In March 2025, Meta, the owner of platforms such as Facebook, Instagram, and WhatsApp, announced several steps reflecting its relentless efforts to enhance its investments in artificial intelligence technology. The company’s capital expenditures for expanding its infrastructure increased from 40 billion to 40 billionto 65 billion between 2024 and 2025. In just one week, the tech giant announced the testing of its first AI training chip to reduce reliance on external suppliers. Additionally, Meta launched an update to the WhatsApp application, allowing the addition of a standalone interface for the smart assistant (Meta AI). There are also reports of the company’s plans to launch a standalone application for Meta AI in the second quarter of the current year.
Meta’s policies are not unique, but rather part of strategies adopted by its social media counterparts. These strategies involve integrating smart models into their platforms, not only within recommendation and filtering systems and user behavior monitoring, but also for content generation through the integration of generative AI applications. These platforms were established to build communities, enhance communication, and generate collaborative human content. This raises many questions about the impact of these strategies on the future of social communication in the virtual space.
Meta’s Smart Assistant:
Meta AI falls under the category of conversational AI technologies, which are intelligent systems designed to engage in natural conversations with humans. These systems rely on a combination of natural language processing (NLP) techniques and machine learning to recognize speech and text inputs, generate responses, and translate meanings across different languages.
Meta’s journey in AI began in 2013 with the establishment of the Facebook AI Research (FAIR) lab, which focused on developing machine learning systems, generative models, and testing and developing natural language processing models, image analysis, computer vision, text classification, spam message detection, and improving the accuracy of machine translation. This represented a strategic turning point for the company towards integrating AI technologies to enhance its platforms and user interactions.
As a more advanced step, Meta introduced the LLaMA (Large Language Model Meta AI) project. The first version, Llama, was released in 2022 as a large language model based on machine learning for text generation. The second version, Llama 2, was released in 2023, introducing significant improvements in performance and the ability to answer complex questions. In 2023, Llama 3 was released, which is the latest and most advanced version, offering additional improvements in areas such as language understanding and analysis. In 2024, Llama 3.3 was released, containing 70 billion parameters and capable of handling contexts up to 128 kilobytes. This enables it to handle complex questions and multiple contexts, as well as generate and analyze text and perform machine translation. The model supports over 150 languages, making it capable of reaching diverse language markets and contexts.
In September 2023, the company launched the Meta AI chatbot and introduced it as a generative digital assistant capable of providing responses and creating images based on user requests within its current applications. In April 2024, Meta placed Meta AI at the forefront of its applications when it replaced the smart chatbot feature with the search function in Facebook, Instagram, WhatsApp, and Messenger. This was followed by the launch of the Meta AI Studio service in July 2024, based on the Llama 3.1 model, as a platform that allows users to create and discover AI characters. It also enables creators to build AI as an extension of themselves to reach more fans.
These increasing investments reflect Meta’s relentless pursuit to be at the forefront of companies competing in the field of generative AI, especially with the announcement of its readiness to launch a standalone application for its smart assistant (Meta AI) in the second quarter of 2025. This application will offer advanced search capabilities, image creation and editing, and the conversion of hand-drawn graphics into animated images. This puts the company in direct competition with applications like ChatGPT from OpenAI, Gemini from Alphabet, as well as rising Chinese competitors such as DeepSeek from Liang Wenfeng and Qwen from Alibaba. However, Meta’s application has a strategic advantage by being released as an open-source model, which fosters a collaborative system that encourages researchers and developers to innovate without ownership restrictions and find opportunities to improve or adapt it to new tasks and use cases.
Automated Chats:
The applications of AI in social media are diverse, ranging from image and video creation to text generation, as well as chat applications in fields such as advertising and marketing. These applications are accelerating with initiatives to integrate smart applications with social media interfaces, so that users no longer need to use external applications for search, content generation, or asking questions. This redefines the interactive relationship between users and social applications, especially with the integration of virtual assistant features and AI-powered conversations through these interfaces.
The integration of generative AI features in social applications is not limited to those owned by Meta alone. TikTok, at its global peak in May 2024, launched the Symphony tool, which allows video generation, editing, and the creation of profile pictures, as well as video translation and dubbing, and the generation of voice comments. TikTok also launched a chatbot called Genie to guide users, following its experimental application of smart conversations to users in the Philippines (Tako).
YouTube also offers a smart conversation tool for its paid service users in the United States, allowing them to engage in conversations and ask questions about the video content they are watching. This tool generates responses using large language models that rely on information from YouTube and the internet, while also collecting data on user interactions to develop recommendation and filtering systems. YouTube previously announced the launch of a feature to generate six-second video clips that can be created and integrated into YouTube Shorts through a feature supported by Google’s DeepMind AI technology.
On the other hand, Snapchat relies on several generative AI features, such as AI Lenses, the paid AI Snaps service for automated post generation, and the AI My chatbot launched by the company in August 2023 to provide users with an interactive experience by responding to inquiries and providing recommendations.
Future Implications:
AI technologies and big data are significantly changing the social media industry, with future implications including:
1- Accelerated growth of the AI market in social media: The value of this market is expected to rise from 2.69billion in 2025 to 2.69 billion in2025 to 9.25 billion by 2030, with a compound annual growth rate of 28.04% over the next five years. This means the generation of more solutions and increased investments in this rising market.
2- Enhancing social AI with more advanced and cost-effective machine learning systems: In addition to multimodal AI systems that allow models to work with various forms of inputs, Meta is taking a more advanced step by developing iterative AI for large language models (Multimodal Iterative LLM Solver) “MILS”. This provides a more intelligent way to interpret multimodal data, relying on zero-shot machine learning, which works differently from traditional models without relying on pre-existing classifications. It continuously improves its outputs using an iterative logging system, enhancing its accuracy without the need for additional training. This means more accurate and flexible AI compared to traditional machine learning models that rely on labeled datasets and large computational resources, resulting in rigid and task-specific systems. This also means more accurate future social networks in image labeling, video analysis, and media generation.
3- Enhancing personalized and predictive experiences across social networks: Social applications powered by generative AI can generate smart and personalized responses based on AI-powered hyper-personalization capabilities. This is achieved by analyzing highly accurate real-time data collected in real-time, allowing for the creation of a unique individual experience for each user, enhancing their experience. This also gains predictive features through predictive personalization techniques, with the development of more accurate approaches to delivering personalized content or services based on the use of data in more accurate and detailed ways that go beyond the idea of providing general content or recommendations based on broad user categories. This involves analyzing comprehensive and immediate behavioral data about users, such as their geographic location, activities on the platforms, and personal preferences. With the development of AI and machine learning capabilities, personalization has evolved from recommendations based on a static view of past behavior to predicting how customer needs and tastes will evolve in the future. By predicting preferences and needs, brands can offer customers the products and services they want, even before they realize they want them.
4- Automation of social creative content: A study conducted by Originality AI, which included nearly 9,000 long posts on Facebook, found that about 41% of these posts were likely to be generated automatically. This has increased significantly with the emergence of the ChatGPT application, with the average monthly percentage of AI-generated posts on Facebook reaching 24.5% from 2023 to November 2024. Capterra, a research company, predicts that half of the social media content created by companies will be AI-generated by 2026. In addition to the potential impacts on the business market and jobs related to social networks, particularly marketing and advertising, these statistics also have significant implications for the future of human interaction on the internet and the potential effects of increasing automation and over-reliance on automated inputs across social networks. This may strip social communication of its human essence and erode authentic human content, not only at the individual level but also at the level of creativity and strategic thinking.
5- Innovation of new automated models for commerce through social networks: This is evident in the expansion of the conversational commerce market, which relies on conversations and messaging through social media applications to provide product recommendations, offers, answer common questions, and even assist in completing transactions. This largely relies on generative AI through chatbots and voice assistants that engage in complex conversations with customers, not only generating human-like responses but also analyzing customer behavior and predicting their next steps. The global market value of chatbot solutions in 2023 was estimated at around $504 billion, with over 80% of companies worldwide using some form of them. The number of sales bots on the Facebook Messenger application exceeded 330,000 in 2023. It is worth noting that bots are programs or AI systems designed to perform specific tasks without direct human intervention, and they are used in areas such as customer service, e-commerce, marketing, and more.
6- Ethical concerns: The widespread access to user data raises privacy and misuse concerns, while AI-generated content, such as deepfakes and deceptive synthetic media, poses a challenge even with companies’ commitments and announcements of governance frameworks and responsible AI. This is in addition to the challenges related to who owns the rights and takes responsibility for the consequences of automatically generated content.
7- Effects of generative echo chambers: Despite the prevalence of the term “echo chamber” to describe polarized web communities that circulate the same opinions, reinforcing users’ current biases, American researchers found that chatbots have a similar effect. In a study conducted on 272 participants to examine whether searching on these AI-powered applications reinforces selective exposure and limits exposure to diverse opinions, they confirmed that these chatbots tend to repeat the opinions of the people using them, creating generative echo chambers that reinforce user bias.
Automated and Human Innovation:
Generative AI applications have brought several advantages to social networks, such as breaking down language barriers and creating extensions of creative capabilities by enabling the generation of content patterns that previously required specialized skills, such as video editing, color correction, and graphic design. Users no longer need these skills to generate engaging multimedia content. Additionally, chatbots enhance the features of social application interfaces for search, support, and answering inquiries, further enhancing the personalization and customization capabilities of these applications. The intensive use of social media platforms and AI-enhanced image editing and text generation applications is a source of data collection about communities around the world, as well as for crafting more impactful personalized content than the general messages broadcasted by radio and television channels.
There is no doubt that the entry of a company the size of Meta, with its billion-user base, into the competition with giant chatbots and the provision of social applications with smart interfaces through the integration of AI technologies, means millions of multilingual, multimedia, demographic, and other variable data inputs capable of driving the development of these models with strength and efficiency. This, in addition to changing the way we consume and interact with social media, from personalized content recommendations to the creation of creative content, has a transformative impact on the adoption and integration of generative AI into social media strategies, and its impact on the business market and digital communication models. This renews the ongoing struggle between automated and human innovation.
Really interesting points raised here. Meta’s aggressive push into generative AI, especially with Llama and Meta AI, shows how fast the social media landscape is evolving. The piece also thoughtfully highlights the risks—like loss of authenticity, privacy issues, and algorithmic echo chambers. It’s a timely reminder of the balance needed between innovation and human connection in the digital world.