CHESA

The NAB Show 2025 – held in Las Vegas this April – was nothing short of the media tech industry’s Super Bowl, drawing over 100,000 professionals from more than 160 countries. CHESA was proud to be there as a sponsor and exhibitor, immersing our team in the latest innovations on the show floor. As a leading systems integrator, we view events like NAB as invaluable – a chance to see cutting-edge solutions in action, meet face-to-face with the partners behind the products, and brainstorm with clients about how these breakthroughs can solve real workflow challenges. “We try to walk around and talk to the people behind the products so we can see what their vision is… It’s also exciting to walk around… with our clients and see what piques their interest”. After catching our breath post-show, we’ve gathered our thoughts on the most compelling trends we saw at NAB 2025 and what they mean for the future of media workflows from CHESA’s integrator perspective.

IP Workflows Come of Age (ST 2110 & Beyond)

One clear theme was the evolution of IP-based workflows for broadcast production. It’s no longer hype – IP infrastructure is now a practical reality for studios large and small. Our partner Imagine Communications underscored this by showcasing SMPTE ST 2110 in action as the backbone of next-gen facilities. Imagine’s demonstrations in their booth (W2067) highlighted how far IP video transport has come: uncompressed signals flowing seamlessly over COTS networks, with their Selenio Network Processor (SNP) and Magellan control system simplifying the transition from SDI to IP. In fact, Imagine’s John Mailhot noted that this tried-and-tested IP combo has “made IP transformation practical for any size operation, enabling more efficient live production across the industry — even for projects incorporating HDR and UHD”. For CHESA and our clients, the takeaway is clear – IP workflows are maturing. We’re seeing broadcasters gain the flexibility to scale and reconfigure systems without the limitations of SDI routers, which means our integration strategies must ensure new systems can seamlessly route signals over IP networks. The health of the industry was on full display: standards like ST 2110 are broadly adopted, and CHESA is already leveraging that momentum to design future-proof, hybrid IP systems that protect clients’ existing investments while opening the door to cloud and UHD workflows.

Immersive & Interactive Broadcast Experiences (XR + Social Media)

Another show highlight was the rise of immersive, interactive broadcast experiences – blending augmented reality, virtual production, and even social media integration to captivate audiences in new ways. A stunning example came from Vizrt. At their booth, Vizrt (in partnership with startup blinx) demonstrated a world-first: an extended reality (XR) virtual studio where the audience could drive the content in real time via TikTok Live. In this proof-of-concept stream, viewers’ TikTok “gifts” weren’t just icons on a screen – they actually transformed the on-screen environment. For instance, a user sending a virtual “Galaxy” gift would cause the studio background to explode into a galactic 3D animation, even displaying that viewer’s name within the scene – a dynamic, real-time shoutout. This clever fusion of gaming-like interactivity with live broadcast graphics had NAB attendees buzzing. Vizrt’s team emphasized that such XR-driven engagement isn’t just gimmickry; it opens up new revenue models. With TikTok users spending in the hundreds of millions on virtual gifts, a live production that taps into that participatory energy can “drive transactions with deeply immersive entertainment opportunities… without the hard sell”. From CHESA’s perspective, this trend signals that broadcasters and content creators are keen to merge traditional production quality with interactive tech to win over younger, online-native audiences. Whether it’s integrating Unreal Engine-driven virtual sets or connecting social media APIs to on-air graphics, we anticipate more projects where CHESA will be asked to connect these technologies. The goal will be to create seamless workflows that allow our clients to deliver immersive storytelling – where viewers don’t just watch, but actually influence the story in real time.

AI-Powered Workflows: Smarter Captioning, Metadata & Creativity

If one trend permeated every hall at NAB 2025, it was the influence of artificial intelligence on media workflows. From automating rote tasks to augmenting creative decisions, AI-driven tools are rapidly becoming mainstream in our industry. A prime example came from Telestream: they unveiled new AI-powered automation for captions, subtitles, metadata tagging, and even content summaries in their Vantage platform. This means a video file ingested into a workflow can have high-quality speech-to-text captions generated almost instantly, multilingual subtitles prepared, descriptive metadata auto-populated, and short synopsis content drafted – all via AI. It’s a game-changer for efficiency: think of compliance captioning, localization, and content indexing being done in a fraction of the time, with less manual effort. Our integration partner SNS (Studio Network Solutions) offered a complementary peek at AI’s role in creative asset management. At SNS’s booth, they set up an on-premises “AI Playground” – a hands-on demo where attendees could explore AI’s power in media management. We tried out tools that let you search a massive media library by describing a scene, or automatically identify duplicate images and even pinpoint specific moments in video by their content. For example, an editor could query, “find all clips where the CEO appears on stage at CES,” and an AI engine would sift the archives to find those shots – no manual tagging needed. SNS’s approach here is to show how AI can enrich metadata in situ and trigger complex workflows behind the scenes. In fact, their upcoming integration with Ortana’s Cubix orchestration platform will let users kick off automated tasks (like file moves or cloud backups) just by setting a tag in the SNS ShareBrowser MAM – essentially using AI and orchestration to connect storage, MAM, and cloud services intelligently. “These new integrations highlight our commitment to providing users with flexible tools that enhance collaboration and drive efficiency,” said SNS co-founder Eric Newbauer, underscoring that the end goal is an end-to-end workflow where mundane tasks are handled by smart systems and creative people can focus on higher-value work.

On the content creation side, AI is also stepping up to tackle one of the industry’s perennial challenges: making content accessible to broader audiences. Perhaps the most jaw-dropping example we saw was AI-Media’s debut of LEXI Voice, an AI-powered live translation solution. Imagine broadcasting a live event in English and, virtually in real time, offering viewers alternate audio tracks in Spanish, French, Mandarin, or over 100 languages – without an army of human interpreters. AI-Media’s LEXI Voice does exactly this: it listens to the program audio and generates natural-sounding synthetic voice-overs in multiple languages with only ~8 seconds of latency. The system impressed many broadcasters at NAB by showing that a single-language feed can be transformed into a multi-language experience on the fly. “Customers are telling us LEXI Voice delivers exactly what they need – accuracy, scale, and simplicity, at a disruptive price,” shared James Ward, AI-Media’s Chief Sales Officer. For global media companies and even event producers, this AI-driven approach could break language barriers and dramatically cut the cost of multi-language live content (AI-Media estimates up to 90% cost reduction versus traditional methods) while maintaining broadcast-grade quality. For CHESA, which often helps clients integrate captioning and translation workflows, these AI advancements are exciting. We foresee incorporating more AI services – whether it’s auto-captioning for compliance, cognitive metadata tagging for asset management, or AI voice translation for live streams – as modular components in the solutions we design. The key will be ensuring these AI tools hook seamlessly into our clients’ existing systems (MAMs, DAMs, playout, etc.), so that captions, metadata, and even creative rough-cuts flow automatically, saving time and enabling content teams to do more with less.

Cloud, Streaming & Remote Production Breakthroughs

NAB 2025 also reinforced how much cloud and remote production technologies have advanced. Over the past few years, necessity (and yes, the pandemic) proved that quality live production can be done from almost anywhere – and the new gear and services on display cemented that remote and cloud-based workflows are here to stay. For instance, our partner Wowza showcased updates that make deploying streaming infrastructure in the cloud or hybrid environments easier than ever. Their streaming platform can now be spun up in whatever configuration a client needs – on-premises, in private cloud, or as a service – while still delivering the low-latency, scalable performance broadcasters expect. This kind of flexibility is crucial for CHESA’s clients who demand reliability for live events but also want the agility and global reach of cloud distribution. We witnessed demos of Wowza’s software dynamically adapting video workflows across protocols (from WebRTC to LL-HLS) to ensure viewers get a smooth experience on any device. The message was clear: cloud-native streaming has matured to the point where even high-profile, mission-critical streams can be managed with confidence in virtualized environments.

On the live contribution and production side, LiveU made a strong showing with its latest remote production ecosystem. LiveU has been a pioneer of cellular bonding (letting broadcasters go live from anywhere via combined 4G/5G networks), but this year they took it up a notch. They unveiled an expanded IP-video EcoSystem that is remarkably modular and software-driven. “The EcoSystem is a powerful set of modular components that can be deployed and redeployed in a variety of workflows to answer every type of live production challenge,” explained LiveU’s COO Gideon Gilboa. In practice, this means a production team can spin up a configuration for a multi-camera sports shoot in the field, then re-tool the same LiveU gear and cloud services the next day for a totally different scenario (say, a hybrid cloud/ground news broadcast) without needing entirely separate kits. One highlight was LiveU Studio, a cloud-native vision mixer and production suite that enables a single operator to produce a fully switched, multi-source live show from a web browser – complete with graphics, replays, and branded layouts. Another headline innovation was LiveU’s new bonded transmission mode with ultra-low delay: we’re talking mere milliseconds of latency from camera to cloud. Seeing this in action was impressive – it means remote cameras can truly be in sync with on-site production, opening the door to more REMI (remote integration) workflows where a director in a central control room can cut live between feeds coming from practically anywhere, with no noticeable delay. CHESA recognizes that this level of refinement in remote production tech is a boon for our clients: it reduces the cost and logistical burden of on-site production (fewer trucks and crew traveling) while maintaining broadcast quality and responsiveness. We’ve already been integrating solutions like LiveU for clients who need mobile, nimble production setups, and at NAB we saw that those solutions now offer even greater reliability, video quality (e.g. 4K over 5G), and cloud management capabilities.

Even the traditionally hardware-bound pieces of broadcast are joining the cloud/remote revolution. Companies like Riedel – known for studio intercoms and signal distribution – showed off IP-based solutions that make communications and infrastructure more decentralized. Riedel’s new StageLink family of smart edge devices, for example, lets you connect cameras, mics, intercom panels, and other gear to a standard network and route audio/video signals over IP with minimal setup. In plain terms, it virtualizes a lot of what used to require dedicated audio cabling and matrices. We see this as “smart infrastructure” that eliminates traditional barriers: an engineer can extend a production’s I/O simply by adding another StageLink node to the network, rather than pulling a bunch of copper cables. For remote productions, this means field units can tie back into the home base over ordinary internet connections, yet with the robustness and low latency of an on-site system. Riedel also previewed a Virtual SmartPanel app that puts an intercom panel on a laptop or mobile device. Imagine a producer at home with an iPad, talking in real time to camera operators and engineers across the world as if they were on the same local intercom – that’s now reality. For CHESA, whose projects often involve tying together communication systems and control rooms, these developments from LiveU, Wowza, Riedel and others mean we can architect workflows that are truly location-agnostic. Whether our client is a sports league wanting to centralize their control room, or a corporate media team trying to produce events from home offices, the technology is in place to make remote and cloud production feel just as responsive and secure as traditional setups.

Smart Infrastructure & Workflow Orchestration

The final theme we noted is a bit more behind-the-scenes but critically important: the growth of smart infrastructure and orchestration tools to manage all this complexity. As integrators, we know that deploying one shiny new product isn’t enough – the real value comes from how you connect systems together and automate their interaction. At NAB 2025, many vendors echoed this, introducing solutions that orchestrate workflows across disparate systems. We’ve already touched on Riedel’s IP-based infrastructure making physical connections smarter, and SNS’s integration platform leveraging AI and tags to automate tasks. To expand on the SNS example: they announced a native integration with Ortana’s Cubix workflow orchestration software that takes automation to the next level. With SNS’s EVO storage plus Cubix, a media operation can do things like: automatically move or duplicate files between on-prem storage, LTO archives, and cloud tiers, triggered by policies or even a simple user action in the MAM; or enrich assets with AI-generated metadata in place (send files to an AI service for tagging as they land in storage); or spin up entire processing jobs through a single metadata tag. In a demo, SNS showed how setting a “Ready for Archive” tag on a clip could kick off a cascade: the file gets transcoded to a preservation format, sent to cloud object storage (with a backup to a Storj distributed cloud for good measure), and the MAM is updated – all without manual intervention. This kind of event-driven orchestration is incredibly powerful. It means our clients can save time and reduce errors by letting the system handle repetitive workflow steps according to rules we help them define. CHESA has long championed this approach (we often deploy orchestration engines alongside storage and MAM solutions), and it was validating to see so many partners focusing on it at NAB.

“Smart” infrastructure also refers to hardware getting more integrated smarts. We saw this in Riedel’s new Smart Audio Mixing Engine (SAME) – essentially a software-based audio engine that can live on COTS servers and apply a suite of audio processing (EQ, leveling, mixing, channel routing) across an IP network. Instead of separate audio consoles or DSP hardware, the mixing can be orchestrated in software and scaled easily by adding server nodes. This aligns with the general trend of moving functionality to software that’s orchestrated centrally. For CHESA’s clients, it means future facilities will be more flexible and scalable. Need more processing? Spin up another virtual instance. Reconfigure signal paths? Use a software controller that knows all the endpoints. The days of fixed-function gear are fading, replaced by what you might call an ecosystem of services that can be mixed-and-matched. Our job as an integrator is to design that ecosystem so that it’s reliable and user-friendly despite the complexity under the hood. The good news from NAB 2025 is that our partners are providing great tools to do this – from unified management dashboards to open APIs that let us hook systems together. We came away confident that the industry is embracing interoperability and orchestration, which are key to building solutions that adapt as our clients’ needs evolve.

Conclusion: From Show Floor to Real-World Workflows

After an exciting week at NAB 2025, the CHESA team is returning home with fresh insights and inspiration. We want to extend our thanks to our key technology partners – Imagine Communications, Vizrt, Telestream, SNS, Wowza, LiveU, Ai-Media, and Riedel – for sharing their innovations and visions with us at the show. Each of these companies contributed to a clearer picture of where media technology is headed, from IP and cloud convergence to AI-assisted creativity and immersive viewer experiences. For CHESA, these advancements aren’t just flashy demos; they’re the building blocks we’ll use to solve our clients’ complex workflow puzzles. Our role as an integrator is ultimately about connecting the right technologies in the right way – turning a collection of products into a seamless, tailored workflow that empowers content creators. NAB Show 2025 reinforced that we have an incredible toolbox to work with, and it affirmed CHESA’s commitment to staying at the forefront of media tech. We’re excited to take what we absorbed at NAB and translate it into real-world solutions for our clients, helping them create, manage, and deliver content more efficiently and imaginatively than ever. In the fast-evolving world of media workflows, CHESA stands ready to guide our clients through the innovation – from big picture strategy down to every last system integration detail – just as we have for over twenty years. Here’s to the future of media, and see you at NAB 2026!

I often get asked, “What is the best MAM?” Eager eyes await my answer at client meetings and conferences. With a smile, I respond, “That’s an easy one—the best MAM is the one that fits your requirements.” While it may sound simple, the reality is more complex. Hidden in this answer are a series of crucial questions and specific use cases, many of which organizations have yet to document.

Identify the Market and Roadmap

Every MAM vendor follows a development cycle influenced by feature requests from sales teams, solutions architects, or client engagements. These product roadmaps are driven by the need to fulfill use case requirements. Some MAMs have robust features designed for image-based workflows, while others are tailored for video management. Yet, each vendor will claim their product is the best, within their defined market, of course. To narrow your options, start by identifying the types of assets and files you need to manage and the features required for your workflows.

Define Your Use Cases

To find the right MAM for your organization, begin by defining your specific use cases and how your workflows operate. Detail the system functionalities and requirements you need. Weigh these functional requirements with a measurable metric, which will help during the system assessment and ultimately determine deployment success, KPI achievements, and ROI.

Understand Workflows and Integrations

Consider what legacy or future technology is part of your environment. Using the 3-5-7 Flywheel methodology from our previous blog, evaluate how your workflows have evolved. What new codecs or systems are you implementing? What languages and API parameters will be necessary for smooth cross-application functionality? Identify your “source of truth” for data and how it flows throughout the data landscape. How do you want your workflows to operate, and how should users progress through them? What storage types are being used, what connectivity and protocols are being used, and where are those storage located? These considerations are vital to ensure functional requirements align with use cases and that the system integrates well within your ecosystem.

Engage Stakeholders and Measure Fulfillment

Involving key stakeholders is crucial. Make sure you gather feedback from a diverse range of users, not just the typical producers and editors. Then, create a matrix to assess how well the system fulfills your requirements, and another to evaluate usability. Some systems may seem like an obvious choice on paper, but may impose rigid processes that users find difficult to adapt to. When users fail system acceptance tests or create workarounds, ROI and KPIs suffer.

Seek Professional Guidance

Most organizations have existing relationships with systems integrators or IT providers—use these resources to bridge knowledge gaps. Engage with engineering teams, ans subject matter experts to gather additional insights, and document key takeaways to explore during testing or proof of concept (POC). When conducting a POC, involve the vendor’s professional services team. A simple integration built by the vendor can reveal their responsiveness and ability to meet your needs.

Conclusion

As the saying goes, “Fail to plan, plan to fail.” This is especially true when choosing and implementing a MAM, DAM, or PAM. With careful planning and attention to the steps mentioned, you’ll be on track to selecting the best system for your organization.

The media and entertainment industry is experiencing a significant transformation, driven by cloud and hybrid infrastructures. These technologies enable unprecedented scalability and cost-efficiency, allowing media companies to adapt to the rising demand for high-quality, instantly accessible content. In an era defined by global connectivity, the ability to scale operations and manage costs effectively is crucial. This article explores how cloud and hybrid infrastructures are shaping scalability, streamlining costs, and revolutionizing the future of media workflows.

Scalability: Meeting the Demands of a Growing Industry

Elastic Scalability in the Cloud

Cloud platforms like AWS, Google Cloud, and Microsoft Azure offer elastic scalability, enabling businesses to expand or contract resources based on demand. During peak events such as live sports or major show premieres, these platforms allow broadcasters to handle traffic surges without investing in physical infrastructure.

Key benefits include:

• Real-time scaling during high-demand periods.
• Cost-effective global content distribution with low latency.
• Seamless streaming performance for millions of concurrent users.

Hybrid Cloud for Tailored Flexibility

A hybrid cloud model blends on-premises systems with cloud services, ensuring scalability while maintaining control over critical assets. For example:

• On-premises systems handle latency-sensitive or high-security tasks.
• Cloud platforms manage tasks like rendering and storage of non-critical assets.

This balanced approach optimizes resource usage while preserving security and performance.

Scalability for Real-Time Media Delivery

Media companies increasingly rely on real-time delivery for live broadcasts and interactive content. Cloud-based architectures distribute workloads efficiently across global regions, reducing latency and ensuring uninterrupted service to a dispersed audience.

Cost Management: Reducing Expenses and Boosting Efficiency

Pay-As-You-Go Flexibility

Unlike traditional on-premises systems, cloud platforms utilize a subscription-based model. Media companies pay only for the resources consumed, leading to significant cost reductions:

• Avoid capital investments in underutilized hardware.
• Allocate resources dynamically to prevent waste.

Optimized Resource Allocation

For episodic projects like live broadcasts or film productions, cloud infrastructure eliminates the need for permanent, high-cost hardware. Teams can scale resources for tasks such as rendering and media storage, then scale down afterward, saving operational costs.

Automated Workflows for Efficiency

Cloud platforms incorporate AI and ML tools to automate repetitive tasks, reducing human workload and improving efficiency:

• Metadata tagging.
• Content encoding and transcoding.
• Automated file backups and organization.

This automation allows creative teams to focus on higher-value activities, streamlining operations and reducing overall costs.

Improved Collaboration and Faster Time-to-Market

Global Collaboration with the Cloud

The decentralized nature of modern media production requires seamless remote collaboration. Cloud platforms enable:

• Simultaneous project access for geographically dispersed teams.
• Faster production cycles through shared real-time workflows.

Hybrid Solutions for Security and Flexibility

Hybrid infrastructures empower companies to store sensitive data on-premises while leveraging the cloud for demanding tasks like real-time editing and rendering. This blend ensures security without compromising production speed.

Disaster Recovery and Content Security

Resilient Disaster Recovery Systems

Cloud infrastructure ensures business continuity through data replication across geographically diverse servers. Key advantages include:

• Rapid recovery during outages.
• Built-in redundancy to safeguard content.

Enhanced Security with Hybrid Infrastructure

For sensitive content, hybrid solutions offer robust protection by keeping critical data on-premises while leveraging cloud scalability. This model supports:

• Advanced encryption.
• Digital rights management (DRM).
• Prevention of unauthorized access.

Future Technologies Enhancing Scalability and Cost Management

Edge Computing for Low-Latency Delivery

Edge computing processes data closer to end-users, reducing latency and enhancing experiences for live streaming and interactive media.

5G for Seamless Media Delivery

The rollout of 5G networks complements cloud and hybrid infrastructures by:

• Enabling faster content delivery.
• Supporting high-bandwidth applications like ultra-HD streaming and immersive VR experiences.

Conclusion

The adoption of cloud and hybrid infrastructures is revolutionizing the media and entertainment industry. With elastic scalability, cost-efficient operations, and robust security, these technologies provide the foundation for a future-ready, competitive landscape. Companies embracing these innovations today will enjoy enhanced flexibility, reduced costs, and the agility to navigate an ever-evolving digital ecosystem.

The media industry, with its complex web of content creation, distribution, and monetization, faced unprecedented challenges in 2024. From rapid technological shifts and escalating cybersecurity threats to disruptions in content pipelines and regulatory scrutiny, the vulnerabilities in the media supply chain have been exposed in ways that demand urgent attention. This year’s disruptions have underscored the need for a resilient, adaptable, and future-proof media supply chain capable of thriving in an era of rapid change.

Cybersecurity Breaches

With the growing reliance on cloud-based workflows and digital collaboration tools, media organizations have become prime targets for cyberattacks. Hackers exploit vulnerabilities in content storage and distribution systems, leading to data theft, intellectual property leaks, and operational disruptions.

Disrupted Content Pipelines

The rise of global crises, including political conflicts and environmental disasters, has hampered location-based productions and delayed delivery schedules. These disruptions have forced companies to rethink their approach to content creation, remote production and planning.

Complex Rights Management

As media companies expand their offerings across multiple platforms and regions, managing licensing agreements and royalties has become increasingly complicated. Mismanagement of intellectual property (IP) rights can lead to legal disputes and revenue loss. Organizations are also rewriting Personal Data Policies to include image and likeness, directly affecting retention and archival policies.

Technology Fragmentation

The integration of new technologies such as AI, VR, and 5G has created both opportunities and challenges. Legacy systems often struggle to keep up with these innovations, resulting in inefficiencies and compatibility issues within the media supply chain.

Regulatory Pressures

Heightened scrutiny over data privacy, content moderation, and intellectual property rights has added another layer of complexity. Compliance with regional and global regulations demands significant resources and operational agility.

Strategies to Address Media Supply Chain Vulnerabilities

Adopting End-to-End Digital Workflows

The transition to cloud-based, fully digital workflows can streamline content production and distribution while improving scalability. Advanced media asset management (MAM) systems allow real-time collaboration and ensure secure content storage and transfer.

Strengthening Cybersecurity Measures

Media companies must adopt robust cybersecurity protocols, such as encryption, multi-factor authentication, and regular audits. Partnering with cybersecurity firms and leveraging AI-driven threat detection tools can help mitigate risks.

Enhancing Production Resilience

To combat disruptions, media organizations should diversify production locations and leverage virtual production technologies. Virtual sets and AI-assisted post-production tools can reduce dependency on physical environments and accelerate timelines.

Optimizing Rights and Royalty Management

Blockchain technology offers a transparent and efficient way to manage licensing agreements and royalty payments. Automating rights management systems can reduce errors, ensure compliance, and provide real-time tracking of revenue streams.

Investing in Interoperable Systems

To overcome technology fragmentation, media organizations should adopt interoperable tools and standards that integrate seamlessly with existing systems. This ensures smooth workflows and reduces downtime when implementing new technologies.

Navigating Regulatory Compliance

Proactive engagement with policymakers and industry groups can help media companies stay ahead of regulatory changes. Establishing dedicated compliance teams and leveraging AI for real-time monitoring of content and data usage can streamline adherence to legal requirements.

The Role of Collaboration and Innovation

The media supply chain is no longer a linear process—it is a dynamic ecosystem requiring collaboration across stakeholders. Partnerships with technology providers, production houses, and distribution platforms can drive innovation and unlock new revenue streams. Additionally, fostering a culture of experimentation with emerging technologies like generative AI, immersive media, and personalized content delivery can create competitive advantages.

Conclusion

The challenges of 2024 have revealed critical vulnerabilities in the media supply chain, but they have also highlighted opportunities for transformation. By embracing technology, fostering collaboration, and prioritizing resilience, media organizations can turn these challenges into catalysts for growth.

In an industry where change is the only constant, the ability to adapt and innovate will define the leaders of tomorrow. Now is the time for media companies to fortify their supply chains, ensuring they are prepared to meet future disruptions head-on.

The media and entertainment (M&E) industry is in the midst of a digital revolution, with technologies like cloud computing, artificial intelligence (AI), blockchain, and 5G reshaping content creation, distribution, and consumption. However, this transformation is not without its hurdles. Key challenges such as interoperability and security have emerged as critical roadblocks, complicating the adoption and integration of new technologies. Addressing these challenges is essential for the industry to fully harness the potential of digital innovation and remain competitive in an increasingly tech-driven landscape.

Understanding the Integration Challenges

Understanding integration challenges is forefront for the media and entertainment (M&E) industry because these challenges directly impact the industry’s ability to innovate, operate efficiently, and remain competitive

Interoperability Issues

The media supply chain consists of a diverse ecosystem of tools, platforms, and workflows, many of which were developed independently. This fragmentation creates significant barriers:

• Legacy Systems: Many organizations rely on legacy infrastructure that struggles to integrate with modern solutions, leading to inefficiencies and bottlenecks. Legacy Data Migrations are typically the quagmire in any project.
• Vendor Lock-in: Proprietary technologies often limit flexibility, making it difficult to collaborate across platforms or switch providers. Proprietary databases tend to lock or limit options for data flexibility, or system transitions.
• Lack of Standards: The absence of universal standards for media formats, metadata, and protocols creates inconsistencies in workflows, particularly when dealing with international partners. There are many codecs that still do not have a baseline standard.

Security Vulnerabilities

As the M&E industry becomes more digital, it also becomes a bigger target for cyberattacks. Key security concerns include:

• Data Breaches: Sensitive data, including unreleased content and customer information, is vulnerable to theft during production, storage, or distribution.
• Content Piracy: Unauthorized access to high-value media assets can lead to substantial revenue losses and reputational damage. International trademark laws also complicate Content Piracy, as markets broaden.
• Cloud Security: The shift to cloud-based workflows introduces risks, such as misconfigured storage or unauthorized access to shared environments.

Cultural and Operational Resistance

Adopting new technologies often disrupts established workflows, leading to resistance from teams accustomed to traditional methods. This resistance can slow down implementation and reduce the overall effectiveness of technological upgrades.

Strategies to Overcome Interoperability Challenges

Having strategies to overcome interoperability challenges is critical for the media and entertainment (M&E) industry because these challenges directly impact efficiency, scalability, security, and innovation. Addressing interoperability ensures that different technologies, systems, and processes work seamlessly together, enabling organizations to achieve their goals in a competitive and rapidly evolving market.

Adopt Open Standards

Industry-wide adoption of open standards for file formats, metadata, and APIs can ensure seamless compatibility between tools and systems. Initiatives like SMPTE’s (Society of Motion Picture and Television Engineers) standards for media asset management are steps in the right direction.

Embrace Cloud-Native Solutions

Cloud-native applications, designed for scalability and integration, can bridge the gap between legacy systems and modern tools. Cloud native technology can also ease the transition from Onprem to Hybrid to full Cloud. Cloud platforms also enable real-time collaboration across geographies, reducing the need for complex physical setups.

Invest in Middleware

Middleware solutions can act as a bridge between disparate systems, facilitating communication and data exchange without requiring a complete overhaul of existing infrastructure.

Foster Collaborative Ecosystems

Encouraging collaboration between technology providers, industry bodies, and content creators can lead to the development of more interoperable solutions. Shared innovation initiatives can accelerate progress while reducing fragmentation.

Addressing Security Challenges

Addressing security challenges is crucial for the media and entertainment (M&E) industry due to the highly sensitive nature of its assets, the increasing reliance on digital technologies, and the growing threat landscape.

Implement Zero Trust Architecture

Zero Trust principles ensure that no device, user, or application is trusted by default, requiring continuous verification for access to critical resources. This approach is vital in protecting high-value content.

Leverage AI for Threat Detection

AI-powered cybersecurity tools can monitor network activity, identify anomalies, and respond to threats in real-time. Such tools are particularly useful in detecting ransomware attacks and phishing attempts targeting media workflows.

Adopt Encryption Best Practices

Encrypting data at rest and in transit ensures that even if unauthorized access occurs, the content remains protected. End-to-end encryption is especially critical for cloud-based storage and transfers.

Conduct Regular Security Audits

Routine vulnerability assessments and penetration testing can help identify and address potential security gaps before they are exploited by malicious actors.

Train Teams in Cyber Hygiene

Employees are often the weakest link in cybersecurity. Comprehensive training programs can raise awareness about phishing, password management, and secure handling of sensitive media assets.

Conclusion

The adoption and integration challenges in the media and entertainment industry are complex but not insurmountable. By prioritizing interoperability, fortifying security, and fostering a culture of adaptability, M&E companies can overcome these hurdles and unlock the full potential of emerging technologies.

As the industry evolves, those who invest in robust integration strategies and proactive security measures will be well-positioned to lead the next wave of innovation, future proof their technology roadmap and deliver compelling experiences to audiences worldwide.

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 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.

What Are AI-Generated Influencers?

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.

Historical Timeline: The Evolution of Virtual Influencers

1930s: Cynthia the Mannequin

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.

1950s: Alvin and the Chipmunks

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.

1980s: Max Headroom

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.

2000s: Hatsune Miku

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.

2016: Lil Miquela and the Age of AI Influencers

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.

The Technology Behind AI-Generated Influencers

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.

The Benefits of AI-Generated Influencers in Marketing

1. Control, Consistency, and Adaptability

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.

2. Cost Efficiency

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.

3. Global Reach and Availability

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.

Challenges and Ethical Concern

1. Lack of Authenticity

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.

2. Audience Skepticism

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.

3. Unrealistic Beauty Standards

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.

4. Ethical Use and Transparency

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.

The Future of AI-Generated Influencers

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.

Conclusion

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.

Sources:

• “The Rise of AI-Generated Influencers: A New Phase in Marketing”
  • https://communities.springernature.com/posts/the-rise-of-ai-generated-influencers-a-new-phase-in-marketing
• “Virtual Influencers: What Brands and Marketers Need to Know”
  • https://sproutsocial.com/insights/virtual-influencers/
• “Virtual Influencers: The Power of AI-Generated Influencer Marketing”
  • https://wearebrain.com/blog/ai-generated-virtual-influencers/
• “Artificial Intelligence and the Future of Music”
  • https://no-taste.com/no-taste-blog/artificial-intelligence-and-the-future-of-music-3573/

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 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.

The Rise of AI Virtual Actors

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.

A Historical Timeline of Virtual and Digital Actors

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:

1930s: The First Virtual Performers – Mechanical Mannequins

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.

1950s: Animated Performers – Alvin and the Chipmunks

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.

1980s: The Birth of Virtual Actors – Max Headroom

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.

2001: The First Digital Lead – Final Fantasy: The Spirits Within

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.

2010s: Digital Resurrection of Deceased Actors

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.

2020s: AI-Generated Actors

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.

How AI Virtual Actors Work

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.

The Benefits of AI Virtual Actors

1. Flexibility and Creative Control

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.

2. Cost and Time Efficiency

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.

Ethical Implications of AI Actors

1. Creativity Versus Profit

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.

2. Resurrecting the Dead: Who Owns an Actor’s Image?

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.

The Future of AI in Hollywood

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.

Conclusion

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.

Sources:

• “What Generative AI Could Look Like for Actors”
  • https://www.backstage.com/magazine/what-generative-ai-could-look-like-for-actors/
• “How AI is Resurrecting Dead Actors”
  • https://www.bbc.com/news/entertainment-arts-66156323
• “AI Actors and Directors: The Rise in Hollywood”
  • https://aisteals.com/ai-actors-and-directors-the-rise-in-hollywood/

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, 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.

Virtual Musicians: AI Voices That Never Existed

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.

The Virtual Idol Scene in Japan

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.

Resurrecting Music Legends with AI

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.

A Threat to Artistic Integrity?

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.

AI as a Creative Tool: Enhancement, Not Replacement

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.

New Generations Embrace AI Music

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.

Ethical and Creative Implications of AI Music

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 Future of AI in Music: Collaboration or Competition?

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.

Conclusion: Embracing AI, but Protecting Creativity

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.

Sources:

• “How AI Is Transforming Music”
  • https://time.com/6340294/ai-transform-music-2023/
• “Orchestrating The Future—AI In The Music Industry”
  • https://www.forbes.com/sites/davidhenkin/2023/12/05/orchestrating-the-future-ai-in-the-music-industry/
• “Is AI a Threat to Musicians?”
  • https://aimm.edu/blog/is-ai-a-threat-to-musicians
• “Billie Eilish, Nicki Minaj, Musicians Warn Against Replacing Human Artists with AI”
  • https://www.axios.com/2024/04/02/musicians-letter-ai-replace-artists
• “Virtual Bands and K-Pop: AI’s Role in the Future of Music”
  • https://www.cnn.com/style/kpop-virtual-bands-ai-intl-hnk/index.html
• “10 Famous Virtual Idols in Japan”
  • https://animost.com/industry-updates/10-famous-virtual-idols-in-japan/