evaluating how their Cloud provider or MSP addresses their current and future needs.
Cloud storage, while following the 3-5-7 model, presents unique challenges with its ongoing costs. Unlike Linear Tape-Open (LTO) storage, which incurs no additional expenses after archiving, cloud storage continues to rack up charges even for dormant data. This has led many organizations to reevaluate their data retention strategies, aiming to keep less data over time. By evaluating data relevance every three years, organizations can optimize costs more effectively. For instance, general “Dated” b-roll footage might be deleted after five years, reflecting its reduced utility, while only content deemed “Historic” after seven years is reserved for long-term use.
Conclusion: A Foundation for Future-Proof Technology Investments
While the 3-5-7 model isn’t a magic bullet, it establishes a solid foundation for maintaining a technology flywheel, ensuring investments continue to meet evolving needs and maintaining a competitive edge. Overall, the 3-5-7 model provides a structured approach to technology lifecycle management. Tweaks and adjustments will occur depending on organizational initiatives, such as sustainability, trends and evolutions in the industry or economic and market dynamics. Organizations might increasingly look to customize this model to fit their particular circumstances, ensuring that their technology investments are both strategic and sustainable.
The post The 357 Model appeared first on CHESA.]]>Embracing the Future of Broadcasting: What comes after SDI? The prominent buzzword at the 2024 NAB Show was Artificial Intelligence (AI). Still, if you look beyond the vast AI offerings, you will notice that the broadcasting industry is witnessing a significant transformation in infrastructure. The industry is moving from traditional infrastructure models to more flexible, […]
The post Embracing the Future of Broadcasting: What comes after SDI? appeared first on CHESA.]]>The prominent buzzword at the 2024 NAB Show was Artificial Intelligence (AI). Still, if you look beyond the vast AI offerings, you will notice that the broadcasting industry is witnessing a significant transformation in infrastructure. The industry is moving from traditional infrastructure models to more flexible, IP-based solutions. This results in leaner and easily scalable systems that are ready to bridge the gap between true software-based solutions and newly imagined workflows. The SMPTE ST 2110 family of standards and Network Device Interface (NDI) technology are at the forefront of this revolution. These IP-based transport solutions redefine how content is created and delivered and shape the future of production. These changes involve adopting and merging long-standing IT-based technologies with new media technologies and workflows. For those familiar with the concepts of SMPTE ST 2110 and NDI but new to their practical application, here’s a look at implementing these technologies effectively.
Understanding SMPTE ST 2110 in Practice
The SMPTE ST 2110 family of standards offers a robust IP-based broadcasting framework, separating video (uncompressed or compressed), audio, and metadata into different essence streams. This separation is crucial for enhancing the flexibility and scalability of broadcast operations. It’s important to remember that ST 2110 is a media data-plane transport protocol based on RTP (Real-Time Transport Protocol) for sending media over a network. The network, Typically called a media fabric, is the infrastructure, but it’s not uncommon to refer to the combined protocol and the media fabric as ST 2110.
Key Considerations for Implementation:
Integrating Network Device Interface (NDI) into Live Productions
NDI complements IP workflows by providing a versatile and low-latency compressed method for video transmission over IP networks. It is particularly beneficial in live production environments where speed and flexibility are paramount. NDI is software-centric and relies on video compression to move media across existing or lower-bandwidth network fabrics efficiently, compared to ST 2110-20, which requires a dedicated high-bandwidth network for uncompressed video.
Practical Steps for NDI Integration:
Adapting to Industry Changes with Flexible IP Technologies
The shift towards technologies like ST 2110 and NDI is driven by their potential to create more dynamic, scalable, and high-value production environments. As the industry adapts, the flexibility of IP-based solutions becomes increasingly critical.
IP greatly enhances remote production capabilities allowing broadcast teams to manage and coordinate productions from multiple locations, reducing the need for extensive on-site personnel and equipment. This shift cuts down on logistical costs and enables a more agile response to changing production requirements.
Moreover, integrating ST 2110 or NDI into broadcast infrastructures is also a strategic move towards future-proofing. These technologies are designed to accommodate future video and audio technology advancements, including higher resolutions, emerging media formats, and immutable software infrastructure. By embracing these standards and systems now, organizations are better prepared to adapt to new trends and innovations, ensuring their systems remain relevant and highly functional in the evolving media landscape.
In conclusion, practical integration into existing systems can unlock unprecedented flexibility and efficiency for broadcasting professionals familiar with the theoretical aspects of SMPTE ST 2110 and NDI. By focusing on proper network infrastructure, synchronization, and compatibility, broadcasters can harness the full potential of these IP-based technologies to revolutionize their production workflows, making broadcasts more adaptable and future-ready. As the industry continues to evolve, embracing these changes will be key to staying competitive and meeting the increasingly complex demands of audiences worldwide.
The post Embracing the Future of Broadcasting: What comes after SDI? appeared first on CHESA.]]>Welcome to Our “Future of Broadcast Infrastructure Technology” Series Dive into the heart of innovation with us as we embark on a journey through the evolving world of broadcast infrastructure technology. This series is a window into the dynamic shifts shaping the industry’s future, whether you’re a seasoned professional or a curious enthusiast. A Journey […]
The post SDI – The Backbone of Broadcast appeared first on CHESA.]]>Dive into the heart of innovation with us as we embark on a journey through the evolving world of broadcast infrastructure technology. This series is a window into the dynamic shifts shaping the industry’s future, whether you’re a seasoned professional or a curious enthusiast.
A Journey Through Time: The Evolution of Broadcast Technology
Imagine a world where the magic of broadcasting was a novel marvel — that’s where our story begins. Giulio Marconi’s pioneering radio broadcast in 1895 set the stage for a revolution in communication. Fast forward from the fuzzy black-and-white imagery to today’s ultra-sharp high-definition videos. The milestones have been nothing short of extraordinary. Remember the times of meticulously cutting analog sync cables? Contrast that with today’s systems, which are nearing a self-timing brilliance. The leap from analog to digital has been a game-changer, enhancing the quality and reach of broadcast content. Now, as we edge closer to IP-based systems and other emerging tech, we’re witnessing the dawn of a new era. But where does this leave the trusty SDI?
Demystifying Serial Digital Interface (SDI)
For years, SDI has been the backbone of broadcast facilities around the globe. But let’s break it down: What is SDI, really? Birthed by the SMPTE 259M standard in 1989, SDI is the reliable workhorse for transmitting pristine digital video via coaxial cable, ensuring integrity, latency-free, and lossless delivery. Evolving over the decades, SDI now supports 4K workflows, thanks to SMPTE ST 2082, managing 12Gbps signals and 2160p resolution at 60FPS. Yet, the real question is whether SDI can keep pace with the industry’s insatiable appetite for growth and innovation.
SDI: The Past, Present, and Future in Broadcasting
SDI’s legacy of reliability and quality is undisputed. Its simplicity has made high-quality broadcasting an achievable standard. However, the relentless march of progress doesn’t play favorites, and SDI has little room to evolve beyond its current capabilities without significant technological breakthroughs. While transitioning to IP-based or cloud-based workflows becomes increasingly common, SDI’s relevance remains strong. But with scalability as its Achilles’ heel, SDI’s future is a hot topic of debate. Considering the economics of cabling, from coaxial to CAT6A to fiber, we’re at a crossroads where cost and technology intersect, guiding us to what’s next.
On the Horizon: What’s Coming Next
This conversation is just the beginning. In the next installments, we’ll delve into the promise of IP-based systems like ST 2110, the transformative role of NDI in live production, and the groundbreaking potential of technologies like 4K/8K, HDR, and cloud workflows.
We’ve only started peeling back the layers of the broadcasting world’s future. Join us as we navigate through the technologies, carving out the path forward, their implications for the industry, and what these changes could mean for you. Look out for our next installment in April and engage with us. Your insights, inquiries, and perspectives are the pulse of this exploration.
Join the Dialogue
Your voice is integral to our series. Share your thoughts, spark a discussion, or simply ask questions. We’re here to delve into the future together. Follow our journey, contribute to the narrative, and let’s decode the complexities of broadcast infrastructure technology as one.
The post SDI – The Backbone of Broadcast appeared first on CHESA.]]>